Marketing-related applications represent one of the biggest opportunities for the pharmaceutical industry to increase revenue and reduce costs with artificial intelligence. Two of his recent analyzes by well-known consultancy firms provide dollar amounts for some of the potential profits.
These consultancies estimate that by applying generative AI to commercial operations, life sciences companies could generate billions of dollars over five years and tens of billions more over the long term.
Aditya Kudumala, Deloitte's global AI leader for life sciences, reported that the tone of AI conversations in pharmaceutical company boardrooms is changing.
“Nine months ago, some of them were still believers, some weren't, and some were wondering what to do,” he said. “If you go back to today, they are looking at deploying artificial intelligence as a strategic tool.”
A report released this month by Deloitte estimates that life sciences companies will gain between $5 billion and $7 billion in value over five years from the use of AI, with 25% to 35% of the opportunity coming from commercial functions. occupied. This report is based on a study of 20 end-to-end AI use cases.
This is less than research and development, which accounts for 30% to 40% of value. However, this proportion exceeds that of the manufacturing and supply chain, which accounts for 15-25%.
A report released earlier this year by the McKinsey Global Institute estimates the annual commercial economic value of Gen AI in the pharmaceutical and medical products industry to be between $18 billion and $30 billion, based on an analysis of 63 individual use cases. It is estimated at $.
Where exactly does that value sit in the life sciences value chain? On the marketing side, from brand strategy to creative brief, content development, deployment, and production, content including medical, legal, and regulatory reviews. A huge amount of money is spent on the life cycle of
Pfizer has reportedly begun rolling out its next-generation AI platform for pharmaceutical marketing (dubbed 'Charlie') to its internal marketing and brand teams, as well as its external agencies Publicis Groupe and IPG. The initiative is seen as part of a strategy focused on improving the content supply chain.
Such a rollout would cost billions of dollars, but it would help in several areas, including reducing costs across the company, commercializing new products, and engaging with healthcare providers, pharmacists, payers, and patients. We are seeing clear results. According to McKinsey, Gen AI can reduce content creation costs, strengthen production pipelines, and improve content approval speed.
“Depending on the client, content and production takes three to six months, including medico-legal review,” says Kudumara. “The power of Gen AI allows him to get his first draft in production in 11 days. It can be translated into different languages in different regions.”
In fact, Deloitte says commercial capabilities have a faster accretion schedule than other capabilities in terms of realizing the impact of AI. In other words, the proportion of peak value realized in year 1 (38%) exceeds the proportion of R&D, supply chain, and other areas.
The promise of AI goes beyond marketing-related applications. On the sales side, AI can help companies do better predictive and brand intelligence, microsegmentation, and HCP targeting.
This technology also has the potential to facilitate the patient experience by deciphering the complexities of reimbursement so patients have a better way of starting and staying compliant with their prescriptions. Within the realm of market access, AI can assist with contracts, create value documents, and help medicines enter prescriptions.
Many of Deloitte's pharmaceutical clients are asking themselves, “What will the future of commerce look like in the next two to three years if we leverage AI at scale?” Kudumala explained. “Can I transform my marketing organization to increase marketing ROI by 3x to 6x? Or reduce content spend by 50% or more?” Reduce revenue leakage and improve patient outcomes. Can we improve adherence?”
Conceptually, they seek to connect these goals, or “North Stars,” by leveraging existing investments in data, analytics, and traditional AI, Kudumala added.
In addition to overcoming silos, the biggest hurdle remains mindset and implementing change. However, as AI becomes more fluent, ideological barriers are becoming lower. The second challenge involves having the right data infrastructure in place, especially sourcing the right structured data.
Companies can also encounter pitfalls when securing leadership support. A bottom-up, decentralized approach allows you to move faster than a top-down, platform-based approach. McKinsey suggested that leaders may need to move between both rather than sticking to one operating model or the other.
To accelerate Gen AI momentum, Deloitte recommends that organizations collaborate with business and IT/digital departments to identify what the company calls “the bet you won't regret,” or initial goals that align with your priority areas. I am.
Four other actions organizations can take to accelerate AI value creation efforts include establishing a leadership mission and aligning with a strategic blueprint, creating a minimum viable governance framework, and piloting. Start your solution.
After years of digital transformation at a measured pace, Kudumara said more companies are seeing next-generation AI as a speed and power multiplier, moving into innovator mode or fast follower mode. I see this as encouraging a shift.
“Everything they do in the next three to five years will demonstrate a high degree of AI maturity, and some will show less,” he predicted. “But they increasingly felt that if they didn't know how to use this ability, they would be outmaneuvered or put at a disadvantage. Rather than say, or valueless technology. That mindset has changed.”
