Beyond Drug Discovery
AI is sweet as pie
For pharma executives, citing artificial intelligence (AI) as a driver of growth is more contagious than measles in a room full of unvaccinated individuals—nearly all mentioned its benefits in their JPM Healthcare presentation. Roger Perlmutter, the CEO of Eikon Therapeutics and former R&D executive at Merck and Amgen, regards AI as “the defining tool of our era.”
However, AI is a broad term and the avenues for growth in pharma are as wide and as numerous as those in New York City. While the orbit of AI is skewed by the gravity of drug discovery, its other use cases are gaining traction. “One of the magic bullets of AI is operational efficiency, so it does not take a decade to get from discovery to a marketed product,” said Bari Kowal SVP of development operations at Regeneron at the Financial Times US Pharma and Biotech Summit.
Kowal has a point. At an average of 10 to 15 years, the US is the market leader in time taken to bring a drug to market. Given this timeline, efficiency in all stages of the drug development process becomes a necessity. “This means using AI to streamline the patient journey, minimize dropout rates, and ensure that patients stay in trials,” said Paula Brown Stafford, CEO of Allucent, a contract research organization.
This also means utilizing AI for formulation development. “AI minimizes the number of required trials, improves production consistency, and enhances scalability from R&D to commercial manufacturing, all while reducing the operational footprint for long acting injectables compared to traditional methods,” said Jay Shukla, president and CEO of Nivagen Pharmaceuticals, a sterile fill finish CDMO.
AI can also help unlock capital flow, said Saaurabh Shharma, partner and CEO at Agram Konnect, a strategic communication company: “AI-powered 3D visualization is revolutionizing how we communicate scientific concepts. For instance, a biotech firm launching a new molecule can use 3D animation to illustrate its mechanism of action for investors.”
One of the most overlooked opportunities for efficiency lies in validation—the complex process of proving that everything in drug development works as intended. ValGenesis, a leader in digitizing the validation processes, integrated AI and machine learning into its Validation Lifecycle Management System (VLMS) across three key areas: protocol generation, validation execution, and review by exception. “Companies typically allocate 30% of their total costs to validation, and we are working to reduce this by at least half,” Siva Samy, CEO and chief product strategist, stated.
Thanks to its AI-powered bots, ValGenesis can now generate validation protocols in five to eight minutes (a task that traditionally took two to three weeks) and reduce review and approval cycles from weeks to hours.
Ultimately, improving operational efficiency is not just about trimming budgets—it means getting therapies to patients faster, with greater precision and safety. Even regulators are adapting, as AI helps companies proactively manage quality and compliance in real time, an emerging shift from traditional reactive models.
From the cherry on top to the filling
AI is becoming embedded in the industry’s infrastructure. However you slice the AI pie, you will find it filled with efficiency, something baked into the DNA of technology firms operating in the pharma space.
One transformational avenue for pharma is generative AI (gen AI). McKinsey found gen AI could unlock between US$60 billion and US$110 billion a year in economic value for the pharmaceutical and medical products industries. While the recipe sounds promising, most companies are still in the prep phase. In a recent McKinsey survey of 100 executives leading gen AI efforts in pharma and medtech, 100% reported experimenting with the technology. However, when it came to realizing gen AI as a competitive differentiator generating consistent and significant financial value, only 5% of leaders fell into this category. GenAI budgets are forecasted to increase from 2024 to 2025, with 20% of McKinsey’s surveyed leaders planning to allocate over US$10 million in 2025. Translating this investment into returns will be pivotal.
One reason for the suboptimal returns is the inability to apply it effectively. Scaling gen AI is not simply a matter of implementing a new technology; it is about rewiring the organization’s operating model and culture to support new AI-driven ways of working. WhizAI, a company that developed a generative AI platform that allows companies to talk to their data like it is ChatGPT, highlighted that the industry has a lot of room to realize this potential. “We are just at the beginning stages of generative AI, much like the internet in the 90s. The true transformation will occur when AI is integrated into products that solve real business problems,” said Rohit Vashisht, the company’s cofounder and CEO.
Integration is key. 70% of digital transformations fail because leaders ignored change management, not because of technical issues. “For every dollar spent on a new technology, US$5 is required for change management to successfully drive capability building, adoption, buy-in, and value capture over time,” according to McKinsey.
Where is my AI?
Despite the benefits, many of AI’s applications remain out of reach. The limiting factor? Siloed, inaccessible data. “We found that most customers—including big pharma—were not ready with their data, despite significant investments in cloud and analytics. Many are at an inflection point where they need applications but lack the necessary data foundation,” underscored Rajiv Anand, founder and CEO of Quartic.ai.
In many companies, manual data processes persist, making any advanced analytics at scale or speed impossible. The solution is a DataOps approach which can enable pharmaceutical companies to extract more value from their data and more quickly advance and scale their digital and analytics initiatives. Companies like Quartic.ai recognized this drawback in the market and now offer an out-of-the-box DataOps solutions.
AI is not just a cherry on top—it is the filling. As pharma continues baking its digital transformation, one thing is clear: the recipe is changing, and efficiency is now the main ingredient.
Article header image courtesy of GSK