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

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.

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.

One of the biggest signals for distinguishing different types of AI work is the expected outcome—whether the goal is automation, prediction, or content generation.
For example, if the focus is on insights and forecasting, it’s likely predictive AI; if it’s about creating text, images, or code, it points to generative AI.
What often goes wrong is when everything gets labeled simply as “AI” without clarifying the use case. This can lead to unrealistic expectations, poor tool selection, and misalignment with business objectives.
I’ve definitely been in conversations where “AI” meant different things to different stakeholders. Usually, I notice it when requirements are vague—like “we should use AI to improve efficiency” without defining how. That’s when I step in to ask clarifying questions about the problem we’re trying to solve, the data available, and the desired outcomes.
In my experience, the key is to shift the conversation from “using AI” to “solving a specific business problem with the right AI approach.”

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.