Trainings

Category: AI

Splended Partners with TAIMI to Introduce Hyperpersonalized AI Coaching

Splended is happy to announce an exciting partnership with TAIMI, a platform allowing its users to learn in a dialogue with AI. I’m involved as a founding advisor, and I want to share why this matters to me.

For years, workplace learning has followed the same pattern: courses, content libraries, and learning platforms designed around completion rather than development. We know the result. People attend trainings, click through materials, and return to work largely unchanged.

At the same time, generative AI has changed expectations. Work changes faster, roles develop continuously, and the need to learn doesn’t come in neat packages anymore. Learning has to happen alongside work, not next to it.

This is where TAIMI takes a different approach.

TAIMI is built around active learning. Each learner works with an AI tutor that adapts to their role, context, and current skill level. Learning content isn’t static or delivered in a closed off format with one correct answer. Organisations can create, update, and refine learning as work changes.

What convinced me to join was not the technology itself, but the intent behind it.

TAIMI is not about replacing people or speeding through learning. It’s about supporting life-long learning at work in a way that is realistic for adults with real jobs. Learning becomes something you do continuously, not something you complete and move on from.

If you’re responsible for competence development and feel that your current learning setup no longer matches how work actually happens, TAIMI is worth exploring.

Marjut Sadeharju

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

Accelerating AI Adoption at Diak: From Skepticism to Strategic Implementation

When Diakonia University of Applied Sciences (Diak) reached out for help with their AI transformation, we recognized a familiar scenario: an academic institution ready to move beyond cautious experiments with AI toward actually using it strategically. What made this partnership special was watching two very different groups (admin staff and teachers) change how they think about AI in their daily work. We worked together with Santeri Kallio on this initiative and learned more on how to support change in the university context.

Starting Where People Are, Not Where We Want Them to Be

Diak’s staff weren’t complete beginners. As one participant put it, “AI is familiar and has been in use, for example in phones, and it’s seen as a helpful support.” So our challenge was really about helping them channel their existing curiosity into use cases that would actually make their work lives easier.

By interviewing the staff and analysing feedback data, we created a roadmap that acknowledged this reality. Instead of drowning people in theoretical possibilities, we focused on quick wins that could immediately improve their daily workflows.

The Power of Differentiated Learning Paths

We split our workshops into two tracks: one for administrators and support functions and another for teachers. This made sense because these groups had completely different worries and needs.

For Administrative Staff: From Fear to Efficiency
At first, people were both excited and worried. One person admitted: “I have to verify the information myself and be familiar with the subject.” But by the end they were expressing practical ideas:

1. Automating funding channel sorting and grant application refinement

    2. Creating multi-format learning materials

    3. Streamlining document summarization and mind mapping

    One participant’s comment really captured this change in thinking: “The most important thing I learned was that there are different AI tools for different purposes, using them together was something we all shared.“

    For Teachers: Confronting the Pedagogy Challenge
    Teachers had entirely different challenges: How do you maintain academic integrity while embracing all this new tech?

    The breakthrough came when teachers started seeing AI as a sophisticated tutoring partner. They got excited about using AI for:

    1. Creating patient practice scenarios for healthcare students

      2. Developing customized exercise sets for language learning

      3. Generating assessment rubrics that automatized giving feedback to students

      Addressing the Elephant in the Room: Trust and Verification

      We didn’t pretend AI was perfect. Instead, we made its limitations a key part of the learning experience.

      “Misinformation can be mixed with correct information, making it hard to notice,” one participant pointed out. But here’s what was interesting: this awareness actually made people more confident. They went from either blindly trusting AI or being totally skeptical to using it smartly.

      We saw people developing really practical strategies:

      1. Always double-checking sources, especially for academic references

        2. Using AI to brainstorm, then applying their own expertise to validate

        3. Running things through multiple AI tools to cross-check outputs

        NotebookLM: The Unexpected Star

        Google’s NotebookLM became everyone’s favorite tool, especially for dealing with huge document sets and creating different learning formats. Teachers absolutely loved it for:

        1. Converting dense documents into digestible summaries

        2. Creating podcast-style content from written materials

        3. Ideating study materials from course readings

        One teacher perfectly summed up its potential: “I can use this for processing extensive materials when I want a first-phase summary. The mind map worked beautifully when I defined the sources precisely.“

        Measuring Success: Beyond the Metrics

        The majority of participants (85%) gave us positive feedback, which was encouraging. But the real win came from what people were saying. We watched them go from “AI might save time” to having actual, specific plans for using it.

        Admin staff stopped asking “Am I allowed to do this?” and started asking “How can I do this responsibly?”

        Teachers started to shift from worrying “Will students cheat?” to thinking “How can AI actually help students learn better?”

        Lessons for Other Institutions

        If you’re thinking about doing something similar at your organization, here’s what we learned:

        1. Start with what people already know: Most staff already use AI somehow. They need help using it strategically, not starting from scratch.

          2. Separate your groups: Different roles have different concerns. Address them specifically.

          3. Don’t hide the problems: Talking about when AI fails actually builds more trust.

          4. Keep it practical: Every exercise should connect to actual work people do every day.

          A Partnership Model That Works

          What made this engagement successful was the combination technical expertise, thoughtful learning design, and Splended‘s practical, hands-on approach with Diak’s openness to change.

          As one participant reflected: “The most important thing was the safe and collaborative experimentation, the tips we received, and the time given to us.” This captures what we aim for in every engagement: creating space for supported exploration that leads to confident independence.

          The change from AI skepticism to strategic adoption requires reimagining how work gets done. The question now is how AI will transform academic work as an inherent part bringing value rather than risks.

          Marjut Sadeharju

          Insights from Netum’s AI Champion Training: Bringing Practical AI to Development Work

          Splended had the pleasure of participating in and organizing an internal AI Champion training for Netum, where we guided a team of developers and specialists through the possibilities and practicalities of using AI in software development. The training was led by Marjut Sadeharju from Splended, with technical instruction by Aappo Pulkkinen from Forge Digital, and it turned into a development of AI-assisted coding and champion skills.

          Exploring AI Through Code and Collaboration

          Aappo describes the learning Spring with enthusiasm: throughout the Sprint, we dove into the use of various tools for AI-assisted software development and how they can support developers in everyday tasks. We explored the architecture and implementation of AI agents, experimented with Retrieval-Augmented Generation (RAG) models for more intelligent information retrieval, and examined the wide range of AI services and models freely available online. Participants had the chance to build, test, and iterate on real AI components, gaining hands-on experience in applying these technologies to real-world coding scenarios.

          The participants reported their sentiment accordingly: it was exciting to see how accessible these tools have become. Integrating AI into software no longer requires deep expertise in machine learning. Instead, developers can tap into prebuilt models and APIs that offer surprisingly robust capabilities. And when those tools are explored together with colleagues, the learning experience becomes even richer. Discussions during the sessions often opened up new perspectives and revealed use cases that might have gone unnoticed in a solo setting.

          Key Takeaways from the Sprint

          One of the main takeaways was just how approachable AI has become. There are powerful, ready-made tools that can be integrated into development projects without needing to build models from scratch. This lowers the barrier to entry significantly and allows teams to experiment with AI even in smaller-scale applications.

          Another important realization was the sheer volume and variety of AI models and services available online. From open-source models to commercial APIs, the ecosystem is already vast and growing rapidly. However, with this rapid growth comes a lack of established standards. We touched on this during the training, especially around technologies like MCP, which aim to bring more structure and standards into building AI systems.

          A particularly eye-opening aspect was the cost structure of using AI via APIs. While tools like GitHub Copilot might seem “free” to the end user, there are also various different pricing models where costs can also scale based on usage, speed and model quality. It’s something that developers and organizations alike need to keep in mind when planning how to use AI in production environments.

          Lastly, the theoretical side of AI turned out to be both fascinating and approachable. While the underlying mechanics of AI models can be complex, it became clear that a deep understanding of the math isn’t necessary for building effective solutions. Still, knowing the basics and understanding the logic behind the tools can give developers a much stronger foundation to build on.

          A Shared Learning Experience

          Perhaps the most valuable part of the training was the community that started to form around it. With a strong group of Netum participants bringing curiosity and experience to the table, we were able to build not just knowledge but also confidence. The Champion team is now ready to take on a systematic learning and development initiative, scaling learning within the organization.

          Thank You to the Team

          We want to extend our warmest thanks to the Netum team for their enthusiastic participation and openness to learning. It was a pleasure to facilitate a space where developers could grow their understanding of AI, and we’re excited to see how these insights will shape their future projects.

          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

          Learning Sprint on GenAI and LLMs: Valmet Developers Level Up

          At Splended, we believe in the power of learning sprints to bring fast, impactful, and practical learning experiences to teams. Recently, we partnered with Nieve Consulting to conduct a specialized sprint on Generative AI (GenAI) and Large Language Models (LLMs) for one of our valued clients, Valmet. The coach for this sprint, Iván Moreno, is an AI specialist with experience in guiding tech teams through the latest innovations in AI. The Learning Sprint was conducted fully online,

          The Challenge

          Valmet’s team of 14 developers wanted to upskill in AI-related topics, focusing on gaining practical knowledge in GenAI and LLMs. With AI evolving fast, they needed more than just theory; they wanted to apply AI to real-world projects and feel confident using these technologies in their day-to-day work.

          The Sprint

          The learning sprint, led by Iván Moreno, was structured to give the team a comprehensive understanding of GenAI and LLMs while emphasizing hands-on learning. The sprint was tailored to meet Valmet’s needs, ensuring that the developers could:

          • Grasp the foundational concepts of GenAI
          • Experiment with real-world applications like Retrieval-Augmented Generation (RAG) in their projects.
          • Build confidence to integrate these AI tools into their workflow.

          The Outcome

          The feedback from Valmet’s developers was positive. Here are some of their reflections:

          “I managed to get a general understanding of GenAI, which was exactly the target of the course!”

          “I made a fun little magic: A Proof of Concept (PoC) with RAG!”

          This feedback speaks to the effectiveness of learning by doing. The team didn’t just learn AI concepts—they applied them immediately.

          Why Learning Sprints Work

          This sprint exemplifies why we at Splended are strong advocates of the learning sprint model:

          • Focused content: By zeroing in on GenAI and LLMs, we ensured the team was able to understand relevant topics.
          • Practical application: Each developer had the chance to experiment and build during the sprint, making the learning experience stick.
          • Expert coaching: With Iván Moreno’s expertise, the team received tailored guidance and support, which boosted their confidence and competence in AI development.

          Partnering with Nieve Consulting and seeing the success at Valmet reinforces our belief that hands-on, tailored learning sprints are the way forward in upskilling tech teams in cutting-edge topics like AI.

          Marjut Sadeharju