The AI Implementation Playbook

Before you touch any AI tool, you need to understand how your business actually runs. Not how you think it runs. Not how the org chart says it runs. How it actually runs, day to day.

Document every major business process. Who does what, how long it takes, where handoffs happen, what gets dropped. This is the boring part, and it's the most important part.

Most businesses skip this step and jump straight to tools. That's how you end up with expensive software nobody uses.

Now you're looking for the bottlenecks, manual steps, error-prone handoffs, and time sinks. These are your opportunities.

The best AI opportunities aren't where you'd expect. They're usually not the big, glamorous processes. They're the small, repetitive tasks that eat 30 minutes here, an hour there, across multiple people.

Look for anywhere humans are doing work that's predictable, pattern-based, or involves moving data between systems. That's where AI shines.

Not everything is worth automating. This is where most DIY AI projects go wrong. They pick the most exciting opportunity instead of the most valuable one.

Rate each pain point on three dimensions: AI suitability (can AI actually do this well?), business impact (how much time or money does this save?), and implementation difficulty (how hard is it to build and maintain?).

A simple scoring matrix keeps you honest. The best opportunities have high suitability, high impact, and low difficulty. Start there.

For the top opportunities, identify specific tools and approaches. This is where you decide: build custom, buy off-the-shelf, or combine both.

The answer is almost never "build everything from scratch." Existing tools handle 80% of most use cases. Custom work should fill the gaps, not replace what already works.

Be specific. "Use AI for customer service" isn't a solution. "Route incoming emails through Claude to draft responses, reviewed by a human before sending" is a solution.

Prioritize by quick wins first. Build momentum before tackling complex projects. Nothing kills an AI initiative faster than spending three months on something nobody can see working.

Your first implementation should take days, not months. It should deliver visible value to someone in the business within the first week. That early win gets buy-in for everything that follows.