The Monday Morning Action Plan

If you're convinced that internal AI capability matters, here's what to do starting Monday:

1. Unlock the tools

Give every employee access to AI. Buy Claude Pro, ChatGPT Plus, or both. Make it standard, not special. Set clear data guidelines (no customer PII, no confidential financials), but otherwise let people use AI freely.

Cost: €20-30/month per person. For a 1,000-person organization, that's €20,000-30,000/month - less than the cost of one mid-level consultant.

Why this matters: People can't learn to use AI if they don't have access. Most organizations are so scared of AI tools that they block them entirely. This is like banning email in 1995 because "it might be insecure." It guarantees your people fall behind. Yes, this requires clear data guidelines. No, that shouldn't take months to figure out. The risk of waiting exceeds the risk of imperfect initial guidelines that improve through use.

2. Build the skills

AI literacy doesn't happen by accident. You need structured learning. That means training - not one-off workshops, but ongoing skill-building in:

• Prompting techniques (how to get better results from AI)
• Context engineering (how to give AI the right information)
• Building and using AI agents (how to automate workflows)

This doesn't have to be expensive. Start with internal lunch-and-learns. Ask people who are already using AI well to share what they've learned. Create office hours where questions get answered. Build communities where experimentation is encouraged. Bring in external trainers if needed, but focus on practical, hands-on learning.

3. Apply to real work

The best way to learn AI is to use it on actual projects - not sandbox experiments or hackathons, but real work that matters.

Give your teams challenges:

• "Use AI to reduce the time it takes to onboard a new customer"
• "Use AI agents to automate our monthly reporting process"
• "Come up with three ways AI could improve the customer experience"

Not hackathons. Not innovation theater. Actual projects with actual deadlines solving actual problems. The learning comes from using AI where it matters, failing fast, and adjusting.

Then, create space for experimentation. Let people try things, fail, and iterate. Celebrate learning, not just success. This is how capability compounds. People learn by doing. They share what works. Best practices emerge. And over time, AI literacy becomes embedded in how your organization works.

This approach feels risky to executives used to comprehensive change programs with detailed roadmaps and stage gates. But the bigger risk is moving slowly while capability compounds elsewhere.

The timing matters. We're roughly 3 years into the AI era. Organizations that started building capability 12 months ago have a head start. But it's not insurmountable yet.

In another 18-24 months, the gap will be harder to close. The organizations that have been learning continuously will have compounded their capability. They'll move faster on new developments. They'll make better decisions about where AI adds value. They'll have organizational muscle memory that can't be bought.