The model also couldn’t recognize what experienced Franchise operators noticed immediately. One location had excellent demographics but poor visibility from the street. Another was difficult to enter because of traffic patterns and an awkward parking lot.
One neighborhood looked ideal because it was busy during the workweek. Most of that activity, however, came from nearby businesses during lunchtime. On weekends, the area became surprisingly quiet.
None of those realities existed in the data.
The AI wasn’t wrong. It simply didn’t have the context. That wasn’t a failure of the model. It was a reminder that some business knowledge isn’t captured in historical data. It lives in the experience of people who know the customers, understand the operations and recognize what data alone can’t measure.
What I learned was that the real question wasn’t whether AI was right or wrong. It was where AI belonged in the decision-making process. That’s the decision line.
That’s where AI creates the most value. AI contributes to the analysis. People contribute the context.
Together, they produce a better decision than either could have made alone.
Every organization will draw the decision line differently
One of the biggest lessons I’ve learned is that there isn’t a universal decision line. Every organization has its own business model, customers, operating priorities and confidence in its data, so every organization should draw the line differently.
I also don’t think the decision line is permanent.
As organizations improve the quality of their data, strengthen their business processes and gain confidence in AI, the line will naturally move.
Decisions that require human involvement today may become routine tomorrow.
That’s the process.
But I also believe some decisions will always require people.
Not because AI isn’t capable. Because some decisions require accountability, context and judgment that extend beyond what data alone can provide.
The goal isn’t to move as many decisions as possible to AI.
The goal is to decide intentionally where AI creates the most value and where human judgment and experience create the greatest impact.
It’s also a conversation many CIOs are having as AI governance moves from theory to day-to-day leadership, a topic CIO.com has explored in its coverage of AI governance.
Formal AI governance frameworks are valuable, but they don’t answer an important question: Where should AI participate in your decision-making process?
That’s a business decision. And it’s one that every leadership team has to answer for itself.
Before implementing AI at scale, I ask four questions:
- Which decisions are truly routine and repeatable?
- Where does human judgment materially improve the outcome?
- Where should accountability always remain with people?
- What would need to change before we move another decision below the decision line?
I’ve found those questions often lead to better conversations than asking where AI can be used. They shift the discussion from technology to business value.
When I think back to the conversations I’ve had over the past year, I’ve realized the biggest question isn’t how quickly organizations adopt AI. It’s where AI belongs.
Some decisions clearly belong with AI. Other decisions clearly belong with people.
Most organizations will spend the next several years deciding everything in between. Every Organization implementing AI is making one of the biggest decisions about AI.
Make sure it’s one you’ve made intentionally.
That’s the decision line.