Reports: Applying gen AI to marketing can help life-sci firms unlock billions

Marketing-related uses represent one of the pharma industry’s biggest opportunities for revenue lift and cost savings from artificial intelligence. Two recent analyses from well-known consultancies put a dollar figure on some of the potential windfalls.

By applying generative AI to commercial activities, these consultancies estimate, life science firms can unlock several billion dollars over five years—and tens of billions more in the long run.

“Nine months ago, some of them were still believers, some were non-believers and some were trying to figure out what to do with it,” he said. “Fast-forward to today, and they’re thinking about deploying gen AI as a strategic lever.” 

Of the estimated $5 billion to $7 billion in value that life science firms stand to gain from using AI over five years, opportunities from the commercial function account for 25% to 35%, according to a report released this month by Deloitte. The report was based on its study of 20 end-to-end AI use cases. 

That’s not as much as R&D, which represents 30% to 40% of value. But it’s more than manufacturing/supply chain, which accounts for 15% to 25%.

A report released earlier this year by McKinsey Global Institute, based on its analysis of 63 individual use cases, reckons gen AI’s annual economic value on the commercial side at between $18 billion and $30 billion for the pharma and medical product industries.

Where specifically does that value sit within the life sciences value chain? On the marketing side, a huge amount is spent on the content lifecycle—everything from brand strategy to creative brief to content development, deployment and production, including medical, legal and regulatory review. 

Pfizer has reportedly begun deploying its next-gen AI platform for pharma marketing—dubbed Charlie—across its internal marketing and brand teams, along with external agencies Publicis Groupe and IPG. The effort is seen as part of its strategic focus on improving the content supply chain.

Such rollouts cost billions, but there’s a clear payoff in several areas, including reducing enterprise-wide costs, commercialising new products and engaging with care providers, pharmacists, payers and patients. According to McKinsey, gen AI can slash content creation costs, enhance production pipelines and boost the speed of content approval.

“Depending on the client, content and production, including med-legal review, takes anywhere from three to six months,” said Kudumala. “With the power of gen AI, we’re able to do the first drafts in production in 11 days. You’re able not only to create the content, depending on the behavior of people you’re connecting with, but translate it into different languages for different regions at a faster clip.”

In fact, Deloitte says the commercial function has a faster accretion schedule than other functions in terms of realising AI’s impact. In other words, the percentage of peak value realized in year one (38%) outpaces that of R&D, supply chain and other areas.

AI’s promise goes beyond marketing-related uses. On the sales side, AI can help firms do a better job of forecasting and brand intelligence, micro-segmentation and HCP targeting. 

The technology may also be able to facilitate the patient experience by decoding reimbursement complexity so that patients have a better shot at starting and staying adherent with prescriptions. Within the realm of market access, AI can assist with contracting, creating value dossiers and helping drugs get placed on formulary. 

Many of Deloitte’s pharma clients are asking themselves, “‘What would the future of commercial look like in the next two to three years if I truly leverage AI at scale?’” explained Kudumala. “‘Can I transform my marketing organisation and create 3-times to 6-times of marketing ROI or reduce my content spend by 50% or more? Can I reduce revenue leakage and improve patient outcomes and adherence?’”

Conceptually, they’re connecting these goals, or “north stars,” together by leveraging existing investments they’ve made in data, analytics and traditional AI, Kudumala added. 

In addition to overcoming silos, the biggest obstacle remains mindset or change adoption. Ideological barriers are falling, though, as AI fluency improves. The second challenge involves having the right data foundation in place, specifically procuring the appropriate structured data. 

Companies may also encounter pitfalls when ensuring leadership support. A bottom-up, decentralised approach allows them to act faster than a top-down, platform-based one. Rather than commit to one or the other operating model, leaders may need to move between both, McKinsey suggested.

To build momentum with gen AI, Deloitte recommends that organisations enlist their business units and their IT/digital departments to identify what it calls “no regrets bets,” initial goals that align to priority areas.

Four other actions organizations can take to facilitate their gen AI value journey include establishing a leadership mandate and aligning on a strategic blueprint, creating minimum viable governance and launching pilot solutions.

After years of proceeding at a measured pace with digital transformation, Kudumala sees more companies viewing gen AI as a speed and force multiplier, prompting a move into either innovator or fast-follower mode.

“Everything they do in the next three to five years…some will exhibit high AI maturity, others not,” he predicted. “But more and more, the feeling they have is that if they don’t know how to work with this capability, they’re going to be outsmarted or be at a disadvantage, versus saying that this is not going to be another high- or no-value technology. That mindset has changed.”