This article gives a practical framework (theory → patterns → playbook) and case studies (Grammarly, Notion, Slack) with scholarly and practitioner references so you can implement behavioural engineering in your product roadmap and roadmap decisions.

Why behavioural engineering matters for AI in SaaS

1. Users don’t just buy capabilities — they buy predictable habits and institutional practices. Habit formation research shows that repeated performance in consistent contexts creates automaticity; products that anchor new workflows into users’ routines create durable behavioural lock-in.

2. Trust determines whether people accept, verify, or ignore AI outputs. Research on trust in automation demonstrates that design choices influence “appropriate reliance” — too little trust and users ignore the AI; too much and they over-rely (automation bias). Designing for calibrated trust is essential when AI outputs affect decisions or workflows.

3. Human–AI combinations are not automatically synergistic. A large meta-analysis shows that human–AI systems, on average, do not outperform the best single agent (human or AI) in many tasks; gains depend on task type, interface design, and relative competencies. This means behavioural design determines whether your AI augments or undermines value.

4. Network effects and institutional complementarities keep winners dominant. Economic theory on network externalities explains why platforms with strong usage networks or complementary assets are harder to displace — and behavioural engineering is how you create those complementary assets (shared memory, workflows, templates, norms).

A theory stack for behavioural engineering (a deeper primer)

Behavioural engineering for AI-enabled SaaS is not one discipline.
It is a stack — where each layer answers a different failure mode of AI adoption.

Think of your work as synthesising five distinct literatures, each solving a specific problem that “AI-first” thinking often ignores.

1. Habit & behaviour change

How repeated product use becomes automatic

The first question is deceptively simple:

Why would a user return to this AI feature tomorrow — without being reminded?

Two foundational models matter here:

Fogg Behaviour Model (FBM)

Behaviour happens when Motivation × Ability × Prompt converge.

In SaaS terms:

• Motivation → perceived value, urgency, emotional payoff

• Ability → cognitive effort, friction, learning cost

• Prompt → contextual trigger (time, event, social cue)

AI features often fail because:

• They assume high motivation (“this is obviously useful”)

• They underestimate ability constraints (verification, prompt-writing, interpretation)

• They rely on weak prompts (“Try our AI!”)

Behavioural engineering implication

• Lower ability before increasing motivation

• Design prompts that appear inside existing workflows, not as separate calls-to-action

• Treat friction as a design variable, not an accident

The Hook Model (Trigger → Action → Reward → Investment)

Popularised by Nir Eyal, this model explains how routines form over time.

In AI SaaS:

• Trigger: “Document opened”, “Deal moved to stage”, “PR created”

• Action: small AI-assisted step

• Reward: speed, clarity, confidence, relief

• Investment: stored context, preferences, templates

Key insight

AI becomes sticky when each use makes the next use easier or more valuable.

This is why ephemeral AI suggestions don’t create habits — but AI that stores context does.

2. Habit formation evidence

How long does behaviour actually take to stick

Product teams often assume:

• “If it’s useful, people will adopt it”

• “If adoption doesn’t happen in 2 weeks, it failed”

Empirical research strongly disagrees.

Lally et al. (2010): Habit formation in the real world

Key findings:

• Median time to automaticity ≈ 66 days

• Range: 18 to 254 days

• Consistency matters more than intensity

Why this matters for AI products

• Expecting “AI adoption” in a sprint or two is unrealistic

• Early friction is not failure — it’s part of learning

• Habits form when behaviour is repeatable in a stable context

Behavioural engineering implication

• Design AI features that support frequent, low-effort repetition

• Measure trajectory, not just short-term conversion

• Run retention experiments with realistic time horizons

If your AI requires high novelty or heavy prompting every time, it will never become habitual.

3. Trust & reliance

Why users either ignore AI or trust it too much

Trust in AI is not binary — it’s calibrated reliance.

Human factors research (Lee & See, 2004) shows that:

• Under-trust → users ignore automation

• Over-trust → users blindly follow automation (automation bias)

Both are dangerous.

Core principles from trust-in-automation literature

1. Transparency

   • Users should know what the AI is doing and why

2. Predictability

   • Similar inputs should produce similar behaviour

3. Graceful failure

   • When wrong, the system should fail visibly and recoverably

Common SaaS failure

• AI “sounds confident” even when uncertain

• Errors feel arbitrary

• Users don’t know when to double-check

Behavioural engineering implication

• Trust must be earned gradually, not demanded

• Design explicit confidence cues, provenance, and fallback paths

• Trust should increase with experience, not by default

The goal is not trust — it is appropriate reliance.

4. Human–AI interaction & safety

How humans and AI actually collaborate

This literature answers a critical question:

When does AI improve human judgment — and when does it degrade it?

Research in Human–AI Interaction (HAI / HAIC) shows:

• Human + AI is not automatically better than either alone

• Performance depends on task structure, interface, and delegation model

Three concepts matter most

a) Interpretability (Doshi-Velez & Kim)

Interpretability is not universal — it is context-dependent.

• High-stakes decisions → explanations matter

• Low-stakes, repetitive tasks → speed matters more

Implication
Don’t over-explain everything.
Explain where behaviour or accountability depends on it.

b) Confidence calibration

AI systems should:

• Express uncertainty when appropriate

• Avoid false precision

Humans are poor at detecting overconfidence — UI must help.

c) Conditional delegation

Instead of “AI always acts” or “AI always asks”:

Let users define when the AI can act autonomously and when it must defer.

This:

• Reduces cognitive load

• Preserves human judgment

• Improves long-term trust

Behavioural engineering implication
Design collaboration rules — not just capabilities.

5. Platform economics

Why some AI features create moats and others leak value

Finally, behavioural engineering must align with economic defensibility.

Platform economics explains why.

Katz & Shapiro: Network externalities

Value increases as:

• More users adopt the same system

• More shared artefacts accumulate

• Expectations converge on a standard

In AI SaaS, this means:

• Shared templates

• Institutional memory

• Team-level workflows

Switching costs (behavioural, not contractual)

True switching costs are:

• Habits

• Muscle memory

• Social coordination

• Embedded workflows

AI that lives outside workflows is easy to replace.
AI that lives inside them is not.

Composability vs embedding

• Interoperate when the AI value is generic

• Embed deeply when behaviour and memory matter

Behavioural engineering implication
Ask not:

“Can competitors copy this feature?”

Ask:

“Can they replicate the behaviours this feature has already shaped?”

Practical design patterns (how to translate theory into product features)

Below are repeatable patterns you can use when integrating AI into your SaaS product to produce defensibility through behaviour.

1) Persistent contextual memory (the product’s “institutional memory”)

What: Store and surface conversation/interaction history, decisions, templates, and rationale so the product remembers context across sessions and users.
Why: Persistent memory amplifies cumulative value — each interaction makes the system more useful to that customer and creates a behavioural sunk cost that’s hard to replicate.
How: Make memory explicit and exportable, provide revision history, and allow teams to curate shared knowledge (not just personal caches). Ensure privacy and access controls.

2) Conditional delegation (human-in-the-loop rules)

What: Allow users to set rules or “trust zones” where the AI can act autonomously and where it should require human sign-off.
Why: Reduces verification burdens while avoiding automation bias in high-risk contexts; improves calibrated trust. Research shows that conditional delegation can improve human–AI workflows.

3) Progressive disclosure + explainability

What: Start with simple suggestions; offer layered explanations and provenance on demand (why this suggestion, confidence, data sources).
Why: Interpretability matters when users make consequential decisions; it reduces over-reliance and increases acceptance where appropriate. Doshi-Velez & Kim outline when interpretability is needed and how to evaluate it.

4) Micro-habits & trigger engineering

What: Break desired workflows into tiny, low-friction actions and use contextual prompts (time, event, teammate action) to trigger them. Combine with small variable rewards (progress bars, micro-feedback). Designs should follow Fogg and Hook's model principles as elaborated above

5) Community templates & shared artefacts

What: Enable users to create, share, and adapt templates, automations, and playbooks that reflect real workflows.
Why: Community artefacts are social proof and accelerate adoption; they create social lock-in and learning economies (Notion and others use this).

6) Default + opt-in safety

What: Choose safe defaults (conservative automation, opt-in for destructive actions), while letting power users opt into more aggressive automation.
Why: Preserves trust, reduces liability, and avoids mass automation failures that cause reputational damage.

7) Social/organizational affordances

What: Design shared annotations, approvals, and audit trails that make AI outputs part of an organization’s process rather than an individual’s black box.
Why: Organisational embedding creates switching friction that is behavioural and institutional.

8) Ethics & consent baked into UX

What: Communicate how data is used, offer consent nudges, and allow data minimization and deletion flows. Adopt a transparent “choice architecture” consistent with accepted ethical frameworks (nudge ethics, persuasive tech critiques).

Case studies (what worked — and why)

Grammarly — from spellchecks to habit-forming writing assistant

What they did:
Evolved from a passive spellchecker into an always-on, context-aware writing assistant across browsers, editors, and enterprise suites. They combined AI suggestions, inline feedback, and productivity reporting to make users rely on and internalize better writing habits. For enterprise customers, Grammarly layers administrative controls and style guides, embedding it in organizational norms.

Why behavioural engineering mattered:
Grammarly’s persistent integration across contexts and consistent feedback loop created automaticity in users’ writing workflows. The product’s cross-device presence and team settings became organizational defaults (institutional memory + social proof).

Takeaway:
For AI features, owning the context (where the user writes) + persistent, personalized feedback + team conventions produces stickiness.

Notion — templates, community, and shared workflows

What they did:
Notion made the product extremely malleable and then seeded a vast template ecosystem and community. Templates lower the activation energy for new workflows; community sharing accelerates adoption and creates social norms. Notion’s templates and public pages become shared artefacts that teams adopt and adapt.

Why behavioural engineering mattered:
By lowering ability (in Fogg terms) and providing prompts (templates + community), Notion triggered habitual usage and embedded itself inside team workflows — a behavioural lock that’s hard for point AI tools to dislodge.

Takeaway:
If your AI enables a workflow template that teams adopt (e.g., candidate screening playbooks, meeting summarization templates), you win institutional embedding.

Slack — network effects + ritualization

What they did:
Slack turned communication into a habit by making it the default interaction layer for teams (real-time channels, notifications, reactions). Teams ritualized Slack usage (standups, incident channels), and integrations embedded third-party tools into Slack’s context.

Why behavioural engineering mattered:
Slack’s value is social: the more teams use it, the more valuable it becomes. AI features (summaries, thread insights) must respect and enhance these rituals rather than interrupt them.

Takeaway:
When AI features support existing social rituals and reduce coordination friction — and when the product stores shared artefacts and signals — they strengthen network effects.

Duolingo — AI layered on top of habit, not novelty

What they did:
Duolingo built one of the strongest habit-forming consumer products before AI became fashionable. As AI matured, Duolingo layered personalisation, adaptive difficulty, and feedback on top of an already robust behavioural system built around streaks, micro-lessons, and gamified progression. AI was used to fine-tune pacing, content sequencing, and error correction — not to replace the core learning loop.

Why behavioural engineering mattered:
Duolingo’s success is driven by ritualisation. Daily usage is anchored by streaks and loss aversion, while lessons are deliberately short to reduce ability barriers. AI works because it reinforces an existing habit loop rather than asking users to learn a new one. The product optimises for consistency over intensity, aligning closely with empirical habit formation research.

Takeaway:
AI accelerates adoption only when behaviour is already designed. If your SaaS product lacks a repeatable usage ritual, adding AI personalisation will not magically create one.

Figma — AI that respects creative and social workflows

What they did:
Figma embedded AI assistance into an already dominant collaborative design workflow. Instead of introducing AI as a disruptive “mode,” Figma integrated it into existing actions — generating variants, assisting layout, accelerating iteration — while preserving real-time collaboration, comments, and shared ownership of artefacts.

Why behavioural engineering mattered:
Design work is inherently social and iterative. Figma’s behavioural moat comes from shared files, visible decision-making, and collective accountability. AI features succeed because they augment creative rituals rather than bypass them. Importantly, Figma avoided premature full automation of judgment-heavy tasks, maintaining trust and preserving human authorship.

Takeaway:
AI strengthens products when it enhances existing social rituals and shared artefacts. When AI shortcuts collaboration or removes explainability, it erodes trust instead of compounding value.

Counterintuitive case studies (where “more AI” was not the advantage)

Linear — minimal AI, maximal behavioural discipline

What they did:
Linear succeeded in an AI-saturated project management market by doing something countercultural: less. Instead of competing on AI surface area, Linear enforced opinionated workflows, low-noise defaults, and clear expectations for how teams should manage work. The product deliberately constrained choice in favour of speed, clarity, and consistency.

Why behavioural engineering mattered:
Linear engineered discipline over flexibility. By reducing cognitive overhead and eliminating configuration sprawl, it created predictable team rituals around issue tracking and prioritisation. Teams didn’t need AI to tell them what to do — the product’s structure itself guided behaviour.

Takeaway:
Sometimes behavioural constraint is more defensible than an AI augmentation. Clarity beats intelligence when coordination is the core problem.

Calendly — automation without “AI theatre”

What they did:
Calendly eliminated scheduling friction not through intelligence, but through behavioural redesign. Calendly shifted scheduling from a socially awkward, back-and-forth negotiation to a simple asynchronous expectation. Users share availability once; the system enforces the norm.

Why behavioural engineering mattered:
Calendly normalised a new social behaviour. By reducing anxiety and ambiguity around time coordination, it created a predictable interaction pattern that required no explanation, training, or trust calibration. The automation was silent — and therefore widely accepted.

Takeaway:
Not every problem needs AI. Some need a rethinking of social behaviour and norms.

Failure & cautionary tales: when behavioural engineering was ignored

These cases matter more than the successes — because they show how AI fails even when technically strong.

Enterprise CRM chatbots — high capability, low adoption

What they did:
Many CRM platforms introduced AI chat assistants that generated summaries, suggested next steps, and auto-filled fields — all logically useful capabilities.

Why behavioural engineering failed:
There were no clear trust boundaries, no accountability when AI was wrong, and no reduction in verification effort. Instead of saving time, AI increased cognitive anxiety.

Behavioural outcome:
Users reverted to manual workflows. AI became a novelty rather than a habit. Fear of automation bias led to disengagement.

Key lesson:
If AI increases cognitive load or anxiety, users disengage — regardless of accuracy.

Auto-ML platforms — democratised AI, orphaned behaviour

What they did:
Auto-ML tools promised to make advanced modelling accessible to non-technical users by abstracting complexity behind automated pipelines.

Why behavioural engineering failed:
Business users distrusted opaque outputs. Data scientists resisted loss of control. Outputs lacked organisational legitimacy because no one clearly “owned” decisions.

Behavioural outcome:
No shared accountability, no institutional embedding, and no learning loop between humans and models.

Net result:
Technically impressive. Behaviourally orphaned.

Voice assistants in enterprise contexts — interface novelty, context failure

What they did:
Voice AI systems (e.g., enterprise voice assistants) attempted to translate consumer success into workplace productivity.

Why behavioural engineering failed:
Workplace norms demand auditability, shared artefacts, and traceability. Voice interactions produced none of these. Errors were socially costly, invisible, and hard to recover from.

Behavioural mismatch:
No persistent memory. No social visibility. No institutional trace.

Lesson:
Behavioural context matters more than interface novelty.

AI code review tools that over-automate

What they did:
Some AI tools attempted fully automated code reviews, bypassing human judgment and discussion.

Why behavioural engineering failed:
When edge cases slipped through, trust collapsed. Teams rejected black-box approvals—social learning — understanding why something was flagged — disappeared.

Behavioural outcome:
Mentorship was removed, shared understanding eroded, and risk aversion increased.

Net result:
Teams reverted to human review — or adopted tools that assist, not replace.

Synthesis: a behavioural failure pattern library

Across these failures, the same anti-patterns recur:

❌ AI introduced without behavioural scaffolding
❌ Automation without accountability
❌ Intelligence without explainability
❌ Speed without trust calibration
❌ Individual optimisation without organisational embedding

Meanwhile, winning products consistently:

✅ Preserve or strengthen rituals
✅ Create shared artefacts
✅ Engineer habit loops
✅ Allow conditional delegation
✅ Make AI socially legible

“AI fails not when models are weak — but when behaviour is left unmanaged.”

A playbook for product leaders — 10 concrete steps

1. Map the behavioural loop. For each AI feature, explicitly map triggers, actions, rewards, and investment (Hook) and annotate where trust & verification occur.

2. Prioritize durable context. Ask: Does this AI produce an artefact (document, template, annotation) that’s stored, shareable, and discoverable? Prioritize features that create persistent artefacts.

3. Design conditional delegation. Let users specify when the AI can act autonomously vs when it must ask. Track delegation outcomes to refine trust policies.

4. Measure behavioural outcomes, not just model metrics. Track habit measures (repeat rates, time-to-automaticity), calibration (how often users verify), and institutional adoption (team share, artefact reuse).

5. Implement progressive explainability. Provide lightweight explanations in the UI and deeper provenance on demand. Evaluate whether each explanation changes behaviour.

6. Create social scaffolding. Templates, playbooks, and community galleries turn private gains public and accelerate adoption (Notion model).

7. Protect against automation bias. Use UI cues, forced verification in risky operations, and training to reduce blind trust. Cite literature on automation bias and design countermeasures.

8. Ethical defaults & consent flows. Make data use transparent, provide clear opt-outs, and use safe defaults for actions with irreversible consequences.

9. Build composable interoperability — deliberately. Decide where to compete vs. where to interoperate. Composability can widen your footprint, but make sure interop surfaces feed your persistent memory or social artefacts to keep lock-in (rather than externalizing the artefact).

10. Run longitudinal pilots. Habit formation and trust calibration take weeks to months. So, it is advisable to run design experiments with realistic timelines (Lally et al.) and track automaticity and institutional uptake.

Ethics, regulation, and limits — when behavioural engineering becomes manipulation

Persuasive design and nudging can improve outcomes, but they can also cross ethical lines. The literature on persuasive technology warns product teams to be explicit about goals and consent; nudge theory offers frameworks but also critiques. Build guardrails: independent ethics review, transparent logging of nudges/automation, and “explain this to my manager” features for organizational accountability.

Measuring success: the right KPIs

Move beyond model accuracy to behavioural KPIs:

• Adoption velocity (team activation, artefact reuse)

• Automaticity index (proxy: % users who perform X without a prompt after T days)

• Calibration score (ratio of verified vs accepted suggestions; false acceptance rate)

• Task outcome delta (does human + AI outperform the best single agent for the task?)

• Organizational embedding (templates shared, internal docs referencing outputs)

Use both quantitative experiments and qualitative interviews to surface trust and workflow friction.

Final note: why behavioural engineering is a defensibility strategy

Technology cycles make functionality fungible — today’s best-of-breed can be tomorrow’s library. Behavioural engineering creates social, cognitive, and institutional lock-in: the product becomes part of how people work, remember, and coordinate. That is the kind of defensibility that survives modularization and composability.

If you focus on three ingredients — persistent context, calibrated trust, and social artefacts — you’ll create AI features that are not only useful but integrated into company workflows and habits. Those behavioural ties, paired with sound ethics and rigorous measurement, are the strategic moat for AI-enabled SaaS.

Selected references & further reading

Scholarly & foundational

• Lee, J. D., & See, K. A. (2004). Trust in Automation: Designing for Appropriate Reliance. Human Factors.

• Lally, P., van Jaarsveld, C. H. M., Potts, H. W. W., & Wardle, J. (2010). How are habits formed: Modelling habit formation in the real world. European Journal of Social Psychology.

• Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv.

• Katz, M. L., & Shapiro, C. (1985). Network Externalities, Competition, and Compatibility. American Economic Review.

• Mosier, K. L., & Skitka, L. J. (1996/1997). Automation bias and decision-making. (See reviews on automation bias).

Human–AI interaction & evaluation

• Evaluating Human–AI Collaboration: A Review and Methodological Framework (2024).

• Vaccaro, M. et al. (2024). When combinations of humans and AI are useful. Nature Human Behaviour (meta-analysis on human–AI systems).

Ethics & persuasive tech

• Berdichevsky, D., & Neuenschwander, E. (1999). Toward an Ethics of Persuasive Technology. Communications of the ACM.

• Thaler, R., & Sunstein, C. (2008). Nudge: Improving Decisions about Health, Wealth, and Happiness.

Practitioner & case analyses

• Farnam Street / The Knowledge Project — Nir Eyal on habit design and being “indistractable.” (interview & Hook model discussion).

• Grammarly: Forrester/Grammarly materials on GenAI adoption and enterprise impact.

• Notion: community-led growth and templates as growth fuel (various practitioner writeups).

• Slack: product-led growth and network effects case studies.

📌 Chapter Synopsis

1. The Puzzle of Smart Companies Failing – Why giants like IBM and Kodak collapse despite doing “everything right.”

2. The Persona Problem – How customer personas explain who people are but fail to explain why they act.

3. Jobs to Be Done Theory – The mindset shift: customers hire products for progress, not features.

4. Creative Destruction in Action – Kodak, Blockbuster, and other cases of jobs staying the same but products getting “fired.”

5. JTBD vs. Traditional Innovation – Why JTBD boosts innovation success rates from 17% to 86%.

6. JTBD Meets AI – How to identify the jobs where AI can replace human effort with massive efficiency gains.

7. The Crucial Question – Stop asking “Who is my customer?” and start asking “What job am I being hired for?”

Agentic AI systems based on multi-agent architectures don’t just automate tasks—they orchestrate intelligence as discussed in our previous article. In this article, we shall dive deeper into the different types of Multi-Agent architectures and the possibilities that it unlocks. Let’s start by doubling down on what the Multi-Agent architectural shift enables:

1. Distributed Task Execution & Specialization

Instead of forcing a single model to do everything, multi-agent systems assign specific roles to specialized agents.

• Case in point: In Magentic-One’s architecture, an Orchestrator agent delegates tasks to a cast of specialists—like WebSurfer for web tasks, FileSurfer for file operations, or Coder for code generation.

• Each agent operates with a narrow context window, which improves performance, reduces hallucinations, and enhances output precision.

• This modular approach is akin to object-oriented programming—agents can be independently developed, maintained, and reused across applications.

The result? Efficient division of labor and higher-quality execution for complex workflows.

2. Parallelism for Speed & Efficiency

Multi-agent systems operate in parallel, dramatically improving speed and throughput:

• Anthropic’s Research System leveraged parallel subagents using multiple tools simultaneously, slashing research time by up to 90% on intricate queries.

• Operational gains: In some industries, such agent-based automation has led to productivity increases of up to 40%.

Instead of waiting on a single agent to sequentially process subtasks, parallel subagents can handle multiple queries, branches, or scenarios at once.

3. Superior Problem-Solving & Adaptability

One of the greatest strengths of a multi-agent system is emergent intelligence through collaboration.

• Agents combine diverse skills and perspectives, creating a system that can think more broadly and act more intelligently than any single agent.

• They can adapt roles on the fly, responding to dynamic scenarios in real-time—ideal for unstructured tasks like research, planning, or negotiation.

• For example, in agentic research systems, agents may challenge, refine, or validate each other’s outputs—mimicking collaborative human problem-solving.

This architecture unlocks the ability to tackle open-ended, ill-structured problems that are typically out of reach for conventional AI.

4. Coordinated Decision-Making & Orchestration

A single intelligent orchestrator is the linchpin of any effective multi-agent system:

• Tools like LangGraph and Magentic-One’s Orchestrator go beyond static planning. They support:

  • Dynamic team formation

  • Context-aware task delegation

  • Real-time course correction

• Orchestrators maintain persistent context, use nested loops to revise plans, and apply corrective logic to recover from failures or ambiguity.

Advanced features like payload referencing allow agents to exchange large content blocks (e.g., code snippets) efficiently, improving coordination in technical tasks.

5. Scalability & Resilience by Design

Unlike single-agent systems, multi-agent frameworks scale horizontally.

• Need more capacity? Add more agents.

• One agent crashes? Others keep the system running.

• CrewAI is built with enterprise-grade reliability, offering fault tolerance crucial for real-time environments like healthcare, logistics, and operations.

This resilience ensures business continuity even when components fail—mirroring how human teams absorb shocks through redundancy.

6. Smarter Context Management

One of the core limitations of LLMs is their context window. Multi-agent systems elegantly work around this:

• Different agents handle different slices of context in parallel.

• They compress and summarize results for a lead agent to synthesize.

• This avoids overloading any single agent and enables longer, more coherent interactions over extended workflows.

Multi-agent setups don’t just scale compute—they scale memory, attention, and nuance.

Types of Multi-Agent Architectures

Multi-agent architectures can have a wide variety of organizations at any level of complexity. The sources identify several primary categories:

1. Hierarchical Architectures (Vertical):

   • Core Idea: One agent acts as a leader (Master/Orchestrator) and has other agents report directly to them.

   • Control Flow: Centralized, with the manager agent retaining control and invoking other agents as callable tools.

   • Responsibility: The main agent controls and coordinates, while sub-agents perform specific tasks at each level. This allows LLMs behind each agent to maintain a limited context relevant to their specific role.

   • Delegation: The root agent delegates responsibilities to sub-agents, who then take control to handle the task.

   • Advantages (Agents-as-Tools / Manager Pattern): Unified user experience, good for multi-step workflows, maintains full context, flexible orchestration, enables parallel queries (with external orchestration), and low-latency routing.

   • Disadvantages (Agents-as-Tools / Manager Pattern): Complex manager prompt, difficult tool selection logic, costly with many tool calls, more failure points, requires consistent tool output, and is a single point of failure in the manager.

   • Preferable For: Customer support with distinct issue types, task stages handled separately, domain-isolated problems, and low-latency routing/escalation flows.

   • Examples:

     • Magentic-One: Features an Orchestrator agent that oversees and manages the entire system, planning, tracking progress, and re-planning to recover from errors. It directs specialized agents like WebSurfer, FileSurfer, Coder, and ComputerTerminal to execute subtasks. The Orchestrator uses nested loops and ledgers to maintain context, devise plans, and take corrective actions, allowing it to recover from errors and persist through uncertainty.

     • Customer Support Agents: A Master agent ("Customer Support Agent") acts as a supervisor, with an Orchestrator agent below it that divides tasks among various micro-agents (e.g., User Experience Agent, FAQ Agent, Issue Resolution Agent). In ADK, the root agent is responsible for delegating work to other agents, and once delegated, the sub-agent takes full control of the response.

     • Anthropic's Research System: Uses an orchestrator-worker pattern, where a lead agent coordinates the process while delegating to specialized subagents that operate in parallel. The LeadResearcher plans, creates subagents for specific research tasks, and synthesizes findings.

     • LangGraph: Allows for defining a multi-agent system with a supervisor of supervisors, generalizing the supervisor architecture for more complex control flows.

2. Supervisor (Tool-Calling) Architecture:

   • A special variant of the supervisor architecture where individual agents are represented as tools.

   • A supervisor agent uses a tool-calling LLM to decide which of these agent tools to call and the arguments to pass to them.

   • The supervisor agent operates in a loop, calling tools until it decides to stop.

3. Network Architectures (Horizontal):

   • Core Idea: All agents are treated as equals and are part of one group discussion about the task.

   • Communication: Occurs in a shared thread where each agent can see all messages from others.

   • Task Assignment: Agents can volunteer to complete tasks or call tools, not needing assignment by a leader.

   • Advantages: Generally used for tasks where collaboration, feedback, and group discussion are key to success.

   • Disadvantages: Can lead to unproductive chatter and difficulty in intelligent message sharing, especially in shared group chats.

   • Examples:

     • DyLAN (Dynamic LLM-Agent Network): Creates a dynamic agent structure for complex tasks like reasoning and code generation. It's horizontal as agents share information without a defined leader, with a step to re-evaluate and rank agent contributions dynamically.

     • AgentVerse: Multi-agent architectures like AgentVerse define distinct phases for group planning, improving reasoning and problem-solving. It includes stages for recruitment, collaborative decision-making, independent action execution, and evaluation.

4. Custom Multi-Agent Workflow:

   • Agents communicate with only a subset of other agents.

   • Parts of the flow are deterministic, with only some agents able to decide which other agents to call next.

   • Can involve explicit control flow (pre-defined sequence via graph edges) or dynamic control flow (LLMs deciding parts of the flow using Command or tool-calling).

   • Example: ADK (Agent Development Kit) allows for different workflows, including sequential, parallel, and loop agents.

Specific Multi-Agent Frameworks/Approaches:

• CrewAI: A lean, fast Python framework independent of LangChain, designed for multi-agent automation.

  • Crews: Teams of AI agents with autonomy and agency, working through role-based collaboration to accomplish complex tasks. They enable natural, autonomous decision-making, dynamic task delegation, specialized roles, and flexible problem-solving. CrewAI encourages agents to assume roles, share goals, and have backstories.

  • Flows: Production-ready, event-driven workflows that provide precise control over complex automations. They offer fine-grained control over execution paths, secure and consistent state management, clean integration with Python code, and conditional branching.

  • Synergy: The true power emerges when combining Crews and Flows to balance autonomy with precise control for complex, production-grade applications.

  • Processes: CrewAI supports sequential and hierarchical processes, automatically assigning a manager in the latter to coordinate tasks through delegation and validation.

  • Examples: News retriever, website scraper, AI news writer, and file writer agents working together. A lead qualification pipeline with validator, scorer, and recommender agents working sequentially. Parallel agents for computer analytics (CPU, memory, disk).

• AutoGen: A multi-agent conversation framework that allows agents to communicate and collaborate by sharing information and refining outputs through iterative interactions. Magentic-One is built on AutoGen.

• LangGraph: Uses a graph-based orchestrator that combines structured task execution with LLM-driven dynamic decision-making. Agents are represented as graph nodes, executing steps and deciding whether to finish or route to another agent (including looping). It allows for explicit control flow or dynamic control flow via Command objects, which carry implicit decisions for routing and state updates.

  • Handoffs: A common pattern where one agent transfers control and information (payload) to another. This can be implemented by returning Command objects from agent nodes.

  • State Management: Agents can communicate via a shared message list, and intermediate messages can be stored separately for subagents.

• ADK (Agent Development Kit): A framework by Google for building agents, which emphasizes a root agent (delegator/manager) that delegates work to sub-agents. ADK allows for sequential, parallel, and loop-based workflows.

  • Delegation Focus: In basic multi-agent ADK systems, the root agent delegates work to the best-suited sub-agent, and that sub-agent is responsible for the final response, unlike CrewAI which emphasizes multiple agents collaborating on one task.

  • Shared State: Allows multi-agent systems to be more intelligent by sharing state among different agents, enabling them to behave differently based on that state.

Core Considerations for Designing Agentic Architectures

Regardless of the chosen architecture, several key elements are crucial for effective agent systems:

• Clear Leadership and Task Division: Establishes clear roles and streamlines task assignment within multi-agent teams.

• Dedicated Reasoning/Planning-Execution-Evaluation Phases: Agents need to plan, act, observe, and reflect, potentially multiple times, especially for complex tasks.

• Intelligent Message Filtering: Reduces conversational noise and ensures agents only receive information relevant to their tasks, improving efficiency, especially in horizontal architectures.

• Dynamic Teams: Agents can be brought in and out of the system based on need, ensuring all participating agents are fit for the current task.

• Human or Agentic Feedback: Essential for self-correction and iterative refinement, helping agents to stay on course and align with human expectations. CrewAI fully supports human-in-the-loop workflows.

• Context Engineering: Crucial for reliability in long-running agents, involves automatically and dynamically providing relevant context to agents. This includes sharing full agent traces, not just individual messages.

• Payload Referencing: A mechanism to efficiently exchange large content blocks, particularly code snippets, by allowing direct injection of extracted text from past multi-party communication, reducing communication overhead and latency.

• Dynamic Agent Routing: Selectively bypasses full orchestration when a request is simple and relevant to a single specialized agent, improving efficiency for latency-sensitive use cases.

Overall, while single agents are suitable for well-defined, straightforward tasks, multi-agent systems are increasingly preferred for complex, multi-faceted problems that benefit from specialization, collaboration, and adaptability, offering enhanced efficiency and problem-solving capabilities.

What About the Tradeoffs?

Of course, this shift comes with new challenges:

• Designing and orchestrating multiple agents requires more complex engineering.

• Higher communication overhead and latency can emerge if not managed well.

• Decision-making may become unpredictable in emergent behavior scenarios.

But these challenges are actively being addressed. New frameworks like CrewAI, LangGraph, and AutoGen are making it easier to manage agent teams, control workflows, and limit drift or redundancy.

Recommended Reading & Frameworks

• CrewAI: https://crewai.io

• LangGraph: https://www.langgraph.dev

• AutoGen by Microsoft: https://microsoft.github.io/autogen

• Magentic-One Agents: Architecture breakdown

References

1. Fourney, A., Bansal, G., Mozannar, H., Dibia, V., & Amershi, S. (2024, November 4). Magentic-One: A generalist multi-agent system for solving complex tasks. Microsoft Research.

2. Google Developers Blog. (2025, June 23). Announcing the Agent2Agent Protocol (A2A). Google Developers Blog.

3. Hadfield, J., Zhang, B., Lien, K., Scholz, F., Fox, J., & Ford, D. (2025, June 13). How we built our multi-agent research system. Anthropic.

4. Hosseini, S., & Seilani, H. (2025). Agentic AI: A detailed analysis of its implications for a Smart Future and challenges. Array, 26, 100399. https://doi.org/10.1016/j.array.2025.100399.

5. Irfan, T. (2025, June). AI agent orchestration with OpenAI Agents SDK. Apify.

6. Lo, F. P.-W., Qiu, J., Wang, Z., Yu, H., Chen, Y., Zhang, G., & Lo, B. (2025). AI hiring with LLMs: A context-aware and explainable multi-agent framework for resume screening [Preprint]. arXiv.

7. Lyzr Team. (2025, July 2). Multi agent vs single agent AI: A detailed guide. Lyzr.

8. Masterman, T., Besen, S., Sawtell, M., & Chao, A. (2024). THE LANDSCAPE OF EMERGING AI AGENT ARCHITECTURES FOR REASONING, PLANNING, AND TOOL CALLING: A SURVEY [Preprint]. arXiv.

9. On Products, AI & Strategy. (n.d.). Difference between agents and tools in multi-agent architecture.

10. Shu, R., Das, N., Yuan, M., Sunkara, M., & Zhang, Y. (2024). Towards effective GenAI multi-agent collaboration: Design and evaluation for enterprise applications [Preprint]. arXiv.

11. Xiao, J., LJW, & Zhao, J. (2025, June 24). MCPs: Value creation, capture, and destruction—Lessons from the API era. The Thesis by Leonis.

12. Yan, W. (2025, June 12). Don’t build multi-agents. Cognition.

When discussing Agentic AI architectures, it's crucial to differentiate between single-agent and multi-agent contexts, as each is suited for different types of problems and comes with its own set of design principles and challenges.

Single Agent Context

A single-agent AI system functions like a solo specialist, designed to operate independently, collecting data, making decisions, and executing actions on its own logic and models. This architecture is powered by one language model that performs all the reasoning, planning, and tool execution.

Key Characteristics of Single-Agent AI:

• Autonomy: Operates independently without requiring input from other agents, simplifying management.

• Task Specialization: Built to solve a specific problem or operate within a single domain, such as finance or HR.

• Predictability: Follows structured logic and rules, making its outputs easy to trace and explain.

• Lower Computational Overhead: Requires fewer computing resources compared to systems running multiple agents.

• Simpler Development and Maintenance: Its focused scope results in fewer moving parts and faster testing cycles.

Advantages of Single-Agent AI:

• Faster Decision-Making: Fewer processing steps lead to quicker outcomes.

• Easier to Develop and Deploy: A narrow focus means less complex infrastructure is needed.

• Cost-Effective: Uses less compute, making it accessible for teams with limited budgets.

• Efficient for Repetitive Tasks: Performs well in environments with clear rules and stable patterns.

Limitations of Single-Agent AI:

• Lack of Collaboration: Cannot coordinate with other systems or share decision-making processes.

• Scalability Challenges: Struggles with layered workflows or multiple goals.

• Limited Adaptability: Not ideal for unpredictable or rapidly evolving scenarios.

• May get stuck in an endless execution loop and fail to accomplish a given task if reasoning and refinement capabilities are not robust.

• Its operational model does not inherently support the division of responsibilities across different execution threads, requiring sequential planning and execution.

When Single-Agent Systems are Preferable:

• When tasks are focused and linear (e.g., resume screening, answering policy FAQs, scheduling interviews).

• When only one system or domain is involved (e.g., pulling data from an Applicant Tracking System or HR Information System without needing cross-platform logic).

• When a fast prototype or lightweight solution is needed.

• When decisions do not depend on multiple specialized roles, allowing one agent to follow a clear set of instructions end-to-end.

• When tasks involve a narrowly defined list of tools and well-defined processes.

• They do not face limitations like poor feedback from other agents or distracting chatter.

Examples of Single-Agent AI in Action:

• Banking fraud detection: Monitoring transactions for unusual behavior based on preset thresholds.

• IT helpdesk ticket routing: Reading support tickets and assigning them to the correct team.

• Basic resume screening: Scanning resumes for keywords and basic qualifications.

• ReAct (Reason + Act) method: An agent writes a thought, performs an action, and observes the output, repeating the cycle until the task is complete, demonstrating improved effectiveness over zero-shot prompting.

• RAISE (Retrieval Augmented Instruction Selection & Execution): Improves context retention and performance in conversational agents, though it struggles with complex logic and can hallucinate roles or knowledge if not fine-tuned.

Multi-Agent Context

A multi-agent AI system brings together multiple AI agents, each responsible for a part of a complex problem, allowing them to communicate, collaborate, and adapt in real time. This approach addresses challenges that exceed the capabilities of single AI agents.

Key Characteristics of Multi-Agent AI:

• Collaboration: Agents share data and learnings in real time to improve outcomes.

• Distributed Task Execution: Each agent handles a different part of the job, reducing overload and increasing accuracy.

• High Adaptability: Capable of shifting roles and responses as situations evolve, ideal for unpredictable environments.

• Parallel Processing: Multiple agents work simultaneously, making large-scale workflows faster.

• Fault Tolerance: If one agent fails or lags, others can continue, ensuring system resilience.

Advantages of Multi-Agent AI:

• Scalability: Allows organizations to expand operations by adding more agents without disrupting the entire system.

• Enhanced Efficiency: Distributing the workload leads to faster execution and fewer bottlenecks.

• Greater Problem-Solving Ability: Diverse strengths and perspectives from different agents enable tackling complex tasks more effectively.

• Resilience: Critical for always-on environments (e.g., healthcare, logistics) as the system stays up even if one component fails.

• Simplified Development and Reusability: Encapsulating distinct skills in separate agents simplifies development, similar to object-oriented programming, and promotes reusability.

• Cost Optimization: Orchestrators can reduce costs by avoiding redundant API calls.

• Superior Problem-Solving and Adaptability: Multi-agent collaboration is designed to tackle complex, multi-faceted, and open-ended problems that single agents cannot handle.

• Efficient Context Management: Can scale token usage for complex tasks by allowing subagents to operate in parallel with their own context windows, preventing overflow while maintaining conversational coherence.

Limitations and Challenges of Multi-Agent AI:

• Complex Development: Requires solid architecture, coordination logic, and well-defined communication protocols.

• Higher Computational Requirements: More agents and data lead to higher processing power needs.

• Potential Communication Overhead: Constant information sharing can introduce delays or conflicts if not optimized.

• Decision-Making Complexity: Deciding which agent to call in complex scenarios can be challenging.

• Fragility: Can result in fragile systems due to dispersed decision-making and insufficient context sharing between agents. Errors can compound, leading to unpredictable outcomes.

• Risk of Sycophantic Behavior: Agents might conform to feedback from other agents, even if unsound, leading to faulty plans.

• Debugging Challenges: Dynamic and non-deterministic behavior makes debugging harder, requiring full production tracing and high-level observability.

• Synchronous Execution Bottlenecks: If subagents execute synchronously, it creates bottlenecks in information flow and prevents real-time steering or coordination between subagents.

When Multi-Agent Systems are Preferable:

Multi-agent AI systems are generally preferable for complex, multi-faceted, and open-ended problems that exceed the capabilities of single AI agents. This preference stems from their ability to leverage collaboration, specialization, efficiency, and adaptability.

Here's a breakdown of when multi-agent systems are preferable:

• Tackling Complex and Multi-Faceted Problems

  • They are designed to address challenges that are too complex for a single AI agent, especially those requiring multiple distinct execution paths.

  • For open-ended problems like research, where required steps are difficult to predict in advance and the process is dynamic and path-dependent, multi-agent systems are particularly well-suited.

• Distributed Task Execution and Specialization

  • Multi-agent systems enable an intelligent division of labor, with each agent responsible for a specific part of a complex problem, based on their unique skills and expertise. This allows Large Language Models (LLMs) behind each agent to maintain a limited context relevant to their specific role.

  • Agents can be independently developed, optimized, and configured for their strengths, simplifying development and promoting reusability.

  • Examples include multi-agent systems for resume screening where core agents handle extraction, evaluation, summarization, and formatting, with sub-agents for deeper analysis.

• Enhanced Efficiency and Speed through Parallelization

  • By distributing the workload, multiple agents can work simultaneously (parallel processing), leading to faster execution and fewer bottlenecks.

  • Anthropic's Research system, for instance, implemented parallelization by spinning up multiple subagents and having them use tools in parallel, which cut research time by up to 90% for complex queries.

  • For tasks requiring significant work, parallel agents can achieve much faster results compared to sequential execution.

• Superior Problem-Solving and Adaptability

  • Different agents bring diverse strengths and perspectives, allowing the system to collectively "think broader, solve faster, and respond smarter".

  • They exhibit high adaptability, capable of shifting roles and responses as situations evolve, making them ideal for unpredictable environments.

  • Multi-agent teams can be dynamically constructed and reorganized for different planning, execution, and evaluation phases, leading to superior results by matching agent roles and skills to the task at hand.

• Improved Coordination and Decision-Making

  • An orchestrator (which can be an LLM itself) coordinates multiple specialized AI agents to achieve specific goals, preventing issues like duplicate work, wasted resources, or task failures.

  • Key features for effective multi-agent architectures include clear leadership, dynamic team construction, and efficient information sharing. Studies show that agent teams with an organized leader can complete tasks nearly 10% faster.

  • Frameworks like LangGraph use graph-based orchestrators that combine structured task execution with LLM-driven dynamic decision-making. Magentic-One's Orchestrator uses nested loops and ledgers to maintain context, devise plans, and take corrective actions, allowing it to recover from errors and persist through uncertainty.

  • Payload referencing enhances knowledge exchange by allowing agents to efficiently share large content blocks like code snippets, which significantly reduces communication overhead and improves reliability in code-heavy tasks.

  • Dynamic agent routing improves efficiency by selectively bypassing full orchestration when requests are simple and relevant to a single specialist agent, reducing latency.

• Scalability and Resilience

  • Multi-agent systems are highly scalable, allowing organizations to expand operations by adding more agents without disrupting the entire system.

  • They offer fault tolerance, meaning if one agent fails or lags, others can continue, ensuring system resilience in always-on environments like healthcare or logistics.

• Efficient Context Management

  • Multi-agent architectures can effectively scale token usage for complex tasks that exceed the context window limits of single agents. Subagents can operate in parallel with their own context windows, exploring different aspects of a query simultaneously and condensing important information for a lead agent, preventing context overflow while maintaining conversational coherence.

• Human-in-the-Loop Workflows

  • Multi-agent systems fully support human oversight and feedback, which is crucial for reliability and alignment with human expectations, especially as AI systems still tend to "snowball" errors.

• Real-World Applications and Enterprise Deployments

  • They are well-suited when tasks involve multiple distinct roles or responsibilities (e.g., screening candidates, coordinating with hiring managers, handling onboarding).

  • When the process spans across various tools and teams (e.g., integrating with Slack, ATS, payroll systems, and internal databases).

  • Multi-agent systems have demonstrated effectiveness in domains like smart traffic management, collaborative warehouse robotics, and hospital patient flow management.

  • Frameworks like CrewAI are explicitly designed for reliability, stability, and scalability in enterprise deployments.

While multi-agent systems offer significant advantages, they also introduce challenges such as complex development, higher computational costs, and potential communication overhead, which necessitate robust architecture, coordination logic, and communication protocols.

Real-world Examples of Multi-Agent AI:

• Smart traffic systems: Managing intersections, traffic lights, and congestion patterns in sync.

• Collaborative warehouse robotics: Coordinating shelf movement, sorting, and delivery preparation.

• Hospital patient flow management: Managing ER triage, room allocation, and doctor assignments.

• Anthropic's Research feature: Uses a lead agent to plan and create parallel subagents for information search, cutting research time by up to 90% for complex queries.

• Magentic-One: A generalist multi-agent system for open-ended web and file-based tasks, with an Orchestrator agent directing specialized agents like WebSurfer, FileSurfer, Coder, and ComputerTerminal.

• AI hiring (resume screening): A framework with core agents for resume extraction, evaluation, summarization, and score formatting, capable of dynamic criteria adaptation via RAG and enhanced explainability through modularity. The summarizer agent can even contain sub-agents like CEO, CTO, and HR agents for refined feedback.

• Travel planning systems: Coordinating agents for flight search, hotel booking, local events, and weather.

Final Thoughts: From Monoliths to Ecosystems

Agentic AI is not just a technological trend—it’s an architectural rethink. Just as microservices revolutionized software development by breaking monoliths into modular services, multi-agent systems are breaking up monolithic AI into cooperative teams of specialists.

This is how we move from intelligence to collective intelligence.

In the years ahead, expect to see multi-agent systems embedded in everything from research assistants and customer service bots to enterprise automation and even autonomous organizations. The next couple of years will be all about building smarter agent teams.

Recommended Reading & Frameworks

• CrewAI: https://crewai.io

• LangGraph: https://www.langgraph.dev

• AutoGen by Microsoft: https://microsoft.github.io/autogen

• Magentic-One Agents: Architecture breakdown

References

1. Fourney, A., Bansal, G., Mozannar, H., Dibia, V., & Amershi, S. (2024, November 4). Magentic-One: A generalist multi-agent system for solving complex tasks. Microsoft Research.

2. Google Developers Blog. (2025, June 23). Announcing the Agent2Agent Protocol (A2A). Google Developers Blog.

3. Hadfield, J., Zhang, B., Lien, K., Scholz, F., Fox, J., & Ford, D. (2025, June 13). How we built our multi-agent research system. Anthropic.

4. Hosseini, S., & Seilani, H. (2025). Agentic AI: A detailed analysis of its implications for a Smart Future and challenges. Array, 26, 100399. https://doi.org/10.1016/j.array.2025.100399.

5. Irfan, T. (2025, June). AI agent orchestration with OpenAI Agents SDK. Apify.

6. Lo, F. P.-W., Qiu, J., Wang, Z., Yu, H., Chen, Y., Zhang, G., & Lo, B. (2025). AI hiring with LLMs: A context-aware and explainable multi-agent framework for resume screening [Preprint]. arXiv.

7. Lyzr Team. (2025, July 2). Multi agent vs single agent AI: A detailed guide. Lyzr.

8. Masterman, T., Besen, S., Sawtell, M., & Chao, A. (2024). THE LANDSCAPE OF EMERGING AI AGENT ARCHITECTURES FOR REASONING, PLANNING, AND TOOL CALLING: A SURVEY [Preprint]. arXiv.

9. On Products, AI & Strategy. (n.d.). Difference between agents and tools in multi-agent architecture.

10. Shu, R., Das, N., Yuan, M., Sunkara, M., & Zhang, Y. (2024). Towards effective GenAI multi-agent collaboration: Design and evaluation for enterprise applications [Preprint]. arXiv.

11. Xiao, J., LJW, & Zhao, J. (2025, June 24). MCPs: Value creation, capture, and destruction—Lessons from the API era. The Thesis by Leonis.

12. Yan, W. (2025, June 12). Don’t build multi-agents. Cognition.

In this 7-minute primer (generated by NotebookLM with my study notes, collection of articles, and instructions), we explore:

• How are companies using AI agents to create goal-based intelligent workflow automations?

• Why the AI outsourcing market is projected to exceed $1.3 trillion by 2030.

• The role of universal standards like Agent-to-Agent (A2A) and Multi-Agent Collaboration Protocols (MCP).

• The big question: will AI agents be our ultimate assistants, our digital colleagues, or the invisible infrastructure of the future?

The Age of Agents has only just begun. 🚀

Several factors contribute to users providing information that is not entirely accurate or truthful:

• Social Desirability Bias: People are often conscious of how they are perceived and may withhold information or present themselves and their behaviors in a certain light to look good. This means they might tell you what they think you want to hear, rather than the unvarnished truth.

• Fear of Judgment or Negative Reflection: Users might avoid discussing aspects that could reflect poorly on them, even if the interviewer isn't looking to judge. This can lead to them omitting crucial details or providing vague answers.

• Protecting Privacy: Falsification is a standard method individuals use to protect their personal data, especially if they perceive the requested information as sensitive or irrelevant to the conversation's context. Users may employ various strategies for this, such as providing invalid information, completely untrue but validly formatted data, or partially true information (e.g., a city name instead of a full address).

• Lack of Trust and Anticipation of Functionality: If users don't trust the interviewer or the system being discussed, they might be hesitant to reveal their true needs. They could even be "frightened by the possibility of ‘biased’ search results when confronted directly" with concepts like adaptive systems, leading them to hold back or give guarded responses.

• "Translation Competence" and Tacit Knowledge: Users, particularly experts in their domain, might simplify their complex knowledge into terms they believe the interviewer will understand, rather than articulating the full, precise truth. Additionally, much of an expert's problem-solving knowledge becomes automatic or tacit through extensive use, making it difficult for them to articulate, even if they want to.

• Desire for Approval ("The Pathos Problem"): If interviewers explicitly seek approval or expose their ego, participants may feel compelled to offer compliments or "fluffy mis-truths" to be supportive or to end the conversation. This also ties into a general "polite response bias," where people respond politely even to computer surveys.

• Overly Optimistic Future Projections: When asked about hypothetical future actions or purchases (e.g., "Would you buy X?" or "How much would you pay for X?"), people tend to be wildly optimistic, leading to worthless "yes" answers and inflated price expectations. "Anything involving the future is an over-optimistic lie".

• Unclear Relevance/Context: Participants are more likely to falsify information if they don't perceive the requested data as relevant to the scenario or context.

How to Get the Truth: Strategies and Techniques for Real Insights

Given these challenges, eliciting genuine, actionable insights requires a deliberate and strategic approach:

1. Embrace "The Mom Test" for Question Framing The core principle is that you shouldn't ask anyone if your business idea is good. Instead, focus on gathering concrete facts about their lives and worldviews. The "Mom Test" provides three simple rules for crafting questions that even those closest to you can't lie about:

• Talk about their life instead of your idea. Avoid mentioning your product or solution too early, as this can bias the conversation.

• Ask about specifics in the past instead of generics or opinions about the future. Learn about their actual behaviors and past experiences, as these are harder to lie about. For example, instead of "Would you buy X?", ask "How did you currently solve X the last time it came up?".

• Talk less and listen more. A successful interview means the participant does most of the talking (e.g., 80-90% of the time). Interrupting or dominating the conversation prevents you from gaining valuable insights into their mental model.

2. Detect and Deflect Bad Data Be vigilant against common forms of untruthful data and guide the conversation back to valuable information:

• Deflect compliments: Phrases like "That's really cool. I love it!" are "fool's gold" and provide zero data. Instead of accepting them, deflect by apologizing for "pitch mode" and redirecting to questions about their current situation or problems.

• Anchor fluff: Generic claims ("I usually do X") or future promises ("I would definitely buy that") are unreliable. Immediately follow up with questions like "When's the last time that happened?" to get specific, verifiable instances.

• Dig beneath ideas, requests, and emotions: Don't just collect feature requests; understand the motivations or "why" behind them. Similarly, if a user expresses strong emotion (e.g., "That's the worst part of my day"), dig deeper to understand the root cause and implications.

• Identify if the problem truly matters: Ask about the implications of the problem to determine if it's a minor annoyance or something they would pay to solve. Also, ask "What else have you tried?" or "How are you dealing with it now?" to gauge if they've actively sought solutions. If they haven't tried to solve it, they likely won't buy your solution.

• Avoid "premature zooming": Don't dive into the details of a specific problem before confirming that the user considers it a high priority or "must-solve" problem. Start with broader questions about their goals and challenges to understand their overall priorities.

3. Optimize Your Interview Environment and Conduct The setting and your approach significantly impact the quality of insights:

• Interview in the user's natural environment: This provides invaluable contextual cues and allows you to observe unstated behaviors, workarounds, and artifacts (e.g., sticky notes, cable organization) that provide a richer understanding of their world.

• Build rapport: Make participants feel comfortable by starting with easy, non-threatening questions, maintaining eye contact, nodding, and acknowledging their responses without judgment. Avoid interrupting or rushing them.

• Adopt an "Advisory Flip" mindset: Approach the conversation not as a sales pitch, but as an opportunity to find industry or customer advisors. This shifts the power dynamic, putting you in control and encouraging more objective insights.

• Show genuine naïveté: Be open to learning and allow participants to teach you. If you're asking questions that might seem "stupid" from their expert perspective, you're likely on the right track to uncovering their deep knowledge.

• Adapt your language: Incorporate terminology and phrases that the user naturally uses to enhance credibility and build rapport, but ensure you understand what new terms mean before using them.

• Use probing questions: Have a list of versatile probes like "Tell me more about that," "Can you expand on that?", or "Why is that important to you?" to uncover motivations, mental models, and deeper perceptions.

• Leverage silence: Don't rush to fill pauses. An uncomfortable silence will often prompt the participant to offer more information.

• Ask for "hidden gems" at the end: Conclude with open-ended questions like "Is there anything else I should have asked?" or "Is there anything we didn’t cover that you expected us to?" These often yield surprising and valuable insights after the formal questions are done.

4. Implement a Structured Process for Consistency and Collaboration To ensure reliable and actionable data, integrate interviews into a broader, team-oriented research process:

• Define clear research goals: Before any interview, specify exactly what you aim to learn. Vague goals lead to irrelevant data.

• Prepare and pilot an interview guide: Develop a flexible guide with topics, questions, and probes. Pilot it with colleagues or target users to refine questions and flow.

• Interview in teams and debrief: Ideally, have two researchers (one to ask questions, one to take notes). After each interview, debrief with your team to consolidate different impressions, discuss commonalities and contrasts, and identify critical factors. This prevents learning bottlenecks where insights remain siloed in one person's head.

• Pre-plan your "3 big questions": Before each set of interviews, determine the three most important, and potentially "scary," questions you need answered—those that could completely change or disprove your business idea. This ensures focus and courage.

• Document thoroughly and review: Take good notes, ideally capturing exact quotes, and use shorthand symbols for quick reference (e.g., for specifics, feature requests, money, people, follow-up tasks). Review notes with your team promptly to disseminate learning and update collective beliefs and plans.

By adopting these strategies, you can navigate the inherent biases and complexities of customer interviews. The goal isn't to validate your existing ideas, but to uncover the truth of your users' world—even if that means disproving your initial assumptions. As Rob Fitzpatrick suggests, there's more reliable information in a "meh" response than a "Wow!", and learning that your beliefs are wrong is actually progress toward finding a real problem and a good market.

Yet, despite its frequent name-drop in UX and product circles, Hick’s Law is often misunderstood, oversimplified, or misapplied. It’s commonly reduced to “just show fewer options,” when in reality, it’s about how human brains navigate choice, and how smart design can reduce the cognitive cost of decisions without dumbing things down.

In this article, we’ll take a deep dive into what Hick’s Law really says, why it matters to digital product builders, and how to apply it not dogmatically, but thoughtfully—especially when designing complex interfaces, onboarding flows, or decision-heavy journeys.

🎓 The Real Hick’s Law: A Logarithmic Understanding of Choice

Back in 1952, psychologists William Edmund Hick and Ray Hyman conducted a series of experiments to understand how humans respond to increasing numbers of options. They weren’t designing apps; they were studying reaction time in controlled lab conditions. But their findings laid the groundwork for one of the most enduring ideas in UX design.

They found a logarithmic relationship between the number of choices presented and the time it took for people to make a decision:

RT=a+b⋅log⁡2(N)RT = a + b \cdot \log_2(N)RT=a+b⋅log2​(N)

Where:

• RT is the reaction time (how long someone takes to decide),

• N is the number of choices,

• log₂(N) reflects the information content (in bits), and

• a and b are constants based on task and context.

What this formula tells us is fascinating: doubling the number of choices doesn't double the decision time. Instead, it increases it incrementally. Why? Because our brains don’t evaluate every choice linearly. We categorize, cluster, dismiss, and prioritize—especially when the information is familiar or structured.

This is a profound insight for design: users aren’t paralyzed by options per se, they’re paralyzed by unstructured, unfamiliar, or meaningless options.

🧩 From Theory to Practice: What Hick’s Law Teaches Product Designers

So, how does a 1950s psychology experiment help us design better websites, apps, and digital flows in 2025?

Hick’s Law is best seen not as a rigid constraint, but as a cognitive lens—a way to anticipate how users might feel when confronted with a set of decisions. It tells us that:

• Every choice has a mental processing cost.

• That cost accumulates logarithmically.

• But we can offset or reduce that cost with smart design choices.

Let’s explore how this plays out.

1. Fewer Options, But With Purpose

The first and most common takeaway from Hick’s Law is to reduce visible options, especially at key decision points like landing pages, sign-up screens, or call-to-action menus.

But it’s not just about fewer items. It’s about fewer distractions, clearer intentions, and higher confidence. A cluttered page overwhelms not because it's long, but because the brain is forced to parse irrelevant options or interpret unclear ones.

💡 Good design asks: “What decision is the user trying to make here?” and removes everything that isn’t in service of that.

2. Progressive Disclosure: Show Less, Reveal More

One of the most powerful UX patterns derived from Hick’s Law is progressive disclosure. This means breaking down complex workflows into step-by-step sequences, only showing the relevant options at each stage.

Think of a multi-step checkout process, or a job application wizard. Users are more likely to complete these flows when they're led one logical step at a time, even if that means more total screens.

🧠 Why it works: it distributes cognitive load and leverages short-term memory more efficiently.

3. Information Architecture: The Power of Categorization

Here’s where Hick’s Law meets information design.

Humans love categories. We’re wired to group things. When you cluster items into categories and subcategories, users can employ what cognitive scientists call a “divide and conquer” strategy. Instead of evaluating 100 choices, the brain scans 5 categories and then 10 items inside one of them. The decision time drops dramatically.

This is why Amazon’s "Shop by Department" model or Spotify’s genre filters work so well. The options are still vast, but you never face the full firehose at once.

🧠 Cognitive load isn’t just about how many choices there are—it’s about how they’re structured and perceived.

4. Language and Labeling: Words as UX Primitives

Even with only three options on a page, if the labels are ambiguous (“Do Stuff”, “More Info”, “Go”), the decision becomes harder.

Hick’s Law is deeply tied to how fast we can comprehend a choice. Labels that are clear, expected, and unambiguous reduce decision time. Those that are clever, technical, or nonstandard increase it.

✍️ Rule of thumb: use the words your users use. Not your team. Not your stakeholders. Your users.

5. Breadth vs. Depth: Rethinking the “3-Click Rule”

There's a myth in UX that “users should be able to get anywhere in 3 clicks.” But Hick’s Law suggests a more nuanced truth: users don’t mind multiple steps, as long as each step is obvious and frictionless.

It’s better to design a shallow, broad hierarchy with clear, predictable choices at each level, rather than a deep tree that hides content behind confusing categories.

Steve Krug (author of Don’t Make Me Think) puts it best: “I don’t mind clicking, as long as each click is a mindless, unambiguous choice.”

🤯 Misunderstandings and Misapplications of Hick’s Law

Like many psychological laws, Hick’s Law can be over-applied or misinterpreted. Let’s bust a few myths.

❌ Myth #1: “Less is always better.”

Truth: Not always. If users know what they’re looking for, they’ll scan a long list fast—especially if it’s ordered alphabetically, numerically, or spatially. Cutting down options can hurt when it hides relevant items behind vague labels or additional clicks.

❌ Myth #2: Hick’s Law = Visual Clutter Management

Truth: Hick’s Law isn’t about aesthetics. It’s about reaction time and decision complexity. Clean design helps, but it’s the cognitive structure, not just the visual layout, that matters most.

❌ Myth #3: Hick’s Law applies uniformly across all users

Truth: Not quite. Reaction time is affected by familiarity, practice, and stimulus-response compatibility. For example, users who use a product daily won’t experience the same delays as new users. Hick’s Law is most relevant at first-use or decision bottlenecks.

🧪 Case Studies: Hick’s Law in Real Products

Let’s examine how Hick’s Law plays out in some real-world systems:

Case Study 1: Amazon's E-commerce Navigation System

Problem: Large e-commerce platforms like Amazon offer an enormous variety of products, leading to a potentially overwhelming number of choices for users. If all available links were presented without structure, users would be "bombarded with choices," potentially causing them to be "stuck in the decision-making process" or even abandon the site.

Hick's Law Application: Amazon addresses this by categorizing choice. Instead of a flat list of every product or category, menu items are organized into high-level categories that "slowly expand as the users select options". This creates a compartmentalized decision-making process where options are kept hidden until they are actually needed. This strategy leverages the understanding that a person's response time increases logarithmically with the number of choices. By minimizing the number of visible choices at any given moment, Amazon applies the "less is better" principle to reduce cognitive load.

Usefulness/Impact: This application simplifies the interface and the shopping process significantly. It helps prevent "choice paralysis" and avoids bombarding users with options, which could intimidate them. By reducing the cognitive stress and competition for the user's attention, it contributes to a more user-friendly and natural experience, making it "much lighter to find the relevant information". This approach is vital for user engagement and conversion rates.

Case Study 2: Online Checkout and Registration Forms

Problem: Digital processes that require multiple steps, such as completing a purchase or registering for an account, can appear very complex and daunting if all fields and options are displayed on a single screen. This can lead to "choice paralysis" and users abandoning the process.

Hick's Law Application (Progressive Disclosure): Designers break down complex processes into smaller, more manageable screens, a technique often referred to as "progressive disclosure" or "obscuring complexity". Instead of one long form, a multi-page series of smaller forms is used, where only the immediately relevant information or actions are presented at each step. For example, a payment process might first prompt for email and password, then show shopping cart details, and then collect delivery information on subsequent screens. The use of a "Completeness Meter" (e.g., for LinkedIn profiles) can also guide users through these steps and reduce friction [Script, uxpin_interaction_design_best_practices, 651].

Usefulness/Impact: This method makes processes feel more user-friendly and less overwhelming. Even though a single long form might theoretically take less total time, the perception of effort is reduced with smaller, paced steps, making it more likely that the user will complete the process and not abandon their action. The goal is to make the interface self-evident, obvious, and self-explanatory at each stage, requiring minimal effort from the user.

Case Study 3: Self-Service Scales in Supermarkets

Problem: Older self-service scales in supermarkets often present a single, flat list of many numbered buttons, each corresponding to a fruit or vegetable. The numerical association is arbitrary and changes frequently, meaning there's "no useful criterion in the arrangement of options" from the user's perspective, leading to a linear (high) decision time.

Hick's Law Application (Splitting Heterogeneous Choices): A more functional model adopted by modern scales applies Hick's Law principles by splitting the choices into two levels. Users first select a general category (e.g., "Fresh Fruit," "Vegetables," "Dried Fruit") from a concise, consistent list. Once a category is chosen, a second-level menu with fewer, more homogeneous items becomes visible. This is a key strategy to "reduce the number and the heterogeneity of the options".

Usefulness/Impact: Even though this introduces more levels to the interaction, it re-establishes a "consistent list" at the first level and reduces the immediate number of options displayed. This allows users to cluster options meaningfully and focus their attention on a subset, leading to a sub-linear (low) time of choice. This approach directly addresses the "paradox of choice" by focusing on the quality of how choices are organized and presented, rather than just the quantity.

Case Study 4: Long, Ordered Menu Lists (e.g., Contact Lists, State Selectors)

Problem: Some applications require displaying inherently long lists of items, such as contact directories, country lists, or lists of states, where minimizing the total number of items is not feasible. A naive application of Hick's Law might suggest these lists would always result in very long decision times.

Hick's Law Application (Contextual Nuance): While Hick's Law generally states that more items lead to longer selection times, there's a critical nuance: if the list is ordered (e.g., alphabetically) and the items are familiar or known to the user, they can be remarkably efficient at scanning. Users don't process each item sequentially; instead, their "eye lands first" and they "adjust their gaze accordingly" to quickly skip over irrelevant content because they know the name and order of the item they're looking for. Steve Krug's "second law of usability" also states that "it doesn't matter how many times I have to click, as long as each click is a mindless, unambiguous choice," implying that the speed of ignoring is key for long, ordered lists.

Usefulness/Impact: This understanding allows designers to effectively utilize long, ordered lists where appropriate, even if they contain many items. It prevents unnecessary design complexity to shorten such lists, acknowledging that users can navigate them efficiently by quickly finding and selecting their desired, known item. This means users are effectively "ignoring the 99 items that aren't my name" to find what they need.

🧩 Summarizing

These case studies demonstrate that Hick's Law is not merely a theoretical formula but a fundamental principle that guides designers in creating intuitive, efficient, and user-friendly products across various contexts. By understanding its implications, designers can strategically simplify decision-making, manage complexity, and ultimately enhance the user experience.

📚 References

1. Hick, W. E. (1952). On the rate of gain of information. Quarterly Journal of Experimental Psychology, 4(1), 11–26. https://doi.org/10.1080/17470215208416600

2. Hyman, R. (1953). Stimulus information as a determinant of reaction time. Journal of Experimental Psychology, 45(3), 188–196. https://doi.org/10.1037/h0056940

3. Krug, S. (2014). Don’t Make Me Think, Revisited: A Common Sense Approach to Web Usability (3rd ed.). New Riders.

4. Norman, D. A. (2013). The Design of Everyday Things: Revised and Expanded Edition. Basic Books.

5. Johnson, J. (2020). Designing with the Mind in Mind: Simple Guide to Understanding User Interface Design Guidelines (3rd ed.). Morgan Kaufmann.

6. Tidwell, J., Brewer, C., & Valencia, A. (2020). Designing Interfaces: Patterns for Effective Interaction Design (3rd ed.). O'Reilly Media.

7. UXPin. (n.d.). The Ultimate Guide to Interaction Design Best Practices. Retrieved from https://www.uxpin.com/studio/ebooks/interaction-design-best-practices/

8. Nielsen Norman Group. (n.d.). Progressive Disclosure: Reducing Cognitive Load. Retrieved from https://www.nngroup.com/articles/progressive-disclosure/

9. Budiu, R. (2015). Organizing Content: Information Architecture Basics. Nielsen Norman Group. https://www.nngroup.com/articles/information-architecture/

10. Schneiderman, B., Plaisant, C., Cohen, M., Jacobs, S., & Elmqvist, N. (2016). Designing the User Interface: Strategies for Effective Human-Computer Interaction (6th ed.). Pearson.

11. Iyengar, S. S., & Lepper, M. R. (2000). When Choice is Demotivating: Can One Desire Too Much of a Good Thing? Journal of Personality and Social Psychology, 79(6), 995–1006. https://doi.org/10.1037/0022-3514.79.6.995

12. Tognazzini, B. (2014). First Principles of Interaction Design (Revised & Expanded). Retrieved from https://asktog.com/atc/principles-of-interaction-design/

A Large Language Model (LLM) is a type of artificial intelligence model designed to process and generate human-like text. Built on advanced deep learning architectures, typically transformers, LLMs are trained on vast datasets of text to understand language patterns, context, and meaning. They are used for a wide range of tasks, including text generation, translation, summarization, question-answering, and conversational AI.

How Does an LLM Work?

LLMs operate by predicting the next word in a sequence, given a context of preceding words. This is achieved through a two-phase process:

1. Training Phase:

   • LLMs are trained on large-scale corpora, such as books, articles, websites, and codebases.

   • The training involves maximizing the likelihood of correct word predictions using self-supervised learning, where the model learns from the structure of text itself without requiring explicit labels.

   • Training uses GPUs or TPUs to handle computations involving billions of parameters over many iterations.

2. Inference Phase:

   • After training, LLMs can generate responses or perform tasks based on input queries (prompts).

   • In this phase, the model leverages the patterns and relationships learned during training to produce coherent and contextually relevant text.

Figure 1 - Basic Flow of LLMs

Key Concepts in LLMs

1. Transformers:

   • LLMs are typically built on the Transformer architecture, introduced in 2017. Transformers use self-attention mechanisms to focus on relevant parts of input data, allowing them to process long sequences of text efficiently.

   • Self-Attention enables the model to weigh the importance of words relative to others in a sentence, capturing context effectively.

2. Tokens and Tokenization:

   • LLMs process text as discrete units called tokens, which may represent words, subwords, or even characters.

   • Tokenization is the process of splitting text into these units, enabling models to handle diverse languages and structures.

3. Parameters:

   • Parameters are the model’s internal values learned during training. They determine how the model weighs different aspects of the input.

   • LLMs like GPT-4 may have hundreds of billions of parameters, allowing them to store and apply complex language patterns.

4. Pretraining and Fine-Tuning:

   • Pretraining: The model is initially trained on general-purpose text datasets to learn foundational language skills.

   • Fine-tuning: The pretrained model is further trained on task-specific data to specialize in particular applications (e.g., customer support, medical QA).

5. Embedding:

   • Text is converted into numerical representations called embeddings that capture semantic meaning. These embeddings enable the model to process and compare text efficiently.

6. Loss Function:

   • The training process uses a loss function, typically cross-entropy loss, to measure the difference between predicted and actual tokens. The model minimizes this loss to improve accuracy.

7. Reinforcement Learning with Human Feedback (RLHF):

   • For applications like conversational AI, models are fine-tuned with human feedback to align their outputs with user expectations and ethical considerations.

Applications and Challenges

Applications:

• Text completion (e.g., autocomplete in IDEs)

• Chatbots and virtual assistants (e.g., ChatGPT)

• Machine translation

• Sentiment analysis and text classification

• Document summarization

Challenges:

• Scalability: Training LLMs requires significant computational resources and energy.

• Bias and Fairness: Models can inadvertently learn and reproduce biases present in their training data.

• Interpretability: Understanding why LLMs make certain predictions remains difficult.

• Data Privacy: Using sensitive or proprietary text data raises privacy concerns.

Transformer Architecture in LLMs

The transformer architecture is the backbone of most modern Large Language Models (LLMs). It was introduced in the seminal paper "Attention is All You Need" (Vaswani et al., 2017). Transformers revolutionized natural language processing (NLP) by enabling models to process entire input sequences simultaneously, as opposed to earlier sequential approaches like Recurrent Neural Networks (RNNs).

Figure 2 - Transformation Architecture Model

Key Components of Transformer Architecture

1. Self-Attention Mechanism:

   • Self-attention allows the model to weigh the importance of each token in the input sequence relative to others, enabling context-aware understanding.

   • It computes attention scores to capture relationships between words, regardless of their position in the text.

2. Multi-Head Attention:

   • Instead of computing attention once, multi-head attention computes it multiple times with different learned projections, allowing the model to capture diverse relationships.

3. Positional Encoding:

   • Transformers process input sequences in parallel, so positional encodings are added to tokens to represent their order in the sequence.

4. Feed-Forward Layers:

   • Fully connected neural layers applied independently to each token position, enabling complex transformations of representations.

5. Residual Connections and Layer Normalization:

   • Residual connections improve gradient flow during training, while layer normalization ensures stable training dynamics.

6. Encoder-Decoder Structure:

   • The transformer architecture originally consisted of:

     • Encoder: Processes input data into a context-aware representation.

     • Decoder: Generates output sequences based on encoder outputs.

7. In LLMs like GPT, only the decoder is used for autoregressive tasks, whereas models like BERT use only the encoder for bidirectional tasks.

Advantages of Transformers

1. Parallelism:

   • Unlike RNNs, which process tokens sequentially, transformers process entire sequences simultaneously, making them faster to train.

2. Contextual Understanding:

   • Self-attention allows models to understand long-range dependencies between words in a sequence.

3. Scalability:

   • Transformers scale well to very large datasets and model sizes, which is essential for LLMs like GPT-4, PaLM, and LLaMA.

4. Versatility:

   • The architecture can be applied to various domains beyond NLP, including computer vision (Vision Transformers) and audio processing.

Other Architectures Used in NLP and LLMs

Although transformers dominate the landscape of LLMs, earlier architectures and some modern innovations also play a role:

1. Recurrent Neural Networks (RNNs)

• Sequential models that process one token at a time.

• Include variations like LSTMs (Long Short-Term Memory) and GRUs (Gated Recurrent Units).

• Limitations: Struggle with long-range dependencies and lack parallelism.

2. Convolutional Neural Networks (CNNs) for NLP

• Originally used in computer vision, CNNs have been adapted for NLP tasks by applying convolutions over sequences of words or characters.

• Faster than RNNs but less capable of capturing long-term dependencies compared to transformers.

3. Attention-Based Models (Pre-Transformer)

• Models like seq2seq with attention introduced attention mechanisms to improve performance on tasks like machine translation.

• The transformer architecture generalized these concepts to all layers.

4. Hybrid Architectures

• Combine RNNs or CNNs with transformers to leverage the strengths of each.

• Example: Transformer-XL, which extends the transformer with recurrence for better handling of long sequences.

5. Sparse Transformers

• Introduced to handle long sequences more efficiently by computing attention only for a subset of tokens.

• Example: Longformer for documents and Reformer for memory-efficient computation.

6. Retrieval-Augmented Models

• Combine transformers with external retrieval systems (e.g., Retrieval-Augmented Generation or RAG).

• Use retrieval to enhance the generative model’s outputs with specific knowledge.

7. Sequence-to-Sequence Models (Seq2Seq)

• Used in tasks like machine translation.

• The transformer itself was a significant improvement on seq2seq models with attention.

8. Diffusion Models (Emerging)

• While primarily used in image generation (e.g., DALL-E), diffusion models are being explored for generative tasks in NLP.

9. Graph Neural Networks (GNNs)

• Focus on relationships and structures in data, such as knowledge graphs.

• Typically used alongside transformers to improve context understanding for tasks like recommendation systems.

Comparison of Architectures

• Sparse Transformers

  • Excellent at context understanding.

  • Efficiently handle long sequences with minimal computational cost.

  • Low computational cost.

• Transformers

  • Excel at understanding long-range dependencies.

  • Highly effective but computationally expensive.

  • High computational cost.

• Recurrent Neural Networks (RNNs)

  • Cost-effective for sequential data.

  • Struggle with capturing long-range dependencies.

  • Limited context understanding.

• Convolutional Neural Networks (CNNs)

  • Computationally intensive.

  • Limited ability to understand global context.

  • Limited context understanding.

Transformers vs. Other Architectures

• Transformers dominate modern NLP because of their unparalleled ability to capture global context, scalability, and flexibility.

• Other architectures, like RNNs and CNNs, are largely relegated to specific tasks or integrated as components in hybrid systems.

Transformers have proven versatile enough to power not only LLMs like GPT-4, BERT, and PaLM but also cutting-edge applications in other domains, marking a paradigm shift in machine learning architecture.

RAG (Retrieval- Augmented Generation)

RAG (Retrieval-Augmented Generation) is a technique in Natural Language Processing (NLP) that combines a retrieval system with a generation model (usually a Large Language Model, or LLM) to enhance the quality and relevance of responses. It is particularly useful in scenarios where the LLM might lack up-to-date or domain-specific knowledge, or when the model's training data does not cover specific user queries.

How RAG Works

RAG operates in two main stages:

1. Retrieval Phase:

   • Relevant information is fetched from an external knowledge base, database, or document repository using a retrieval system.

   • Retrieval methods typically use tools like:

     • Vector search: Finds semantically similar documents using embeddings (e.g., with tools like FAISS or Pinecone).

     • BM25: A traditional keyword-based retrieval algorithm.

2. Generation Phase:

   • The retrieved documents are fed into an LLM (e.g., GPT, BERT-based models) along with the user query.

   • The model generates a response by combining its generative capabilities with the context provided by the retrieved documents.

Key Components of RAG

1. Retrieval System:

   • Finds and ranks relevant documents or data from an external source.

   • May involve semantic search or keyword-based search.

2. Large Language Model:

   • Generates coherent and contextually accurate responses using retrieved data and its own linguistic understanding.

3. Knowledge Base:

   • The external source of information, such as a database, a set of documents, or an enterprise knowledge repository.

Why Use RAG?

1. Knowledge Updating:

   • LLMs like GPT-4 are trained on static datasets and may lack recent or specialized knowledge. RAG enables them to access up-to-date information.

2. Scalability:

   • Instead of embedding vast amounts of domain-specific data into the LLM, RAG dynamically retrieves information, reducing computational and storage costs.

3. Domain Expertise:

   • RAG allows the model to incorporate specific, fine-grained knowledge that may not be present in its training data.

4. Reduced Hallucinations:

   • By grounding responses in retrieved factual documents, RAG minimizes the risk of the model generating incorrect or fabricated information.

Use Cases of RAG in LLMs

• Customer Support

  • Answers complex queries by retrieving company policies or FAQs.

  • Example: Chatbots integrated with enterprise knowledge bases.

• Legal Document Analysis

  • Provides answers or summaries from legal text corpora.

  • Example: AI-powered legal assistants (e.g., contract analysis tools).

• Healthcare Applications

  • Offers medical advice by referencing clinical guidelines or literature.

  • Example: AI assistants for doctors (e.g., retrieving studies or drug info).

• Educational Tools

  • Generates answers or content using textbooks or scientific papers.

  • Example: AI tutors referencing syllabi or research publications.

• E-Commerce Search

  • Recommends products and resolves customer issues by accessing catalogs or manuals.

  • Example: Intelligent search features on platforms like Amazon.

• Enterprise Knowledge Management

  • Helps employees retrieve relevant documents from internal databases.

  • Example: Microsoft Copilot, Salesforce Einstein GPT.

• Research Assistance

  • Fetches relevant academic papers and scientific findings for researchers.

  • Example: Semantic Scholar, tools integrated with ArXiv.

Advantages of RAG

1. Efficiency:

   • Dynamically retrieves specific information instead of relying on the LLM's memory alone.

2. Flexibility:

   • Works with a wide variety of external data sources, including structured and unstructured data.

3. Accuracy:

   • Grounded in factual, retrieved data, improving the reliability of responses.

4. Cost-Effective:

   • Eliminates the need to fine-tune LLMs on every domain-specific dataset.

Challenges in RAG

1. Retrieval Quality:

   • The effectiveness of RAG depends heavily on the retrieval system's ability to fetch relevant documents.

2. Latency:

   • The two-step process (retrieval and generation) can increase response times.

3. Data Management:

   • Ensuring the knowledge base is accurate, up-to-date, and free of sensitive information is critical.

4. Context Handling:

   • Integrating retrieved information seamlessly into the LLM's response while maintaining coherence can be complex.

Popular RAG Implementations

1. OpenAI GPT with Retrieval:

   • Integration of tools like Pinecone or FAISS to retrieve context before feeding queries to GPT models.

2. LangChain Framework:

   • A Python-based framework for building RAG pipelines by combining LLMs with external data retrieval.

3. Hybrid Search Systems:

   • Combining traditional search techniques (BM25) with embedding-based methods for improved retrieval.

4. Google Bard:

   • Uses retrieval-augmented techniques to deliver up-to-date and relevant information to user queries.

Applications of Various Large Language Models (LLMs)

Large Language Models (LLMs) have revolutionized natural language processing (NLP) and artificial intelligence (AI), finding applications across diverse industries. For chatbots and conversational AI, models like GPT-4, Claude, and ChatGLM power customer support and virtual assistants. In text generation, GPT-4, GPT-3.5, PaLM, and Falcon are used for creative writing and marketing content. Summarization tasks such as condensing legal briefs and meeting notes rely on GPT-4, BERT, T5, and Flan-T5. For translation, models like PaLM 2, Bloom, and ChatGLM handle multilingual support and document translation.

In sentiment analysis, Cohere, BERT, and Bloom help companies process social media and customer feedback. Question answering systems like GPT-4, Claude, and Falcon are used in chatbots, FAQs, and knowledge retrieval. Code assistance is supported by Codex, GPT-4, and CodeT5, helping developers with IDE integration and coding suggestions. For personalized recommendations like product suggestions or playlist generation, companies use GPT-4, Claude, and Bloom.

Document analysis tasks such as contract review and resume screening are powered by Claude, Ernie Bot, and LLaMA. In medical applications, GPT-4, MedPaLM, and Bloom assist with diagnosis suggestions and patient Q&A. In education and training, GPT-4, Duolingo AI, and PaLM create tutoring experiences and educational materials. Search engine enhancements, including semantic and voice search, are driven by GPT-4, Claude, and Ernie Bot.

In gaming, GPT-4 and DALL-E enable dynamic storytelling and quest creation. For legal applications, GPT-4, LLaMA, and Cohere support contract drafting and legal research. In news and journalism, GPT-4, Flan-T5, and Bloom help automate article writing and news summarization. Data analysis and insights such as market trend analysis and financial summaries are powered by GPT-4, Claude, and Falcon.

Multimodal applications — combining text with images or video — use GPT-4, CLIP, and PaLM 2 for tasks like image captioning and video summarization. Content moderation, ensuring social media compliance, is supported by GPT-4, Cohere, and Ernie Bot. In social media analysis, GPT-4, Bloom, and BERT detect trends and measure campaign performance.

For cybersecurity, Falcon, GPT-4, and Granite are used for threat detection and log analysis. In recruitment and HR, GPT-4, Claude, and BERT streamline resume screening and job description generation. Creative design projects, like ad campaigns and storyboarding, are assisted by DALL-E and GPT-4. In financial services, GPT-4, Claude, and Falcon help with fraud detection and risk assessment.

Voice assistance for smart homes and vehicles is powered by GPT-4, ChatGPT, and PaLM. Finally, environmental monitoring tasks, such as weather prediction and environmental impact assessment, are supported by GPT-4, Cohere, and Falcon.

Robert B. Cialdini’s seminal book, Influence: The Psychology of Persuasion, offers invaluable insights into the art and science of persuasion. In this blog, we’ll explore how product managers can apply Cialdini’s six principles of influence—Reciprocation, Consistency, Social Proof, Liking, Authority, and Scarcity—to lead cross-functional teams effectively and drive impact.

1. Reciprocation: Building Trust Through Giving

Reciprocation takes into account the two key behavioral facts of human beings:

1. Human beings are social animals and always try to behave in a socially approved way.

2. Humanity’s “Web of Indebtedness” – Human beings tend to feel obligated to return the favor/reciprocate back when you do something for them

Reciprocation is a powerful method of persuasion as it generally leaves someone with two choices: Agree to the request of the person who has done something for you – the socially approved way, or decline and face the social shaming as per the long-established rules of the society. A good example is set by charitable organizations when they send small gifts along with their request for donations.

Another act of reciprocation is what is called the “rejection-then-retreat” method – you concede to someone who has previously conceded to you. In this case, the persuader first makes an unrealistic request being aware that he will be turned down, and then smartly brings it down to the actual request which he wants you to agree upon. Smart right, did that make you remember a few meetings with sales professionals?

Reciprocity is rooted in the human instinct to repay kindness. As a PM, you can leverage this principle by offering value first—whether it’s sharing insights, solving a team’s pain points, or simply expressing genuine appreciation. For example: A PM working with a design team noticed they were struggling with outdated tools. By advocating for and securing better resources, the PM earned the team’s trust and goodwill. When the time came to push tight deadlines, the designers were more willing to go the extra mile.

2. Consistency and Commitment: Aligning Goals

Human beings try to portray consistency and commitment under social pressure and with an internal desire to build a self-image of their own. This, in turn, results in fooling oneself to maintain thoughts and belief systems consistently. PMs can harness this by involving team members early in decision-making. When individuals feel ownership of an idea, they are more likely to stay committed.

• Example: In a roadmap planning session, a PM invited engineers to propose solutions to major technical challenges. By validating their input and incorporating their suggestions, the PM ensured buy-in and accountability throughout the project.

A key tip: Document agreements and commitments during meetings. Referring back to these records reinforces the importance of consistency.

3. Social Proof: Leveraging Collective Influence

Humans often look to others for cues on how to act, especially in ambiguous situations. People tend to look up to others with similar choices and traits like age, language, demography, etc. They tend to associate themselves with a social group. Hence, the behavior is largely dependent on this and social proof is the most powerful and ambiguous influencing tactic. Why ambiguous? Take the example of cult followers or fan following of celebrities who do crazy things for the sake of their associations.

Social proof is used as a powerful tool to bias one’s thought process and decision making. Take the examples of the advertising domain or for that matter, TV shows use laugh tracks. They try to showcase socially accepted evidence to make you laugh or buy something.

Another aspect of social proof is explained by the author with “Werther Effect”, that elaborates on the series of suicides that happened across Europe, is the result of the suicide story of the hero of the classic book The Sorrows of Young Werther by Johann Von Goethe. In modern-day as well ‘Werther Effect’ influences societies that often result in a series of suicides or accidents following a suicide making the news.

PMs can use this principle to build momentum by showcasing examples of success.

• Case Study: A PM at a startup faced resistance to adopting a new analytics tool. By highlighting testimonials from other teams who had successfully implemented the tool and sharing measurable benefits, they convinced the skeptical teams to give it a try.

Similarly, celebrate small wins publicly. When a team sees their peers achieving milestones, they’re more likely to follow suit.

4. Liking: Creating Authentic Connections

People are more inclined to work with those they like. One tends to be very agreeable to someone who they like, thanks to “The Halo Effect”. Liking, as an influencing tactic, has an emotional basis to it. Liking can be based on various factors: physical characteristics like good looks or being handsome, familiarity with people who you have close connections with, hospitable people who serve you food and drinks, etc. These people cast a positive “halo” because of your liking for them. This gives them greater influence over you. A good example of this, as per the author, is one Canadian election where good-looking candidates won two and a half times more votes than their competitors.

For PMs, building rapport is not just about being friendly—it’s about understanding and empathizing with your team’s needs and motivations.

• Case Study: A PM leading a distributed team made a point to learn about each member’s interests outside of work. Sharing casual conversations about shared hobbies fostered goodwill and collaboration during critical sprints.

To amplify this principle, practice active listening, acknowledge contributions, and celebrate team members’ successes. As Cialdini notes, people respond to the "halo effect”—positive feelings about one aspect (likeability) can extend to others (your ideas).

5. Authority: Leading with Expertise

Authority is a powerful tool of persuasion. When someone speaks with authority, people tend to be more agreeable to them. A good example of authority is aircraft flight crew following their captain’s instructions even when they make no sense. The rationale behind this is also explained by the author with the help of an experiment where, as per the author, “With each increase in status, the same man grew in perceived height by an average of a half-inch, so that as the ‘professor’ he was seen as two and a half inches taller than as the “student”. So, people tend to perceive someone with authority differently and this perception plays the trick when it comes to persuasion.

While PMs may lack formal authority, they can establish credibility through expertise. Demonstrating deep knowledge of your product, market, and user needs earns respect and trust.

• Example: During a contentious prioritization meeting, a PM presented user research data and competitive analysis to back their recommendations. The clear, data-driven approach swayed even the most skeptical stakeholders.

Borrowing authority can also be effective. Quote industry leaders or reference successful competitors when advocating for a strategy.

6. Scarcity: Creating Urgency

Scarce resources are always considered more appealing and valuable. Limited the supply or shorter the time-frame of access, the more the demand. This is the reason why every coupon has an expiration date (implicitly urging you to use it before it expires). Scarcity is deeply instilled in our brains and it instigates the fear of loss or the fear of loss of choice. This tends to make the scarce resources important.

Scarcity is a key sales and marketing tactic with limited-period offers, the core of which lies in creating a desire to avoid missing out on something. So, it is important to recognize the situations when someone plays this tactic, step back, and examine before rushing to make a decision.

Scarcity drives action by making opportunities feel time-sensitive or exclusive. PMs can use this principle to prioritize initiatives and rally teams around deadlines.

• Example: To meet a tight launch date, a PM framed the timeline as an opportunity to capture a competitive advantage in a growing market. By emphasizing the potential loss of market share, they motivated the team to deliver on time.

Beware of overusing this tactic, as it can lead to burnout. Use scarcity sparingly and authentically.

Quotes from Great Minds

“The key to successful leadership today is influence, not authority.” – Ken Blanchard

“Leadership is not about being in charge. It is about taking care of those in your charge.” – Simon Sinek

Final Thoughts

Influence is not about manipulation; it’s about understanding human behavior and creating environments where teams feel motivated and empowered to succeed. By mastering the principles of influence, product managers can build strong relationships, drive alignment, and lead their teams to deliver exceptional outcomes.

In the words of Robert B. Cialdini, “People prefer to say yes to those they like and trust.” As a PM, your ability to inspire and influence will define your success more than any title ever could.

More reading: Cialdini, Robert B. (2006). Influence: The Psychology of Persuasion. Harper Collins Business Essentials

However, building and selling software products for small and medium businesses is no joke. The entrepreneurs and product managers who have uphold this responsibility can clearly empathize with this. It’s an unique challenge only if you are poised and excited to embrace it, else it can drain you. Why so?

Because, firstly it is easy to identify an underlying problem(s) of small businesses and build a solution around it. But then to go about and acquire customers who would actually appreciate the solution that has been built and will be ready to pay empathetically is the biggest challenge. Yes, the challenge is scaling and monetizing the product profitably. Small businesses are often unorganized and have build their own processes around as they grew. So they seek customizations in the product that can make it work in tune to their ways. And, all these product customizations that they seek are in absence of the dollars that they are willing to pay. So, it’s an arduous task to meet customer demands and acquire them. Acquisition is one aspect and then there is a huge challenge to retain them. SMB SaaS typically sees a high churn rate.  So, in short it takes years to scale your product as in initial phase the growth in customers might just be linear. It may take years to reach the 'Knee Point' where growth will see an exponential aspect to it and scaling will be much faster thereafter.

That's the reason you will find very less software products for SMB space who have made it big like Intuit.

So, to summarize, yeah its hard. If you are not persistent and perseverant about your product and have a solid balance sheet behind sustenance of your company, you might end in giving up or taking an exit.

Sometime back I was leading product endeavors at greytHR, a SaaS based HR & Payroll software for SMBs in India. For the uninitiated, greytHR is a market-leader in India and Middle-east in SMB segment for HRIS software. When I was associated with them they had already close to 7000 paying customers (till March 2019) and were growing pretty rapidly. greytHR is slightly more than a decade old. And it was a great learning experience for me understanding this unique market segment, working with thousands of customers and building products for them. I am going to share a few key learnings in this post which can help budding product managers and entrepreneurs in SMB space to manage their products better.

1. Make friends with your first 25-50 Customers:This is a key starting point. Once you have acquired your first 25-50 customers, its very important to work with them closely. It makes sense to lie down with them all the time. See how they use the product, what hiccups they face, what is their feedback, do they see any value in using the product, is it helping them succeed? This will help you in furthering the viability of your product in the market, give you cues into bettering the product features and will help you redefine the next set of key features that you should be adding. Also, a good relationship with these customers can help in spreading that initial word about your product and defining key marketing/sales assets (1 pager value props, success stories, testimonials, case studies, app reviews, etc.) that will aid in acquiring next 500 customers.

2. Emphasize on the 'Perceived' Value: As SMBs are unorganized, they look forward to a software to standardize and help them automate their business processes that will save their time for focusing on their growth trajectory. So, the software should be able to emphasize the value to your customers that they can perceive, acknowledge and appreciate. For this you might have to focus on making the software easy to use and getting your customers to succeed with your product.

   1. Make it easy to use: It is very important to think through the user experience and simplicity while defining the product features across each touch point right from product evaluation to onboarding to first usage in the product. One has to understand that the users in SMB space are not that seasoned or tech. savvy like one will see in large enterprises. For example, in case of HR product for SMBs, users are not even HRs in most cases. They are small business owners, fresh MBA-HR grads, people managers, IT admin or anyone who has been assigned with that business process responsibility. So, its key to simplify or rather de-mystify the business process for the user and make it seamless for him to realize the value.

   2. Customer Success Matters: It is key to gauge the customer's usage of the product and health score of the account. As churn is very high in SMB space, it is always advisable to periodically touch base with your customers share relevant data that demonstrates a measurable and trackable ROI to your customers, especially in initial few months. Have forums where your product experts engage with customers, lend their helping hand by addressing their concerns, remove their initial purchase anxiety and constantly nurture them with product benefits, use cases, tips and tricks which can help foster the product usage and thereby perceive the product value.

3. Focus on Effective Product Discovery: Engineering a product or a key feature is a costly affair. Hence, as a PM, when you commit on building a product or key feature, it is very crucial to do it 'First Time Right'. This calls for an effective product discovery. Product discovery is all about gauging on incoming product ideas/feature requests and then methodically picking up select few for incorporation in the product. As a PM, you can start by weighing the incoming product ideas with different parameters like business viability, technical complexity and operational feasibility. Its best to start with a secondary research of market potential by deep diving through market insights and competition to see if the idea is viable for your business and then reaffirm the same with customer interviews. Once you have selected the product feature that you want to build, its best write a detailed product narrative covering use cases and benefits that your customers will derive out of this and partner with a UX designer to envision it with the help of a prototype (usually click-through works best to assess the usability and user experience). It makes sense to validate this prototype with your key stakeholders within the company to avoid any hassles later during engineering. Lastly, user testing is a must to validate if your users see any value in the product feature, and make sure you consider their feedback before you transition the feature for development.

4. Genericity vs Specificity: While continuously building and developing features for your product, It's always wise to think through various use cases and have a product or feature vision in mind with solid research into domain/industry/market and competitive landscape. Keeping the product or feature generic reduces the functional debt over a period of time and aids to easy scalability of the product. Do not build literally whatever customers ask for. While addressing customer asks are key for a growing business, it is better to understand the root problem and solve that. Most customer asks will be around the issue that they directly face and almost always superficial in nature (around UX like give me a button to do this do that or let me have a checkbox of options to select, can I have an API to do this, etc.). But it makes sense here to talk to customers, understand the reason they wish to do it this way and identify the root problem. With this root problem, you will be in a better position to see if this problem needs to be solved, does it make sense for your key customer segments, can this be productized. If yes, then you can take this root problem and can derive use cases on how your key customer groups are going to solve their problem(s) using this feature and put forward an elegant solution by focusing on extensibility, scalability and usability.

5. Be Data Informed: Its best to define key success metrics for your product or feature and set up a product analytics dashboard. This is irrespective of the product phase or the number of customers you have. Data that goes into this dashboard can be software telemetry, system events like clicks, scrolls, etc., usage data, task completion rate, behavioral data or explicit user created data like feedbacks, ratings after a key user journey within the product. These can give you cues with respect to usage and user interactions within the product or simply put can help you stay informed on the whether your product is delighting customers or not. It should also be of note that in SMB or enterprise space you have to be sensitive about the data that you are tracking else it can get you into legal hassles. Mostly software telemetry data is harmless and customers should not have any concerns but its better to add it to terms of usage and make them agree before they start using your product.

6. Define the Boundary conditions: It's key to define boundary conditions while developing a feature. Boundary condition is all about placing limitations around your product features keeping the product capabilities and use cases in mind in order to optimize the product performance and provide a better user experience. You can consider this as max. and min. definitions of a feature. For example, if you have a table in your feature which can have custom columns, it's important to define the minimum number of system provided columns that will be available out of the box, maximum number of custom columns that it can have, maximum number of rows that the table can support, maximum file size for import capabilities if the table has import, etc. If you do not define this, then there are high chances of users misusing the feature capabilities (of course unknowingly). As discussed above, SMBs are unorganized and while growing they define and re-define processes which are not the industry standards but work-arounds which can quicken their growth trajectory (sometimes well known as 'jugaad'). To cite an example of the same, in the previous HRIS product that I had managed, we were once surprised to see the various ways our customers were using (abusing) the employee document management system. From storing company and historical HR files/records to manage employee performance to use it like almost like a Dropbox, customers on their own have worked out (frugally innovated)various number of use cases in absence of any set boundary condition defined within the system. This is value for money for them but in SaaS it’s a cost of cloud for the vendor. Additionally, it impacts product performance and many a times attract unnecessary support requests which as a PM you won't want for your product.

Last but not the least, its also important to understand the market dynamics (esp. competition) and improvise on the pricing tactics like having a freemium-limited use pricing plan, distinct product plans separately catering to the needs of small businesses and mid-market segments, etc.

Happy building classy products & delighting your customers!

More readings and references:

1. Druker, P.F., 2009. Innovation and Entrepreneurship, New York: Harper Collins.

2. NEAGU, C., 2016, The importance and role of small and medium-sized businesses, Theoretical and Applied Economics Volume XXIII (2016), No. 3(608), Autumn, pp. 331-338

3. https://medium.com/product-to-product/saas-product-management-explained-by-6-product-managers-182e46082380

4. https://www.productplan.com/saas-product-metrics-pyramid/

5. https://www.salesforce.com/hub/sales/small-business-sales-guide/#

6. https://hackernoon.com/why-you-should-be-data-informed-and-not-data-driven-76079d187989

7. https://economictimes.indiatimes.com/small-biz/sme-sector/smbs-fast-learning-and-adopting-new-technologies-google-india/articleshow/69957362.cms?from=mdr

8. https://financesonline.com/software-for-small-business/

Basic Essentials (for Getting Started)

1. Inspired by Marty Cagan

Marty Cagan’s “Inspired” is an absolute cornerstone for aspiring Product Managers. It explores the intricacies of creating innovative, user-centered products and emphasizes the importance of product leadership within an organization.

2. Getting Real by 37 Signals

For a pragmatic approach to product development, look no further than Getting Real by 37 Signals. It advocates for simplicity, agility, and effective team collaboration—a must-read for anyone looking to build great products efficiently.

3. [For Interviews] Decode and Conquer by Lewis C Lin

Preparing for PM interviews? Lin’s book is your secret weapon, offering a wealth of insights, strategies, and example interview questions to help you navigate the challenging PM interview process.

4. [For Interviews] Cracking the PM Interview by Gayle McDowell and Jackie Bavaro

Another invaluable resource for interview preparation, this book provides comprehensive guidance on mastering PM interviews, complete with real interview questions and solutions.

Product and Experience Design

5. The Design of Everyday Things by Don Norman

Don Norman’s classic delves into the principles of user-centered design, shedding light on how good design can make products more intuitive and enjoyable to use.

6. Hooked: How to Build Habit-Forming Products by Nir Eyal

Nir Eyal’s exploration of habit-forming products is a goldmine for PMs aiming to create user engagement that lasts. Learn how to craft products that people can’t resist.

7. Don’t Make Me Think by Steve Krug

Usability is paramount, and Steve Krug’s book teaches you how to design interfaces and websites that are intuitive and user-friendly—essential for a PM involved in digital products.

8. Change by Design by Tim Brown

Tim Brown’s insights into design thinking are instrumental in shaping a product manager’s approach to innovation and problem-solving. Discover how design can drive positive change.

Rapid Experimentation, Growth, and Customer Development

9. Sprint: How To Solve Big Problems and Test New Ideas in Just Five Days by Jake Knapp

Incorporating the principles of Google Ventures’ design sprint, Jake Knapp provides a blueprint for rapid ideation and testing, an indispensable skill for modern PMs.

10. The Lean Startup by Eric Ries

Eric Ries’ book revolutionized how startups and enterprises approach product development. Dive into lean methodologies, MVPs, and validated learning to build successful products efficiently.

11. The Four Steps to Epiphany by Steve Blank

Steve Blank lays the foundation for customer development—an essential practice for Product Managers aiming to create products that truly resonate with their audience.

12. Zero to One by Peter Thiel

Peter Thiel’s book challenges conventional thinking and encourages innovation. It’s a treasure trove of entrepreneurial wisdom and a valuable read for PMs navigating disruptive landscapes.

Data-Driven Product Management

13. Naked Statistics by Charles Wheelan

A grasp of statistics is pivotal in making informed product decisions. Charles Wheelan’s book makes this often-daunting subject accessible and enjoyable.

14. Lean Analytics by Alistair Croll

Learn how to measure, analyze, and iterate your way to success with Alistair Croll’s guide to lean analytics. It’s a roadmap for data-driven decision-making in product management.

Product Leadership

15. Influence: The Psychology of Persuasion by Robert Cialdini

Understanding the psychology of influence is crucial for product managers. Robert Cialdini’s book provides insights into persuasion that can enhance your product’s appeal.

16. Deep Work by Carl Newport

Carl Newport’s book offers practical advice on achieving deep focus and productivity, essential for juggling the multiple demands of product management.

17. The Hard Thing About Hard Things by Ben Horowitz

Drawing from his own experiences, Ben Horowitz’s book delves into the challenges and tough decisions faced by leaders in the tech industry, providing invaluable lessons for product managers.

18. The Innovator’s Dilemma by Clayton Christensen

Clayton Christensen’s seminal work explores why great companies often fail when faced with disruptive innovation. Understanding this dilemma is essential for PMs navigating evolving markets.

19. Measure What Matters by John Doerr

John Doerr’s OKR (Objectives and Key Results) framework has been a driving force behind the success of companies like Google. This book details how OKRs can propel your product and team forward.

20. The Effective Executive by Peter Drucker

In this timeless classic, Peter Drucker outlines principles for effective leadership and management, offering insights that can help you excel in your role as a product manager.

Incorporating these books into your reading list can elevate your skills, broaden your perspectives, and ultimately make you a more effective and innovative Product Manager. Remember, the journey of learning is continuous, and the wisdom gained from these texts can serve as a guiding light on your path to excellence in the world of product management. Happy reading!

Fundamentally, we can divide the skills required for any professional into three categories: Technical Skills, Functional Skills, and Operational Skills. At the beginning of the career, professionals are more focused on acquiring technical skills followed by functional skills. Mid-career professionals are very skilled technically and focus more on mastering the functional skills plus building up operational skills. As they mature in the functional skillset and add strength to their operational skills they take a leap into the leadership positions. Let’s take an example to understand this more, a software engineer during the beginning of the career tries to build his/her grasp on programming languages (Java, Python, etc.). With career advancement, they build their functional skill set that is system design and architecture principles and finally as they graduate to the leadership position they master enterprise architecture, technology leadership, and people management skills.

However, product management has a slight deviation to this because most professionals enter into product management at a mid-career level from different backgrounds. Hence, they are expected to be proficient technically in their area of expertise (say engineering, business or design), competent functionally and sound operationally. Those Product Managers, who join fresh after an MBA or MS program, are usually put into rotational product management internship programs (like that of Facebook or Google), post which they start as associate product managers. These product managers at the associate level are also expected to learn fast and acquire the necessary competencies for them to graduate as Product managers. So, for product managers, the operational skills do make much of a difference as in how good they are at product management. For example, if you have joined as a product manager from software engineering background you would already be skilled in system design principles, you can quickly grasp the requirements management and prioritization techniques but what will be challenging for you is to lead different cross-functional teams without authority and get things done from them. So, let us discuss some of these key operational skills that can make a lot of difference in you as a product manager and help you succeed.

1. Influence

Influence is the culminated outcome and cyclical effect of five key behavioral characteristics that help product managers to build trust and earn respect from various stakeholders of software product organization, hence lead them progressively. These five key behavioral characteristics are: Listening, Understanding, Inspiring, Persuading, Leading. These behavioral characteristics are closely tied to each other. Good product managers are the ones who are great listeners. They listen to their team, their customers, and other stakeholders with who they have to work closely to build and ship the product. So, a lot of listening helps them to understand their needs, concerns, and challenges. Listening is different from just hearing. It is about making sense out of what the other person is saying in a more observant way, which helps in deriving insights out of other person’s statements. Hence, great listening leads to a good understanding. A good understanding of the needs and issues helps product managers to build trust and confidence with these stakeholders. The product manager speaks to them in their language, resonate with their thoughts. This, in turn, helps the product manager to inspire these stakeholders by creating a sense of purpose. Inspiration acts as a means to persuade them to align towards the product vision and strategic objectives and help the product manager to lead them in getting things done and achieving the product milestones.

2. Communication

After influence, I would consider ‘Communication’ as one key operational skill that helps good product managers stand out. First of all, let’s look at the host of information that product managers need to communicate. It can be as simple as a user story to as strategic as the product vision. In between you have customer insights, market intelligence, product narratives, functional and non-functional requirements, release information, metrics, and the list can go and on. All these have to be communicated to different stakeholders in a manner that it can be rightly assimilated by them. Hence product managers are expected to be great communicators to effect maximum collaboration from them. Communication has three key aspects: Oral Communication, Written Communication, and Visual Communication. Oral communication is the use of both verbal communication and non-verbal cues to clearly and crisply express thoughts, ideas, and information. Similarly, Written Communication is all about concisely delivering the information to the right audience in a contextual manner. In the case of both oral and written communication, product managers need emotional intelligence and a stronghold of language and style to communicate in the right tone.

Product managers are also involved in a lot of training, coaching and mentoring activities. They are required to impart product training, knowledge transition sessions, customer webinars, product roadshows and mentoring junior product managers. Visual communication works best in these sessions to aptly impart the required information. Visual Communication is the effective use of images, charts, infographics and interactive content to communicate meaningfully.

3. Product Thinking

Product thinking involves the application of both design and systems thinking to conceptualize a product solution. Product thinking involves a user-centered approach to design and define the product. It starts from understanding and framing the problem that the product will solve for its users and then goes into the design and define a product that can help users achieve their goals in a very directed and coherent manner. As a product manager, you think in terms of a product as a whole and not as a bunch of systems that interact with one another.

Product thinking is at the core of product management craft that defines great product managers. Good product managers first understand the problem and its underlying issues. They then apply design thinking to creatively come up with ideas or concepts that can possibly solve the problem. These ideas or concepts are validated and tested by asking the right questions with vetted assumptions. Once an idea is validated then product managers apply systems thinking to define a scalable set of system components, their associated behaviors, and interactions. Though it involves both technical and functional skills, holistically one needs to be operationally sound at this to design a great product.

4. Customer Advocacy

Product managers are the voice of the customers in the software product organization. They wear customers’ hat during the product discovery and play the role of customers’ representative while working with the engineering team during the development of the product. Hence, customer advocacy is an integral operative of the product manager. Customer Advocacy is the ability to understand and empathize with customers to understand their needs, their problems and their challenges to be able to be their voice throughout the product life cycle. This helps in building products that are great in terms of user experience and usability. In turn, it is well received by the market, gets a good adoption rate and leapfrogs the product business.

5. Product Leadership

Product Leadership is all about leading the product organization towards the product vision and closer to the company’s business objectives. Product Leadership is very different from the other organizational leadership areas because product managers lead without authority. Product leadership relies heavily on having good emotional intelligence and intricate people skills. This is required to tackle different stakeholders by helping them understand the desired future state and show them the associated mutual interests so that there are less resistance and more collaboration. Product leadership brings everyone together, motivates them to work together to achieve product goals and objectives. Product managers are required to facilitate and lead discussions, smartly resolve conflict, and take apt decisions especially during the times of deadlock. This also helps them to earn the required respect which is much needed for them to lead the product in the appropriate direction.

A great product leader is the one who makes stars align, who makes things happen, who plays both the role of a Rockstar as well as a Servant Leader, all this to build and ship a great product. And, to that, these 5 skills according to me makes a lot of difference and helps the product manager stand out. Hope this makes sense. Let me know what you think.

Happy building classy products!

More readings and references:

1. Cialdini, Robert B. (2006). Influence:  The Psychology of Persuasion. Harper Collins Business Essentials

2. https://medium.com/build-acl/pixars-rules-of-storytelling-applied-to-product-managers-ux-designers-420cec0a18a6

3. https://a16z.com/2012/06/15/good-product-managerbad-product-manager/

4. BABOK v2 by IIBA

Product Manager is responsible for coming up with ideas for the product.

There is all hype about product managers being those smart folks who solve challenging problems, come up with great ideas and build world-class products. We have discussed these as in how the truth is far from it, in our previous articles. The very concept that idea generation is one of the critical product management jobs is a big myth. In reality, most ideas come from customer feedback, and market opportunities (via sales and marketing channels). Having said that, an idea or an innovative concept can come from anyone be it customers, engineering team members, support executives, or sales & marketing folks.  Product Managers are responsible for validating the idea from a business angle and see if it fits into the product vision and long term product strategy. They then put the validated idea onto the roadmap and further refine them into product requirements that can be picked up by engineers for development. They uphold the product strategy throughout the product life cycle.

Product Manager is the same as the Project Manager with different title.

This is a common myth especially among those who have heard about product manager roles but do not understand it properly. Mostly it is based on the hear-say that product manager is a project manager in software product organizations. This is not true. Product manager and project Manager are two discrete roles. A project manager is the owner of a project and is responsible for project management activities like Project Planning, Budgeting, Allocating Resources, Managing Timelines, Project Coordination, Project Deliverables, and Stakeholder Reporting. They are usually not responsible for Product or Project Requirements, hence they do not need a domain or product knowledge.

Product Managers, on the contrary, set product vision, define product strategy, manage product requirements throughout the product lifecycle and are responsible for customer development and growing product business. This is a bigger responsibility than just managing the project, which they perhaps also do in certain organizations where they own product delivery as well. We have elaborated this in our article ‘Who is a Product Manager?‘

Product Manager is the same as the Product Owner.

This is a popular misconception as the product manager also plays the role of a product owner in many software product organizations. However, it may be noted that the Product Owner is an agile role and is responsible for defining user stories, driving execution of the stories and, managing product backlog for the scrum team. That is just one aspect of product management: Requirements Management. Product Managers’ role is much more than just ‘Requirements Management’. They are also responsible for Product Strategy and Customer Development.

Product Manager sets the dates and timelines for feature delivery.

This is something most would say is true. But it is also a common misconception about the role of product manager. The product manager cannot decide on the dates and timelines as he is not going to build the product by himself. The engineering team is responsible for product development, hence as a team, they decide on what could be a reasonable estimate based on the complexity of the user story and their capacity. Again this is an estimate, the actuals may vary. So, if there are external commitments that Product Managers have to abide by, then they have to use their requirement prioritization skills to see how can they best deliver a reasonable feature set within the stated deadlines.

So, that was all about a few myths around the role of Product Manager. I hope this article helped in debunking these myths and added more clarity on what product manager is, what they do and what they don’t.

Happy building classy products!

More readings and references:

1. https://www.pmi.org/about/learn-about-pmi/who-are-project-managers

2. https://www.scrum.org/resources/what-is-a-product-owner

3. https://www.mindtheproduct.com/what-exactly-is-a-product-manager/

Thus, simply put: “A Product Manager is the one who is responsible for building and maintaining the product throughout its lifecycle.” He is responsible for building the product, maintaining it, scaling it and growing the product business throughout the entire lifecycle of the product.

A Product Manager in an organization is responsible for,

• Setting up a compelling PRODUCT VISION, a stable and long-term one, which shows the big picture of how the product can translate the ‘Problem space’ to ‘Solution space.’

• Formulating a great PRODUCT STRATEGY, which illustrates how this vision is going to be achieved by the product organization.

• Leading the PRODUCT DESIGN & EXECUTION, which is all about getting things done. This is the most challenging aspect of product management as it needs collaboration from multiple teams, right from User experience to Engineering to Technical Writing to Sales, Marketing and Ops. It demands the product manager to be communicative and persuasive to push that needle to make all-stars align so that the product can be shipped and monetized.

• Driving the CUSTOMER DEVELOPMENT initiatives, to achieve the product-market fit and then putting up a customer acquisition process in place for growing the customer base and increasing the adoption of the product.

A Product manager plays a different set of roles in different phases of the product life-cycle. In the product discovery phase, the product manager plays the role of ‘Product Designer’, where they closely work with the user experience designers and domain experts (of the problem area) to conceptualize the product, define the product requirements and translate it to a product prototype.

In the development phase, they work closely with the engineering team as a program manager to provide them guidance and direction on building the product. They help the engineering team in release planning, owning and managing the product backlog, scoping the user stories, guiding the team on prioritization and finally accepting them for the product release.

During the product introduction phase, one of the challenging aspects of the product manager is to prepare a go-to-market strategy for the product and acquire the first few customers. The product manager plays the role of subject matter expert in this phase for the rest of the organization and the early adopters of the product. They are responsible for evangelizing the product, creating awareness about the product and its problem space, and the benefits that it brings to the table. Product managers work closely with the first few customers, especially those who are recruited as part of a beta or early adopter program, to test the minimum viable product. This helps in identifying the product delta and getting the feedback early into the product to improve it further and achieve the product-market fit.

Once the product-market fit is achieved, in the growth phase, the product manager plays the role of a growth hacker to aggressively acquire customers, increase the adoption of the product and scale the customer base. They work closely with the growth marketers to define customer journeys, identify problem areas and then do rapid experimentation to accelerate the growth.

In the maturity phase, the product manager plays the role of a business strategist. The idea is to focus more on the business aspects of the product by retaining the customer base, increasing customer satisfaction and reducing the churn rate and sustaining the market share. Focus areas in this phase are to work on a competitive pricing strategy, market expansion, evolving the product with new use cases and added value proposition. This is the phase where product managers battle with technology debts and find it difficult to innovate. So, they look for strategies to add other channels of monetization like exposing APIs as a service, adding integration partners who have innovative solutions in the same space, etc. In parallel, they also look for other avenues to innovate with platform re-architecting or building a contemporary product from scratch or acquiring a product and integrating it with the existing product offering. These strategies can help defer the decline phase and in some case might also renew the entire product lifecycle. If none of these strategies work and the product enters into the decline phase, then product managers also take the hard decisions to deprecate certain features of the product which are obsolete and adding to the overhead cost or might decide on phasing it out altogether.

The role of a product manager does vary a bit from company to company depending on the company’s culture and focus area. Software product organizations are typically either Sales-focused or Engineering-focused or Market-focused. In each of these cases, the role of product management organization varies a lot.

• Sales-Focused: As the name suggests, in a sales-focused organization, more focus is on the sales numbers and maximizing the return on investment on the product. Product management, in this case, is more focused on enabling sales and marketing by acting as a product marketing organization. They create sales enablement artefacts like product factsheets, battle cards, case studies, etc. Also, as they are also responsible to build and maintaining the product, they do act as a program manager for the engineering organization providing them guidance on product requirements. Product Leaders in a sales-focused organization own the P&L responsibilities and ensure they meet their revenue KPIs. Hence product priorities are decided in a top-down manner, with product leaders defining their objectives and objectives of that of their product teams.

• Engineering-Focused: Engineering-focused organization is more invested in technology and innovation. They love experimenting and adopting new technologies. So, they need a strong technical product manager who can closely work with them to understand customer needs, translate them into product requirements and then drive execution.

• Market-Focused: Market-focused organizations are more invested in customer needs and are obsessed with solving customer problems. Product managers are very proactive, data-driven and result-oriented. Hence product management is more strategic and has a market-centric & customer-focused approach. So, product priorities are bottom-up in this case with each product manager setting goals and objectives for their teams based on the market requirements and product leadership guides the same.

So, that was all about Product Managers and their role in different organizations. In our coming articles, we will further explore this space, bust a few myths and build on the knowledge. Hope you found this information useful.

Happy building classy products!

P.S: Learn more on the Product Management concepts, processes, and strategies from my free videos on Antwak.

More readings and references:

1. https://www.mindtheproduct.com/what-exactly-is-a-product-manager/

2. Cagan, Marty (2018). Inspired: How to create tech products customers love (2nd ed.). Wiley India

3. https://www.productplan.com/product-management-role-product-lifecycle/

This ‘someone’, the blue-eyed person for the organization is ‘Product Manager’ and the practice that this person belongs to is called ‘Product Management’.

In our first video of product management foundations series, we have tried to answer this very basic question, what is product management?

P.S: Although very basic, there are some interesting concepts and we have busted a few myths. So, do watch the video. Learn more on the Product Management concepts, processes, and strategies from my free videos on Antwak.

1. FMCG industry takes credit for the birth of the Product Management discipline. History of product management goes way back to 1931 when the role of ‘Brand Men’ was conceptualized by Neil H McElroy of Procter & Gamble for managing products and its marketing mix

2. Later, McElroy, as an advisor at Stanford, influences young entrepreneurs Bill Hewlett and David Packard, who later go on to form the divisional structure of a product group at HP. Each product group is an autonomous, self-sustaining entity responsible for conceptualizing, producing and marketing its products. At HP, the product manager is considered as the voice of customers and their customer-centric approach helped them to set an unbroken 50-year growth record of 20% YoY from 1943 to 1993.

3. HP Product alumni adopt the successful lean manufacturing and continuous improvement principles of Toyota Production System, hence product management perspectives evolve to integrate customer-centricity, brand vertical and lean manufacturing. The success of product management groups at HP soon gets popular among many hardware and software product organizations in Silicon Valley.

4. 1970s: Technology companies realized that product management role in the technology domain is got to be different from FMCG where the product manager only owns the marketing mix (packaging, pricing, brand management, and promotions). Product development is key in the technology world. It is complicated, costly with a lot of investment in inventions and innovations, hence it needed customer perspectives to be deeply infused in this process. This led to the alignment of the product management role with the product development process.

5. 1980s: Intuit’s founder Scott Cook who was a former ‘brand man’ at Procter & Gamble, starts incorporating ‘brand management’ principles to its first software product, Quicken. Quicken, although a finance software, was aimed for home users and non-financial users. Scott’s deep customer empathy and obsession to solve the customer problems led to the ‘Follow me home’ program where the product team was involved in studying how customers use the product in a real-life situation. Thus, Intuit gets the credit to be first among the software companies to adopt the role of product management.

6. 1990s: Microsoft also follows this movement, like Intuit, of adopting product management principles into a role of what it called ‘Program Management’. This started when Microsoft got into developing Excel for Macintosh. Program managers were responsible for understanding customer needs, identify product/feature requirements and then translate them to technical/software requirements that would make sense for the engineers to develop the product.

7. 2000s-till date: Leading software companies like Google, Apple, Amazon, etc. adopt product management principles as a dedicated practice that works closely with their engineering, design and business areas.

More readings and references:

1. https://www.mindtheproduct.com/history-evolution-product-management/

2. https://medium.com/pminsider/the-history-and-evolution-of-product-management-part-1-23cb7a858f05

3. https://medium.com/pminsider/the-history-and-evolution-of-product-management-part-2-9c987fdc4ac

4. https://medium.com/pminsider/the-history-and-evolution-of-product-management-part-3-c91698c889ca

(Source: “What, exactly, is a Product Manager?” by Martin Eriksson)

This is true to an extent, that product management is all about having a good understanding of business, technology, and user experience and hence bringing about synergy in these three areas to build successful products. But it overtly simplifies the product management craft that is required for the success of both the Product Manager and the organization.

In my opinion, product management is much more than just a role. It is an organizational practice, a discipline in itself and not an interdisciplinary role. Sure, it needs a PM to work with multiple stakeholders from engineering, UX, sales, marketing, customer success, etc. But, hey is it not the case with most teams today. All organizations today operate in cross-functional zones to stay ahead of the game in their respective business. There is no room for siloed operations. The same is the case with product management.

Product management is an established practice in most of the successful product organizations. There are exceptions for sure in a few cases where product managers belong to the Engineering group to elicit and manage requirements or to marketing groups for brand management and customer development activities. And this depends upon whether the organization is engineering-driven or sales-driven. However, in most successful market-driven and customer-obsessed organizations, product management is a dedicated group of people. This is required because product management is not about just building new features/products but to build it right and make it successful by adding value to both your customers and business. It’s a practice of succeeding day after day, every day. It’s a practice about continued focus and continuous improvements.

Based on my study and analysis of various product teams in successful product organizations, its quite evident that product management practice broadly constitutes of 3 key pillars: Strategy & Envisioning, Requirements Management, and Customer Development.

• Strategy & Envisioning: To build a successful product, it is imminent to have a well-defined product vision that is aligned with the business objectives. It is key to have a stable and long-term product vision. Vision is the foundation on which product strategy rests. A compelling product strategy chalks out the exact plan on how the product is going to go about and achieve market leadership. Typically it is both short term and long term and  It demands product managers orient themselves towards business goals and set a direction for their product towards achieving that. Like any plan, the product strategy has to be re-visited periodically and re-iterated based on changing customer needs, competitive landscape, and market dynamics.

• Requirements Management: For the product to be successful, it is key to encapsulate the exact set of product/feature requirements that should go into the product. This needs a systematic approach to identify product requirements by analyzing the market needs and deep-diving into customer problems with a solutions approach. Once the product requirements are identified, it has to be defined as software requirements for the software solution to be implemented and constantly prioritized and re-prioritized based on multiple factors such as business need, technological capabilities, complexity and availability of limited resources such as engineering capacity and time. The goal is not to spend too much time on just building the product but to get a minimum viable product out that can be taken to the customers and see its reception in the market.

• Customer Development: This is all about how to take your product to the market, acquire customers and make a business out of it. As explained by Steve Blank in his book ‘The Four Steps to Epiphany’, customer development constitutes of four steps: customer discovery, customer validation, customer acquisition and scaling the product business. Customer discovery and validation aims to find the right product-market fit. It includes identifying the customer segment, a market for your product and then assesses problem-solution fit for the product that you have conceptualized. This is a process in itself that includes continuously defining hypothesis based on your opportunity analysis and then rapid experimentation with your minimum viable product (or product concept) until you find the right market. Once you have achieved the product-market fit, then you have to work all your energies out on various customer acquisition strategies and push for customer adoption and then scale your product and operations to leapfrog your product business.

Now, it is not that these key pillars are explicit in product organizations but broadly covers what product teams do. Many product companies have even dedicated roles like product strategists, product planners, product strategy managers who work on product strategy alone. Similarly, product marketing specialists and allied roles do exist in various companies which are meant for customer development and agile methodologies itself to describe a dedicated role of a ‘product owner’ to plan and manage requirements. Amidst all these various roles, as product management practice continues to evolve it would be interesting to see if there would be a 3 in a box (Product Strategist + Product Owner + Product Marketer) approach adoption by product organizations in near future.

Hope it makes sense. Happy building classy products & delighting your customers!

P.S: Learn more on the Product Management concepts, processes, and strategies from my free videos on Antwak.

More readings and references:

1. https://www.mindtheproduct.com/what-exactly-is-a-product-manager/

2. Cagan, Marty (2018). Inspired: How to create tech products customers love (2nd ed.). Wiley India

3. Blank, Steve (2005). The Four Steps to the Epiphany: Successful Strategies for Startups That Win (3rd ed.). K&S Ranch