Hi Niina, let’s start off by learning a little bit about you.
Thanks! So, I'm Niina Hagman and I have just celebrated my first 100 days at Konecranes. I came to the company from the data & AI consultancy side, where my work sat at the intersection of strategy, governance and real-world data and AI delivery.
Prior to that, I spent seven years at a Finnish telecom operator, where I worked in corporate strategy, market intelligence and then transitioned to the wider fields of digital, data and analytics.
I have always been interested how advancing technologies reshape what’s possible in business — enabling smarter decisions and better business outcomes end-to-end.
How did you find your way to AI?
I actually started by studying economics, marketing and accounting. It’s quite common for people to move into AI from diverse backgrounds, reflecting how multifaceted the field is.
Obviously, you need to have certain a technical and mathematical understanding, but it also depends on how deep you want to go: right into the deep technical end of things, or staying on a more general and wider level?
I later deepened my studies in computer science and AI at university, but my goal has always been clear: to bridge real business needs with technological opportunities, while relying on true experts for the most advanced AI research and implementation. Overall, in the AI field you’re never “finished”; technology evolves so fast that continuous learning isn’t optional – it’s instrumental in staying relevant.
And how did you find your way to Konecranes?
I really enjoyed my years in consulting, working with international clients across many industries and helping them build the foundations to make the most of data and AI. That work constantly reminded me that technology alone is not a silver bullet to happiness: you also need the right structures, targeted investments, people, skills and processes, as well as a clear conviction to use AI to create real business value.
Over time, though, I felt a stronger pull towards being on the inside of one organization – staying long enough to actually develop and scale those capabilities wider and deeper, especially now when the pace and impact of AI-driven change are accelerating so dramatically. That thinking led me to Konecranes – a highly respected global company with high-quality products and services, a meaningful role in society, and strong ambitions to transform material handling across the wider ecosystem. I’m genuinely grateful to be part of that journey now.
What did you find out about Konecranes and AI when you walked through the door?
Coming from the consultancy side, I have worked with a number of companies in the manufacturing industry that were using AI, but the experience was more about, for example, limited focus areas, and often heavily relying on third-party AI solutions/features only. Many of these companies also had a lot of work to do in establishing the basic data capabilities.
Konecranes, on the other hand, had already spent years investing in the building blocks of Industry 4.0, like establishing its capabilities in cloud, data storage, connected equipment, data science competences, etc. Obviously, data itself is an infinite game, it’s never complete, but Konecranes already has a solid foundation that it could leverage.
Across the company we’re now able to build on strong foundational IT and data capabilities, which means AI is not just a vision or a distinct software feature, but a tangible capability that is generating measurable business benefits. At the same time, rapid advances in technology over just the last 12–18 months have opened up entirely new opportunities that simply didn’t exist before. Together, these create a great moment to take our next big steps in AI use – and the million-dollar question is which steps should those be?
OK, what should those steps be?
I’m glad you asked! First, it’s important to recognize that every company is suited to leverage AI and usually more than they do today, regardless of industry. The angles differ, of course, but all companies have customers, products and services, and a range of supporting functions where AI can create value. Unlocking this improved AI value creation requires developing AI skills and understanding across the company.
Secondly, we need to be better and faster in translating newly learnt skills into action. Ideas are cheap, implementation matters.
Konecranes is poised to use AI much more than what we’re doing today – or many other companies are doing today.
Can you give us some more examples about improved AI use?
Sure! I could divide this into three categories.
First, everyone can use AI at Konecranes. We have the citizen AI tools, and they are becoming more potent, so we should become much more creative and systematic in this type of tool usage. Think of Copilot, SAP Joule or similar AI assistants. Not only prompting these assistants, but setting up automated, trigger-based workflows with these capabilities.
Second, while we already have many existing AI applications in areas like sales, service, etc., we should also look at the wider role AI could play in the processes around these applications. If we were to design certain processes or sub-processes from scratch, what would that look like, what role would humans play, and what would we leave for automation and AI agents? There are big benefits in this field.
The third category or opportunity is in areas like supply chain and the core of our manufacturing. Building on the idea of rethinking processes, the next step is to consider how AI can reshape entire operational ecosystems. Supply chain and manufacturing are not isolated functions – they are deeply interconnected with planning, procurement, logistics, etc.
As AI capabilities evolve, we move from incremental improvements to true convergence – systems that are not just integrated but dynamically orchestrated. Imagine predictive models that anticipate demand shifts and automatically adjust production schedules, or AI agents that synchronize supplier networks with real-time factory performance.
Then, as a bonus, there are the physical products themselves. As I mentioned, many of our products might already be “smart”, i.e. from the perspective that they are connected, but looking ahead I'm certain that they will become a lot smarter. They will gain more perception, become multimodal. Just think of what we could do there with AI!
To close, how will AI change Konecranes in, let’s say, the next seven years?
It's a super-interesting question. So, to state the obvious, I think that the world will look a lot different, we can already see that from the technology today. It’s evolving so rapidly but at the same time, change will never be as slow as it is now.
I don’t have a crystal ball handy, but I would say that in seven years AI will be deeply integrated into almost every domain that we have in the company. People will be using AI in seamless collaboration with machines, so it’s human-machine collaboration in all forms. That’s “machines” meaning hardware as well as digital machines. We will be finding ways to serve customers in the material handling space even better than we do today.
» Listen to the Futucast episode where Niina talks about AI enablement (in Finnish)
» Read more: Turning data into actionable insights with artificial intelligence
