From Mindset to Execution: Why 95% of teams fail at AI transformation (and how to be among those who succeed)

Today I added a short post where I wrote that Mindset > Toolset.

Today I'll tell you a truth nobody wants to hear: most Czech teams won't manage AI transformation.

Not because of technology. Because of themselves.

Local mindset vs. global reality

I see it every day. Developers, designers, managers, they all have the same pattern:

An AI tool saves them 80% of the work → they focus on the 20% of shortcomings → "This is useless, I'd rather do it myself."

Instead of appreciating that they did an 8-hour task in 2.

A concrete example from last week:

A developer was given the task of finishing a web application: frontend + backend + database.

Traditionally: 4 weeks of work.

With Cursor: AI in 5 days:

• Generated a complete React frontend
• Wrote the entire Node.js backend with an API
• Created the database schema and migrations
• Refactored old code
• Implemented authentication and validation

His reaction? "Cursor is useless, I have to redo it all."

The reality? 80% of the app worked perfectly, 20% needed debugging and tweaks.

The result? Instead of 2 days he spent a whole week "redoing" it and complaining that AI can't program.

This isn't an AI problem. This is a mindset problem.

Why Czech developers reject AI (and why they lose by it)

After more than ten years of going through IT projects, manufacturing processes, e-commerce platforms and product design, I see a clear pattern:

Traditional teams resist change. AI saves them 80% of the work, but instead of appreciating that, they focus on the 20% of shortcomings and prefer to claim it's better to do it all again by hand.

Typical excuses:

❌ "AI code is poor quality" → but 80% of tasks don't need enterprise-grade code

❌ "I don't want to learn new tools" → but the competition is learning

❌ "GitHub Copilot is enough for me" → while the world uses 20+ AI tools daily

❌ "I'd rather do it myself" → and spend 5x more time

The reality of the global market:

✅ An Indian developer with AI tools delivers an MVP in a week

✅ An American startup of 3 people competes with a Czech team of 15

✅ AI-first agencies offer the same quality for 50% of the price

While we discuss, they get on with it.

Why does this happen?

1. A local bubble:

Czech developers compare themselves with Czech developers. Not with global competition.

2. Perfectionism: (I struggled with this extremely myself)

Rather a 100% solution in 10 hours than a 90% solution in 2 hours.

3. Resistance to change:

• "We've always done it this way" vs. "How can we do it faster?"

4. A lack of business context:

They don't see that speed > perfection in 90% of cases.

The world is "shrinking": whoever doesn't get it will be left behind

Thanks to AI the world works in an ever more globally connected way.

What this means in practice:

Speed decides

Today it no longer matters whether you're a startup from Nigeria or an agency in Prague. AI tools even out differences in experience and skills. What used to decide was the number of senior people on the team. Today it's the ability to deliver a working solution quickly. Time-to-market is more important than perfect code, whoever delivers first has the lead.

Prices fall

A task that a year ago would have cost 100 thousand, AI handles today for a thousand. The global talent pool is available to anyone, from anywhere, thanks to AI assistants. The local "premium" is ceasing to exist. If you build only on being in the right place, you'll soon hit a wall, AI erases geographic and price differences.

Specialisation is changing

Everyone with the support of AI can program (or soon will be able to). More important than writing code itself is the ability to steer AI and plug it into the process. People with multidisciplinary thinking win, those who can connect technology, business and design. Fast adaptation today has more value than years of experience in one narrow field.

An example from my own practice:

Project: E-commerce redesign for a client

The traditional Czech approach:

• 3 months of planning
• A team of 8 people
• A budget of 800k

Result: Good quality, but slow

Our AI-first approach:

• 2 weeks of execution
• A team of 3 people with an AI-first mindset + AI tools
• A budget of 200k

Result: The same quality, 4x faster, 4x cheaper

The client chose us. Why? Because his competition was already using a similar approach.

How to recognise an AI-ready developer (and how to become one)

After dozens of interviews and projects I know what a developer who will succeed in the AI world looks like:

AI-ready mindset:

Experiments actively:

• Tests new AI tools in their free time
• Shares discoveries with the team
• Looks for ways to speed up the workflow

Business-wise mentality:

• Understands that a 90% solution in 20% of the time is often better than 100% in 100% of the time
• Measures value, not just code quality
• Sees the wider context of the project

🚀 Adapts quickly:

• Doesn't resist process changes
• Learns from AI outputs instead of criticising them
• Combines AI tools creatively

Outdated mindset:

Resistance to change:

• "We've always done it this way"
• Looks for reasons why AI can't be used
• Waits for "perfect" tools

Perfectionism:

• Focuses on the 20% of shortcomings instead of the 80% of benefits
• Would rather rewrite AI code than improve it
• Quality > speed in all situations

Rigid thinking:

• Doesn't want to learn new tools
• Relies only on traditional procedures
• Doesn't see the business context

How to change:

An AI-first culture doesn't start with a giant strategy, but with small steps. Take one repeating task, try a tool that speeds it up, and measure the difference in time. Within a week you'll see concrete results.

Then share them with the team. Show what works, be specific with numbers and inspire others to try it too.

And above all, don't stop at one attempt. Test new tools every week, combine them creatively and track the impact on productivity. AI is not about a one-off experiment, but about a culture of quick iterations and measurable results.

Leadership in AI transformation: the hard reality

As a leader you have to be brutally honest:

What works:

Leading by example:

AI needs to be used daily and visibly. Share your experiments with the team so everyone can see what works and what doesn't. And don't forget to measure results and communicate openly, only that way will you get the most out of AI.

⚡ Fast decisions

Tools need to be tested within weeks, not months. Implement what proves itself immediately. And above all, stop the endless analyses that only slow down real progress.

The right people:

Invest in people who have the appetite to experiment and look for new ways. Part ways with those who refuse to grow and hold the team back. And don't forget to reward those who bring AI innovations and move the organisation forward.

What doesn't work:

• Waiting for "mature" tools → the competition runs ahead in the meantime
• Compromises with opponents of change → they hold back the whole team
• Gradual implementation → the AI advantage is in speed

Numbers that don't lie

Global reality:

• 72% of companies have AI actively in operation (McKinsey 2024), up from 50% in previous years
• $252.3 billion of global AI investment in 2024 (Stanford AI Index 2025)
• AI-first startups grow 3x faster than traditional ones (confirmed by research)
• Developers with AI skills earn 8-40% more depending on specialisation (Levels.fyi 2024)

Local reality:

• 39% of Czech companies use AI in 2025, a threefold increase in a year (Czech Daily 2025)
• 70% of Czech companies still haven't adopted AI (Czech Chamber of Commerce 2025
• The average Czech IT project takes 2x longer than the global standard (confirmed by industry reports)

Brain drain: the best are leaving for AI-first companies (the trend continues)

Additional insights:

• 60% of large Czech companies have AI technologies implemented (Prague Forum 2024)
• 25% of Czech companies are in the process of adopting AI solutions
• The Czech Republic fell from 7th to 9th place in the IT Competitiveness Index (2023→2024)

The National AI Strategy of the Czech Republic 2030 was approved in July 2024

Brain drain, the best are leaving for AI-first companies

Conclusion: Either you adapt, or you will be replaced by someone who adapted.

🚀 Concrete steps: how to start tomorrow

For developers:

Week 1:

• AI audit
• Identify 3 repeating tasks
• Find an AI tool for each
• Measure the time before/after

Week 2: Experimenting

• Test 1 new AI tool a day
• Document the results
• Share with the team

Week 3:

• Optimisation
• Combine the tools into a workflow
• Automate as much as possible
• Measure the overall impact

For leaders:

Day 1:

• A personal AI routine
• Start using AI for your own tasks
• Be visible with experiments
• Share results with the team

Week 1:

• Team assessment
• Identify AI-ready vs. opponents
• Set clear goals and deadlines
• Prepare for hard decisions

Month 1:

• Implementation
• Roll out AI tools to the whole team
• Measure productivity weekly
• Adapt processes based on data

My question for you:

Are you among the 5% of teams that will manage AI transformation?

• Or are you still in the 95% that:
• Discuss instead of testing
• Look for reasons why it can't be done
• Compare themselves with local competition instead of global

Self-reflection:

How many AI tools do you really actively use today? When did you last change your process just because AI allowed you to work differently? Do you track productivity before and after implementing AI at all? And how quickly can you deliver a first MVP?

If you don't have a clear answer to these questions, you have a problem...

Conclusion: a global game, local players

Thanks to AI the world is "shrinking". Geographic advantages are disappearing. The local premium is ending.

Those who can think globally instead of locally win. Those who adapt quickly, even when it isn't perfect. Those who can measure real value, not just quality on paper. And above all those who have the courage to experiment instead of just discussing.

Czech developers and teams still have that chance. But the window is closing fast.

The question has long since ceased to be whether AI will change your industry. The question is whether you will be among those who lead that change, or among those who will only clumsily chase it.

What do you think? Am I too critical, or do you see the same problems? 👇

P.S. If you have a concrete example of AI transformation (successful or unsuccessful), I'd be glad if you shared it. We all learn, mostly from mistakes, and I personally have made more than enough.