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AI Transformation

If you do not lead AI,
it will lead you astray.

We have been doing a lot with AI for some time. No one can tell me what is paying off.

MLI helps leadership teams structure the AI transformation and set it in motion. We bring together the expertise required so that they can set out with clarity and resolve.

AI is changing business models, processes and roles. We help you prioritize value pools, clarify technical guardrails and steer the expected benefit in measurable terms.

Get the AI Leadership Compass See the AI Course Setting

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What it is about Three theses Challenges The team Approach Evidence Formats The compass Questions

What it is about

From AI hyperactivity to targeted business impact.

Many companies have long had assistants, pilots and platforms in place. But activity for its own sake is not impact. Quite the opposite: people can paralyze one another with quickly generated AI documents, because it still takes humans to read them.

Business impact with AI means:

We can use AI to measurably improve the customer experience or product quality. With a positive contribution to value.
We can enable employees to achieve better performance and results for our company, faster — in product development, for example.
We can save costs and reduce risks through reliable automation.

The two greatest dangers are waiting it out and letting chaotic sprawl take hold. On the wave between the two, a strategically effective AI transformation needs meaning and structure.

Why it matters

Three theses on AI transformation.

If you disagree, we are glad to argue the point. Provided you really want to move something.

Waiting is not a neutral position

Those who do not lead AI will be led by it. The technology is developing so fast that companies without AI practice quickly fall behind. Or they rely on external vendors without a decision compass of their own.

AI competence requires an entirely new kind of learning

In the past, people shone through knowledge. Today, AI knows almost everything. What counts is practical experience. An AI transformation has to treat AI like an intelligent new colleague with whom you quickly and practically try out working together.

AI success needs structure and leadership

AI agents can scale the capabilities of employees many times over. But they have to be led. Someone must take responsibility for what they do. And they should create measurable value.

Typical challenges

What costs time and nerves. And what does the opposite.

The same technology, two directions — depending on whether leadership sets the course. None of the patterns on the left is a technology problem; behind each lies a leadership decision that no one has made yet.

What costs time and nervesUnled AI‑transformation

And what does the oppositeLed AI‑transformation

Strategy

Cost and headcount savings in the foreground; employees are afraid. Plenty of activity, no focus on value. Pilots remain pilots: 95% of the GenAI‑initiatives examined showed no measurable effect on results.1

A clear target picture of how humans and AI are to work together and where AI can create the greatest value. A few prioritized use cases at a time — employees see opportunities and prospects. Clear criteria for success and for stopping.

Framework

Shadow AI: many people use private AI for work as well, yet it is hardly discussed on the job. There is no shared framework, and no one knows which data flows where.

Employees have AI support that goes beyond help with wording. Clear rules of play instead of bans, a deliberate time allowance and active enablement for case-based experimentation and exchange.

Quality ofwork

People rely on AI and quickly produce fine-sounding documents that simulate work and create extra work for the recipient — workslop.2 An inflation of documents, applications and concepts that no one can read anymore. Quality of work and focus decline.

A manageable number of topic-specific AI assistants and agents that strengthen employees in a goal-oriented way. AI uses uniform standards; customers experience faster responses and better advice. User reviews and impact measurement are part of every use case.

Data and systems

Three departments, three figures for the same business transaction. The company becomes dependent on external vendors and settles for the standard product. The AI answers with complete conviction, and employees are busy mainly reconciling numbers.

Business-critical terms are defined bindingly. Every agent is trained by experienced experts and accesses the right internal tools according to a consistent logic. Employees learn to lead AI — using AI becomes a matter-of-course skill in daily work.

People and organization

People fear the future and are wary of AI. Customers are fobbed off with AI agents. Responsibility diffuses, the offering becomes interchangeable, the company unattractive as an employer.

People work with AI in a natural way. Agents have a clear identity, their actions are traceable, and there is always a human who leads them and bears the responsibility. People gain time for the situations in which trust or creative ideas emerge.

Which of the two pictures is your company currently extending, and how can you tell?

1 MIT NANDA, The GenAI Divide: State of AI in Business 2025 — analysis of 300 public AI deployments, 150 interviews with executives. 95% of pilot projects without a measurable effect on operating results.

2 BetterUp Labs and Stanford Social Media Lab, Harvard Business Review, September 2025 — survey of 1,150 employees in the US: 40% received workslop in the previous month, with an average of 1 hour 56 minutes of extra work per incident.

The expert team

AI strategy, AI architecture and leadership at one table.

Three perspectives that would otherwise come in three proposals from three providers.

Sebastian Morgner Strategy and leadership

Managing Partner of MLI, twenty years of management consulting and executive coaching, more than 60 strategic transformations accompanied; co-founder of the Zukunftsallianz Mensch & KI, an alliance on humans and AI. In the mandate: leads the process, facilitates the leadership team and is responsible for the execution rhythm.

Waiting is not a neutral position. Those who do not lead will be led.

Dr. Tobias Große-Puppendahl AI architecture

Principal Enterprise Architect Data & AI at Porsche AG, where he is responsible for building the group-wide GenAI platform for some 60,000 employees; PhD in computer science (TU Darmstadt), more than 30 patents. In the mandate: examines questions of architecture, data and governance and makes effort estimates realistic.

You swap models in days. Your terms, processes and rules — not so.

Tristan Post AI strategy

Founder and CEO of the AI Strategy Institute, Faculty Member for GenAI at Boston Consulting Group, lecturer at the Technical University of Munich; author of AI Native (Hanser). In the mandate: assesses use cases by business benefit and brings market comparison into prioritization.

Whoever instructs an AI is also responsible for what comes out of it.

What we do

Seven steps, one logic: value before tools.

From the declaration of intent to the reimagined organization — in the order that has proven itself in practice.

Define ambition and target picture 2 daysAn honest assessment: where do we stand? What would be possible with AI? Where do we want to go? Without a yardstick for success, every later assessment remains a matter of taste.
Optimize the prerequisites 1–3 monthsAppropriate AI infrastructure and support at the workplace. Binding rules of play. Time allowances for experimenting with AI. Enablement for effective use.
Cross-functional AI coalition 2–4 weeksAI pioneers and multipliers who are ready to build AI competence for the company and to contribute to executing AI projects.
Prioritize use cases 1 monthWhere does human work offer the greater value, and where does the use of AI? Which use cases hold the greatest potential, and where do AI agents make the most sense? A facilitated design thinking process.

AI sprints 4 weeks each, ongoingAI pilots are implemented and refined until they are ready for roll-out — with user reviews and impact measurement. A pit stop every three months: what do we continue, what do we stop, what do we try anew?
Scaling and roll-out ongoingBring successful pilots to scale. Ensure employees handle AI solutions consistently. Continuous quality control and further development along the lines of KAIZEN.
A future-ready organization 1–3 monthsJob profiles for humans and AI, decision rights, transparency about decision paths. Every agent receives a role profile and a named person responsible for it.

What every scaled use case needs

an expected contribution to value
a person responsible
measurable success criteria
known costs and risks
a stop criterion
a decision date: scale, adjust or stop

What we are responsible for, and what we are not

MLI is responsible for strategy, leadership process, prioritization and execution rhythm. Technical implementation is delivered by your internal teams or specialized implementation partners. If needed, we help clarify requirements and select suitable partners, and receive no commission for doing so.

The evidence

Two case studies.

Case 01 · Techpilot

From an AI roadmap to an AI-native business model

A new market — within nine months.

Starting point: Europe’s leading procurement platform for mechanical engineering wanted to assess the opportunities and risks of AI for its business model — many ideas, but no shared target picture and no prioritization.
Approach: Compass assessment, one-day future workshop with the management team, prioritization with the AI Utilization Matrix, AI roadmap with accountabilities.
Result: On this basis, Techpilot developed its business model further into an AI-native one within nine months and opened up a new market: increasingly automated procurement processes.
For decision-makers: AI can open an existing business model to new customer groups — beyond process automation.

Case 02 · Financial software

From slow development to market-ready output

Time to market: from 24 to 7 months.

Starting point: A market leader in financial software for German mid-sized companies — the name remains confidential — with slow development, a complex process and a three-year backlog of requirements.
Approach: Organizational analysis in the CIO and CPO units, a shared AI vision, new roles and ways of working, integration of AI agents, support for the leadership team.
Result: The three-year backlog was cleared; the time to market for new product features fell from 24 to 7 months.
For decision-makers: The value comes from the interplay of technology, roles, development process and leadership, not from additional tools.

“Together with Sebastian Morgner and MLI, we worked out the opportunities and risks of artificial intelligence for our business model. It was a perfect mix of structure and inspiration. We received strategic input, but we also saw very concrete examples of the intelligent use of AI. In the process, we recognized new possibilities. By the end of the workshop, our management team had a shared picture of how to use AI, prioritized use cases and a clear plan to embed and execute the topic systematically.”

Frank Sattler · CEO, Techpilot

The entry point

A valuable way in: the AI Course Setting.

What. A compact workshop for your management team — top management and selected key people.

With whom. MLI experts who, as neutral sparring partners, cover all relevant aspects — strategy and leadership, AI architecture, AI strategy. Experience meets experience, business expertise meets transformation know-how.

What for. The right moment for a common thread to emerge — more than that: clarity and structure. At the end there are decisions rather than declarations of intent; what comes next follows from them.

AI Course Setting at a glance

Duration: One compact working day, about 5½ hours
Participants: Top management and key people, 6 to 15 people
Fees: €9,500 flat fee for two MLI experts, preparation, delivery and documentation of results; plus VAT
Preparation: Your team’s compass profiles, analyzed before the session
Travel expenses: None in the Greater Munich area; otherwise at actual cost

Request the AI Course Setting

Send us an e-mail — you will receive a proposed date and the preparation materials.

The agenda · an example

Input: what decides the AI transformation 30 min
Taking stock with the AI Leadership Compass — success factors for your company 90 min
Target picture: the AI-strengthened organization 60 min
Leadership in the age of AI 90 min
Decisions and next steps 15 min

We adapt the sequence to your situation — taking stock always remains the core.

On request

The AI Future Workshop for your entire leadership team

When the whole leadership level needs to come on board: an activation format for up to 50 people, work in facilitated groups, an aggregated assessment of where you stand. Fees depend on group size and facilitation design.

Request the Future Workshop

What you have after the session

an aggregated profile of where leadership currently stands
the three most important bottlenecks
prioritized value pools and use cases
a shared target picture
named accountabilities
open questions on architecture, data, governance and competencies
a next decision date
a clear recommendation: start, deepen, adjust, or stop

Who it is for

Who we are a good fit for.

The entry point pays off in a particular situation — we would rather say so before than after.

A good fit

Companies in which AI is already in use — but without a shared framework
A top management team that actively takes part itself
A willingness to stop ongoing initiatives if they cannot prove their benefit
The ambition to build AI competence of your own rather than buying it in permanently
An organization that also cares about the human aspects of the transformation

Not such a good fit

Those who want to delegate the AI transformation entirely to IT or innovation
Those who need a maturity score for the supervisory board — the compass delivers a profile, not a benchmark
Those who want to justify a job reduction that has already been decided
Those who see the AI transformation primarily as cost-cutting

The next step

Let’s talk about your situation.

45 minutes

Personal conversation

A first meeting, an exchange about your needs, not a sales pitch. Choose a time directly in the calendar, with no intermediate step and at no cost.

Choose a time

Five fields

Contact by e-mail

Describe your topic briefly: one or two sentences are enough. We reply within two business days.
Prefer to talk? +49 8807 244446-0

Go to the contact form

PDF · 21 pages

Institute dossier

Methodology, survey instrument, reference architectures, fee models and network profiles — for forwarding internally when an initiative still needs to be justified.

Download the dossier

Workbook · free of charge

Where do you stand in the AI transformation?

Take stock across eight fields of action, assessed on the verifiable current state, not on intentions.

Questions and answers

What decision-makers ask us first.

Five short answers. In more depth, gladly, in conversation.

What is the AI Leadership Compass, and what is it not?

The AI Leadership Compass is a structured instrument for reflection and dialogue in leadership teams, giving a complete picture of where the AI transformation stands. It covers eight relevant fields of action and 48 statements. Key people assess these against the verifiable state of the past six months — first alone, then in comparison across the team, where the divergence matters, not the average. It is not a validated diagnostic test and not a maturity model, but a strategic instrument for recognizing the levers of a successful AI transformation.

We already have an AI strategy. What does the AI Course Setting add?

The conditions for AI transformation are changing rapidly right now. You cannot formulate a long-term strategy; you need clear principles and continuous iteration to meet this challenge. The AI Course Setting is a strategic pit stop in which a leadership team systematically assesses the relevant levers and decides which next steps create the greatest value for the company.

What helps here is the discussion with experts who have an overview of current technological developments and of what has worked in other companies and organizations. The distance between a strategic decision and demonstrable activity is regularly the real subject.

Are you really neutral?

MLI sells no software, no licenses and no implementation, and receives no commission when we help select implementation partners. That is why we can also advise against an investment. In MLI mandates, our experts work exclusively in an advisory role; should a recommendation touch on the interests of anyone involved, we disclose it on our own initiative before a decision is made.

What does the entry point cost, and what comes after?

The AI Course Setting costs a flat €9,500 — for two MLI experts, preparation, delivery and documentation of results, plus VAT and, where applicable, travel expenses; we charge none in the Greater Munich area. The AI Future Workshop for the entire leadership level is quoted according to group size and facilitation design. What comes next is your decision: a follow-on mandate is neither a condition nor the purpose of the session.

What is your stance on co-determination and the EU AI Act?

We treat co-determination as a prerequisite, not an obstacle: employees and their works councils belong at the table early, otherwise the transformation tips over later. Four statements in the compass concern minimum requirements such as an inventory of applications, named responsibility and shutdown procedures — we read them individually, not as an average. This does not replace a legal or data protection review; the reference to the AI Act is a pointer, not legal advice.

What is the best way to approach an AI transformation?

That depends on the context and the starting conditions. In our work, this sequence has proven itself: first, define the ambition, that is, top management’s acceptance criteria; second, open the learning journey, so that all employees read the effort as an opportunity; third, a Big Think Day at which the strategically most important use cases are prioritized; fourth, mission-driven teams that pilot these cases, released from other duties; fifth, a sprint rhythm on a quarterly cycle; sixth, bundle the knowledge into AI identities, that is, assistants for each area; seventh, rethink the organization, with new task profiles and led agents.

The Leadership Insights article Seven stages, in this order describes this in detail.

More of what we offer

Strategy Activation

If you do not lead AI,
it will lead you astray.

From AI hyperactivity to targeted business impact.