AI strategy, product and enablement for regulated (pharma) enterprises.
Over 11 years across enterprise software and pharma.
How I think about AI
AI is a chance to rethink how work gets done.
In practice, AI initiatives often fail in three predictable ways: they start with a tool looking for a problem, patch AI onto yesterday’s workflow, and get stuck in pilot mode. Avoiding those traps means shifting the focus at each stage.
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AI Strategy
Choose
Start with the outcome that matters.
Define what winning looks like. Identify the constraint with the greatest impact on that outcome, then choose the intervention best suited to shift it. AI is one option, not the starting assumption.
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AI Product
Build
Redesign how the work gets done.
Start with the future-state workflow. Decide what AI should automate, augment or inform, where human judgement and accountability remain critical, and how decisions and handoffs should change. Then build the product around that new way of working.
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AI Enablement
Scale
Build the system around the product.
Define clear ownership, industrialise the foundations, put workable governance in place, and prove the economics beyond the pilot. Then embed the new way of working through deliberate rollout, adoption and continuous improvement.
Where value is moving
As AI becomes abundant, the sources of value begin to shift.
The strategic question moves from what the technology can do to what remains scarce, distinctive and hard to replicate.
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Market
Access to capable models
Proprietary advantage
Context, data, customer relationships and differentiated products and experiences.
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Work
Routine application of expertise
Judgement and creation
Problem framing, difficult decisions, innovation and exceptions.
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Organisation
Information and coordination mechanisms
Adaptive operating models
Decision rights, team structures, governance and capability building.
Latest insights
Insights.
Coming soon
Coming soon
Coming soon
About Maria
From building AI to shaping
the decisions around it.
I started close to the technology, building machine learning products and platforms. Over time, the questions that interested me most moved upstream: Which problems are worth solving? How should AI change the work around it? What makes people trust and adopt it? And what has to change for it to scale?
My work has taken me across SAP, Roche and Novartis, from predictive ML and AutoML to AI for scientific discovery, clinical operations and enterprise GenAI. That mix has shaped how I think about AI today: as a product, a business decision and an organisational change at the same time.
Selected experience
Over 11 yearsin AI & data
From machine learning to scientific discovery and enterprise GenAIAcross industries and use cases
Product, strategy & scaleFrom idea to real-world impact
Get in touch
Let’s compare notes.
If you are working through similar questions, or have a different view, I’d like to hear from you.