AI transformation is a leadership commitment, not a technology project

At our recent AI-themed event, one message stood out above all others. AI transformation is not first and foremost a technology challenge. It is a leadership commitment.

Reflections from our CXO Breakfast Club event "Lead and breathe in the era of AI" , with Kai Telanne, CEO of Alma Media, and Anniina Brusi, Head of Commercial at Budbee (part of the Instabee group).

Much has been written about the gap between AI ambition and AI reality: the data that is not yet in order, the scattered pilots, the groundwork that keeps getting skipped. What is often underdiscussed is the question sitting underneath all of it. Who is actually responsible for making this work?

The answer that emerged from the event is refreshingly simple, and perhaps a little uncomfortable for anyone hoping it would be "the technology team". It is the leader.

Start from the vision, not the tools

Organizations often begin their AI work by focusing on tools and technologies. Both Kai and Anniina argued for a different starting point. Like any development effort meant to drive profitable growth, investments in AI should be tied to the company's long-term vision and strategic priorities, and weighed against them. The first question is not "which tools should we adopt?" but "what role does AI play in reaching where we want to be?"

Seen this way, the leader's role becomes clearer. It is to define what success looks like, communicate a clear direction, and lead by example. AI is not really a technology change. It is a change in how people work, make decisions, and create value, and that kind of change takes time. Rather than chasing quick wins, leaders do better to treat AI as a long-term journey for the whole organization.

The three phases of AI adoption

Both speakers described a clear progression in how organizations put AI to work.

1. Improving productivity

   Most organizations start here, helping people and teams work smarter. It is the easiest entry point, because the benefits are tangible and fairly easy to measure. But productivity gains on their own are not the destination. Often this phase alone opens the door to the next ones, as it prompts us to question old processes, the data sources we use and the ways we create value.
   
   

2. Creating new customer value

   The next phase is to solve real customer problems. Anniina Brusi shared a clear example from Budbee. A recurring challenge in parcel delivery, especially in Finland, is the high number of redirections. By starting from that concrete pain point, Budbee has used AI to cut unnecessary redirections. The benefit works on two levels: a better experience for the end customer, and a real edge for the e-commerce clients Budbee serves.
   
   

3. Redesigning the business

   The third phase is the most far-reaching. Instead of adding AI on top of existing processes, the organization rethinks how it operates. AI becomes part of workflows, decision-making, and the business model itself. At this stage it is no longer a separate initiative, but part of how the company creates value.

The lesson running through all three phases is that good AI work starts from customer needs, not from what the technology can do. When an organization truly understands its customers' problems, it can see where AI will make the biggest difference. In that sense, an AI decision is no different from any other sound business decisions: it starts with value.

What this asks of leaders

If AI is a leadership choice tied to vision and strategy, what does that look like in practice? The speakers offered a few concrete starting points.

• Get the data foundation right. AI is only as good as the data behind it. No matter how advanced the solution, its value depends on the quality and availability of the underlying data. For many organizations, building trustworthy, accessible data is the single most important enabler of everything that follows.
• Move step by step. Change does not happen overnight. Small, measurable progress builds momentum and confidence.
• Make time to redesign work. Rethinking processes and roles is work in itself, and it needs dedicated time and resources.
• Align around the customer. Understanding customer pain points helps focus AI efforts where they create the most value.
• Set learning targets. AI skills should not sit only with technical teams. Leaders, managers and employees all need room to learn and experiment, with progress followed up.
• Create a safe space to experiment. Make sure teams have an environment for development that meets current technology and security standards.
• Combine AI with human judgement. Once the data is in place, AI can surface opportunities and propose solutions. Leaders are still needed to judge what is operationally feasible, commercially worthwhile, and strategically right.

The real question

The challenge today is not whether AI exists. It does. The real question is whether leaders are ready to make the choices that put it to good use: investing in data, making space for learning, redesigning work, and aligning the organization around customer value. Technology can enable the change. Leadership decides whether it happens.