Buying the tool is easy. Being AI ready is the real work.

In São Paulo I recently spoke about the Challenges of AI Readiness to a room full of people. Many of us already use a variety of Gen AI tools. Many of us are exploring AI Agents. The strong response and honest questions showed something important: the same challenges appear in organizations across countries and sectors.

This shared problem is not a lack of tools. The problem is that a tool can arrive at an organization (or in private use) before the organization is ready to use it well. People may not know which or what data is safe to use. leaders of organizations may not agree on the business problem. A usefull pilot may stay in one team. This is exactly why AI readiness truly matters.

AI readiness is an organizational capability: the ability to use AI, GenAI and AI agents safely, usefully and repeatedly.

AI Access is not AI Readiness

The numbers make the gap visible. McKinsey reports that 88% of survey respondents say their organization regularly uses AI in at least one business function. Yet only about one-third say their organization has started scaling AI across the enterprise. Put simply, many organizations have adopted AI tools, but most are still learning how to integrate them into everyday work.

This is not bad news. It's a useful reality check. Access to AI is no longer the challenge. The real challenge is using AI in a safe, effective, and repeatable way that creates value across the organization. During my presentation in Brazil, I used a metaphor (in memory of Ayrton Senna). AI can be a fast car in a garage. The engine is strong, but the tyres are flat, there is no road, and the driver has not been trained. Buying the car does not create a journey. In the same way, buying a licence does not create AI value.

This is not bad news. It is a useful reality check. Access to AI is no longer the challenge. The real challenge is using AI in a safe, effective, and repeatable way that creates value across the organization.

The eight readiness challenges

1. AI Literacy is not equal across the organization

   As AI spreads across organizations, not everyone moves at the same pace. Some teams embrace it quickly, while others are more cautious or lack the support to use it effectively. This creates an AI Literacy gap. The solution is not the same training for everyone. A finance team, a customer service team, and a manager each need different examples, skills, and guidance to use AI with confidence.

2. Leadership must make AI a real change

   AI is not just an IT project. It affects how people make decisions, how work gets done, and even how roles grow, or change over time. That is why leadership matters. Leaders need to set a clear direction, create space for learning, and demonstrate responsible use of AI themselves. When organizations focus only on buying technology, adoption often remains limited, and the full value of AI is never realized.

3. Data must be ready for AI

   AI is only as reliable as the data it uses. To deliver meaningful results, AI needs data that is accurate, up to date, well understood, and properly protected. When data is outdated, incomplete, or unclear, AI can produce weak or misleading answers, often with great confidence. That is why it is important to look critically at the data behind the technology. Before using important data with AI, ask a few simple questions: Where did it come from? Is it still current? Who is responsible for it? And is it appropriate for AI to use it?

4. Many initiatives need one clear map

   Many AI initiatives start with good intentions. Teams experiment, explore new tools, and look for ways to create value. But when these efforts happen in isolation, organizations can end up with multiple solutions that never connect. Keeping an AI register can help create visibility and coordination. By recording the use case, tool, data source, owner, risk level, expected value, and next steps, organizations gain a clearer view of their AI landscape. It also helps identify shadow AI: tools for AI agents that are being used outside agreed processes or governance frameworks.

5. Culture eats AI for breakfast

   Technology alone does not determine the success or adoption of AI. Organizational culture plays a just as important part in this change process. People need the space to learn, experiment, ask questions, and share what works. They also need the time to develop new skills and the new way of working. A culture of blind trust in AI can lead to bad,or poor decisions, but next to that, a culture of fear will prevent innovation and learning. The most effective approach lies somewhere in between: encouraging curiosity while maintaining a critical mindset.

6. Governance helps AI scale

   Governance should not be seen as a barrier to AI adoption (it should not be the brake). Instead, it provides the structure and confidence needed to scale AI in a responsible way. Clear roles, guidelines and guardrails help people move faster because they know what is expected and what is allowed. For each important AI use case, organizations should define who is accountable for the outcome, whic data and systems can be used, when human oversight is required, and how success will be measured. Effective governance is not about slowing innovation down. It's about ensuring that AI creates real and sustainable business value.

7. Training must become a daily practice

   Training is where the journey begins, not where it ends. People may complete an AI course and still not use AI in their daily work. To turn learning into adoption, there is a need to build practice into everyday workflows. Colleagues, ambassadors, and AI champions can play an important role by providing support when it is needed most. The real measure of success is not how many people attended a training session, but how many use AI effectively and create value with it.

8. Do not only protect the garage

   Managing risks is an important part of any AI strategy, but is should not be the end goal. Organizations that focus only on preventing risks, may miss many of the amazing opportunities that AI has to offer. The real challenge is to balance protection with progress. Responsible AI is about creating value while managing risk. That means building the right foundations, helping people to develop the skills they need, and having a clear vision of where AI can make a difference. In other words: build the road, train the driver, and choose the destination.

   
   

In short:

• AI literacy is role-specific (not everyone needs to be a data scientist)

• Leadership drives change (create that clear direction)

• Data must be AI-ready (this is why Data Governance and AI governance are connected)

• Many AI initiatives, too litlte direction (create that map)

  Culture enables adoption (do not let it bite you)

• Governance helps AI scale (we need guidelines and guardrails)

• Training only works when it becomes daily practice (create time, space to practice)

• Move from defence to responsible value creation (responsible AI starts with YOU!)

In the next blog, we will explore this topic in more detail.