At Aalto EE, we have been providing training on digital technologies for decades, and have seen cycles of new technology adoption. Our roots in the Helsinki University of Technology continuing education give us a long perspective on what it actually takes for organizations to absorb and apply new technology, not just learn about it. We have been at the forefront of AI training since establishing the Diploma in AI in 2016, developed together with the Finnish Center of Artificial Intelligence (FCAI).
A Shift in How Organizations Approach AI
In the past half a year, however, there has been a clear shift in the conversations we are having with leaders across Northern Europe. Even smaller organizations and industries traditionally very resistant to new digital technologies are realizing that the question is no longer whether to act on AI, but how to do so and how to bring their people up to speed in the best possible way.
We would like to share the latest examples of what works for organizations in building AI capabilities and creating results with AI. The solutions are always organization-specific; even so, I hope you will find things that inspire you in these examples.
Example 1: Making AI Tools Usable at Scale
Not long ago, one of the top three AI companies in the world called us with an unusual problem: How to help their regular professionals use their AI tools effectively across Europe? The professionals found their library of on-demand courses too vast to navigate, and some were resistant to AI out of fear that it would take their jobs. We designed a learning program that was pedagogically sound, addressed those fears directly through open discussion, and included a human trainer, live interaction, and hands-on practice with the tools in day-to-day tasks.
Example 2: Upskilling Top Leadership in a Traditional Industry
A construction-related company with limited AI experience wanted to upskill its entire top leadership (approximately 60 people). We arranged a series of short sessions interlaced with independent online learning. The company specifically requested training deeply rooted in their industry context, so we utilized Aalto faculty who had worked hands-on in construction for years prior to their research careers.
Example 3: AI Training for Thousands Across a Large Organization
A large retailer requested AI training for thousands of employees at all levels and gave us significant autonomy in the design. We designed a program that met the needs of all major groups and built interconnections between them, facilitated by AI. This allows leadership to see exactly how each unit is progressing and to receive real-time results, questions, and comments to inform their strategic planning.
Example 4: Finding the Right Starting Point for AI Adoption
An energy company wanted to increase its use of AI but was uncertain of the optimal approach. We designed a series of AI-aided workshops with a leading Finnish AI and software company to help discover which roles would benefit most from AI training, and what specific form that training should take.
These examples are quite different from one another in industry, scale, and starting point. Yet they point to a few principles that seem to hold across all of them.
Shared Lessons Across Very Different Organizations
Principle 1: Human Elements Matter in AI Learning
Even one of the world's best AI technology companies benefitted from a structured, pedagogic approach and from human elements as part of the training – live workshops, Q&A sessions, human trainers and assistants. This is true for all companies. Human elements in learning can add great value without adding great cost when well planned.
Principle 2: Effective AI Training Starts with Understanding Learners
Good training is not only about the content, but it is also fundamentally about understanding learners as humans. This starts with creating a safe learning environment, the foundation for all learning, and includes identifying roles, learning paths, and the most suitable learning methods, as seen in all of these examples.
Principle 3: Hands-On Tools and Timeless Fundamentals
All of these training programs include hands-on experience with AI tools. Everyone needs to understand how the tools work in practice, but learning needs to go deeper than which button to press or how to write a good prompt in ChatGPT version X.X. That kind of information expires quickly as technology develops. It is equally important to understand the underlying principles that remain stable as tools change. Few of us ever need to touch the battery of our car, yet understanding a few basic principles of how electricity works helps us avoid being stranded or know what to do if we are. For most people, these principles are best taught in plain, everyday language rather than through diagrams, code, or maths.
Principle 4: From Individual Productivity to Organizational Transformation
Learning to use AI tools is just the beginning. Individuals becoming better at their current tasks through AI is valuable, but the most significant organizational benefits come when processes, business models, and ecosystems change. Getting there requires more than tool training. It requires leaders, developers, and others to think differently about how work is structured. This is why the most successful programs are built through close cooperation between unit leaders, HR, and professionals who understand learning – not just AI.
If any of these examples resonate with a challenge you are facing, or raise questions about what might work in your organization, I would be happy to continue the conversation.
Please contact us for further discussion
Jonni Junkkari
Senior Solutions Director, ExEd Programs
+358 10 837 3860
jonni.junkkari@aaltoee.fi
