AI innovation has been accelerating since the beginning of the year. What was cutting-edge six months ago is now table stakes. But here’s the thing: most leaders feel behind, even when they’re not. Where to start, which tool to pick, and what training to roll out to whom remain the questions that block them from moving quickly.
In this environment, speed matters as much as making the optimal choice does. Moving faster without losing control is the new normal that companies winning right now are embracing. But where should you start? That’s why I invited Doug Shannon, Generative AI & Automation Leader, to join me on “What’s the BUZZ?” and explore this topic together. Here’s what we talked about...
The Speed of AI Innovation isn’t Slowing Down
Remember when we thought AI moved fast? That was before Claude Code, before OpenAI’s autonomous agents, before Anthropic’s latest models started finding security vulnerabilities that vendors didn’t even know existed. The pace of change has gone from “every six months is two years of AI time” to something even more compressed. And here’s what that means for you: if your roadmap is older than eight weeks, you’re already behind.
Companies are shipping software faster. Models are getting better. New capabilities are dropping constantly. The question is increasingly how to stay relevant when the ground keeps shifting beneath your feet. Most leaders see the pace of innovation and think, “We need to move faster,” but also, “We need to be careful.” Both things are true. The trick is figuring out how to do both at the same time, which is harder than it sounds when you’re trying to maintain governance, protect your data, and not break things that are already working.
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Here’s something that might surprise you: mid-market companies have a massive advantage right now. They don’t have the legacy systems, the legacy data, the legacy organizational structures that slow down enterprises. But they also don’t have the resources of the big tech companies. So what do they do? They build.
The winning move is to enable your people to build what you actually need, internally, using whatever tools work best for your specific situation. A mechanical engineer at a small agricultural manufacturer who built an invoice processing app using Claude Code is a great example. He didn’t need a software engineering degree. He had an idea, access to the right tools, and permission to try. His finance colleague didn’t lose her job. Instead, she got her time back.
This is where the Europe-versus-the-US conversation gets interesting. Instead of asking “How do we cut costs?” ask “What if our people became 10X more productive?” Keep them. Train them. Give them access to AI tools. Let them build. Because when you do that, you’re not just automating work, you’re also building internal products that are tailored to how your business actually works. You’re keeping your intellectual property inside your walls. You’re building a moat that’s actually defensible. And you’re staying agnostic about which models and tools you use, which means you’re not locked into any one vendor when the next thing comes along.
Governance Doesn’t Mean Slow, But Smart
The biggest fear leaders share is this: “If we let people build, won’t it get messy? Won’t we end up with a thousand different tools and processes and nobody knowing what’s actually running?” The answer is yes, unless you set it up right.
Give people a safe place to experiment. Then have a center of excellence that understands automation, how to connect things, and how to establish a lightweight governance. Their job is to say yes, but to do so smartly. When someone builds something cool in the sandbox, that team takes it, makes it 10X better, connects it to your actual systems, and safely brings it into the enterprise. Now everybody wins. The person who built it feels heard. The business gets a better solution. And you actually know what’s running in your systems.
This is also where automation becomes your friend. Use traditional automation to handle the data work like filtering, categorizing, cleaning. Then use AI on top of that. Use internal tools instead of external ones when you can. Build your own MCPs, your own integrations, your own stack. It’s safer. It’s faster. And it costs less in the long run because you’re paying for tokens and energy, not licenses to ten different vendors.
Summary
The speed of AI isn’t slowing down. Your roadmaps need to reflect that. But speed doesn’t mean chaos, and it doesn’t mean firing people and hoping for the best. The companies winning right now are the ones that enable their people to build, stay flexible about which tools they use, and create smart governance structures that say “yes” instead of “no”.
If you’re in a mid-market company, you have an advantage. Use it. If you’re in an enterprise, you have inertia to overcome, but you also have resources. Start small. Pick a process. Enable a team. Build something internally. See what happens. And if you’re sitting on the sidelines waiting for the “best practice” to emerge, stop waiting. The best practice is moving too fast to catch. The real practice is learning as you go, staying connected to what’s actually happening in the market, and making sure your people have the tools and permission to move with it.
What you can do now: Pick one process in your business that could be better. Find someone on your team who’s curious about AI. Give them access to Claude Code or similar tools, and give them permission to try. See what they build. That’s how this starts.
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August 18 - Michael Carroll (Fellow at LNS Research) will share how to adapt traditional approaches for AI agents to recommend and buy your product. [More details to follow on my LinkedIn profile…]
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