AI-enabled, human-interpreted: the future of pharma marketing insights is precision intelligence

Pharma doesn’t have an insight problem – it has an operating model problem. Despite unprecedented investment in data, research and content development, outcomes continue to disappoint. 77% of pharma content never reaches its intended audience.1 Half of launches miss expectations, and one in four delivers less than half its forecast.2

This is not a failure of ambition or effort; it is a structural issue.
Most insight functions within global pharma companies are still organised for a slower, more predictable world – one where plans are set annually, data lives in silos and market research happens in bursts. Insights are generated, reported and handed from one team to the next, often too late to change the outcome.

The trouble is, that model can’t keep pace with the speed of today’s markets.

Over the past 18 months, the GLP-1 category has shown just how quickly competitive dynamics can shift. Published analyses of US prescription data show Eli Lilly overtaking Novo Nordisk in key segments in a matter of months.3 The launch of oral GLP-1s is already reshaping the category again, reopening competitive advantage and triggering another phase of disruption.

In this environment, insight delivered quarterly, or even monthly, is already out of date.

Pharma needs to change how insights are generated. Not by commissioning more research, but by moving towards always-on intelligence using AI approaches designed to detect change early and guide human-led action fast.

In recent work supporting a global immunology franchise, Day One Strategy observed unexpected physician switching within three to four months of a competitor’s entry. Existing competitive intelligence relied heavily on historical claims and syndicated data, meaning the impact only became visible once switching was already underway.

The team shifted from retrospective reporting to a continuous threat detection system. Signals across access dynamics, competitor activity and customer interactions were connected into a single intelligence stream. This revealed the specific triggers driving switching behaviour weeks before they would have appeared in standard market data.

Instead of launching a traditional defensive campaign, the brand responded through a series of Micro Battles – focused six- to eight-week sprints designed to test, refine and launch targeted defensive actions. Marketing, medical and market access aligned around a shared view of the threat and a clear response narrative.
The result was earlier detection, faster alignment and a focused intervention that stabilised switching trends.

What the immunology example illustrates is a different way of generating and applying insight. We call it precision intelligence – the ability to cut through noise and surface highly relevant, timely insight – so strategic decisions become clearer and faster. It comes from hybrid thinking – human judgement sharpened by technology for pharma.

Not another tool or methodology, but a capability built for the realities of modern pharma, where competitive dynamics shift quickly and decisions cannot wait for quarterly reports.

Precision intelligence operates as a continuous loop rather than a linear process – detecting emerging signals, interpreting what matters, deciding whether to act and learning through rapid response.

Data from a range of sources – such as claims, CRM, customer interactions, patient support and brand tracking – is connected in a way that reveals early shifts in behaviour and sentiment that a single data set misses. Regular reporting is replaced with always-on intelligence that is ready to act on, but with human judgement at the centre.

Pharma is spending more than ever on insights, messaging and activation. But linear planning, episodic research and disconnected data cannot support markets that now move at speed.

Precision intelligence offers a path forward – pharma-specific, human-led and already in use by teams managing complex, competitive franchises. But this approach demands new ways of working, with leadership willing to prioritise speed and an acceptance that slow insight is a commercial risk.

The immunology brand that stabilised switching trends didn’t succeed because it had better technology. It succeeded because it changed how insights operate. And that is the shift that matters most.

Find out more
Day One Strategy has published a white paper that explains why pharma’s operating model needs to change and reveals how a precision intelligence approach can be put into practice. Read real-world use cases from clients adopting their proprietary framework:
www.dayonestrategy.com/precision-intelligence.

References
1. Veeva Pulse data, Global, January 2022 – December 2022
https://www.veeva.com/wp-content/uploads/2025/05/Veeva-Pulse-Report-Top-Insights-1Q25.pdf
2. Bain & Company 2017
https://www.bain.com/insights/how-to-make-your-drug-launch-a-success-invivo/?utm_source=chatgpt.com
3. MarketMinute article, September 2025 https://markets.financialcontent.com/talkmarkets/article/marketminute-2025-9-19-glp-1-titans-clash-eli-lilly-challenges-novo-nordisks-dominance-in-a-spiraling-market-battle?utm_source=chatgpt.com

Abigail Stuart is Founding Partner at Day One Strategy