Novo Nordisk
Using AI to turn fragmented customer data into strategic advantage
Novo Nordisk had no shortage of data, but no joined-up view of their customers. Insight lived in silos across market research, CRM, medical systems and engagement platforms: useful alone, disconnected together. We built Intellibots to change that.
The challenge
The problem wasn't a lack of data. It was that no single, trusted view of the customer existed. Multiple sources described the same healthcare professionals from different angles, but they were rarely brought together. In practice, each team worked from its own siloed insight, in a vacuum.
Our analysis revealed three costly drains: rising research spend as teams re-commissioned studies they couldn't easily access or reuse; fragmented customer understanding, because insights were often not combined at all and each team acted on its own slice; and strategy that was disconnected from behaviour, because what HCPs said in research and what they actually did were rarely looked at side by side.
What we did
We designed Intellibots as an AI-powered insight layer that brought multiple data sources into one continuously updated customer view, one you could actually talk to.
Unify the sources
We connected five domains, market research, CRM master data, notes from the field, competitor channel insights and engagement data, into a single, trusted view of each segment, so historical research became a living asset instead of a static report archive.
Conversational PersonaBots
Built on an agent-based PersonaBot architecture, Intellibots let a user pick a segment and instantly explore who they were, what mattered strategically, and how to shape content and engagement, asking questions and getting answers in plain language.
Insight that stayed live
Because the system was integrated directly with source data, insights stayed current. Work that once needed cross-functional coordination and months of planning could now move in minutes.
The agent set-up
The results
Perhaps the biggest gain was joining up what HCPs said in market research with what they actually did. The two often diverged, and seeing them side by side let strategy be anchored in real behaviour rather than stated intent.
Smarter personalisation: faster, more precise segment identification and AI-supported content guidance meant communications were better tailored to real HCP needs.
Lower research spend and major efficiency gains: by activating existing research and linking it to live data, Intellibots reduced repeat commissioning and compressed planning, segmentation and content development from months into minutes.
Consistency at scale: standardised logic reduced variation across teams and markets, so different functions finally worked from the same trusted picture rather than their own.
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