Kim Pot
Speaker and trainer on AI adoption
Kim Pot has helped more than 75 organizations over the past years shape their collaboration with generative AI. Not aimed at speed or efficiency, but at the quality of the work itself. More is not better, she says. More of better is. According to Kim, the biggest challenge with AI is not the technology. The real challenge lies in organizations having to explicitly define what quality means, in order to determine where AI adds value and which decisions people must make themselves. She combines a background in public administration and organizational science, change management and marketing with a straightforward perspective on technology and plenty of humor. After her keynote, the audience looks at AI differently.
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Biography
Public Administration, Change Management and Marketing
Kim Pot combines a background in administrative and organizational science, change management and marketing with years of practical experience in AI adoption. This mix is unusual in a field dominated by technologists: she looks at how organizations actually function, and only then at what technology can do.
Over 75 organizations further
She has since helped more than 75 organizations implement AI and translate it into better work. As founder of KIM (Artificial Intelligence & Humanity), she helps organizations not only use AI, but actually benefit from it. In addition to being an author, speaker and trainer, she is increasingly working as an advisor.
Quality as a starting point
AI forces organizations to rethink quality, because AI can only contribute if people understand what good work must meet. This insight is the starting point of every keynote: with humor, sharp analysis and recognizable practical examples, Kim shows how AI becomes a lever for better work instead of more work.
It's about people, not AI
She prefers working with teams, because that's where collaboration happens, learning takes place, and the standards for good work are set. Her drive isn't the technology itself, but people who want to deliver their very best work with joy. It's part of who she is that she likes to include cat pictures in her slides and often brings candy along.
Expertise
Making Quality Explicit
The core of Kim's work: AI can only contribute to good work when an organization knows and can articulate what good work *is*. In practice, this is rarely documented — it lives in the heads of experienced colleagues. Kim helps teams bring that implicit standard to the surface, translate it into instructions that AI can work with, and embed it in processes. In doing so, AI also reveals how well an organization actually performs.
AI adoption as a behavioral challenge
Rolling out tools is not the same as using them. Kim approaches adoption as change management: time must be allocated, frameworks need to be established for what is and isn't allowed, knowledge must be shared centrally, and usage must be tied to concrete goals. Without these four elements, AI remains confined to a handful of enthusiastic colleagues — and disappears as soon as they leave.
The Division of Roles between Humans and Machines
Kim knows where AI fails and where it excels, and that distinction is the most useful part of her story. Which decisions remain with people, which thinking steps must you continue to take yourself, and at which points is human judgment irreplaceable? Answering those questions determines whether collaboration with AI can be steered.
Translating for non-technical professionals
Her audience consists of people who want to apply AI practically in their own work, not developers. Kim tailors content and level to the room and delivers her story without jargon, with humor and relatable examples. That's precisely what makes people actually do things differently after her talk.
Themes
AI and quality
Why AI forces organisations to reconsider quality, and how to make explicit what good work looks like so both humans and machines can work with it.
AI adoption and change management
Why AI adoption doesn't start in the boardroom and what's actually needed: making time, setting frameworks, sharing knowledge centrally, and linking AI to objectives.
AI Literacy and Practical Applications
The basics of generative AI and how to apply it concretely in your own work. For professionals without technical background or prior knowledge.
Critical Thinking in an AI World
What thinking steps professionals must master to deploy AI safely and effectively, and where human judgment remains indispensable.
Data and AI for Non-Techies
What AI can and cannot do with data, which applications are useful in your work, and how to interpret results.
AI in marketing and communication
Why those fields are now in focus, and how to make your content benefit from AI without losing its unique character.
Why Kim
75 organizations of practical experience
Kim knows where AI implementations fail in practice, because she has witnessed it firsthand dozens of times. That's a different story than an overview of what's theoretically possible.
An own thesis
While many AI speakers list possibilities, Kim takes a position: the challenge isn't in the technology but in making quality explicit. An audience can agree or disagree with this, and that's what turns it into a conversation rather than a presentation.
Administrative knowledge and communicative expertise
A rare combination in this field. She understands how decision-making and change work in organizations, and is able to convey this to people without a technical background.
She inspires action
Kim translates complex developments into practical steps that change behavior. Because that's what generative AI demands: reorganizing the work itself, not just adding another tool.
Down to earth and funny
No hype and no doomsday scenarios. Her tone is dry and light, which keeps even the skeptics in the room listening.
Always tailored to the client
Content and format align with the organization and knowledge level in the room, from beginner to advanced. According to her own account, she consistently delivers more than what was requested.
Talks
AI and the Question of Quality
45 to 60 minutesThe challenge is not in the technique
Around AI, it's usually about faster, more efficient, and more of everything. Kim starts somewhere different: only when people know what good work must meet, can AI add value to it. More is not better, but more of what's 'better' certainly is.
AI reveals how well your organization works
The moment you try to describe what quality is, you discover how much was implicit. Through sharp analysis and relatable practical examples, Kim shows what organizations encounter and why that presents an opportunity.
Where people stay engaged
Participants leave with insights into where AI adds value, where human judgment remains essential, and how to make quality explicit so that AI actually enhances it. The content is tailored to the knowledge level in the room and requires no technical background.
AI Training Tailored to Your Needs
Half day to full dayFirst determine what your team needs
Kim tailors content and form completely to your organization and works with both teams and managers. Beginner or advanced: you get to work on your own work, not a standard example.
Choose your topic
From her programs you can choose: AI adoption for management (the AI team compass, behavior and working agreements) · AI strategy and organizational transformation · AI and quality · AI and content · data analysis with AI for beginners · tool trainings for ChatGPT, Claude and Copilot, among others.
Until behavior actually changes
Using AI optimally requires different behavior, and that doesn't change by itself. Kim won't let you go until she and you see the change you had in mind.
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