1. Half still have no real touchpoint

Around 50% of participants have never opened an LLM before the course. This isn’t about the technology—it’s about caution, uncertainty, and the lack of guidance. Once people start experimenting in a supported environment, the hesitation fades quickly. Many express the same thought: “I didn’t know what I was supposed to ask, so I didn’t dare to start.”

2. Usage falls into three clear levels

Among those with at least some experience, usage forms a consistent three-tier structure:

• 90% use LLMs very lightly: translations, summaries, and simple edits.
• 10% use them more actively, but still at a surface level. Few users actually use frameworks, build consistent logic, or design workflows around the model.
• Deep usage is extremely rare.
  • Custom GPTs, structured tools, and process-level thinking are still marginal.

This pattern repeats across almost every organisation.

3. Agents generate interest – but they are not simple

Agents and custom GPTs now come up in almost every discussion compared to early 2025. Interest is strong, but real hands-on work is still limited. Across our training groups:

• About 50 people have built a custom GPT.
• About 20 people have built an agent (usually as a team).

Building an agent isn’t a “just do it” button. It requires quiet technical intuition, process thinking, and a willingness to experiment. Many are only now developing these foundational skills. This is why claims that “2025 is the year of agents” is more about marketing than reality. The real “agent year” in everyday work is likely closer to be realised end of 2026–2027.

4. Understanding is rising quickly – usage more slowly

Between January and November, the change is clear:

• People now better understand what LLMs do and don’t do.
• They recognise the importance of context.
• The model is seen more as a conversation partner, not just a text machine.

Yet everyday use is still largely task by task, not a continuous partnership with the model.

5. The “sparring partner” mindset works

One of the strongest findings relates to mindset. When the model is seen as a sparring partner, usage becomes more natural and relaxed. In our courses our mission, which is stated at the start of the course, is to make LLMs your sparring partner. At the end we ask how we succeeded and the results are striking:

• 95% of participants respond to our feedback survey.
• Of those, 97% say the model became a sparring partner during the course (N=1000).

Once people understand how an LLM works and where its limits lie, their usage becomes instinctive. The barrier isn’t technical—it’s emotional.

6. The biggest shift is in thinking, not yet in routines

The most notable change in 2025 is not what people do with LLMs. It is that more and more people understand what an LLM is, what it can be used for, and how it should be used. This unlocks internal conversations, role clarity, and new ways of dividing work.

And the one fact: The term “Artificial Intelligence” as a singular is the most misguiding term and should be completely abolished to create better understanding.

What does this tell us about Finnish organisations in 2025?

Based on everything we’ve seen in our training data, the situation looks like this:

🙌 Interest is growing relatively fast, but not like we inside the “AI bubble” think.

🙌 Usage is growing slowly—especially if the emotional barrier isn’t lifted.

🙌 Deep usage is still rare. People think this is a new Google, and naturally that will limit exploration as what you do with Google is already a habit—and as we know habits are hardest to change.

Finland is now in a phase where understanding is expanding, but everyday AI-Human working habits are still forming. This is a natural stage—and right now is the ideal moment for organisations to build their LLM strategy before the next major shift arrives.

Key Takeaways from #Harvest event 9 October 2024

Generative AI (GenAI), and artificial intelligence (AI) in general, is no longer a promise of the future; it’s the reality of today. During the Harvest event on October 9th, 2024, it became clear that AI’s potential, particularly GenAI, is immense, yet many organisations are still standing on the sidelines.

Why? Not because the technology isn’t mature—it is. The real challenge lies in the myths and misunderstandings that continue to slow its widespread adoption. As DB Schenker’s Samuli Salmela explained in his keynote speech, these misconceptions prevent businesses from fully embracing the transformative power that GenAI, especially, offers.

As leaders, it’s time for us to move beyond the myths and recognise AI as a strategic partner, not just a tool. AI can bring profound improvements to our organisational processes and, ultimately, transform the entire business. The question is no longer whether AI is ready – it is. The real question is whether our companies are ready to learn, experiment, and utilise AI in the right way. The businesses that seize AI’s opportunities now will thrive, while those waiting for the “perfect moment” – that never comes – will be left behind.

Let’s explore the key takeaways and insights from the event by addressing the most prominent myths about GenAI and their practical implications for our organisations in overcoming them.

Myth 1: “AI is difficult and expensive”

This belief often stems from a fear of the unknown. In reality, AI has become increasingly accessible, with many easy to use  tools available. The real challenge lies not in the technology itself, but in our willingness as people to adapt. As leaders, we must reframe AI as an investment in both the company’s as well as the people’s future rather than a burdensome expense or a new thing that ends up benefitting no-one.

Myth 2: “GenAI makes mistakes, so it needs constant human supervision”

While it is true that AI is not infallible, neither are humans. The key is to view AI not as a replacement for human intelligence, but as a powerful complement to it. This symbiosis of human intuition and machine processing power can lead to unprecedented insights and innovations for both organisational, as well as wider, good. A need for human oversight in AI related projects depends greatly on the use case and its risk profile. As we learn to understand and trust AI more, we can relax the oversight.

Myth 3: “We don’t have good enough data to use AI”

Perfect data is a mirage. Instead of waiting for an ideal dataset, we should focus on cultivating a data-driven culture where continuous improvement is the norm. AI can actually help us identify gaps in our data and AI can also allow us to start utilising unused or previously difficult-to-use data assets. AI can also refine our collection processes, so again, rather than thinking of it as a technology, think of it as an added competence to your organisation and treat it accordingly. 

Myth 4: “Our data is not safe with GenAI”

In an age of increasing digital threats, this concern is valid. However, it should not paralyse us. Instead, it should motivate us to implement robust data governance frameworks. By doing so, we not only protect our assets but also build trust with our stakeholders. It is also important to use technologies that are secure and enterprise-grade, such as the Microsoft offering.

Myth 5: “AI doesn’t really think”

This myth touches on deep philosophical questions about the nature of intelligence. While AI may not “think” in the human sense, it can process information and identify patterns at a scale beyond human capability in certain cases. Our role as leaders is to harness this power while providing the context, creativity, and ethical considerations and judgement that only humans can offer. This is one of the reasons it is important to focus on your people at the same time as you focus on technology.

Myth 6: “It’s just hype. Companies aren’t getting real benefits”

Scepticism is healthy, but it should not blind us to the real-world impacts of AI. From predictive maintenance to personalised customer experiences, AI is already delivering tangible benefits across industries and will also require a new set of measuring benefits. As leaders, we need to look beyond the hype and focus on practical applications that can drive our businesses forward. AI is like any major technology disruption; its short term implications are overestimated and long term impact under-estimated.

Myth 7: “The models aren’t ready; it is better to wait”

In the rapidly evolving world of AI, waiting for perfection is a luxury we cannot afford. The most successful organisations will be those that adopt a “learn fast” mentality, embracing current AI capabilities, while staying agile enough to adapt to future developments.

Building a Human-Centric, Data-Driven Culture: The Key to Successful AI Adoption

It is essential to remember that adopting AI goes beyond technology—it fundamentally revolves around people. To fully realise AI’s potential, the entire organisation must be engaged in the journey.

Since we’ve been talking about AI like a colleague, we figured, why not ask Copilot  itself, which of these myths resonated the most? “The myth that struck a chord was, ‘GenAI makes mistakes, so it needs human supervision.’ While it’s true that responsible AI use requires oversight in critical fields like healthcare, where accuracy and ethics are non-negotiable, the idea that GenAI always needs human supervision is a bit outdated.

This requires cultivating a culture that embraces data and AI literacy, with human leadership at its core. Such a culture encourages curiosity and experimentation, valuing human creativity and judgement. In this way, employees see AI tools as enablers rather than fearing replacement. This approach creates an environment where both human and artificial intelligence can thrive, laying the groundwork for genuine innovation and growth.

But as any good farmer knows, harvesting is only the beginning. The true value lies in how we refine those fruits—transforming them into products, services, and tangible results that drive real organisational impact. And just as importantly, we must sow new seeds for future growth by budgeting wisely and investing in the AI capabilities that will shape the years ahead. After all, success isn’t just about this harvest; it’s about laying the foundation for future seasons of prosperity.

Authored by:
Kira Sjöberg, GOODIN, Sami Masala & Nino Ilveskero, AIThink & CoPilot, Microsoft. 

Understanding the Learning Stages People in Organizations go through

1. Beginner Level: Grasping the Basics

The journey begins with foundational knowledge. For data literacy, this includes understanding data types, basic data manipulation, and the significance of data in decision-making. For AI literacy, it involves an introduction to AI concepts, what AI can and cannot do, and real-world applications. At this stage, short, introductory sprints focused on key concepts help demystify complexities and lay the groundwork for more advanced learning.

2. Intermediate Level: Enhancing Skills through Application

Once the basics are understood, employees should start applying their knowledge to real-world scenarios. This could involve structured projects or challenges where learners manipulate datasets or build simple AI models relevant to their roles. This stage is crucial for reinforcing concepts and gaining confidence. Naturally understanding that not everyone will utilize data or AI on this level in their roles, but understanding the side of applying is important. Generative AI utilization is however another relevant application mode often in such cases. Organizations can support this through workshops, guided training sessions, and practical hands-on projects that encourage active learning. One example of this is the GOODIN and Splended Data Learning Sprint.

3. Advanced Level: Specializing and Innovating

As learners become more comfortable, they can move into specialized areas such as predictive analytics, machine learning, and advanced data visualization techniques for data literacy. For AI literacy, this might include deep learning, neural networks, or robotics. Advanced learners should engage in longer, more complex sprints that challenge their understanding and encourage innovation within their specific areas of interest. This demands a “fail fast” or “learning by doing” -type of learning culture in organizations and positive and encouraging management practices and commitment that enable failing for learning.

4. Expert Level: Leading and Mentoring

At the expert level, individuals are expected not only to be proficient but to lead initiatives and mentor others. They stay abreast of industry trends and continuously adapt to new technologies. Here, learning involves self-directed projects, leadership in sprints, and contributing to strategic decision-making with data-driven insights become real life value for business.

Implementing Learning by Doing: The Sprint Method

A learning sprint approach can be highly effective in progressing through these stages. Each sprint focuses on a specific skill or project, encouraging rapid learning and application. Here’s how it can work:

• Define Clear Objectives: Each sprint has specific, measurable goals to ensure focus and alignment with business objectives.
• Time-bound Challenges: Limit sprints to a few weeks to maintain urgency and engagement.
• Cross-functional Teams: Include employees from different departments to foster diverse perspectives and collaborative problem-solving.
• Reflect and Iterate: At the end of each sprint, gather feedback and reflect on lessons learned to improve the next cycle.

Supporting the Journey

Supporting employees through this journey requires more than just providing educational resources; it involves creating an ecosystem that promotes continuous learning and application. This includes access to the latest tools and technologies, opportunities for peer learning, and a culture that celebrates experimentation and learning from failure.

As they become more fluent, they will be able to leverage these skills to innovate and lead in their respective fields, ensuring the organization stays competitive in a data-driven future making sure the human drives the AI and not vice versa.