Marketing-related uses represent one of the pharmaceutical industry’s biggest opportunities to increase revenue and reduce costs with artificial intelligence. Two recent analyzes by renowned consulting firms put a dollar figure on some of the potential windfalls.
By applying generative AI to business operations, these consultancies estimate, life sciences companies can unlock several billion dollars over five years – and tens of billions more in the long term.
In the boards of directors of pharmaceutical companies, there has been a change in the content of AI Conversationsreported Aditya Kudumala, global AI leader for life sciences at Deloitte.
“Nine months ago, some of them were still believers, some were not believers and some were trying to figure out what to do with it,” he said. “Fast forward to today, and they are considering deploying generation AI as a strategic lever.”
Of the estimated $5 billion to $7 billion that life sciences companies are expected to gain from using AI over five years, opportunities from the sales function account for 25 to 35 percent, according to one study. report published this month by Deloitte. The report was based on its study of 20 end-to-end AI use cases.
This is less than R&D, which represents 30 to 40% of the value. But it’s not limited to manufacturing and supply chain, which accounts for 15-25%.
A report published earlier this year by the McKinsey Global Institute, based on its analysis of 63 individual use cases, estimates the annual economic value of Generation AI on a commercial level to be between $18 billion and $30 billion for the pharmaceutical and medical products.
Where exactly does this value lie in the life sciences value chain? On the marketing side, huge amounts of money are spent across the content lifecycle – from branding, to creative brief, to content development, deployment and production, to medical review , legal and regulatory.
Pfizer has reportedly begun rolling out its next-generation AI platform for pharmaceutical marketing, dubbed Charlie, within its internal marketing and brand teams, as well as with external agencies Publicis Groupe and IPG. This effort is seen as part of its strategic direction to improve the content supply chain.
Such deployments costs billions, but there are clear benefits in several areas, including reducing enterprise-wide costs, bringing new products to market, and engaging healthcare providers, pharmacists, payers, and patients. According to McKinsey, AI generation can significantly reduce content creation costs, improve production pipelines, and increase the speed of content approval.
“Depending on the client, content and production, including forensic examination, takes three to six months,” Kudumala said. “Thanks to the power of AI generation, we are able to get the first drafts into production in 11 days. You can not only create the content, based on the behavior of the people you connect with, but also translate it faster into different languages for different regions.
In fact, Deloitte says the business function has a faster accretion timetable than other functions in terms of realizing the impact of AI. In other words, the percentage of maximum value realized in the first year (38%) exceeds that of R&D, supply chain and other areas.
The promise of AI goes beyond marketing-related uses. On the sales side, AI can help companies do a better job of forecasting and brand intelligence, micro-segmentation, and HCP targeting.
Technology could also make the patient experience easier by decoding the complexity of reimbursement so patients have a better chance of starting and sticking to their prescriptions. In the area of market access, AI can help conclude contracts, create value records and put medicines on the formulary.
Many of Deloitte’s pharmaceutical clients are wondering, “What would the future of the commercial sector look like in the next two to three years if I actually leveraged AI at scale?” » explained Kudumala. “Can I transform my marketing organization and create 3-6x higher marketing ROI or reduce my content spend by 50% or more? Can I reduce revenue leakage and improve patient outcomes and compliance? »
Conceptually, they connect these goals, or “north stars,” by leveraging existing investments they have made in data, analytics and traditional AI, Kudumala added.
Besides overcoming silos, the biggest obstacle remains mindset or embracing change. Ideological barriers, however, are falling as mastery of AI improves. The second challenge is having an adequate database, particularly sourcing the appropriate structured data.
Companies can also run into pitfalls when it comes to ensuring executive support. A bottom-up, decentralized approach allows them to act more quickly than a top-down, platform-based approach. Rather than committing to one operating model or the other, leaders may need to shift from one to the other, McKinsey suggests.
To build momentum with the AI generation, Deloitte recommends that organizations use their business units and IT/digital departments to identify what it calls “no-regrets bets,” initial goals that align with the AI generation. priority areas.
Four other steps organizations can take to facilitate their AI value creation journey include establishing a leadership mandate and aligning with a strategic plan, creating minimum viable governance, and launch of pilot solutions.
After years of working in a measured pace With digital transformation, Kudumala sees more and more companies viewing the AI generation as a speed and force multiplier, prompting them to move into either innovator mode or fast follower mode.
“Whatever they do over the next three to five years… some will demonstrate great maturity in AI, some will not,” he predicted. “But more and more, they feel that if they don’t know how to use this capability, they will be smarter or at a disadvantage, instead of saying this is not going to be another high or worthless technology. This mindset has changed.
