When someone says, “we should use AI,” how do you unpack what’s really being asked?

Business alignment should be at the core of any decision to use Ai as no single Ai pattern can address all business needs and meet stack holders expectations. A clear understanding of the business needs will help address if Ai is necessary, what kind of Ai is needed based on identified pattern, data needs and availability, etc. The success of any Ai project depends largely on the mentality of the project team than on the technology itself. When projects fail, it’s not the Ai tool that failed, it mostly stems from a lack of proper alignment of business objectives with project expectations and final outcomes by the project teams. This is a mis-match responsible for most Ai project failures. However, the iterative nature of Ai projects makes it easy to reassess, re prioritise and realign business objectives with outcomes before further resources are wasted.

I've actually run into this a few times recently. The moment someone says, "Let's use AI," I stop the conversation and ask, "What do you want it to do?"
I've noticed that everyone has a different definition of AI. Some people are thinking about ChatGPT, some want predictions, some want automation and others just want reports faster. That's usually where the confusion starts.
I've found it's much easier to break the problem down first. Does the system need to recognize something, predict an outcome, detect unusual activity or simply answer questions? Once that's clear, the conversation becomes much more practical and it's easier to decide whether AI is even the right solution.
One thing I've learned is that not every business problem needs AI, and that's okay. Sometimes a simpler solution gets the job done better.

Great question.

When someone says “we should use AI,” the conversation is rarely about technology itself.
It is usually about pressure for speed, efficiency, innovation, or competitive leverage. The first step is to clarify intent.

Three signals help distinguish what is really being asked.

First, decision proximity.
Is AI automating a task, augmenting human judgment, or moving toward managing objectives autonomously?
These are fundamentally different categories of work.
The closer AI gets to consequential decisions, the stronger the need for governance, traceability, and explicit oversight.

Second, problem clarity.
Is there a clearly defined business problem with measurable impact, or is AI being treated as the starting point?
When the solution precedes the problem, misalignment and inflated expectations follow.

Third, accountability design.
Who owns the outcome if an AI-driven recommendation fails?
When responsibility becomes diffuse, risk scales faster than performance.

In many organizations, “AI” simultaneously means efficiency, experimentation, and cost reduction to different stakeholders.
Misalignment becomes visible when decision flows and ownership are unclear.
A common tipping point is when stakeholders use the same word “AI” but describe different success metrics.

The real shift is not from manual to automated.
It is from “man in the loop” to “man in control.” Without deliberate design of responsibility, capability increases while accountability erodes.

Clarity of purpose, category of AI work, and ownership separates disciplined transformation from technological noise.

I had the opportunity to highlight what was interesting about the question posed during the company evaluation: "Why we use artificial intelligence?" PMI PMO of the Year Awards 2026. ✅ Innovation, AI, and sustainability are no longer optional — they are becoming core PMO capabilities