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

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Michael Brinn
PMI Team Member
Product Manager, Learning| PMI Denver, Colorado, United States

What signals help you tell different kinds of AI work apart—and what tends to go wrong when everything gets lumped together?

Have you ever been in a conversation where “AI” meant different things to different people? What tipped you off?

Share your experiences navigating what’s really being asked when someone says “we should use AI” in the comments below.
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RICHARD FISHER Entrepreneur Executive Servant Leader| rwfisher19 Managua, Nicaragua
PURPOSE WHY WHAT FOR begin with discussing these matters, which should help clarify discussions regarding AI; Perhaps begin at the end, where you would like to be after all is said and done, and work your way back to get a better perspective on AI's role and place inside of your organization.
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Bright Gyebi United States
In the Air Force, “we should use AI” usually means someone wants faster decision support, relief from manpower‑heavy tasks, or better situational awareness—but they haven’t yet defined the operational problem. I’ve learned to ask what mission gap they’re trying to close, what data or processes are involved, and what level of risk or autonomy is acceptable so the conversation shifts from hype to an actionable requirement.
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Ming Yeung Adjunct Professor & Acting COO/CPO/CRO (contract)| Blockchain Venture Capital Inc. Toronto, Ontario, Canada
In my experience, the clearest signal is intent: whether the goal is automation, prediction, augmentation, or generation. When someone describes outcomes like efficiency, insight, or creativity, it becomes easier to map the request to task automation, predictive analytics, or generative AI. Another signal is decision proximity, i.e. how close the AI is to influencing consequential choices, which determines the level of governance required. Things go wrong when “AI” becomes a catch‑all term. Misalignment emerges, expectations inflate, and accountability blurs. Without clarifying the problem, data, ownership, and success metrics, organizations end up chasing technology instead of solving real business needs.

Absolutely! The tipping point is almost always vague language. When one person talks about AI as automation, another imagines generative tools, and a third envisions autonomous agents, the disconnect becomes obvious. I notice it when stakeholders use the same word but describe different outcomes, risks, or timelines. Some reference ChatGPT‑style assistants, others think of embedded AI in everyday systems, and some imagine agentic systems capable of acting independently. The moment success metrics diverge, in the forms of efficiency, innovation, cost reduction, or strategic insight, it becomes clear we’re not discussing the same thing. That is when I pause and realign the conversation around purpose and problem definition.
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Prosper Ntitambert NSISSE IHLOUMAM AFOUNATANG Software Engineer Yaoundé, Cameroon
This is a statement that sparks so much controversy.

First of all, it’s worth noting that many people say, “we should use AI,” without having a clear idea of how to integrate it. Many say it simply because it’s trendy, since AI is at the center of everything these days.

For me, I believe that when someone says, “we should use AI,” you should start by identifying the specific problems to be solved, come up with a literal solution for each problem, and then ask yourself if AI is necessary at that stage. If so, explain exactly how and why to use it.
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Arun Prasad Project Manager| KPMG Global Services Bangalore, Karnataka, India
Winning with AI: Start With Real Use Cases, Not Hype
  • A clear, high-impact use case is the real winner. Start by identifying a specific problem where faster, better outcomes matter.
  • Embed AI into your existing workflow. Don’t bolt it on—integrate it where it can streamline steps, remove bottlenecks, and speed decisions.
  • Match tools to tasks. Know which AI capabilities fit each stage of the workflow (e.g., data prep, classification, generation, review, monitoring) to maximize impact.
  • Measure what matters. Define success upfront (time saved, quality improved, cost reduced, risk lowered) and track results from pilot to scale.
  • Iterate and operationalize. Start small, learn fast, refine prompts/process, then scale across teams with clear governance and change management.
  • Stay pragmatic. Don’t adopt AI for the label—adopt it when outcomes are real, quicker, and measurable.
Bottom line: Use case first, workflow integration second, tool selection third. If it doesn’t demonstrably improve outcomes, rethink it.
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Ravi Sankar Kandukuri Project Manager| TCS Hyderabad, India
Well done
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Shashi Bhalla Uttar Pradesh, India
This comes up all the time. People are pointing at very different things with the same word. Strongest Signal is actually what problem are they trying to solve i.e automation, predictive, optimisation etc. If team can’t describe the output without saying “AI will just figure it out,” they probably haven’t understood the core pain area yet.

One person means : Let’s add a Copilot-like assistant.
Another hears:
Let’s rebuild our core system using machine learning.

They both say “AI,” and alignment never actually happened.
Hence, there has to be clarity over what is to be accomplished by asking the right questions such as goal - what specifically IT shall do ? Automation, Generate, Classify, recommend etc. Then, where is the human intervention needed? Before output or after? What would be the risk tolerance criteria? What would be the value creation in terms of Speed, Quality or Cost. If we focus on these questions, we can really get the real work started.
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Shashi Bhalla Uttar Pradesh, India
This comes up all the time. People are pointing at very different things with the same word. Strongest Signal is actually what problem are they trying to solve i.e automation, predictive, optimisation etc. If team can’t describe the output without saying “AI will just figure it out,” they probably haven’t understood the core pain area yet.

One person means : Let’s add a Copilot-like assistant.

Another hears:

Let’s rebuild our core system using machine learning.

They both say “AI,” and alignment never actually happened.

Hence, there has to be clarity over what's t be accomplished by asking the right questions such as goal - what specifically IT shall do ? Automation, Generate, Classify, recommend etc. Then , where is the human intervention needed? before output or after? What's would be the risk tolerance criteria? What would be the value creation in terms of Speed, Quality or Cost. If we focus on these questions, we can really get the real work started.
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Debra Oglesby PM III| WorldPay Inc Acworth, Ga, United States

In response to How to Unpack when someone says, “We Should Use AI”

Start with determining the Purpose. Provide a clear, non‑technical framework to evaluate AI proposals, align stakeholders, and reduce delivery, value, and governance risk.

1. Problem Clarity (Start Here)

☐ What **specific problem** are we trying to solve?

☐ Is this driven by **missed expectations**, **broken or stalled processes**, or **risky decisions**?

☐ What outcome is currently not achievable with existing approaches?

2. Is This Actually an AI Use Case?

☐ Does the work require **classification, prediction, interpretation, or intelligent routing**?

☐ Are we asking the system to **make sense of data**, not just move it?

☐ Would rules‑based automation be insufficient?

3. AI Pattern & Mental Model

☐ Which **AI pattern** does this resemble (e.g., classification, forecasting, personalization)?

☐ What are the expected **inputs and outputs**?

☐ What **mental model** will the team use to reason about this work without technical depth?

4. Data Readiness

☐ Do we have the **right data**, at the right quality, to support this?

☐ Is data preparation, monitoring, and ongoing evaluation understood?

☐ Are there hidden **human‑in‑the‑loop** dependencies?

5. Delivery & Process Impact

☐ How will AI **change the workflow**, not just the toolset?

☐ Will it reduce manual routing, interpretation, or bottlenecks—or introduce new ones?

☐ Are we improving delivery, or layering AI onto a broken process?

6. Value Definition

☐ What **measurable value** is expected (speed, accuracy, personalization, responsiveness)?

☐ Are expectations realistic given the problem and data?

☐ How will value be assessed over time?

7. Risk, Governance, and Oversight

☐ What **ethical, privacy, or compliance risks** are introduced?

☐ Where are **governance guardrails** and review cycles required?

☐ Are we avoiding vendor‑led decisions that don’t match our workflows?

8. Stakeholder Alignment

☐ Do business and delivery teams share a **common language** for what “AI” means here?

☐ Are assumptions documented and understood early?

☐ Is ambiguity reduced before committing to delivery?

The Executive Framing Statement would be:

"Before approving an AI initiative, we confirm the problem, validate that this is truly an AI use case, identify the AI pattern, assess data readiness, define value, and establish governance to manage risk responsibly.”
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Cortney Doherty Waterford, ONTARIO, Canada
When someone mentioned using AI, I find that the are wanting something done quicker with less resources. They do not generally have a firm direction or are looking for additional ideas.
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