Cases/01 · AI investments

Pharmaceutical consultancy.Overhauled Data Science methods, products and general offering.

Pharmaceutical / life sciences·Data Science business unit·Practitioner: Simon

A pharmaceutical consultancy needed to scale its Data Science offering and embed modern product practice across an organisation that had grown organically. The Data Science sat within a department of approximately 30 people, with real demand and real talent — and a structure that had been built for a smaller version of itself.

The Data Science offering needed to scale, but the structure underneath it didn't yet support scale: product definitions were fluid, pricing was based on what the work cost rather than what it was worth, and project delivery ran on improvisation rather than a repeatable system. Add an organic-growth pattern and you get a unit that does great work — and struggles to grow.

The total Data Science offering was redefined end-to-end: products, services, pricing, and operations. Vanilla Scrum was implemented across IT projects. Customer personas, Lean UX Canvas, and Design Thinking were embedded as the cross-organisation methodology — not a poster on the wall but the actual way commercial projects, solution architecture, phases, tools, and knowledge management came to work. An AI-driven omnichannel Customer Data Platform was brought in, adding a complementary revenue stream.

17.33%

YoY topline growth across the business

3x

Revenue growth in the Data Science offering in less than two years

Modern product practice embedded as part of how the organisation operates — not a methodology on a wiki but the working practice across the department.

Simon — Commercial strategy, Product, Operating model, Methodology embedding.

This case maps to 'Our AI investments aren't landing'. A Data Science offering inside a consultancy is itself an AI investment — and this one was failing to land at scale. The bottleneck wasn't the technology; it was the products, pricing, and methodology. The engagement combined an Analysis (defining the right product taxonomy and pricing model) with Implementation (the methodology, the operating cadence) and a Sustain-style window post-rebuild to ensure adoption stuck.