I work with private equity firms and diligence teams that need to know if AI represents real value, what the hidden risks are, and what the AI roadmap for the holding period should look like.
Many AI claims around industrial businesses rely on assumptions that do not hold up under operational scrutiny. For example, assumptions about data quality, system readiness, management capability, or how quickly AI can create measurable value during a holding period. Good judgment requires careful investigation, critical thinking, and a practical understanding of what production reality allows.
My perspective was shaped by building systems where incorrect outputs carry real consequences. After my PhD at the Technical University of Denmark, I co-founded a company building AI for vessel optimization in live maritime operations. Our models were deployed into production environments where errors were immediately visible and operationally costly. I stayed through acquisition, serving as CEO, CTO, and VP of AI.
I have also worked on several AI due diligence projects in industrial M&A, assessing technical realism, data conditions, and value creation potential in acquisition contexts where AI claims needed to be tested against operational reality.
That experience taught me that while building robust models is difficult, judging whether AI can realistically create value inside an existing industrial company is often harder. Industrial AI depends on far more than algorithms. It depends on process maturity, data integrity, system architecture, and management readiness.
Since then I have:
- Built property valuation AI systems used by banks to meet Basel Committee on Banking Supervision requirements
- Architected AI solutions for SaaS companies operating in highly regulated markets
- Designed systems where traceability, evaluation discipline, and decision reconstruction were first-class architectural concerns
My role is typically:
- Assessing downside exposure linked to data, systems, and technical debt
- Evaluating where AI can realistically create value within the investment horizon
- Supporting diligence teams with technical judgment that is commercially relevant
What I bring:
- Experience shipping AI into live operations
- Deep understanding of how technical constraints shape business outcomes
- Creative systems thinking
- Direct, accountable collaboration
I work with only a few clients at any one time. It is important that we begin by making sure there is a good fit for collaboration, because that is the best way of ensuring strong outcomes.