Your Boss Wants an AI Strategy. Now What?
ChatGPT dropped three months ago and suddenly everyone’s panicking
Every executive I’ve talked to lately wants an “AI strategy.” Which is nice, I guess. The enthusiasm is real. The clarity? Not so much. I keep having versions of the same conversation: “We need to use AI. Can you figure out how?”
That’s not a strategy. That’s panic dressed up as initiative. And it usually ends in one of two places: either you spin up a dozen half-baked AI projects that never ship, or you spend six months in meetings and do nothing while other people move.
There’s a better path. It starts with being honest about where you actually are.
So what does this look like day to day?
The first question shouldn’t be “How can we use AI?” It should be “What’s actually broken, and could AI help?”
Obvious, right? And yet. Organizations keep starting with the technology and then hunting for somewhere to plug it in. Backwards. AI is a tool. Like any tool, it only matters when you point it at a real problem.
I’d start with an inventory of the high-pain stuff. Where are people drowning in repetitive, pattern-matching work? Where’s the gap between “we have the data” and “we’re making decisions”? Where do you have tons of unstructured info that nobody can find or synthesize?
That’s where current models can actually help. Not because AI is magic, but because those tasks are the ones where “good enough” automation makes a real difference.
The three tiers I keep coming back to
I’ve found it useful to think about AI adoption in three tiers—roughly by risk and how much you’ll learn.
Tier 1 is internal productivity tools. Give people access to AI for code generation, drafting docs, pulling insights out of data, summarizing meetings. Low risk because humans review everything before it goes anywhere. High learning because the whole org starts building intuition for what AI nails and what it flubs. Examples: AI-assisted coding, chatbots over internal docs, meeting note summarization, search across your knowledge base.
Tier 2 is workflow augmentation. Once you’ve got Tier 1 under your belt, you start weaving AI into specific business flows where it does the heavy lifting and humans stay in the loop. First drafts of customer replies for review, ticket classification, capacity forecasting, anomaly alerts. The human still decides; the AI does the grunt work.
Tier 3 is autonomous systems—AI making decisions or taking actions without human review. Reliability, monitoring, and fallback all need to be serious. Most orgs shouldn’t start here. Content moderation, dynamic pricing, auto-scaling based on predicted load, fraud detection that blocks in real time—all possible, all harder than they look.
What you actually need to run this
One of the bigger misconceptions: that you need to build a massive ML platform from scratch. For most orgs, it’s more modest.
You need a clean way to call model APIs (OpenAI, Anthropic, or whatever you’re running yourself)—API key handling, rate limits, cost tracking, and a thin abstraction so you can swap providers without rewriting everything. You need something to manage prompts—version them, test them, monitor them. Ad-hoc prompts living in application code will become a mess fast. You need some way to evaluate whether your AI stuff is actually working—automated checks, human ratings, A/B tests. Otherwise you’re guessing. And you need cost visibility. AI API bills can spike in weird ways. Dashboards for cost per feature, per user, trends over time. It’s not just finance; it’s how you figure out where AI is worth the spend.
The part nobody wants to talk about
The hard part isn’t technical. It’s the culture stuff.
Fear: lots of people think AI is coming for their jobs. That fear is mostly wrong in the near term, but it’s real and it needs a straight answer. Be clear about what you’re using AI for and why. Emphasize augmentation. And if roles are going to change, say so upfront.
Over-trust: the opposite problem. People treating AI output as gospel. Language models sound confident. They’re articulate. Their mistakes are especially sneaky. You need some basic training on limitations and on why verification matters.
Uneven adoption: some people will jump in fast; others will drag their feet. Don’t mandate it—that just breeds resentment. Create room for experimentation, share what works, and let the value show itself. The skeptics often turn into the strongest advocates once they feel a real productivity bump.
Where this goes from here
We’re still early. Models will get better. Tooling will mature. Stuff we can’t predict will show up. The orgs that start building AI muscle now—even in small, boring ways—will be in a much better spot when the next wave hits.
But “practical” is the word. Don’t chase the hype. Don’t try to boil the ocean. Start with real problems, measure real results, and go from there. The ones that approach this with disciplined pragmatism will do better than the ones that either lose their minds with excitement or freeze entirely.