So You Invested in Generative AI… Now What?
There’s a pattern I’ve seen again and again. A company invests in Generative AI, expecting game-changing results. Six months later, the excitement fades.
- The AI tools work—but the impact? Underwhelming.
- Some teams experiment, but adoption is scattered at best.
- Leadership wants measurable ROI, but no one can confidently point to what AI has actually changed.
If this sounds familiar, you’re not alone. The problem isn’t AI—it’s execution.
Generative AI can absolutely be a competitive advantage. I’ve seen it firsthand. But the way most firms introduce it—just giving people access and hoping for the best—doesn’t work. Here’s why AI adoption struggles and what to do instead.
The Three Costliest Mistakes Companies Make with Generative AI
1. Using AI Like an Advanced Editor (Instead of a Workflow Transformer)
I get it—Generative AI makes writing faster. It’s a fantastic tool for summarizing, rewording, and generating drafts. But if that’s all your team is using it for, you’re barely scratching the surface.
I’ve watched firms roll out AI pilots, only to find that the time savings don’t translate into real business impact. Why? Because shaving off 15 minutes of editing doesn’t change how work gets done. It’s not enough to create a competitive edge.
What actually works: You need to embed AI into workflows, not just into writing. Think bigger. How can AI help your teams make decisions faster? Automate tedious processes? Provide insights at the moment of need?
And here’s the thing—it’s not that hard. AI can even help you figure out where to start. But it’s not an easy button. It takes some intentional effort to integrate Generative AI where it truly matters.
2. Avoiding Experimentation (Because It Feels Like Failure)
Here’s the tough truth: AI success requires experimentation. And most organizations are terrible at experimenting.
I’ve seen this play out in so many firms. They launch an AI tool, but no one wants to risk breaking something that works or looking inefficient. The culture of perfection and excellence gets in the way of learning.
But without learning, there’s no progress. If you’re waiting for AI to be a perfect, plug-and-play solution before rolling it out, you’re guaranteeing slow adoption and limited ROI.
What actually works: Treat AI like an ongoing learning process, not a one-time launch. Instead of waiting for a perfect plan, start with small, real-world experiments and iterate. Let teams try, learn, and refine—because that’s how AI adoption really happens.
3. No Clear Ownership (So AI Adoption Stalls)
Let me ask you a question: Whose job is it to make Generative AI work in your organization?
If the answer isn’t crystal clear, that’s a problem.
In many firms, AI adoption falls into a no-man’s land—IT owns the tool, but not how people use it. Individual teams experiment, but there’s no structure. Leadership expects ROI, but no one is driving execution.
AI has the potential to deliver huge competitive advantages—I know, because I’ve seen it happen. But without clear ownership, those gains won’t materialize.
What actually works: Assign real responsibility. Ensure that each AI initiative has a defined champion. Define who is responsible for turning AI from a tool into a real driver of business outcomes in each department and team where it may be relevant. And be realistic—firm wide adoption won’t happen overnight. However, without ownership, it won’t happen at all.
The Generative AI Execution Scorecard: Find Out Where You Stand
If you’re struggling to get real results from AI, the problem isn’t the tech—it’s how it’s being used.
The Generative AI Execution Scorecard helps COOs and CTOs diagnose:
- Where AI adoption is stalling (and why)
- What’s blocking ROI from your AI investments
- How to turn AI from an experiment into a competitive advantage
Take the free scorecard now and get a custom report on your AI execution strengths and gaps: [Generative AI Execution Scorecard]