Painkillers vs. Vitamins: Choosing Customer Problems That Truly Matter
For Senior Product Managers and Product Leaders navigating the age of AI, LLMs, and Agentic Products
The central act of product strategy is problem selection. Not every customer problem is worth building a product around; not every painful experience translates into a viable market opportunity; and not every problem a customer articulates, when examined carefully, is the actual problem that governs their behavior. Product organizations that skip or underinvest in the analytical work of problem selection—moving rapidly from customer complaint to roadmap item without the intermediate discipline of evaluating the problem along multiple strategic dimensions—tend to build products that are technically adequate and commercially disappointing, owing to the fundamental mismatch between the problems the product addresses and the problems customers are genuinely motivated to solve.
Extant research in entrepreneurship, innovation management, and behavioral economics has generated a rich set of frameworks and empirical findings that illuminate the dimensions along which customer problems should be evaluated. This essay synthesizes the most strategically consequential of these findings into a coherent analytical framework for problem selection—one that attends to the structural distinction between painkillers and vitamins, the interrelated dimensions of frequency, intensity, and willingness to pay, and the behavioral economics literature’s contribution to understanding why customers do and do not act on their own stated preferences.
Painkillers Versus Vitamins: The Most Overused Analogy in Product Management, and Why It Remains Indispensable
The painkiller-vitamin analogy has been so thoroughly circulated in product management discourse that it risks becoming a platitude—invoked reflexively in problem evaluation conversations without the analytical depth that makes it genuinely useful. A more rigorous application of the analogy reveals important nuances that the simplified version obscures.
In its most useful form, the painkiller-vitamin distinction maps onto a structural difference in the customer’s relationship to the problem, not merely the severity of the pain. A painkiller addresses a problem that the customer already recognizes, already experiences acutely, and is already motivated to solve—the solution does not need to educate the customer about the existence of the problem or build the behavioral habit of problem-consciousness; the problem creates its own demand. A vitamin addresses a problem that the customer may acknowledge intellectually but does not feel acutely enough to create urgent behavioral motivation—the solution must not only be effective but must also build and sustain the motivational architecture that drives recurring use.
The strategic implications of this distinction cascade through every aspect of product and go-to-market strategy. Painkiller products typically exhibit faster initial adoption, clearer willingness to pay, and stronger word-of-mouth driven by the relief of genuine pain. Vitamin products typically require longer adoption cycles, more intensive customer education, and a sustained investment in building the habit infrastructure—reminders, streak mechanics, social accountability, measurable outcome feedback—that substitutes for the intrinsic urgency the painkiller customer already possesses. Neither product type is inherently superior from a strategic standpoint; the point is that they require different market entry strategies, different product architectures, and different success metrics—and treating a vitamin product as if it were a painkiller (by assuming that demonstrating effectiveness will be sufficient to drive adoption) is one of the most common strategic errors in B2B software product development.
There is a further nuance that the simple analogy tends to obscure: the category assignment is not intrinsic to the problem but is contextual, varying by customer segment, competitive context, and the customer’s prior experience with the problem space. Slack’s enterprise messaging product was, for one customer segment—teams that were already using fragmented email-and-text workflows and acutely experiencing the coordination cost—a painkiller. For another segment—teams with established internal communication practices that worked well enough—it was a vitamin. The strategic insight is not simply “is this a painkiller or a vitamin?” but “for which segment is this a painkiller, and what is the market size of that segment relative to the vitamin segment?” (Ramadan et al., 2016). The answer to that question shapes the ideal customer profile, the go-to-market strategy, and the investment thesis for the product.
In the context of AI-native products, the painkiller-vitamin distinction has acquired particular strategic importance. The proliferation of AI features and AI-powered workflows in 2023–2025 produced a substantial category of products that were, in the painkiller-vitamin framework, vitamins positioned as painkillers—tools that demonstrably improved certain aspects of user productivity, but whose value did not rise to the level of urgency that would drive the organizational adoption, workflow integration, and sustained usage required for commercial success. Product leaders who apply the painkiller-vitamin lens rigorously to their AI product concepts—asking not “does this AI capability improve user experience?” but “does this AI capability address a problem that users currently experience as urgently enough that they will change their existing workflows to adopt it?”—are more likely to identify the genuinely valuable AI product opportunities from among the larger set of technically impressive but strategically marginal possibilities.
Frequency, Intensity, and Willingness to Pay: The Three-Dimensional Problem Evaluation Framework
Beyond the painkiller-vitamin distinction, a rigorous problem evaluation framework requires attending to three interrelated dimensions: the frequency with which customers encounter the problem, the intensity with which they experience it when they do, and the willingness to pay they exhibit for solutions that address it. These three dimensions interact in complex and sometimes counterintuitive ways that have significant implications for product strategy.
Frequency determines the degree to which a product becomes embedded in the customer’s workflow and behavioral routine. Problems that customers encounter daily tend to produce products with strong habit formation, high retention, and compounding network effects based on the data generated by frequent use. Spotify’s personalization advantage compounds precisely because listening is a daily or near-daily behavior, generating the behavioral signal density required for recommendation algorithms to develop meaningful personalization over time (Railsware, 2024). Problems that customers encounter infrequently—even if intensely experienced when they occur—tend to produce products with weaker habit formation, higher churn risk (owing to the extended intervals between problem encounters during which alternatives can be adopted), and lower data generation rates.
The strategic implication for product leaders is that high-frequency problems tend to produce stronger long-term product positions than high-intensity, low-frequency problems—even when the low-frequency problem is significantly more painful to experience. An annual tax preparation problem is intensely experienced when it occurs, but the low frequency creates a weak behavioral habit, a long window between purchase decisions during which a competitor can win the customer’s next encounter, and limited data generation to support personalization and product improvement.
Intensity captures the depth of the customer’s experienced difficulty with the problem—a dimension that is not always correlated with frequency. Problems of high intensity but low frequency (serious medical conditions, enterprise procurement decisions, legal disputes) tend to produce high willingness to pay per solution encounter but weak recurring engagement. Problems of moderate intensity but high frequency (organizational communication overhead, repetitive data entry, navigating enterprise software complexity) tend to produce lower willingness to pay per interaction but stronger recurring engagement and more durable product dependency.
The interaction between frequency and intensity is strategically revealing: the most commercially attractive product opportunities tend to lie in problems that are both high-frequency and high-intensity—problems that customers encounter regularly and experience acutely enough that they are continuously motivated to invest in better solutions. These problems are, by definition, scarce. Product leaders who are examining a problem along both dimensions and find themselves confronting a high-intensity, low-frequency dynamic should ask whether product design and business model architecture can engineer frequency into the customer’s experience of the solution—through ancillary features, related workflow integration, or a platform approach that creates additional use cases that draw customers into daily engagement.
Willingness to pay is the dimension that most directly connects problem evaluation to commercial viability—and it is the dimension most susceptible to the cognitive biases that Kahneman and Tversky’s (1979) prospect theory and subsequent behavioral economics research have documented. The key insight from this body of research for product leaders is that willingness to pay is not a stable attribute of the customer that can be reliably elicited through direct questioning; it is a judgment that is constructed in context, shaped by the customer’s comparison set, by the framing of the value proposition, and by the reference points against which the proposed price is evaluated (Kahneman & Tversky, 1979; Thaler, 1980).
The practical implication for problem selection is that the willingness to pay exhibited by customers in research settings—interviews, surveys, conjoint analyses—should be interpreted as directional evidence rather than precise measurement. The more strategic question is whether the structural conditions that tend to produce high willingness to pay are present in the problem context: (1) a clear baseline cost of not solving the problem, expressed in terms the customer can readily quantify; (2) a competitive context in which the customer has limited alternative solutions; and (3) a problem severity that the customer is motivated to discuss with budget holders rather than tolerating as an organizational fact of life (Ibbaka, 2024).
Behavioral Economics in Problem Selection: The Gap Between Stated Preference and Revealed Behavior
One of the most consequential and least adequately addressed insights from behavioral economics for product leaders concerns the systematic gap between what customers say they want and what their behavior reveals they will actually pay for, adopt, and continue using. This gap is not a methodological artifact of flawed research; it is a structural feature of human decision-making that Kahneman and Tversky’s (1979) prospect theory, Thaler and Sunstein’s (2008) nudge framework, and a substantial body of subsequent behavioral research have documented with considerable empirical precision.
Several behavioral constructs are particularly consequential for problem selection and product strategy. Loss aversion—the empirically documented tendency for people to weight losses approximately twice as heavily as equivalent gains—has direct implications for how product leaders should evaluate and position customer problems. Problems that customers experience as losses (capabilities degraded, time wasted, revenue foregone, competitive disadvantage accumulated) tend to generate more behavioral motivation and, accordingly, stronger adoption dynamics than problems framed as missed opportunities for gain, even when the objective magnitude of the value differential is comparable. Product leaders who understand this dynamic can use it both in problem selection (prioritizing problems that are experienced as losses rather than deferred gains) and in product positioning (framing the product’s value in terms of loss prevention rather than benefit acquisition, when the underlying problem warrants it).
Status quo bias—the preference for current arrangements that Kahneman, Knetsch, and Thaler (1991) documented as a systematic feature of decision-making—has equally significant implications for problem selection, particularly in enterprise contexts where product adoption requires customers to change established workflows, retire existing tools, or retrain behavioral habits. Products that require customers to abandon significant prior investment in tools, processes, or skills face an adoption friction that is not captured in willingness-to-pay research conducted in the absence of an incumbent solution. Product leaders who evaluate problems without adequately accounting for the incumbent switching cost—the behavioral and organizational inertia that the status quo bias generates—will systematically overestimate adoption velocity and underestimate the investment required to achieve it.
The endowment effect—the tendency for people to value things they already possess more highly than equivalent things they do not—compounds the status quo bias in enterprise software contexts. Customers who have invested in an existing solution, built workflows around it, and developed organizational familiarity with it will evaluate its capabilities more favorably than an objective performance comparison would warrant. This means that the product’s value proposition must overcome not only the functional gap between the incumbent and the challenger but also the behavioral premium the customer places on the incumbent by virtue of existing ownership—a premium that is not rational in the classical economic sense but is highly predictable and operationally significant.
These behavioral dynamics suggest that the most strategically sound problem selection process attends not only to the frequency and intensity of the customer’s experienced problem, but also to the behavioral architecture of the adoption journey—the specific cognitive biases and decision-making patterns that will govern the customer’s evaluation of a new solution and the organizational dynamics that will shape the adoption and retention trajectory. Product leaders who build this behavioral architecture into their problem evaluation process from the outset are better positioned to design products that are not merely valuable in principle but adopted in practice.
The Problem Worth Solving: A Synthesis for the AI Era
In the context of AI-native and agentic product development, the frameworks developed in this essay take on additional strategic significance. The technical ease of building AI-powered products—and the corresponding organizational pressure to demonstrate AI adoption—has created a perverse incentive to identify problems that AI can technically address rather than problems that are strategically worth solving. Product leaders who allow this dynamic to govern their problem selection process will build products that showcase impressive AI capabilities in service of problems that customers are not sufficiently motivated to solve.
The evidence suggests that the most durable AI product opportunities are concentrated in problems that satisfy the full framework outlined here: they are painkiller problems rather than vitamin problems for the target customer segment; they are problems of high frequency and high intensity that generate sufficient behavioral signal to enable the personalization and adaptation advantages that AI capabilities uniquely enable; they generate willingness to pay that is grounded in a clear, quantifiable cost of the unsolved problem; and they create an adoption journey whose behavioral complexity is understood and designed for from the outset (Presta, 2026; AI PM Tools Directory, 2026).
The product leader who asks these questions rigorously—before committing to build—is practicing the kind of problem selection discipline that distinguishes strategic product thinking from technically impressive feature development. The question is not whether the AI capability is impressive. The question is whether the problem it addresses is worth solving.
References
AI PM Tools Directory. (2026). The future of AI in product management: 2026–2030 predictions. https://aipmtools.org/articles/future-of-ai-product-management
Ibbaka. (2024). Core concepts: Willingness to pay. https://www.ibbaka.com/ibbaka-market-blog/core-concepts-willingness-to-pay
Kahneman, D., Knetsch, J. L., & Thaler, R. H. (1991). Anomalies: The endowment effect, loss aversion, and status quo bias. Journal of Economic Perspectives, 5(1), 193–206. https://doi.org/10.1257/jep.5.1.193
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291. https://doi.org/10.2307/1914185
OpenView Partners. (2024). What behavioral economics can teach us about pricing. https://openviewpartners.com/blog/behavioral-pricing/
Presta. (2026). AI product strategy 2026: The founder’s guide to AI-native growth. https://wearepresta.com/ai-product-strategy-2026-the-founders-guide-to-ai-native-growth/
Railsware. (2024). Jobs to be done examples: Spotify, Duolingo, Uber cases. https://railsware.com/blog/jobs-to-be-done-examples/
Ramadan, A., Peterson, D., Lochhead, C., & Maney, K. (2016). Play bigger: How pirates, dreamers, and innovators create and dominate markets. HarperBusiness.
SHS Web of Conferences. (2023). Product features that hit consumers’ pain points may lead to reduced willingness to pay. SHS Web of Conferences, FEMS 2023, 01074. https://www.shs-conferences.org/articles/shsconf/abs/2023/18/shsconf_fems2023_01074/shsconf_fems2023_01074.html
Thaler, R. H. (1980). Toward a positive theory of consumer choice. Journal of Economic Behavior & Organization, 1(1), 39–60. https://doi.org/10.1016/0167-2681(80)90051-7
Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. Yale University Press.