A pragmatic path to AI adoption - 627846 Consulting
Skip the hype. A simple framework for finding AI projects that actually pay off.
Every IT leader has been told to “adopt AI.” Far fewer have been told how — or which problems are actually worth pointing it at. The result is a lot of stalled pilots and tools that never make it into anyone’s daily work.
The technology is rarely the bottleneck. Choosing the right problems is.
Start with the work, not the tool
The best AI opportunities aren’t found by browsing vendor demos. They’re found by looking at where your people spend time on repetitive, rules-light, high-volume work — drafting, summarizing, triaging, classifying, looking things up.
For each candidate, ask three questions:
- Is the value real and measurable? Can you point to hours or dollars it would save?
- Is the data ready? Is the information the model needs accessible, and are the access controls sound?
- Can it reach people’s workflow? A capability no one uses where they already work delivers nothing.
If a use case can’t clear those three, it’s a science project, not a business case.
Pilot small, measure honestly, then scale
Pick one or two use cases that clear the bar. Run a real pilot with guardrails for security, privacy, and accuracy. Measure against a baseline. Only scale what demonstrably works — and be willing to kill what doesn’t.
The organizations getting value from AI aren’t the ones with the most ambitious roadmaps. They’re the ones who picked a few right problems, proved the value, and built from there.
If you’re weighing where AI fits in your organization, a focused opportunity assessment is a good place to separate the real wins from the noise.