When I saw the film Her in the cinema in 2014 — a call-center agent falls in love with an operating system — it was science fiction. Today, forty percent of young AI users would rather chat with the AI than with their real friends.1
I watch with fascination as utopias become reality. What worries me is something else: the way many people in leadership positions deal with this transformation.
Recently I gave a talk to 150 HR managers and was struck by the helplessness in the room. “Do we even have the backing from our management to shape the AI transformation?” someone asked. Or: “We lack the competence to be an important factor.”
I am convinced that the AI transformation is less a technical challenge than a human one. At its core, it is about the future collaboration between people and AI, less about tools and servers. Artificial intelligence can be of great use here, particularly in areas such as onboarding or knowledge management: a field study by Professor Danielle Li at MIT shows that AI assistants can raise the productivity of new employees in customer service by up to forty percent.2
And in the boardrooms?
There you often find hesitation, endless discussions or the classic German “great, let’s cut costs” reflex, while companies in China, India and the USA extend their lead with an entirely different outlook and at high speed.
Understanding together instead of guessing alone
Because no one can survey this development alone, I founded the Zukunftsallianz Mensch & KI, an alliance for the future of people and AI, together with entrepreneurs, decision-makers and experts.
At one of our think days we ventured a pre-mortem: what would you have to do to fail spectacularly? The answers came quickly and recalled patterns that had already ensured Germany was not among the front-runners in digitalization.
- Activism and innovation theater: high-profile appearances and formats without lasting implementation.
- Leaving AI to the technicians: understanding AI as a technology rather than as a development that radically changes how people think and act.
- AI as a pure cost case: the typical “strategy” of unimaginative managers. They believe they can save costs in the short term instead of thinking about how to build lasting competencies and capabilities for customer loyalty.
- Or waiting and eating chocolate, because it surely won’t be that bad.
At another think day we had an AI assistant with us as a sparring partner. When we asked it at the end what the elephant in the room was — the one thing we had not addressed although it was highly relevant — a surprising answer came back: power structures.
Anyone who wants to shape the AI transformation successfully must be prepared to make their own power transparent as well: how do we make decisions? On the basis of which data? Which options did we evaluate, and how?
The AI transformation demands an open, modest understanding of leadership that is oriented towards the matter and the purpose, with personal interests in the background.
When we put our twenty heads together and developed scenarios for the AI future in companies, a dystopia and a vision emerged, and the vision motivated us.
Two scenarios
Neither is fate; both are the consequence of decisions. As you read, check honestly which direction your company is currently heading.
Mindset before technology
Many company leaders believe they first have to formulate a strategy that describes the future in detail. That is often the most elegant excuse for not starting.
What it takes is a narrative that answers three questions.
Where do we stand right now? A radically honest stocktaking. It asks not only which use cases we want to solve, but names the weak points: fragile processes, disordered data, missing competence. Even if it hurts. Anyone who talks only about opportunities builds the target picture on sand.
What is our target picture? Which possibilities does AI open up for becoming a more future-proof company? These possibilities have to be translated into concrete use cases.
What are the next steps? Which results, derived from the target picture, do we want to achieve in the coming months? And how do we organize ourselves so that it actually happens?
Three insights from the alliance:
- No taboos in thinking.
- Data and relationships are the new gold — what matters is data farming, that is, training the AI on your own definitions.
- Stay independent. Listen to independent experts, not to those who want to sell you AI applications. After all, you don’t ask the frogs whether the pond should be drained.
Scale now costs money
Something has shifted. Providers are changing their pricing models, away from flat rates and towards billing by token.
So scaling is no longer free. Costs grow not with the number of users but with the complexity of what they do: an agent that runs through several steps on its own consumes many times a single query.
How serious this is becomes clear from a company that itself sells AI. According to a report in Handelsblatt, SAP plans to budget its own employees’ token consumption — staggered from one hundred euros a month for the broad base to five thousand euros for a few intensive users.3
Unlimited access without a goal burns budget; a ban prevents learning. SAP solves it through transparency and budgets rather than bans. The answer lies in prioritization: whoever knows the most valuable use cases can be generous there and frugal elsewhere.
Success has three letters
Put together a team that combines strategic thinking, technological judgment, the implications for organizational development and practical implementation skills. Bring all key people into one room and derive the most important strategic use cases from the needs of the market and your customers.
The most important thing is to set out together.
One of my clients rebuilt his company, a procurement platform for mechanical engineering, as AI-native in this way. Now he has both: the existing company for buyers who value personal contact, and the AI-native version for customers who want everything handled automatically.
How we at MLI can help accelerate a focused AI transformation is described on the AI Transformation page.
What this text does not do
The two scenarios are pointed. In practice you find elements of both in the same company, often in neighboring departments. The benefit lies not in assigning yourself to one scenario, but in arguing within the leadership circle about which line currently applies.
1 Study “KI-Nutzung im privaten Alltag 2026” by the German health insurer Pronova BKK; 3,485 representatively selected people in Germany were surveyed.
2 Cf. Erik Brynjolfsson, Danielle Li and Lindsey Raymond: Generative AI at Work, NBER Working Paper No. 31161, National Bureau of Economic Research, Cambridge (MA) 2023, p. 1 (abstract) and chapter 4.
3 Handelsblatt, report on the budgeting of token consumption at SAP, 2026.