The 90-Day AI Plan

Let's make this concrete. You've got three months to show that AI is worth the investment. Here's what to do.

In your first month, you're building foundation. Audit what's actually happening — not what's approved, but what's in use. Survey your teams, check your network logs, look at expense reports. You'll discover 40-60% more AI usage than you expected. That's normal. Next, pick your first deployment target. Use the criteria from Part 1: high-volume, repeatable, information-intensive work. Define specific success metrics before you deploy. You should know exactly what "working" looks like. Finally, write down your minimum governance: approved tools, data boundaries, escalation paths. One page is enough.

In your second month, you're deploying to one team and learning from it. Train them properly — mechanics, workflow integration, boundary awareness. Set up measurement from day one. Run weekly check-ins to see what's working and what isn't. Adjust based on actual usage, not assumptions. This is where you learn whether your deployment strategy actually works.

In your third month, you're proving value and preparing to scale. Calculate your ROI. Be honest about the cost: licenses, training time, implementation effort, governance overhead. Be thorough about the value: time saved, errors avoided, revenue influenced. If you can't show a clear return, you have more work to do before expanding. Document what you learned, build a playbook for the next deployment, and decide deliberately where to go next.

At the end of 90 days, you should have measurable impact from one team, a repeatable process for deployment, and data that tells you whether to expand. If you don't have these things, you're not ready to scale. If you do, you have proof that AI works in your specific context — which is exactly what you need to justify continued investment.

What we covered: A concrete 90-day plan with specific actions for each phase.

Coming next: The complete series wrap-up — what success actually looks like, and the difference between companies that win with AI and those that just spend on it.