Michael Eiden
Managing Director @Alvarez & Marsal
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WORK HISTORY
Managing Director @Alvarez & Marsal
London, GB
I lead Applied AI/ML across the EMEA region, advising Fortune 500 boards, C-suites, and PE-backed portfolios. My team is sector-agnostic by design: current and recent programmes span financial services, energy and utilities, telecoms, consumer goods, travel and transport, and healthcare and life sciences. What we do isn\'t AI strategy on its own - it\'s end-to-end transformation. Real engineering, real adoption, real reimagination of the underlying processes, delivered in the A&M style clients know us for: hands-on, outcome-first, accountable. Not POCs that die quietly between slide 40 and production. A specific focus: Causal AI. While most of the market is trumpeting GenAI and agentic AI, we believe the genuinely scarce capability in enterprise is decision science - transparent, auditable AI that incorporates domain knowledge and actually helps leaders answer \"what should I do next?\" rather than just \"what information exists?\" We\'ve rolled out causal systems in high-stakes settings, particularly in financial services, where auditability, traceability, and rigorous scrutiny aren\'t optional and where the size of the prize more than justifies the engineering investment. We also build GenAI and agentic systems - my team was one of the first in Europe on this - but with a strong bias for observability, grounding, and use cases where the value is real and measurable. Across 50+ AI solutions over two decades, the failure modes I see most often are sector-agnostic; so is the recipe for making them work.
EDUCATION
Universität Trier
Diploma, Environmental Sciences (Specialisation on data analysis and modelling)
Universität Trier
Doctor's Degree, Bioinformatics
SKILLS
ABOUT MICHAEL EIDEN
I\'ve been chasing one question ever since: how do you get algorithms to make sense of genuinely complex systems? My early career answered that question in biology. At one of Europe\'s first AI-for-drug-discovery companies I was building neural networks for novel anti-infective compounds and an antemortem BSE test at the height of that crisis. A client once insisted we print out every weight and bias alongside the model. Twoday, this reads like an early version of the interpretability debate we\'re still having in enterprise AI today. PhD in bioinformatics; bioinformatics group at the UK Medical Research Council on metabolic disease; honorary role at Cambridge; then CIO at Specific Diagnostics in California, where we built one of the world\'s first deep-learning systems for olfactory sensor data for near-real-time sepsis detection - now an established technology. In 2018 I moved into consulting, and discovered the pattern-recognition toolkit I\'d built in biology transferred cleanly across sectors. Optimising transport networks has a lot in common with biological signalling networks. Forecasting commodity prices is closer to metabolic modelling than most people realise. As Partner and Global Head of AI/ML at Arthur D. Little I built the firm\'s global AI practice from scratch and ran programmes in financial services, telecoms, transport, oil and gas, pharmaceuticals, media (including two of the world\'s largest), and energy. As Managing Director for AI/ML EMEA at Alvarez & Marsal, I now lead that work at larger scale. Across two decades I\'ve commercialised 50+ AI solutions across healthcare, financial services, telecoms, energy, oil and gas, travel and transport, defence, and consumer goods. A&M clients know us for hands-on transformation; our AI work is cut from the same cloth : end-to-end, sector-agnostic, real engineering, real adoption, not POCs that die quietly between slide 40 and production. My stance: most of the market is trumpeting GenAI and agentic AI. Useful, but not scarce. The scarce capability in enterprise AI is causal - auditable decision science that helps leaders answer \"what should I do next?\" rather than \"what information exists?\" I see the highest returns in financial services and other regulated, high-stakes settings. I write here about what actually works when enterprises scale AI, across sectors - and what silently doesn\'t.
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