AI Reshapes Pharma Jobs and Insight Functions as Companies Build In-House Capabilities

Artificial intelligence is reshaping work across pharma, with companies adding AI functions and building in-house teams rather than reporting major job losses. Drug discovery is the biggest area where life sciences companies will not only add AI functions but jobs as well, while pharma leaders are also being pushed to move from episodic research and disconnected data toward always-on intelligence.

A recent poll of industry executives found that pharma’s C-suite leaders do not think AI will lead to major job losses. “Rather than replacing jobs, AI creates new roles and elevates existing ones, making curiosity, creativity and critical thinking essential skills for the future,” according to a December announcement of a second annual AI Festival. One recruitment executive said AI is not necessarily replacing jobs one-for-one, but is “rehousing and reshaping the types of jobs that are now coming in.”

In fact, AI has added jobs at some large drugmakers. One company said it is working with Nvidia to build an “AI factory for drug discovery” and co-innovation lab in San Francisco that will create new scientific and technical roles. More than half of surveyed biotech executives said AI experts are among the top three roles they need to fill in the coming years, and the search for AI talent is especially critical as life sciences companies invest in and build more in-house AI teams.

Drug discovery is the biggest area where life sciences companies will not only add AI functions but jobs as well. AI is allowing companies to discover therapies and drugs at a fraction of the cost that it took before, and a fraction of the resources, creating demand for AI and machine learning engineering talent in discovery. Commercial analytics roles are also in high demand, with a need for workers who can analyze commercial data and real-world evidence. Positions and departments that directly affect time-to-market and regulatory success are expected to remain in high demand.

Pharma job titles are also being reshaped by AI, creating hybrid roles that merge several functions and capabilities, such as commercial analytics and market access. Companies are asking more for cross-functional skill sets than a technical expert in just one area. One example described a company that combined departments after adding AI capabilities, requiring employees to work more cross-functionally, with no employees displaced.

The push to adopt AI is also changing how pharma generates commercial insights. Despite heavy investment in data, research and content development, outcomes have continued to disappoint: 77% of pharma content never reaches its intended audience, half of launches miss expectations, and one in four delivers less than half its forecast. Most insight functions within global pharma companies are still organised for a slower, more predictable world, where plans are set annually, data lives in silos and market research happens in bursts.

Over the past 18 months, the GLP-1 category has shown how quickly competitive dynamics can shift. Published analyses of US prescription data showed Eli Lilly overtaking Novo Nordisk in key segments in a matter of months, and the launch of oral GLP-1s is already reshaping the category again. In this environment, insight delivered quarterly, or even monthly, is already out of date.

One recent immunology example found 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 connecting signals across access dynamics, competitor activity and customer interactions into a single intelligence stream, revealing specific triggers weeks before they would have appeared in standard market data. The brand responded through a series of six- to eight-week sprints designed to test, refine and launch targeted defensive actions, and the result was earlier detection, faster alignment and a focused intervention that stabilised switching trends.

The approach has been described as precision intelligence, operating as a continuous loop rather than a linear process. Data from 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. 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.