AI is Inevitable — But Where Should You Start?
Introduction: From Hype to Habits
AI is no longer a futuristic concept. It’s here. Embedded in tools we use, platforms we trust, and conversations we’re already having. For executive teams, the inevitability of AI is no longer in question. The only question that matters now is: Where should we start?
Not where it’s most shiny. Not where vendors are loudest. But where it makes a meaningful difference to how your business creates, delivers, or captures value.
In this article, we explore how to move from curiosity to commitment—and choose the AI starting point that actually matters.
The Temptation to Start Everywhere
Once leaders commit to "doing something" with AI, the risk is trying to do everything.
Endless pilots with no connection to strategy
Disconnected teams testing tools in silos
Initiatives optimized for speed, not scale
Dashboards filled with activity, not outcomes
These scattered starts can create the illusion of momentum while producing very little change.
Activity is not adoption. Pilots are not progress.
Start Where It Solves a Real Problem
AI is a capability, not a strategy. To matter, it has to attach to a real tension.
Ask:
What’s a recurring pain point we haven’t been able to solve with traditional tools?
Where do we spend a lot of effort to produce mediocre results?
What decisions are too slow, too manual, or too inconsistent?
This framing grounds AI in value, not novelty.
If you wouldn’t fund the initiative without AI, don’t fund it just because of AI.
Anchor in One of Four Value Lenses
To prioritize use cases, choose one primary value lens:
Efficiency: Can AI help us do the same work faster or with fewer resources?
Effectiveness: Can AI help us make better decisions, predictions, or recommendations?
Experience: Can AI improve the journey for employees or customers?
Exploration: Can AI reveal patterns, risks, or opportunities we couldn’t see before?
Each lens suggests different tools, teams, and success criteria. Clarity on value leads to clarity on action.
Choose a Use Case That Can Scale
You don’t need to bet the company. But you do need to bet on something that can grow.
A good starting point:
Connects to a clear business outcome
Can be delivered with the data you have (not the data you wish you had)
Requires real but not heroic cross-functional collaboration
Can teach you something reusable for future use cases
Use the first use case to build capability, not just credibility.
Design for Learnability, Not Perfection
AI initiatives are often judged too early, too harshly. The goal of a first use case isn’t to change the whole company—it’s to build fluency.
Make learning intentional:
Create feedback loops from users and stakeholders
Track friction, not just performance
Debrief frequently, even if results aren’t final
The question isn’t: Did it work? It’s: What did we learn that will make the next better?
Name an Owner, Not Just a Team
AI won’t move if it’s everyone’s interest but no one’s job.
Name someone accountable for:
Driving alignment across business, data, and tech teams
Making trade-off calls and escalation decisions
Translating between experimentation and execution
Tracking adoption, not just delivery
Momentum needs ownership. Without it, AI gets stuck in the lab.
Manage the Internal Narrative
AI adoption is as much a communications challenge as a technical one.
Craft a clear internal story:
Why did we choose this use case?
What do we expect it to change—and what will stay the same?
How will success be measured?
Where do we need support, feedback, or input?
When the story is clear, resistance drops—and learning travels faster.
Invest in Capabilities, Not Just Outputs
The best first use cases don’t just solve problems. They expose gaps you need to close.
Use your start to:
Build data literacy across functions
Strengthen governance frameworks
Revisit incentive models and decision rights
Identify process gaps or legacy constraints
A great AI start makes the rest of the journey easier—not just shinier.
Conclusion: Start Where It Matters—And Start to Learn
You don’t need a five-year AI strategy to start. But you do need clarity, ownership, and the willingness to treat your first move as an investment in learning.
AI is inevitable. But adoption is not.
Start where the pain is real. Where the value is clear. Where the lessons will travel.
That’s not just a good AI use case.
It’s a strategic one.
