Biology meets commercial thinking.

Scientists adopt trust signals first. The technology comes second.

I surveyed 213 life sciences professionals for my MBA dissertation. The strongest predictor of ML adoption wasn't capability. It was trust.

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The obligate mutualist: customer concentration as coevolution

Biology calls it obligate mutualism. Business calls it your best client. Both can end you.

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The science works. The data is solid. But your audience is not convinced to leap.

Having a great scientific idea that doesn't stick with the market? The problem is rarely in the lab. Kotler's Augmented Product Model can explain why.

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The Ambition Matrix: A strategic lens for life sciences innovation portfolios

As scientists, we were trained to transform the world. What nobody told us: transformation is the result of endless incremental steps. This matrix helps.

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The Ansoff Matrix: A handy compass for Life Sciences Innovation

My favourite framework for rapid R&D assessment. When scientists present new ideas: are we innovating on product, market, or both?

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The Idle Valley: The hidden cost when R&D and product management fall out of sync

There's a timing problem between life sciences R&D cycles and product management. I wrote about it.

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