Why Companies Fail Transformation. It’s not what you think

Let me start with something that surprised us.

We worked with a large global organization that had everything in place for innovation. Strong ambition, strong teams, lots of investment. Exactly the kind of company you would expect to perform well. When we analysed their teams, the data looked good. People scored high on the competencies you would expect for innovation. They were flexible, curious, able to think differently.

But the output was low. That didn’t make sense.

So we looked one level higher, at the leaders of those teams. And that’s where it became clear. Many of these teams were led by leaders who did not show those same innovation-related behaviours. In practice, ideas were not really encouraged, experimentation slowed down, and decisions stayed centralized. The result was predictable: strong teams, weak output.

That was an important lesson. It is not enough to have the right people. The context they operate in, and especially the leadership, determines whether that potential actually turns into results.

This is something we see in many organizations. Most companies don’t struggle with knowing what to do. Strategy is usually clear. There is alignment. There is investment in AI, data and transformation. And still, outcomes differ.

The difference is not the plan. It is the context.

So what actually matters when you get the context right? There are three things that stand out.

First: how decisions move.

In some organizations, decisions flow. People know what to do, take ownership, and move forward. In others, decisions get stuck. They need multiple approvals, move up and down the hierarchy, or simply wait. That alone creates a big difference in speed.

And speed is becoming the real differentiator. It determines how fast you adapt, how quickly you bring ideas to market, and whether you stay relevant.

Second: how people work together.

Culture is often talked about as something abstract. In practice, it is very visible in daily behaviour.

Do people speak up in meetings?
Do they challenge ideas?
Are mistakes used to learn, or to judge?

These small moments matter more than posters on the wall. Through our years of working with Professor Geert‑Jan Hofstede, we learned that these behaviours are not random. The way people communicate and collaborate follows deeper patterns. In some environments, hierarchy plays a big role. In others, people are more open to challenge. These patterns shape how fast organizations learn and adapt.

If people hesitate to speak up, problems surface late.
If people avoid uncertainty, they experiment less.

Again, speed slows down.

Third: who is in the room, and who leads.

Context is built through people. From our data, two capabilities consistently stand out in teams that innovate better and faster than others. The first is the ability to learn quickly and adjust. People who do this don’t wait for perfect information. They test, learn, and move. The second is the ability to influence others. Because in most organizations, nothing important happens without alignment. Together, these capabilities determine whether decisions actually move.

But there is an important nuance. It is not just the team that matters. It is also the leader. As we saw in the example, strong teams can still underperform if leadership blocks progress. Leaders set the pace. They decide whether ideas are encouraged, whether decisions are pushed through, and whether people feel safe to act.

We saw a similar pattern in how teams were structured in that same organization. Collaboration was seen as important, and people were capable of working together. But in reality, teams operated quite independently. Collaboration depended on specific leaders connecting them. When those leaders were not actively involved, communication simply stopped.

The capability was there, but the context did not allow it to work.

And the solution was not more training. It was changing how teams were connected and how leadership supported collaboration.

AI makes existing issues more visible.

This becomes even more visible when companies invest in AI. Many organizations expect AI to improve execution. But in practice, AI makes existing issues more visible.

An AI-first company is not just using AI tools. It is one where AI is part of daily work and decision-making. That requires fast decisions, clear ownership, and strong collaboration. If that is not in place, AI does not speed things up. It slows things down.

We have been in boardrooms where leaders believe that strong performance today is no reason to keep investing in innovation.

The impact of that mindset is rarely immediate failure.
It shows up in a more subtle way: loss of speed.

Decisions start taking longer.
Projects get revisited.
Opportunities are missed.

Over time, the impact becomes significant, and in some cases hard to reverse.

You can see this clearly in IMD’s Future Readiness research. Companies like Mastercard have consistently focused not just on short-term performance, but on investing in innovation and building new capabilities over time. That ability to deliver today while preparing for tomorrow is what keeps them moving, while others, often former market leaders, slowly fall behind.

References

1. IMD (2026). Future Readiness Indicator
2. Zhu et al. (2022). Psychological safety and innovation behavior: A meta-analysis
3. Jin & Peng (2024). Psychological safety and innovation performance
4. Zhang et al. (2023). Organizational culture and innovation performance
5. Hofstede, G. et al. Cultural dimensions theory
6. Michelotto & Joia (2024). Organizational digital transformation readiness
7. Cyfert et al. (2025). Digital transformation: role of leadership, culture and competencies
8. Pera internal research on competency model and performance correlations
9. McKinsey (2025). Superagency in the workplace
10. Pera anonymized global supply chain case study