Organizations are making one of the biggest decisions about AI without realizing it.
Every time we automate a process…
Every time we deploy a generative AI copilot…
Every time we trust an AI recommendation…
We’re answering a question that most leadership teams have never actually discussed.
Who — or what — should be making the decision?
Over the past year, almost every conversation I’ve had with executive peers has eventually turned to AI. The questions are usually the same: How are you using it? Where are you seeing value? How fast should we move?
They are all good questions. But I think they are causing us to skip a much more important one: Where should AI make decisions, where should it advise and where should human experience and judgment always lead?
I’ve spent my career helping organizations navigate major technology shifts: EPR, cloud and analytics all helped people make better decisions.
AI feels different.
For the first time, technology isn’t just helping us make decisions. It’s beginning to participate in them.
In many cases, it already can. The better question is whether it should. Because every time AI makes a decision, we’re making one too.
We’re deciding which decisions belong with AI, and which still belong with people.
As organizations move beyond experimenting with generative AI, the challenge is no longer deploying it. It’s redefining how people with AI work together. Microsoft’s Work Trend Index describes this shift as organizations move from experimenting with AI to fundamentally changing how work gets done.
What I learned was that we were deciding where AI should participate in making decisions.
That realization led me to what I now call the decision line.
Drawing the decision line
I don’t think organizations need another AI framework. What I learned is they need a better way to think about where AI belongs. That’s what the decision line is: A simple way to think about where AI should decide, where it should advise and where human judgment and experience should lead.
Most organizations are putting formal AI governance in place, such as the NIST AI Risk Management Framework. But every leadership team still has to decide where the AI should participate in business decisions. A framework can help organizations manage AI risk, but it can’t determine where AI belongs in your business.
Not every decision deserves the same level of human involvement.
Some decisions are routine, repeatable and governed by well-defined business rules. They’re decisions where consistency matters more than interpretation and where manually reviewing every transaction doesn’t create additional value.
One of the first places I saw this play out was in Accounts Payable. Organizations have long relied on people to perform three-way matching and resolve exceptions between purchase orders, invoices and goods receipts. Most transactions follow established business rules, making them ideal candidates for AI.
When we automated that process, AI performed the routine work, allowing people to focus on the exceptions that actually required experience and judgment. The result wasn’t just greater efficiency — it was a better use of people’s expertise.
I saw the same thing happen in Logistics. AI evaluated transportation costs, truck capacity, inventory, purchase orders and delivery schedules in seconds, allowing planners to spend more time making decisions instead of running calculations.
The question isn’t whether AI can process more information than people. The better question is whether human judgment materially improves the outcome.
In these situations, it often doesn’t.
Those decisions naturally belong below the decision line.
The harder question — and the one I think every organization will wrestle with — is what belongs at the decision line.
Where the decision line really matters
The decisions below the decision line are usually the easiest to identify. The hard decisions are the ones where AI is incredibly valuable, but human judgment still changes the outcome.
The moment this really became clear to me was when we started using AI to answer two important questions about new restaurant locations.
The first was relatively straightforward: Where should we open the next one?
To answer the first question, we built an AI model to analyze sales cannibalization. We tested it against historical data, creating what I called a “time machine” to see how accurately the model could predict outcomes we already knew.
The model consistently predicted sales cannibalization more accurately than the old spreadsheet models we relied on for years.
What I learned was that AI could solve a business problem we had struggled to model for a while.
Then we asked the second question: Where should we open the next restaurant?
The model evaluated demographics, population density, traffic patterns, household characteristics and every criterion we provided. On paper, many of its recommendations looked exactly right.
Then something interesting happened.
Our franchise office started to challenge several of the recommended locations.
Not because they disagreed with the data. Because they knew something the data couldn’t.
They understood that customers don’t always behave the way models predict. Some customers had been going to the same restaurant for more than 5 years. Even when another location opened closer to home, many continued to go where they felt comfortable. They knew the quality of the food. They recognized the wait staff. They had established routines. Convenience wasn’t always measured in miles.
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.
Read More from This Article: The decision line
Source: News