Meaningful Digital Evolution (MDE)
In prior research, the burden of dealing with the consequences of digital transformation has often been placed on the individual. Our view is that digital transformation is a systemic challenge which requires system-level solutions.
Our project contributes to the European Industry 5.0 initiative; empowering workers, addressing the evolving skills and training needs of employees, and increasing the competitiveness of European industry. Our research is funded by Business Finland.
Table of contents
Ctrl C + Ctrl V: What people want AI to do?
One person came up to talk with us at our stand during Business Networking Day 2025 and they told a story we can’t stop thinking about. In their company, everyone has a personal AI assistant that can do all sorts of work for them. Employees send these assistants to mandatory, weekly mass meetings, where the AI will then take notes, summarize, and send the recap back. The only problem? None of the notes from the assistants match each other.
Although the example was funny, it also shows a dystopian example of where we could be heading with workplace AI. People send their digital assistants to a meeting, where they talk with each other, while people get to do the “real” work. This doesn’t mean that the technology itself is bad, but more so raises the question of how we’re using it.
That’s what we at the Meaningful Digital Evolution (MDE) project have set out to explore. We wanted to use the opportunity of attending the event to ask people questions and start conversations about the future of work. With a short survey, we asked visitors three questions:
- Which 3 of your work tasks would you most like to hand over to AI?
- What would you do with the time you saved?
- Which your team's or workplace's process do you think would benefit most from the use of AI?
Automate the boring, not the meaningful
Across all responses, one thing stood out: people don’t want AI to take over the meaningful tasks that require decision-making and critical thinking. Tasks that people mostly wanted rid of were repetitive and manual, the so-called “ctrl c + ctrl v” tasks, like filling out forms or applications.
As the event was hosted by the University of Jyväskylä, there was a variety of people attending: teachers, researchers as well as employees and managers from various companies. Despite their differences, the answers remained similar. For example, a researcher would like to use AI for summarizing certain articles or find the most relevant information from a text, whereas an engineer would like to use AI for extracting key details from a technical standard.
These ideas show that people don’t want to distance themselves from their work but would rather use AI as an assistant or even as a secretary. With so many fragmented systems and tools, simply finding the right information can take a lot of time. If used thoughtfully, AI could help with some of this work. If not, it just brings more systems and tools for people to get lost in.
“Looks like you have a busy week”
Beyond what people want AI to do, many also criticized the way AI is being forced into their work, even in places where people don’t want to use it. One person explained frustration with their work that is heavily filled with meetings. At the beginning of each week Outlook’s AI then tells them, “Looks like you have a busy week coming up!” as if that somehow makes the workload any easier.
Digitalization was supposed to simplify work, but for many, it’s done quite the opposite. All these little interruptions, different systems, the chatbots, suggestions, auto-summaries and -replies that no one asked for, quietly add to the workload instead of streamlining it.
If AI could give you time back — what would you do with it?
Still, people thought that in some cases AI could speed up their work. The responses in our survey showed that they’d spend the time on deep work, learning, or creativity. Others simply said they’d rest, take a walk, talk to colleagues, or finally have time to think. Many of the answers were hopeful and showed that people want to do “human” things.
Sadly enough, some responses showed that people would use the time to do the work that they don’t have enough time to do now. It’s a quiet reminder of the pressure and stress that people feel in their work. The environment that we’re working in is so busy, that even the idea of “freed-up time” feels like a chance to do more work.
Either way, the answers showed that people aren’t dreaming of doing nothing — they’re dreaming of doing something meaningful, especially in a time where we seem to have limited space for that.
Beyond ease: the cost of AI
While it’s easy to get excited about how AI might save us time and make us more efficient, it also requires us to stop and think about the effects of it.
The growing infrastructure behind AI systems requires enormous amounts of energy and water. In some parts of the world, data centers have already begun to strain local water resources. This is where the MDE project becomes especially relevant. If we really want to build a future where digital tools support meaningful, human-centered work, we must also think about the world behind the scenes. Are we creating systems that people truly want to use, or ones that quietly consume us and our resources?
So, what would you let AI do for you?
Our small survey doesn’t represent everyone and all opinions, but it offers a small glimpse into how people think about AI at work. The discussions and the survey at the Business Networking Day provided both reassurance in the work we do, as well as good starting ground for our research.
AI can take notes in your meetings, but it can’t tell you why the meeting matters. It can help you work faster, but it can’t decide what’s worth doing in the first place. Without the right understanding, AI might just sit there, talking to itself. For now, the most important thing seems to be the idea that we should not develop technology for technology’s sake, but rather find places where people want to use it.
As we continue figuring these things out, we would love to hear your thoughts. What would you let AI do for you? How do you want technology to shape your work, time, and values? Do the ideas from our survey reflect your feelings or is there something that we missed? Talk to us!
(Written by Iida Tuuva)
References
Rao, D. (2024, August 30). AI is cannibalizing itself. And creating more AI. Theweek; The Week. https://theweek.com/tech/ai-cannibalization-model-collapse
Camillo, A. (2025, April 24). How Big Tech’s Data Centers Are Draining Water-Stressed Regions - Impakter. Impakter. https://impakter.com/how-big-techs-data-centers-are-draining-water-stre…
Fleury, M. (2025, July 9). “I can’t drink the water” - life next to a US data centre. BBC. https://www.bbc.com/news/articles/cy8gy7lv448o
Meaningful Digital Evolution (MDE). (2025, August). University of Jyväskylä. https://www.jyu.fi/en/projects/meaningful-digital-evolution-mde
Yrityspäivä 2025. (2025, October). Jyväskylän yliopisto. https://www.jyu.fi/fi/tapahtumat/yrityspaiva-2025
At the beginning of September, part of our MDE team had the opportunity to attend TCWorld 2025, the world’s largest conference for technical communication. For me, it was not only my first time at TCWorld, but also one of my first ever conferences as a researcher and technical communication professional. The experience turned out to be… many things. Now that some time has passed and we’ve gotten back to our regular work, it feels like the right time to reflect on the trip and the thoughts we gathered during our stay in Stuttgart.
Before the conference
Our journey began well before we landed in Germany. Like many of us, we managed registrations across multiple platforms, requested travel permissions, created travel invoices, and so on. As an added benefit, two of us received a last-minute email titled “Access to conference blocked” because the system thought our tickets hadn’t been paid.
Some of you might remember our blogpost from the Business networking days, where we did a small survey. We asked three questions, one of them being “Which 3 of your work tasks would you most like to hand over to AI?” People repeatedly hoped AI would help with administrative things: forms, fragmented systems, unclear processes, and manual steps required to get simple things done. I certainly can relate.
Even something as simple as requesting a refund for train tickets requires me to log into a tool, verify my account with a separate mobile app, download my train tickets from the vr.fi website, attach them to the request, manually fill in the costs of the train tickets, and finally fill in short descriptions for the train tickets. Surely this could be simpler?
AI is everywhere, but is it (or can it be) equally understood?
In Stuttgart at the actual conference, it was immediately clear that AI is now part of nearly every discussion in technical communication. In one way or another, almost all sessions, whether about content strategy, knowledge graphs, localization, or content reuse, circled back to AI.
Highlights of the tcworld conference and fair
This was to be expected. What did stand out was the variation in the ways people talked about AI. We saw cutting-edge applications, like LLM-powered conversion of legacy PDF documentation into DITA (Peking University) and AI-assisted documentation production (Accenture). Here AI felt concrete, practical, and useful. Others were at the “AI 101” level, for example explaining that you should not, under any circumstances, paste raw ChatGPT text into a highly technical user manual or you would be legally responsible for the outcomes.
Although at first those tips and sessions felt self-explanatory, it is important to remember that many people still have to navigate AI on their own. In that sense, the basics are crucial for individuals who might not get formal guidance from their organizations and are left to navigate and choose between all of the tools and best practices by themselves.
Workshops and expectations vs. reality
I also attended a workshop on sustainable prompt engineering. Parts of the workshop were great, especially connecting with other technical communication professionals and hearing about their experiences and pain points with AI. As AI skills in the field vary widely, it’s good to have beginner-friendly materials. However, I personally didn’t benefit much from the content as it didn’t really help me refine my own AI workflows.
This is directly related to a central theme for MDE: adopting AI is never just a technical question. Organizations need to find ways to bring people from different toles, teams, and levels of expertise into the same conversation. Without shared processes, understanding, or common language, it’s difficult to adopt even the best of tools.
Why TCWorld mattered for us
What we gained from the conference:
- An overview of the current state of AI adoption in technical communication.
- Real-world use cases that inform our research on meaningful digital work.
- Insight into the challenges people face with tools, boundaries, workflows, and collaboration.
- A chance to connect and network with other technical communication professionals.
It also reaffirmed that technical communication is a great place to start when looking into digital transformation. Technical writers, translators, terminologists, and content strategists work through boundaries of expertise, culture, language, and tools every day. This makes them ideal partners in looking at meaningful digital evolution.
Final thoughts
TCWorld 2025 was a good reminder that while AI is already reshaping workflows, expectations, and skills, there are still a lot of struggles with very basic things.
For researchers, this is both a challenge and an opportunity.
As we continue our work on the MDE project, events like TCWorld help us stay connected to the real world. Rather than staying in our own academic bubble, we talk to the people whose work is most affected by digital transformation. This is how we can better support our communities and colleagues.
It is increasingly clear that in modern organizations, technical communicators are no longer “just writers.” We often end up as information architects, translators between teams and tools, planners who decide how content moves through an organization, and supervisors of terminology and knowledge. As boundaries between roles blur, people need to collaborate across disciplines, locations, and professional backgrounds more than ever. All of this comes down to the question about the human part of work:
When tools become faster and more automated, how does the meaningfulness of work change?
Where does human expertise continue to matter and how do we protect that?
(Written by Iida Tuuva)
