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Accenture’s global life sciences technology lead discusses how the life sciences industry has embraced artificial intelligence.
Accenture recently issued its Technology Vision 2023 for Biopharma report, which revealed that the industry has embraced AI and invested a lot of money into the technology. Accenture’s global life sciences technology lead Shalu Chadha sat down with Applied Clinical Trials' sister publication, Pharmaceutical Executive to discuss the report and the industry’ relationship with AI.
Pharmaceutical Executive: What trends did your report reveal?
Shalu Chadha: This is the annual report that we do specific to biopharma. It involved over 65 biopharma executives, and it was focused on the emerging trends we’ve noticed. We’re seeing convergence, like no time before, of technology and science which is driving innovation in a human and machine world. Whether that’s the scientists, the healthcare professionals, the business leaders, or the patients, they’re all driving that. They’re anchored on several trends: performance frontier, AI, data, and digital identity.
The reason why we’ve anchored on those topics is to ground us on what we’re describing as a client imperative over digital core. Our research shows that where there is a deliberate strategy, and where the clients are setting a new performance frontier with their companies, they’re anchored on a digital core.
Our report has stated 93% of biopharma executives actually are surprised at the acceleration of the science and technology. Our research shows us that AI-mediated drug discovery has grown by about 8%, and that is primarily driving over two-and-a-half billion dollars in investments. This paradigm is here and now, and there isn’t a biopharma company today not making the investments in AI in drug discovery and learning to collaborate with many others.
We see this pace, which we call new science, as being here to stay. The questions, what are the implications from a technology standpoint and how do businesses become the new frontier in this space.
PE: The biopharma industry has been slow to adapt new technologies, but it seems that AI is a different story. Would you agree?
Chadha: Absolutely. I’ve been here for over eight years, and you’re right. The last three years has driven advancement, whether it’s on the discovery or clinical side. There’s a level of adoption in biopharma that is stacking up higher than other industries.
PE: What lessons can the industry learn from this?
Chadha: Using AI as an example, the implications around the new science is that data is critical for any of these novel AI and scientific discoveries. A client’s data management and practices is incredibly important. Secondly, none of this can be done without enough of the partner ecosystem. We’re seeing a very active ecosystem when it comes to partnership that we haven’t seen in biopharma before. Our clients are very clear that the innovation and discovery that needs to be done must be done with platform.
Third, biopharma has traditionally admired itself for working in functions and domains. Now, more than ever, the convergence is causing cross talk between teams. You must be thinking about patient-centricity when considering your supply chain. You must think about your clinical trials and the information you can capture during early discovery. From a culture prospective, those are things that biopharma can learn more from other industries.
On the talent side, there’s a level of cross-skilling there that we’ve never seen before. Every business is now a technology business. The adoption and spread of data and data-based products is ever more important.
PE: How are companies the new importance of data?
Chadha: Data is not just within biopharma, it’s in the ecosystem. For example, a patient diagnosed with epilepsy and is anxious for a treatment could be matched for treatment by a drug company using AI by matching genetic information and personalized treatments for rare diseases and providing that back to the patient. When that patient receives options in less than a day for a specific treatment that is similar to their genetic profile and being able to choose that is a remarkable improvement for where we are.
This kind of example shows us that data is the life blood of innovation. It’s not just data curated from within, but more importantly, that data across the ecosystem.