In this edition of People Beyond the Network, we meet Peter Kukura, Chief Information Officer at CETIN International, a role often associated with platforms, data, and transformation, but lived through talented people and everyday decisions. 

7 questions with Peter, 7 Perspectives on IT Leadership and adoption of AI.

1. Peter, what aspects do you wish more people knew about your field of work?
I think many people still imagine IT and project work as something very technical, structured and predictable: platforms, systems, tickets, timelines, budgets. Of course, all of that is part of it. But behind the scenes, a large part of the work is actually about people, priorities and decisions. At group level, technology leadership is often about connecting different worlds: business ambition, country reality, technology constraints, vendor promises, budget limits and people’s capacity to absorb change. The harder part is making sure we solve the right problem, that people understand why it matters, and of course: that the solution can actually be adopted in real life.

This is becoming even more visible with AI transformation. We are entering a new era where technology is changing so fast that your job is no longer about having years of experience. Very often, it is about learning quickly, testing what is possible, understanding the implications, and helping others make sense of it. Wearing my shoes today means being only a few weeks ahead of everyone else, not years ahead - but using that head start responsibly to guide the organisation, separate hype from real value, and create practical adoption.

2. Looking back, what were the key steps that shaped how you approach leadership in technology, and what did they teach you about working across countries and functions?
You quickly realise that the same slide, same system or same process does not automatically work everywhere. Each company has its own maturity, constraints, history and people dynamics. So my leadership approach became less about “rolling out” solutions and more about creating a common direction while respecting local context. You need a clear group ambition, but you also need enough listening to understand what is realistic in each country. 

Another important lesson is that technology transformation only works when business, IT and operations move together. If IT builds something alone, adoption will be limited. If business defines ambition without understanding technical and data reality, execution becomes painful. The role of leadership is to bridge this gap and keep everyone focused on value, not only activity.

3. This year, in collaboration with all CETIN International entities, you are driving the AI transformation further. Can you give us more detail?
I would describe it as our group-wide AI transformation framework — the place where we connect AI use cases, data, governance, technology and people across CETIN countries.

The ambition is very practical: how can AI help us plan better, operate better and work smarter? In the network area, that means topics like smart network planning, optimization, energy efficiency, anomaly detection, NOC/SOC automation and, step by step, more autonomous operations. But it is also about personal and team productivity — using AI in everyday work for reporting, document preparation, analysis, automation and faster collaboration.

What makes me optimistic is that we are progressing quite fast. Across our countries, we already have more than 60 AI and automation scenarios. Some are live or being scaled, such as AI chatbots and assistants, energy anomaly detection, network anomaly detection, site rental contract AI buddy and RPA robots. At the same time, we are piloting more advanced topics like Smart Network Planning, AI alarm handling, SOC assistant, inventory digitalization, sovereign/on-prem LLM options and agentic platforms.

The biggest lesson so far is that the traditional approach does not really work anymore. Technologies, tools and AI solutions are evolving so fast that solutions are already outdated by the time we deliver it in case we do not act fast. We need to work more in smaller agile developments, fast validation, and visible value delivered in weeks or months, not years.

Another important learning is that we cannot rely only on external partners. They are valuable, especially when they bring specific expertise or help us accelerate, but in many areas they are learning together with us because the solutions themselves nowadays are still developing. And if all the knowledge stays outside, we create a gap the moment the vendor leaves. That is why building internal expertise is one of the top priorities.

And finally, data is the cornerstone. Without the right data, in the right quality, with clear ownership and definitions, AI simply does not work. So it is not only about AI tools. It is also about building the data, people and operating model that allow us to scale AI sustainably across CETIN.

4. A key business theme this year is enabling broad and practical AI adoption. What’s one AI habit or practice you’d love to see become “normal” for colleagues across CETINs, regardless of role?
I would love to see AI become a normal sparring partner in everyday work. Not something special, not something only for IT or data people, but a standard help before we start from a blank page.

For me, personal productivity tools are currently the most advanced and already available, which makes them the fastest and most practical entry point into AI adoption. If used well, they can easily improve individual productivity by 20–30% by helping people prepare faster, structure better, summarise long inputs, automate repetitive tasks and create stronger first drafts. The human remains responsible for judgement, decision and final quality, but AI can remove a lot of low-value effort and give us a much better starting point.

For example, before writing a document, preparing a meeting, analysing a problem or reviewing a long input, people should naturally ask: “Can AI help me create the first version, challenge my thinking, structure the options or find what I may have missed?”

At the same time, I think people should become familiar with multiple AI tools, not only one corporate application. AI is becoming part of everyday life, not only office life. The more people experiment with these tools also in their personal life: for learning, planning, creativity, communication or problem solving, the more naturally they will understand what AI can and cannot do.

5. What is the favourite automation/AI adoption case you’ve seen so far?
In the corporate world, I think the most mature and impressive examples are still conversational AI solutions in customer care — both chat and voice. These have been developing for years, and today they can already handle a large share of standard customer requests end-to-end. What makes them powerful is not only the technology itself, but the fact that they are embedded into real operations, connected to processes and measured by real business outcomes.

In the network area, I have also seen first examples of closed-loop automation, which I find very exciting. This means the system does not only detect an issue, but can also correlate data, identify a probable root cause, recommend or trigger a corrective action, validate whether the service improved, and update the operational workflow. We are not talking about fully autonomous networks everywhere yet, but in selected domains the industry is clearly moving towards more self-optimizing and self-healing operations.

Another interesting example I have seen, especially in smaller and more agile companies, is agent-based change management. AI agents can support the whole flow from a project idea, through requirements, analysis, development, testing and deployment preparation. Of course, human control and approvals are still important, but the speed of moving from idea to working solution can be completely different.

My personal favourite use case is actually from daily life. I follow several content creators in the investment domain, and previously it took me several hours a week to go through videos, posts and updates. Now I use an agent-based screener that monitors selected sources, prepares a regular summary, keeps historical context and highlights what has changed. So instead of spending hours collecting information, I get a structured overview in minutes and can spend my time thinking about the content rather than searching for it.

6. What stayed with you most e& Emerging Talents programme – and what, if anything, changed in how you lead or develop people afterwards?
Several topics stayed with me quite strongly: leadership identity, influence without formal authority, organizational dynamics, and the idea that people often grow through “outsight” - by acting in a bigger role before they fully feel ready for it. It also connected well with the discussion about future skills: curiosity, resilience, analytical thinking, AI and big data, empathy, active listening and the ability to lead across functions.

It reinforced for me that leaders should not only manage delivery, but also create opportunities. Sometimes the best development moment is when you let someone lead a topic, represent the team, challenge senior stakeholders, connect people across countries, or build something new from scratch. Of course, this needs support and a safe environment, but people grow when they are trusted with real responsibility, not only when they attend another training.

7. We know you are a fun manager to have; how do you keep the team’s spirit and friendliness among your team members?
I think team spirit is built mostly in small everyday moments, not only during big team events. It is about how we talk to each other, whether people feel safe to ask questions, whether we can laugh even when things are difficult, and whether we help each other when priorities become intense.

I try to keep the atmosphere open and informal where possible. We deal with serious topics, but we do not have to take ourselves too seriously all the time. A bit of humour helps, especially in transformation work, where nothing is ever perfectly smooth.

For me, it is also important to keep bringing new energy into the team. We regularly cooperate with universities and bring in graduates or young talents. They often come with fresh ideas, a different view of technology, and today also much more naturally AI-enabled thinking. That helps the whole team stay curious and open to new ways of working.

At the same time, I believe we need to look outside the company as well. It is healthy to see how other companies and teams work, how they organize themselves, what dynamics they have and what we can learn from them. This gives us a kind of mirror and prevents us from thinking that our way is the only possible way.

And finally, fun is really important. Going out, having a beer, and talking about something completely different than work can be just as important as a team meeting in the office. A good team is one where people are professional, but also human. We can challenge each other, disagree, push for better results, but still keep respect and friendliness. That is what makes long and complex change manageable.