Trainings

Category: Business

The Biggest Risk for Companies Isn’t AI, It’s the Stagnation of Skills

Across the Reaktor Ecosystem, we have been accelerating our customers’ AI capabilities with the support of partners like Forge Digital, Adventure Club, and Codemate. Together we have helped organisations not only adopt new AI tools but also rethink how they work, learn, and lead in the age of intelligent systems.


But as AI becomes more capable, a new challenge is emerging, one that technology alone cannot solve. It is not the risk of AI replacing people, but the risk of people stopping their own learning.
Technology is developing faster than any education system can adapt. In the age of AI, a company’s success no longer depends on technology itself, but on how quickly people learn to use it wisely and how effectively they share what they learn.

Companies that make learning a structure, not a reaction, stay ahead. When learning is continuous, employees’ thinking developes at the same pace as technology. However, training must be purchased and organised in a way that supports daily work rather than disrupts it. For example, taking a consultant away from client work for a full day may lower billable rates, but short and recurring online workshops can actually improve both quality and speed.


Learning is also a leadership issue. Leaders cannot outsource capability development; they must lead by example, keep learning themselves, and create space for teams to share knowledge openly. A leader should model working with AI, including the mistakes, because it lowers the threshold for teams to experiment.


At the same time, every employee must take responsibility for their own development. Maintaining skills is no longer a luxury; it is a professional duty. Sometimes that means exploring new things outside working hours: reading, experimenting, discussing, prototyping, publishing content.
Entrepreneurs and consultants know this well. Every project pushes them into unfamiliar territory where learning is not optional. The same readiness is now required in every field.
The winners of the future will not be those with the most sophisticated technology, but those who learn the fastest to see what AI makes possible.

Marjut Sadeharju

How to Make Hackathons Work Inside Organizations – Insights from Elo 2025

With Elothon 2025 at Elo Mutual Pension Insurance Company, we set out to enable the cross-pollination of teams and to enable innovation. The challenge was clear and directly tied to Elo’s mission: innovate with modern AI technologies to create real customer value and business opportunities.

What we got was much more: feasible business ideas that would bring real customer value using already existing data and tools. Here are some insights on how to make the most of internal innovation initiatives, hackathons and learning events.

Best Practices

1. Start with real business challenges

Six cross-functional teams worked on problems directly linked to Elo’s goals and targets. This gave everyone focus and created the sense that their ideas could actually matter. At Elo, people came together seamlessly and productively from the start, a cultural strength that made collaboration natural.

2. Provide expert coaching

We brought in Dr. Riku Ruotsalainen and Aleksi Nuuja from Reaktor as coaches. Their role wasn’t to tell teams what to do, but to challenge thinking, sharpen approaches, and push ideas further. Riku also joined the judging panel, ensuring feedback stayed practical and grounded.

3. Include the customer’s voice

This time, a customer judge was part of the process. That changed the dynamic, teams couldn’t just impress internally, they had to think about end-users from the very beginning.

4. Build a path forward

Unlike many hackathons that end with applause and a dead end, the winning team at Elothon earned the chance to pitch their idea directly to Elo’s group of executives. This raised the stakes and gave participants real ownership.

5. Enable rapid prototyping and iteration

We introduced Lovable as a prototyping tool, turning concepts into visible solutions quickly. Teams also had a practice pitch round with coaching and feedback and the improvement between rehearsal and final pitch was remarkable. This proves the power of feedback as a tool for rapid learning.

The Result

Instead of a one-off hustle, Elothon became a step forward in Elo’s innovation strategy. Collaboration was smooth, the format worked, and the feedback was excellent. One participant summed it up well:

“The arrangements in general were well done. Lovable was a fantastic tool. Coaching and pitch practice was useful.”

It was important to have coaches to guide and challenge the participants’ business ideas. Building a feedback loop sped up learning tremendously. A comment from a participant reveals how learning is always tied to feelings:

“I especially liked the encouraging atmosphere and invaluable feedback from the judges. A great day overall!”

At Splended, we’ve learned that hackathons inside organizations only create value when they are designed as learning and innovation processes not just events. With clear business challenges, strong coaching, customer involvement, and a real path forward, hackathons can move from short-term energy bursts to solutions that actually benefit the customer.

Marjut Sadeharju

How to Help Product Owners Think Differently with AI

When we partnered with Riku Kuikka at Visma, the mission was clear: support Product Owners and adjacent roles in rethinking how AI could be used as part of real product work.

The goal was to create space to explore, question, and experiment. Because even in organisations that already use AI, there’s often a gap between knowing something is possible and actually building something new with it.

Coaching with Depth and Curiosity

To make that space meaningful, we brought in two of our trusted partners: Ville Lindfors (Codemate) and Aki-Ville Pöykiö (Adventure Club). Both bring not just technical fluency but deep experience in Product Development, systems thinking, and the art of asking good questions. Their role wasn’t to teach AI. It was to coach people into thinking differently.

Together, we designed and delivered sessions that challenged Product Owners to:

• Shift from “feature thinking” to experimentation thinking

• Understand the real boundary between automation and AI

• Explore how AI can support, not replace, judgment and creativity

• Prototype faster, and with a clearer sense of what’s valuable

Thinking in New Directions

One thing we appreciated from the Visma side was honesty. People shared openly where they were already strong and where the concepts felt either too basic or not yet grounded enough. The interactive sessions sparked meaningful conversations, also afterwards on their online channels and with our coaches, surfacing both excitement and skepticism.

That’s exactly where the best learning happens.

What resonated most:

• Practical examples that felt close to home

• Space for group reflection and discussion

• Clear coaching instead of static content

From Exploration to Execution

As the Learning Sprint progressed, a key shift occurred: thinking moved from experimenting to deploying.

“A great prototype doesn’t guarantee a production-ready solution. Getting to production requires versioning, risk management, transparency – and most of all, clarity about the AI’s role in the product.”

We explored what this means in practice:

• Ensuring privacy, legal compliance, and observability

• Logging and analyzing AI behavior in production

• Collecting user feedback and implementing A/B testing

Making AI-Compatible Products

An insight from Session 4 challenged how many participants viewed their systems:

“Our products don’t exist in isolation. They need to be AI-compatible.”

We’re no longer just building for humans. Modern systems should be accessible to AI agents as well. 

This means:

• Structuring APIs and documentation for machine consumption

• Considering AI personas in user modeling

• Preparing for interoperability with protocols like MCP (Model Context Protocol) and A2A (Agent2Agent)

In short: building for the future means designing systems that humans and AI can both work with.

Riku’s Vision Makes a Difference

What made this collaboration work was the leadership. Riku didn’t approach this as a box-ticking exercise. He saw it as capability-building: an investment in how product teams think, not just what they know. He brought the right people in, asked the right questions, and created room for the team to reflect.

Visma has set ambitious goals to record four AI experimentations per employee this year. With 600 employees, this means 2400 experimentations. Their top-level management is setting a concrete example on how they use AI.

“When an AI feature goes to production, it doesn’t just become ‘real’, it starts changing the entire ecosystem. Data flows shift, expectations evolve, and the way teams work begins to adapt around this new capability.”

It’s this kind of leadership that makes a difference. And it’s the kind of leadership we love to support.

Final Thought

If you’re wondering what real, grounded AI capability development for product roles looks like — it looks like this.

Marjut Sadeharju