Product leaders and investors in generative AI are grappling with a key question: how should we think about products whose outputs can be highly useful yet occasionally wrong? This article introduces the Accuracy-Value framework to map generative AI products and chart a path to sustainable advantage. The core principle is simple: defend your position while you grow. In other words, even if your AI product starts with imperfect accuracy, it can win if it delivers high value and you continuously improve it in ways others cannot easily copy.

Introducing the Accuracy–Value (AV) Matrix

Imagine a graph with Accuracy on the horizontal axis (low to high) and Value on the vertical axis (low to high). A generative AI product’s performance can be represented as a point on this matrix. In this context, accuracy means how correct, reliable, or factually sound the AI’s outputs are. Value is the real-world utility or impact of those outputs for users, essentially how much the product helps them achieve their goals or solve a pressing problem. High value might mean significant time saved, costs reduced, revenue generated, or new capabilities unlocked, even if the content is not always 100% correct. Low value means the product is not addressing a big need, or the benefit to users is marginal. The yellow path shows the strategy: start by delivering high value even if accuracy is not perfect, then move right over time as you raise accuracy, and aim for the high-value, high-accuracy sweet spot.

It’s important to note that not all generative AI use cases require high factual accuracy to deliver high value. For instance, a creative brainstorming tool might regularly “make stuff up,” but if it sparks brilliant ideas for a user, its perceived value remains high despite lower accuracy. This is precisely where defensibility matters most: without moats, a faster-improving rival can copy the value as base-model accuracy rises. Conversely, some applications may produce highly accurate outputs yet still address a trivial problem, so their value remains low. The framework helps visualize these trade-offs. Successful products aim for the top-right quadrant (high accuracy, high value), but the route to get there often begins elsewhere on the chart. High value first is the hook, and accuracy is the compounding.

DEFEND & GROW: Move right by steadily improving accuracy

Improve accuracy while you defend a position worth defending and grow the user base. What is worth defending is a strong initial position or clear product-market fit. In B2B, the feature must deliver immediate, repeatable value. For an assistant inside a SaaS product, a slight month-over-month rise in users is not adoption. Real adoption means most active users use the assistant within the first few weeks and keep using it because it saves time or raises quality. Once you have that, defend by improving reliability, grounding, and tight integrations. Then grow by expanding to new personas and use cases, adding net-new seats, and winning users who previously could not use the product but now can because the assistant reduces required skill and effort.

Lead with high value, then compound with accuracy. Users forgive rough edges when the payoff is real. Do not start with low value and hope that later accuracy will save the product. A common anti-pattern is bolting a generic assistant onto an existing SaaS workflow. Users already know the product, the assistant answers inconsistently, and it adds friction instead of leverage. That is low accuracy times low value. Ship a step change in value, then defend and grow with any durable moat you can build, including proprietary data, deep integrations, differentiated UX, network effects, and distribution.

Focusing on What You Can Control

One trap for product teams is to rely on model vendors’ updates for accuracy gains. When OpenAI or Google releases a new version, everyone benefits from a general performance jump, but that is not a sustainable strategy for differentiation. Those gains are available to your competitors too. Instead, ask: What can we do to make our product more accurate or reliable in ways others cannot easily copy? Typically, the answer lies in using data, tools, services, and design in smart ways:

• Proprietary data and fine-tuning: Use unique data to fine-tune or train small, task-specific models. This drives large accuracy gains that rivals cannot copy. Continuous learning from user corrections personalizes outputs beyond a generic API. Base models trained only on public data offer little moat, so leaders seek exclusive data sources. You do not need big deals. Consistently collect and curate high-signal data from your users and partners for the highest-value tasks. Make sure you have explicit legal rights to use that data.
• Retrieval, tools, and system design: You can raise effective accuracy through product engineering instead of waiting for a new model. Use retrieval augmentation so the AI pulls facts from verified sources at query time. Call external tools for exact answers, such as a calculator, a Python runtime, or a live API for current data. Users care that the answer is right, not how you got there. These patterns ground outputs and reduce hallucinations when it matters. For more on why product integration wins, see my piece on weaving Google Lens into Gemini: Why product integration, not benchmark bragging rights, will decide the next wave of AI winners.
• User experience and interface: Use UX to launch, increase ease of use, and form habits that become your moat. Make the first value instant with clear defaults, zero setup, and sensible templates. Flag uncertainty so users know what to verify. Anchor the product in daily rituals with saved workflows, reusable snippets, brand kits, and deep integrations that reduce context switching. As teams adapt their routines to your design, the product becomes the path of least resistance. At that point, small accuracy gains elsewhere are not enough to make them switch. Habit, speed, and fit become the moat.

In short, do not squander energy on micro-optimizing the AI model in ways that model providers or the open-source community will achieve anyway and share broadly within a few months. Instead, double down on data and design moats. Solve for accuracy in your use case with whatever assets you can muster. Not only will this make your product better, it also reinforces defensibility. For example, your proprietary data fine-tune remains your secret sauce, and your tightly integrated toolset makes switching to a competitor harder because they would have to replicate an entire system, not just an API call. Focus on reaching the “trust threshold,” the point at which users feel the value far outweighs occasional errors and they trust the tool in their workflow. That threshold might be lower than perfection. As models improve beneath you, you can quickly incorporate those upgrades.

B2C: Instant Value, Lasting Habit

​​Consumer products live or die on time-to-value and delight. People tolerate mistakes when the payoff is instant and obvious, or when the experience is entertaining. ChatGPT proved this. It delivered a useful first draft for almost any task in seconds, so many users kept using it even when answers were imperfect. Perplexity took the opposite stance. It built trust first with citations and live browsing, which raised perceived accuracy and made the product feel safe for factual queries. Google leans on distribution and convenience. Because it lives inside Search, Android, and Workspace, it wins by being the default and reducing friction. Grok optimizes for personality and real-time context. Its voice and access to fresh signals on X create a sense of novelty and timeliness that some users prefer over formal accuracy.

For B2C, the bar is clear. Remove setup, show value in the first session, and make sharing or saving effortless. When you claim facts, surface sources or ask clarifying questions. When you deliver creativity or entertainment, lean into style and speed. Pricing and free tiers matter because they feed habit formation and word-of-mouth. Use the Accuracy–Value matrix to check your pitch. What is the instant win for a new user, and how will you raise accuracy without adding friction? Products that answer both earn retention and grow.

B2B: ROI First, Workflow Fit

Unlike consumers who might play with a fun AI toy, businesses evaluate products with ROI in mind. They often prefer solutions that are reliable, where accuracy in outcomes or at least consistency is important, and that integrate well, because value multiplies when the product fits their stack. A product that is extremely accurate but solves a very small problem might not justify a budget line. Conversely, a product that promises to save millions but is erratic will be a tough sell unless there is a clear path to improvement or a human oversight plan. Successful B2B AI products therefore pitch high value out of the gate and explain how they tame the accuracy issue. We see this in many SaaS announcements: they highlight what the AI can do and clarify how they handle errors to show it is enterprise-ready. The Accuracy–Value framework can serve as a checklist for enterprise AI pitches: does the product hit a valuable use case? If yes, do we have a clear story on why today’s accuracy is sufficient and how it will improve, especially with the customer’s data?

Using the Framework to Evolve Your Product Strategy

• Plot your starting point: Place the product on the matrix. Be honest about value and accuracy. If you are low on both, rethink the idea. If you have high accuracy and low value, find a bigger problem to solve.
• Define “move right”: List the top sources of error. Plan fixes you control, such as better data, retrieval, verification steps, and domain rules. Set simple targets for accuracy on key tasks.
• Build moats early: Secure legal rights to use data. Invest in workflow integrations, community, and distribution. These make you hard to copy even as models improve.
• Prioritize by movement: Ship features that move you up or to the right. If a new feature adds value but risks quality, plan follow-up sprints to raise accuracy. If accuracy is the blocker, add grounding and checks first.
• Run “what if” tests: Ask what happens if a rival doubles accuracy tomorrow. Ask if you can win if your model stalls at 85 percent. Adjust your roadmap so you keep users.
• Close the loop with users: Track real adoption and retention. Collect corrections and use them to fine-tune. When you cross the trust threshold, automate more and expand use cases.

The Accuracy-Value framework is a clear lens for building generative AI products. It reminds us that user value is king. If an AI truly solves a problem or delights users, they will forgive early mistakes. Long-term winners do not rely on that forgiveness. They keep improving quality with levers they control, and they strengthen their advantage at the same time.

Whatever your role, ask three questions: Where is the product on Accuracy-Value today? Where can it be in a year? How will it get there? DEFEND & GROW: Move right by steadily improving accuracy. Keep making your AI smarter in ways others cannot copy, and keep the user at the center by solving meaningful problems. Ship value now, compound accuracy, and turn momentum into a moat.