What Decision?

The meeting started with a spreadsheet.

Twelve months, $2.3 million spent, and a machine learning platform that nobody used. The CEO looked across the conference table at his CTO and asked: "What the hell happened?"

What happened was predictable.

They'd bought capability without clarity and built technology without asking what problem it solved. The vendor had delivered exactly what was promised: a sophisticated AI system that could process customer data and generate insights. The only issue? Nobody could explain what decision those insights were supposed to improve.

This isn't a story about bad technology. The platform worked fine. This is a story about the three questions that separate digital transformation from expensive experimentation. Executives often skip these three questions because they seem obvious...but they're not.

The Real Problem with AI Pilots

Every executive I know has heard the AI pilot horror story. Enthusiastic kickoff. Promising demos. Boardroom approval. Then silence. The tool sits unused while teams revert to spreadsheets and gut instinct.

Most leaders think this happens because they picked the wrong technology. Wrong vendor. Wrong use case. Wrong timing.

They're missing the point. AI projects fail because leaders rush past the strategic questions to get to the technical solutions. They focus on what the technology can do instead of what decisions it should improve.

The companies scaling AI successfully start with three questions that sound simple but require uncomfortable honesty to answer.

Question One: What decision are we trying to make better?

Not "What can this AI tool do?"

Not "How can we automate this process?"

But what specific decision, made repeatedly by real people, would benefit from better information or faster processing?

I worked with a retail client who wanted AI for "customer insights." Sounds reasonable. We spent two hours trying to identify how they'd have made a different decision with those insights. They couldn't answer the question.

Turns out, they already knew their best customers bought specific product combinations at certain times of year. They knew which marketing channels worked. They knew their retention patterns.

So...why spend the money on AI? They killed the project and saved $400K.

The test is brutal in its simplicity:

If you can't describe the decision your AI will improve, you're not ready for the technology. You're shopping for solutions to problems you haven't defined.

Question Two: Who makes that decision today...and will they trust a machine?

The gap between AI pilot and AI production is human trust.

Your best decision-makers have years of experience, developed instincts, and hard-won expertise. They've been burned by systems that promised accuracy and delivered confusion. They trust what they can understand and control.

I watched a manufacturing client build a predictive maintenance system that worked beautifully in testing. Ninety-two percent accuracy in predicting equipment failures. The plant manager ignored it for six months.

Why? Because he didn't understand what the system was optimizing for. Was it minimizing downtime? Reducing maintenance costs? Preventing catastrophic failures? The AI team couldn't explain the tradeoffs in terms he could evaluate.

The company rebuilt the system with transparent logic that showed the plant manager exactly which sensor data triggered which recommendations. They explained why the algorithm prioritized some failure modes over others. Only then did he start trusting and using the output.

If your best decision-maker won't use the AI output, your project is just expensive research. You need their expertise to validate the results and their trust to implement the recommendations.

This means involving decision-makers in the design process. Not just the requirements gathering phase but the actual logic development. They need to understand what the system prioritizes and why. They need to trust the output.

Question Three: What changes when we're right? What breaks when we're wrong?

Every AI system fails and makes mistakes.

Most pilots ignore failure scenarios entirely. They optimize for accuracy metrics that look impressive in PowerPoint but mean nothing in practice. Ninety-five percent accuracy sounds great until you realize the five percent failure rate hits your most important customers.

Successful AI implementations plan for both sides: what happens when the system works perfectly, and what happens when it fails spectacularly.

The success planning requires an honest assessment of downstream changes. If your AI perfectly predicts customer churn, who acts on those predictions? How do they prioritize intervention efforts? What workflows change? Which roles evolve?

The failure planning requires even more honesty. What happens if the AI recommendation is wrong fifteen percent of the time? Can your team quickly identify bad outputs? Do you have fallback procedures? Are the consequences manageable?

I helped a financial services client implement fraud detection a few years ago. The system was 97% accurate in testing. But the three percent false positive rate meant flagging legitimate transactions from their highest-value customers. We built extensive manual review processes and quick appeal procedures before going live.

What most don't understand is that those manual processes are expensive. The AI was cheap. Building systems that could handle AI failures while maintaining customer trust was expensive and complex, but worth the investment.

Why These Questions Get Skipped

Four reasons executives avoid these questions:

Vendor pressure. Salespeople want to demonstrate capability, not solve specific problems. They show you what the technology can do, not what decisions it should improve. It's easier to get a budget for "AI capabilities" than "better decisions about customer priority."

Innovation theater. Funding an AI pilot feels progressive. Answering hard strategic questions about decision-making feels boring. One gets you mentioned in board presentations. The other gets you results.

Technical focus. Engineers love solving technical problems. Business problems are messier, more political, and harder to debug. But technical excellence without business clarity creates sophisticated tools that nobody uses.

FOMO. "Everyone else is doing AI" creates urgency without direction. Better to have an AI pilot than no AI at all, right? Wrong. Better to solve real problems with simple tools than create impressive capabilities that improve nothing.

The Alternative Approach

Start with decisions, not technology.

Map the repeated decisions in your organization that could benefit from better data, faster processing, or pattern recognition. Identify who makes them and what they need to trust the input. Next, design for failure scenarios and success workflows.

Then, and only then, evaluate technology options.

This approach takes longer. It requires uncomfortable conversations about decision-making authority, risk tolerance, and change management. It forces you to admit that some problems don't need AI solutions.

But it works.

The companies successfully scaling AI past the pilot phase do this strategic work first. They define success in business terms, not technical metrics. They build systems that people use and trust.

This is the kind of strategic work I do with exec teams before they write the first check for AI tooling. If you're tired of expensive experiments that don't stick, let's have a conversation about what works: ericbrown.com

If you found this post helpful, consider sharing it with another executive grappling with AI, technology, and data. If you want to explore AI and other Technology strategies, grab some time on my calendar, and let's chat.

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