Where AI creates measurable business value, where it does not, and what it takes for the organisation to get there. Advisory at CEO level, combined with daily hands-on use of agent-based AI and 15+ years of leading change inside large tech organisations. Also offered as one-to-one coaching for executives ↓
AI advisory for owner-led companies and my own daily practice with AI agents. The common thread: start from the business problem and the organisation's capacity to absorb change, then decide where AI belongs. Client names are anonymised.
Situation: Production carried significant technical debt: ageing machinery that could not be updated, no data exchange between systems, and manual handling and inspection of finished components. The CEO wanted an outside view on where AI could realistically improve the production workflow.
Action: Interviewed the CEO, the COO and production workers to establish the real state of the machinery and the technical debt. Identified three AI-enabled improvement initiatives and prioritised them with the CEO against value, implementation risk and cost, using the organisation's capacity to absorb change as the starting point. Built a three-month implementation roadmap for the selected initiative: computer-vision inspection of produced parts, with the inspection data captured and analysed to drive efficiency.
Result: The success criterion was met: one AI initiative selected with the CEO on value, risk and cost, and backed by a three-month implementation roadmap scaled to what the organisation could absorb.
Situation: The company supplies segments such as rail, defence, offshore and medical devices, where product certifications are a recurring compliance burden. Collecting and categorising the data behind each certification was manual work. The AI advisory was part of a wider innovation and growth assignment with the CEO and CMO.
Action: Designed two AI workflows for the certification work: one for collecting the data each certification requires, and one for categorising it.
Result: Two AI workflows designed for a recurring compliance task, delivered alongside a growth strategy the CEO has since acted on.
Situation: The company's protected niche position came under pressure as European competitors entered the Danish market. Part of their offensive was to embed AI in their service offerings.
Action: Worked with the CEO and COO over an 18-month engagement. Analysed industry structure and competitors with Porter's Five Forces, and established that the entrants were embedding AI in their services. Recommended a small AI-enabled service pilot run jointly with an existing customer, and placed it in an implementation roadmap together with the market and service initiatives.
Result: Leadership received a clear picture of the AI threat and a concrete, small-scale way to respond. The CEO chose to prioritise market expansion first and defer the AI pilot until after the expansion, a deliberate sequencing decision.
Situation: In most organisations the barrier to AI value is rarely access to tools. It is the ability to turn a business task into something an AI can execute reliably, and to know when the output must be checked.
Action: Designs and instructs AI agents for concrete tasks: automated news monitoring with a daily briefing, a second agent monitoring local planning and municipal decisions, a payroll calculation tool, structured research on the EU AI Act, and document production. Writes the instructions and workflows the agents operate by, and decides where a human verifies the result before it is used. This website was built and migrated to its own hosting with AI.
Result: 25-35% less time spent per week on the two monitoring tasks. First-hand judgement of what agent-based AI does reliably today, where it fails, and what verification it needs. That judgement is what I bring to adoption work with leaders and employees.
Client names and further detail are shared in conversation where confidentiality allows.
One-to-one coaching for executives who are expected to lead AI adoption and want to do it from their own experience. You learn to use AI in your own work first, and then decide what it should mean for your organisation.
Many leadership teams set AI direction without having used the tools in their own work. That makes it harder to judge where AI creates value, to set realistic expectations, and to ask the organisation to change in a credible way. Coaching closes that gap at the top, where adoption is decided.
Hands-on sessions where you use AI on your actual tasks: decision papers, board and steering committee material, analysis, preparation for difficult meetings. With the tools your organisation already has approved.
How to lead AI adoption: what to expect from your teams, what to delegate to AI and what to keep, when output must be verified, and how to handle concerns in the organisation.
Translating your own experience into priorities: where AI can create measurable value in your area, what your leadership team needs to decide, and what the first concrete step is.
Book a 20-minute intro call. No obligation. CV and case material tailored to your client proposal.
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