Decision POC Methodology: From Endless Decks to Real Decisions

The Problem with Planning

Most organizations spend months in planning meetings before building anything. Endless decks, endless discussions, endless delays. I've watched teams spend six months planning an AI initiative, only to discover halfway through that their assumptions were wrong.

The Decision POC methodology flips this. Instead of planning for months, you build a proof of concept in weeks. You test your assumptions with real decision-makers. You learn what works before investing millions.

What is a Decision POC?

A Decision POC is a minimal viable system designed to test whether an AI system can improve a specific decision. It's focused on one decision, built in 2-4 weeks, tested with real decision-makers, and measures impact on decision quality. The investment is minimal, but the learning is maximum.

Here's the key insight: you don't need a perfect system to learn. You need a working system that decision-makers can interact with. That's what teaches you what actually matters.

How It Works

Week 1: Define the Decision

Start by getting crystal clear on what decision you're trying to improve. Who makes this decision? How is it made today? What's the cost of poor decisions? What would better decisions look like?

I worked with a financial services company that wanted to improve credit decisions. They were taking three days to approve or deny applications, with a 15% error rate. That's the baseline. That's what you're trying to beat.

Week 2: Design the System

Now you design the system. What data do you need? What model or AI approach makes sense? How does this integrate into existing processes? What constraints do you work within?

Don't overthink this. You're not building the final system. You're building something that works well enough to test your assumptions.

Weeks 3-4: Build and Test

Gather your data. Build the model or system. Integrate it into the decision process. Get real decision-makers using it. Collect feedback.

This is where the magic happens. You'll discover things you never would have discovered in planning meetings. You'll learn what actually matters to decision-makers. You'll find integration challenges you didn't anticipate. You'll see opportunities you missed.

Week 4: Measure and Learn

Did decisions improve? How much faster? How much more accurate? What did you learn? What would you do differently next time?

The financial services company I mentioned? Their POC showed that they could reduce decision time from three days to one hour and improve accuracy from 85% to 95%. That's when they knew it was worth investing in the full system.

Why This Works

Decision POCs work because they're focused on learning, not perfection. They're fast enough to move before you lose momentum. They're real enough to teach you something. And they're cheap enough that you can afford to be wrong.

I've seen teams use Decision POCs to validate ideas that seemed promising but turned out not to work. I've seen teams discover that their initial approach was wrong, but the POC showed them a better way. And I've seen teams build confidence in an idea by proving it works before scaling.

The key is that you're testing with real decision-makers, not in a lab. You're measuring impact on decision quality, not just model accuracy. You're learning what actually matters to your organization.

When to Use This

Use Decision POC when you have a high-impact decision that's made poorly today. Use it when you're uncertain about whether AI can actually help. Use it when you need to move fast. Use it when you want to learn before investing millions.

Don't use it for low-impact decisions. Don't use it when you already know what you need to build. Don't use it when you have unlimited time and budget.