When someone says, Let's use AI. Then, we first need to understand what is the business problem that we are trying to solve using AI? Which AI tool do we plan to use? How do we want to technically use the AI tool to achieve the expected project outcome. This will help with project planning and lay the foundation for project delivery. Saving Changes...
Kaleigh WattsInfrastructure Operations Project Manager| Lexis NexisAtlanta, Ga, United States
First, I think you need to recognize what kind of statement this is. It's rarely about Tech first. it's usually one of these:
A problem statement in disguise A strategy signal A FOMO reaction A request for validation A vague mandate without ownership Saving Changes...
When Someone says use AI, what comes to mind is perfection Saving Changes...
Dr. Richard LordSecurity Transformation Senior Manager| Accenture Federal ServicesEldersburg, Md, United States
When someone says “we should use AI,” they’re rarely making a technical request. They’re usually expressing an unsatisfied need, an aspiration, or a sense of urgency without yet understanding what to ask for. I first treat the request as a symptom of a problem, not a requirement. I start by surfacing what's behind the statement by asking,
What's happening that makes AI feel necessary?
What would success look like without mentioning AI?
My job is to translate the signal to use AI into a concrete problem and a path to the desired outcome, sometimes with AI sometimes without. Saving Changes...
When someone says “we should use AI,” I hear a signal that something isn’t working as well as it should. My first step is to unpack the real problem, the outcome they’re aiming for, and the assumptions behind AI. I look at where the friction is in the process, what success would look like, and whether the organization is actually ready. Only then do I decide if AI is the right solution—or if a simpler process or automation would deliver more value. Saving Changes...
Yes, I’ve seen this a lot in telecom projects. Someone says “AI,” but one person means network automation, another expects predictive analytics, and someone else thinks of chatbots. You can tell from how they describe outcomes or timelines. I’ve learned to pause early and ask what “AI” should actually do before things go off track. Saving Changes...
RAMON Jr RAYNESAl Ayuni Investment & ContractingSaudi Arabia
When someone says “we should use AI,” they are usually expressing a vague need or pressure, so you should clarify the real problem, define the specific task, check available data, set measurable goals, and confirm that AI is truly the right solution before proceeding. Saving Changes...
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.
Brillian writeup Luis Branco Crisp, Concise, yet perfectly outlines the paradigm. Saving Changes...
Simon TamPM Consultant| Global Business Mangement ConsultantHong Kong, Hong Kong
Feb 19, 2026 1:05 PM
Replying to Luis Branco
...
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.
For the seven patterns for AI, the first thing to do is to clarify what are the problems we like to solve. It is important to be aware the requirements may change and there are always new tools arising. To have the best mental model is better having a good technical skills, important steps to ask for the right question. Saving Changes...
Honestly, when someone says ‘we should use AI,’ I think the first step is to unpack what pain point they’re actually reacting to.
Is it repetitive work,
Slow decisions,
Pressure to scale,
Just fear of being left behind?
The answer matters, because ‘use AI’ by itself isn’t really a strategy, it’s usually a signal that a deeper business or workflow issue needs to be defined first. Saving Changes...
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