Artificial intelligence is now part of our everyday lives, it’s here, it’s powerful, and it’s accessible. Even better, AI “agents”, capable of autonomously performing complex tasks, are already transforming how we work. Yet in most companies, AI adoption remains marginal.
A recent study by writer.com revealed that only 45% of employees (compared to 75% of CxOs) feel their company has been very successful in adopting and using generative AI over the past 12 months. And 35% of employees, dissatisfied with the AI tools provided by their employer, are paying out of pocket for better generative AI solutions.
So why are there such adoption issues?
What can leadership teams do to accelerate AI adoption within their organizations?
AI and AI agents, what are we really talking about?
When we talk about AI today, we often mean generative models (like ChatGPT, Claude, Gemini…) that can produce text, code, images and even make decisions based on natural language instructions.
AI agents go even further: they can chain multiple tasks, interact with systems and take initiative toward a goal.
In theory, we’re ready for a productivity revolution. In practice, it’s a different story.
In most of my client organizations, I see internal AI solutions made available to employees, yet very little actual change in how people work. Deliverables are still created manually, processes remain the same and workloads are just as heavy.
Adoption remains low ; and it’s not about the tools
Powerful tools exist. But adoption is still lagging.
Why?
Because the challenge is no longer (just) technological. It’s human, organizational, and strategic.
Who in a company can truly say they’re fully comfortable with AI concepts and using AI tools?
Sure, you’ll find a few tech-savvy employees who love to experiment. But what about your marketing, product, finance, legal, or sales teams? Let’s be realistic, some of them still struggle with a CRM or Microsoft office. So are they really confident using automation platforms (Zapier, Make, n8n), AI prompting or creating AI agents?
Of course not.
And that’s normal. It’s not their background or their training. We can’t blame them.
But I’m convinced that without at least some basic knowledge and AI fluency across the broader employee population, nothing will change.
Today, you don’t need to know how to code to launch a website, but you do need to understand UX, SEO, and customer journey principles to create a performant one.
You don’t have to be an email marketing expert to send campaigns, but you do need to grasp opt-in, deliverability, design and user experience to make them effective.
Same goes for AI: you don’t need to be a data scientist, but you do need to know how to write an effective prompt, orchestrate tools like Zapier or n8n, and understand limitations, biases and business impact.
Today, how many employees, managers or directors truly know how to prompt? Automate a workflow? Identify a meaningful use case?
Very few.
So we end up with powerful tools on hand but unchanged habits. We keep working the same old way.
10 actions to accelerate AI adoption
I believe AI adoption must be a top-down driven approach.
Here are 10 actions top management team could do right now to accelerate AI adoption within their organization and boost productivity.
- Build AI fluency at every level. Not just for digital teams or innovation leads. The executive committee must understand what AI changes, concretely, for each function and business unit.
- Appoint AI sponsors and champions. Every department should have an AI point of contact who can identify opportunities, evangelize internally, and connect business needs with tech capabilities.
- Invest in continuous training. AI evolves quickly. Skills need to keep up. One-shot workshops aren’t enough. Provide structured learning, time to experiment, and coaching.
- Identify high-impact use cases by department and role. Adoption accelerates when people see tangible value.
- Redesign processes. AI isn’t a patch. It calls for new workflows and new human-machine task distribution.
- Track and measure adoption. Who’s using what? With what results? What’s holding people back? Measure in order to optimize.
- Update goals and performance expectations. If tools evolve but KPIs stay the same, nothing will move.
- Encourage hands-on experimentation. Give space for pilots, POCs, internal tests. Innovation doesn’t only come top-down, it also thrives from bottom-up initiatives.
- Create a clear governance framework. AI raises ethical, legal, and security questions. Address these early to avoid chaos or regulatory deadlocks. A well-governed AI is an adoptable AI.
- Celebrate internal success stories. Highlight use cases that worked. Celebrate productivity gains, time savings, and innovation. Few things inspire more than peer success.
Conclusion
The tools are here. The potential is massive. But without a clear vision, shared culture, and structured enablement, AI will remain stuck in demo mode. It’s up to CxOs to lead by example, not just by talking about AI, but by integrating it into how their organization thinks, operates, and grows.
