Using AI sensibly.
I help organisations turn AI into concrete decisions: Where is it worth using? Which risks and dependencies arise? And how does an initiative become a viable part of processes and the organisation?
How I help.
Advice for the questions that must be answered before and during delivery — from the right area of use to adoption in day-to-day operations.
Strategy & Prioritisation
Identify and prioritise use cases, assess value and effort realistically, prepare make-or-buy decisions and develop a robust roadmap.
Governance & Decision Framework
Define responsibilities, approvals, risk and quality criteria. Account for regulation where it is relevant — without making it an end in itself.
Adoption & Organisation
Align roles, processes, data flows and ways of working with the use of AI. Plan pilots so that a successful test can actually move into operation.
Delivery Support
Sharpen requirements, coordinate internal teams and external vendors, sanity-check technical options and guide decisions through delivery.
Technology is only half the job.
An AI system can work technically and still achieve little in an organisation. Because new technology changes not only tools, but also responsibilities, routines, expectations and decisions.
People don't simply work the way a process diagram prescribes. They develop habits, informal arrangements and their own criteria for which systems they trust. When AI ignores this practice, friction, workarounds or extra effort arise — even when the model is convincing in testing.
That's why I don't treat AI adoption as a pure software question. What matters is how a solution is embedded in existing work: Who decides? Who bears responsibility? Which tasks change? Where does control arise, where relief — and what does that mean for the people meant to work with the system?
Acceptance is not a communication problem
Resistance can't always be solved with training or change communication. Often it points to real problems: missing involvement, unclear responsibility, extra work, or a system planned past actual practice.
Processes are more than sequences
Org charts show responsibilities, but not automatically how decisions actually come about. For AI projects, informal routines, experiential knowledge and dependencies are therefore just as relevant as the technical process description.
Responsibility has to work in practice
Governance is useful when it's clear who may deploy a system, who must review results and who decides in case of doubt. Good rules have to work in daily work — not just on paper.
What we can work on together.
Consulting between decision and delivery.
I am Nicola Jonas Mück. For over ten years I have worked on digital products, projects and business models — in consulting, project leadership and product ownership. Today I focus on AI as a question of management, organisation and delivery.
My studies in sociology and philosophy are not an academic add-on to the actual topic. They help me see organisations as more than technical systems. New technologies always meet existing roles, interests, routines and ideas of what counts as good work or the right decision.
With AI this quickly becomes relevant: a system can save time and create new control at the same time. It can support decisions and simultaneously make responsibility less clear. It can deliver good results on the merits and still go unused if staff don't trust its outputs or if it doesn't match how they actually work.
For me these questions therefore belong in the same engagement as architecture, vendor choice or economics. I use technical depth where it is needed for a decision. I can follow and question prototypes, architectures and vendor arguments. But the tech stack itself is not the product.
What I bring.
Experience from digital product work and consulting, complemented by a perspective on organisation, work, responsibility and the social consequences of technological change.
From concept to operation
Experience with digital products and platforms across their entire lifecycle — not just strategy papers.
Over ten years of practice
Independent consulting, project ownership and work with a range of organisations, teams and stakeholders.
Able to judge the technology
First-hand work with AI applications and prototypes provides enough technical depth to assess feasibility, effort and dependencies realistically.
Understanding technology in organisations
Analysing how roles, routines, interests and informal structures shape the adoption of new technologies — and why a technically good system is therefore not yet an organisationally good one.
Making responsibility precise
Making terms like fairness, transparency or responsibility concrete enough to produce robust decisions rather than abstract principles.
No solution has to be sold
No tie to a particular software vendor or model. The recommendation follows the problem and the context.
Intro call.
In a first conversation we clarify what it is about, where the actual decision lies and whether I'm the right person for it. If not, that can usually be established quickly too.