The 90-Day AI Reality Check

Most AI projects look promising at 30 days, show cracks at 60 days, and face hard decisions by day 90. Success and failure usually come down to whether you're solving a real business problem or just implementing impressive technology.

Three months ago, your AI pilot looked promising. The demos were smooth, the vendor was responsive, and your team was optimistic.

Today? You're sitting in a conference room, wondering where everything went wrong.

This pattern repeats across companies every week. At the 90-day mark, AI projects reveal their true nature. Some emerge as genuine business tools, but most expose themselves as 'not ready for prime time' and outline the uncomfortable facts about readiness, expectations, and leadership.

The 30-Day Illusion

Month one feels good.

The technology works in controlled conditions. Your pilot users give positive feedback. Early metrics look encouraging. The vendor sends congratulatory emails about your "successful deployment."

But 30 days isn't enough time for reality to set in. Users haven't hit edge cases, workflows haven't been stress-tested, and the novelty hasn't worn off.

Most AI projects coast through month one on enthusiasm and vendor support. The real test starts when the training wheels come off.

The 60-Day Questions

By day 60, the questions start.

Why isn't adoption spreading?

Why do people keep reverting to old methods?

Why does the AI recommendation conflict with what experienced staff would do?

This is when you discover whether your team trusts the system. Trust doesn't come from accuracy scores or vendor presentations. Trust comes from watching the system make decisions under pressure and seeing those decisions work.

I've watched companies realize at the 60-day mark that their "AI solution" was just a complicated way to do what spreadsheets already handled. Others discover that their data quality issues, ignored during the pilot, make the AI outputs unreliable.

Smart leaders use month two to ask hard questions: Are we solving the right problem? Do we have the right data? Are we measuring what matters?

The 90-Day Decision Point

Month three is decision time. Keep going, scale up, or cut losses. By now, you know whether the AI adds real value or just impressive demos.

The projects that survive the 90-day test share common traits:

• Clear ownership. Someone specific is responsible for results, not just implementation. They understand both the technology and the business problem.

• Realistic scope. The AI solves a focused problem well rather than trying to transform everything at once. Success builds from small wins, not grand visions.

• User buy-in. The people using the system helped design it. They understand its limits and know when to trust its output.

• Data discipline. The underlying data is clean, relevant, and maintained. Garbage in, garbage out isn't just a fun little thing to say. It's reality.

What Separates Winners from Write-Offs

The projects that make it past 90 days become foundations for broader AI adoption. The ones that fail teach expensive lessons about preparation and realistic expectations.

Winners typically start with a specific business pain point. They measure success in business terms, not technical metrics. They plan for the human side of change, not just the technical implementation.

Write-offs usually begin with technology solutions looking for problems. They chase impressive capabilities rather than solving real business needs, and they underestimate the change management required to shift how people work.

The difference between winners and write-offs often comes down to leadership clarity. Do you know precisely what problem you're solving and how you'll measure success? Or are you implementing AI because it seems like the right thing to do?

Making the 90-Day Test Work for You

If you're starting an AI project, plan for the 90-day reality check. Set specific success criteria that matter to your business. Identify the most significant risks to adoption early.

If you're in the middle of a project hitting roadblocks, ask whether you're solving the right problem or just the one that seemed technically interesting.

And if you're at the 90-day mark facing disappointing results, consider whether the issue is the technology, the implementation, or the problem you chose to solve.

The 90-day test reveals how your organization handles change. Companies that consistently pass this test combine technological capability with business discipline.

This kind of strategic clarity is what I help executive teams develop when they're navigating AI adoption decisions. If you're facing these 90-day realities in your organization, let's talk: ericbrown.com

P.S. The best AI projects I've seen didn't start with AI. They started with business problems that happened to have AI solutions.