Writing by Lee Basnight on AI strategy and implementation

If you ask me what made AI implementation work at the places I've led it for, I'll tell you it was the process. The decision rules, the SOPs, the version control for prompts, the eval protocols. The boring operational layer that nobody puts on a slide.

I think most AI failures look the same up close. A team picks a tool. The pilot looks impressive. Leadership signs off. A quarter goes by, then another. The tool is technically still in the stack, but most of the team has stopped using it. They've gone back to doing things the way they did them before the AI initiative, sometimes with the AI cost still on the budget. Ask anyone on that team why, and you'll hear answers like "it's faster to just do it myself" or "I never know if I can trust the output for this kind of customer." Both of those are process problems. The model works. The team is missing a shared rule for when the model is the right call.

This is what process work actually looks like in practice. It's a document, often a few pages, that sits in a shared drive nobody opens for fun. It says things like "for outbound sales emails to existing customers, AI drafts go to manager review before send. For internal newsletters, AI drafts go straight to send. For anything in legal or compliance categories, AI stays out of the workflow." It's mundane. Writing it takes a week. Nobody wants to be the one writing it. Once it exists, every member of the team makes the same decision the same way, and a manager can stay out of the loop for individual calls. That's where the efficiency gain comes from. At Trane I'd put 40 to 50 percent of the gain on documents like this. The model was a smaller piece of the story than most people assume.

There's also a cultural reason this layer gets skipped. The language is unsexy. Governance, decision criteria, eval protocols, prompt registries, content policies. It sounds like compliance. It sounds like the part of the company that slows things down. So when AI strategy gets discussed, the conversation tends to drift toward tools and capabilities, because those are the parts you can demo. Process is harder to demo. It's just a doc.

If I were starting an AI program tomorrow, I'd budget more time for process design than felt reasonable. I'd write the SOP first, before I picked a tool. I'd be willing to look slow for a quarter to be fast for the next two years. Most teams skip this work, which is why most AI initiatives stall out.

For more info or opportunities, let's talk: leethepolymath@gmail.com

Some morning thoughts. Twelve months from now, even the most trained eye won't be able to tell what's real and what's fabricated online. That's not a prediction meant to spark fear, it's simply where we're heading.

The deeper shift, though, is this: the consumer or client won't care.

We've been here before. People were skeptical of the internet. They doubted streaming. They dismissed blockchain. In every case, once the technology matured, the debate shifted from "Is this real?" to "Is this useful? Does it work for me?"

The same is happening now with AI-driven creative work. In a world where digital output can be perfectly simulated, the only real currency left will be trust and quality. That means businesses and creators will need to let go of their philosophical debates about "purity" or "authenticity" in creative work.

Clients won't ask, "Was this made by a human or a machine?" They'll ask, "Does this solve my problem? Does this move me? Do I trust this brand to deliver consistently?"

This is the inevitability of progress: technology compresses time. What took decades to normalize before is now happening in months. The winners won't be the loudest moral objectors. They'll be the ones who embrace the shift early, without losing sight of quality and trust.

As someone in Design Operations, my job is all about making creative work smoother and more effective. For the past four years, I've taken a deep dive into AI implementation for creative pipelines, seeing firsthand how it can boost efficiency and open new doors.

But there's a flip side to this exciting "AI gold rush." Many companies are scrambling to inject AI everywhere, seeing it as a quick fix for saving money. They're doing this without proper data to train the AI, and without enough usability testing to see if it actually helps people. This quick-fire approach often creates more problems than it solves, especially when it comes to customer satisfaction.

When AI misses the mark: my bank experience

This morning, I had a frustrating experience with my bank that perfectly illustrates this problem. My issue was simple, but I was sent to an AI agent first. This bot, clearly not well-equipped, immediately, incorrectly and hurriedly guessed at my problem, offering useless solutions. It was a loop of frustration, wasting my time.

When I finally managed to explain what I needed to the machine and asked to speak to a human, the system did something that really got to me: it sent me to another AI agent. Whether intentional or not, it felt like they were trying to deceive me into thinking I was talking to a person, or just hoping I'd give up. It left a really sour taste. And the thing is, my problem still wasn't solved.

This wasn't just an inefficiency; it was a clear sign of how poor AI implementation can damage trust and leave customers feeling unheard.

AI is here to stay, but so is humanity

The truth is, AI isn't going anywhere. It's already woven into many professional processes and will only grow. But my experience shows why human involvement, or at least the option to connect with real people, will remain crucial. AI should be a powerful tool that helps humans, not a barrier that prevents genuine connection and problem-solving.

For businesses, this means understanding that true efficiency and customer loyalty come from thoughtful, human-centered AI design. It's about empowering your teams and delighting your customers, not just cutting costs.