The First Step Toward AI? Pick a Problem, Not a Platform
The First Step Toward AI? Pick a Problem, Not a Platform
Introduction: The Wrong Starting Line
Many organizations eager to embrace AI begin in the wrong place: with a platform demo, a procurement process, or a consultant-led roadmap. But AI isn’t a tool to plug in. It’s a capability to build. And the best way to build it isn’t by starting with technology.
It’s by starting with a business problem that’s painful, persistent, and unsolved.
This article explores why the smartest first move with AI is not to pick a platform—but to pick a problem. One that matters enough to rally teams, build momentum, and justify real investment.
The Platform Trap
The allure of platforms is strong. They promise scalability, speed, and a one-stop solution. But when AI adoption starts with platform selection, several risks emerge:
The platform drives the use case, not the other way around
Technical excitement outpaces business clarity
Early adopters run proofs of concept that don’t scale
Talent gets stuck customizing instead of solving
Platform-first thinking is seductive—but it often produces AI that looks good and solves little.
Why Problems Are a Better Starting Point
Problems create focus. They:
Force clarity on what success looks like
Align stakeholders around shared urgency
Reveal data needs, process gaps, and capability shortfalls
Provide a business anchor for experimentation and iteration
And most importantly, real problems make it obvious when AI is—or isn’t—working.
The best AI adoption stories start with someone saying: “We’ve tried everything else.”
What Makes a Problem AI-Worthy?
Not every problem is a good candidate for AI. Look for those that:
Involve repetitive or data-intensive decision-making
Require faster or more consistent outputs than humans can provide
Span functions or systems, creating integration headaches
Have high upside if solved (cost, growth, insight, experience)
A good test: Would solving this problem materially change how we operate or compete?
If the problem isn’t painful or valuable enough, the AI solution won’t matter.
From Pain Point to Use Case
Once you’ve identified a problem, translate it into a use case that’s structured enough to act on.
Define:
The current process or decision point
The volume and variability of inputs
The data sources (structured or unstructured)
The desired improvement: speed, accuracy, consistency, insight
Use this framing to test fit with available AI capabilities—and clarify what would make the use case successful.
A use case is the bridge from strategy to system.
Make It a Business Use Case, Not a Tech One
Many AI use cases stall because they’re designed from the lab out, not the business in.
Anchor your first move in operational reality:
What process will change?
Who needs to adopt it?
What training or change management will be required?
What happens if the model underperforms?
When the business owns the use case, it gets attention. When IT owns it alone, it often stays peripheral.
AI success is 80% adoption, 20% algorithm.
Test for Learnability, Not Just ROI
Your first AI use case is not just a solution. It’s a strategic test.
Choose a problem that allows you to:
Learn how to collaborate across data, business, and tech
Surface internal blockers and governance needs
Prototype roles, rituals, and reporting for AI at scale
Use the project to build habits—not just outputs.
The win isn’t just solving the problem. It’s proving you can solve more.
Assign Ownership and Accountability
To move beyond intent, assign real accountability.
Give someone clear responsibility for:
Scoping and validating the problem
Aligning stakeholders and use case framing
Overseeing solution development and delivery
Tracking outcomes and learnings
Ownership turns exploration into execution.
Without a name, nothing moves.
From First Win to Repeatable Pattern
When your first AI use case delivers, resist the urge to immediately scale it everywhere.
Instead, use it to:
Codify what worked into a playbook
Standardize roles and expectations
Align incentives around usage and insight
Build internal momentum for what comes next
Think of the first win not as a destination, but as a blueprint.
Because success in AI doesn’t scale itself—structure does.
Conclusion: Problem First, Platform Later
If you want AI to matter, start where the pain is.
Don’t chase features or futures. Chase friction. And choose the problem worth solving—not the tool worth showing.
AI is a capability that must be earned, one decision at a time.
So skip the pitch deck. Find the problem. And solve it—well.
