Your AI Problem May Actually Be a Product Problem

Alan

Your AI Problem May Actually Be a Product Problem

Source: RootStone Partners — https://www.rootstonepartners.com/your-ai-problem-may-actually-be-a-product-problem
Author: Alan
Published: Aug 19, 2026
Rights: © 2026 RootStone Partners. All Rights Reserved.

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Premature Solution Commitment

There is a growing industry around prompt engineering, and much of it is genuinely useful. But organizations get distracted by the mechanics of prompting when the larger issue is decision quality. A sophisticated prompt cannot compensate for an undefined problem. If the objective is unclear, AI will fill in the gaps with assumptions you may not share. If constraints are missing, it will optimize against conditions you never intended. If success is undefined, it cannot distinguish a good solution from an interesting one.

Most companies respond to poor AI output by adding more detail to the prompt. That is understandable. But there is a more fundamental question worth asking first: are we giving AI the right problem, or have we already decided on the solution?

AI does not solve premature solution commitment. It makes premature commitment easier to execute.

The Dashboard Trap

Consider a sales organization that wants managers to intervene sooner when opportunities begin to stall. Someone writes a requirement for a dashboard showing opportunities that have been inactive for more than 30 days. That sounds reasonable. It might also be completely wrong. The real problem may be that managers do not know when an opportunity is at risk. A dashboard is one possible answer. Automated notifications are another. Changes to the sales process might be another. Better qualification might eliminate the problem earlier. But once the organization decides "we need a dashboard," every conversation becomes about building the dashboard, and the original problem disappears underneath the solution.

In a mid-sized company, that kind of misalignment can easily consume six months and several hundred thousand dollars before anyone realizes the original problem was never being solved. The result may be excellent. It may also be an excellent answer to the wrong question.

Execution vs. Exploration

The distinction that matters is between two ways of using AI. When you tell AI exactly how to accomplish something, you are asking it to execute your thinking. When you tell AI what you need to accomplish and give it the context to understand why, you are asking it to expand the set of options you can evaluate.

Execution is the obvious value of AI. Exploration is the underused value.

A skeptic might say this sounds like prompt engineering with extra steps. It is not. One improves the interaction. The other determines whether you are asking the right question in the first place.

Define the Decision Before the Solution

Good product leaders do not start every initiative by telling a team exactly what to build. They define the problem, identify who has it, establish what success looks like, and make constraints visible. Then they give the team room to work out how to solve it. AI works better the same way.

The framework is straightforward. Define the outcome. Define the constraints. Define how you will measure success. Then, and only then, generate options.

A user story captures this discipline well: as a sales manager, I need visibility into stalled opportunities so I can intervene before deals are lost. There is no dashboard in that statement. No notification system. No prescribed workflow. It describes the need without deciding the solution.

Now compare two ways of engaging AI. The first is execution:

Create a project plan for a dashboard that shows opportunities inactive for more than 30 days.

The second is exploration:

We have 25 salespeople using an existing CRM. Managers review pipeline reports manually once a week. Opportunities can remain inactive for weeks before anyone notices. We want to reduce the time between an opportunity stalling and manager intervention from seven days to two. We cannot replace the CRM this year.

What are the strongest approaches for achieving this outcome? Compare them on impact, effort, cost, risk, and time to value. Identify the assumptions behind each.

That is a fundamentally different interaction. You have not asked AI to make the decision. You have asked it to help you understand the decision.

Organizations often tell AI what they want without explaining how they will know whether they got it. "Reduce support costs" is an objective, but it does not tell us what success looks like. A better framing gives AI meaningful boundaries without prescribing the solution: reduce cost per customer by 15% within six months. Satisfaction cannot decline. No additional headcount. The existing platform stays. Results must be measurable within six months.

Be specific about the outcome. Be specific about constraints. Be specific about success. Then leave room for the solution.

The Real Constraint

Many business decisions are not held back by a lack of information. They are held back by the number of options people can realistically consider. A leadership team faces a problem, someone proposes a solution that sounds reasonable, and everyone shifts into implementation mode because alternatives take time to generate.

You will gravitate toward the obvious levers, but you will not think of every possible approach. Used well, AI can make the decision space larger without making the decision process slower.

Give it relevant business context, objectives, financial constraints, technology limitations, and organizational history. Then ask it to identify the strongest paths to your outcome. Some recommendations will be familiar. Others would never have made it into the first meeting. AI can widen your option set before the organization locks itself into a direction.

The human still chooses, and that should not change. AI does not know your organization the way you do. It does not carry the political or financial consequences of a bad call. It does not know which risks your leadership will tolerate and which ones kill projects. Those responsibilities stay human. But you can make those human decisions significantly better when you are evaluating a richer set of alternatives.

Challenge Before You Commit

Once you have a preferred solution, do not immediately ask AI to turn it into a project plan. Ask it to challenge the decision. Give it the proposed approach and ask: assume we committed to this six months ago and the initiative failed. What most likely went wrong? Which assumptions should we test before committing?

This creates a structured way to disagree before significant money and time are spent. You can also ask what would have to be true for the second-best option to win, or what information you are missing that could materially change the decision.

Good product organizations have done this for years. What is new is the speed. A small team can now conduct analysis that previously required multiple meetings and subject matter experts. That does not eliminate expertise. It makes expertise more effective.

The Organizational Advantage

The next opportunity is not just automating work. It is compressing the time between a question and a well-informed decision. How quickly can you understand a problem? How quickly can you identify viable options? How quickly can you test assumptions? How quickly can you learn you are wrong?

Those capabilities compound. If your organization can make good decisions faster, projects start with better definitions. Teams spend less time building the wrong things. Executives receive better options. Customers see improvements sooner.

The opportunity is to give AI a better role, not more autonomy. Let it explore. Let it analyze. Let it challenge. Let it propose alternatives. But keep the decision with the people accountable for the outcome. A product leader does not need to design every detail. A CEO does not need to perform every analysis. Their responsibility is making sure the organization solves the right problems, makes sound tradeoffs, and learns quickly when assumptions prove wrong. AI increases the leverage of that responsibility, but it does not remove it.

A Diagnostic for Your Next Planning Meeting

The organizations that get the most from AI will not be the ones with the cleverest prompts. They will be the ones already good at defining problems. They understand outcomes. They make constraints explicit. They challenge assumptions.

Before your next initiative, ask three questions:

Have we defined the problem clearly enough that someone outside the team could understand why it matters?
What evidence would convince us that our preferred solution is the wrong choice?
What is the smallest experiment that could invalidate our core assumption?

The Cost of Getting It Wrong

The cost of a poorly written prompt is a few minutes of revision. The cost of premature commitment is months of work on the wrong thing. The most expensive AI mistake is not getting the wrong answer. It is getting a very good answer to a problem you should never have chosen to solve.