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AI & Automation: How Advanced is Adoption in Life Sciences R&D?  

Forward-thinking life sciences organisations have switched on to the benefits of AI-powered automation, to transform their research and development operations and streamline their path to market while enhancing patient safety. But how far have they come, and where are investments currently focused to maximise the benefits? Here, ArisGlobal’s Emmanuel Belabe examines the progress leading pharma organisations are making and highlights the potential that has yet to be unlocked, drawing on new industry-specific research into evolving aspirations for AI and intelligent automation.

ArisGlobal’s new 2024 Industry Survey Report, Life Sciences R&D Transformation: Ambitions for Intelligent Automation & Today’s Reality, was conducted late last year with 80+ organisations, to understand more about the evolving attitudes toward it and the expectations of AI and intelligent regulatory and safety process automation. The international study, which spanned every patient treatment process domain, from CROs to young biotechs, sought to establish where life sciences companies are currently on the automation value spectrum, and where their ambitions lie; that is if rising data complexity necessitates a more ambitious technology use.

As our interview uncovers, the findings inform companies’ evolving use of intelligent automation, including the application of Generative AI; the growing role of real-world data in expediting the safe delivery of new drugs to market; and where companies plan to invest next.

Q: What was the Most Surprising Finding from the Research, in your View?

A: Emmanuel Belabe, ArisGlobal (EB): It’s probably the mismatch between aspiration and reality. At first sight it seems the Life Sciences industry is already quite mature in its use of next-generation automation, enabled by AI advancement. But a closer inspection confirms that this is largely still an ambition rather than a reflection of companies’ current status. Although more than three-quarters of respondents (75%+ of surveyed organisations) say they already use some form of advanced automation within their processes today – up 13% from 2022 and just 5% in 2020, only 8% have applied advanced automation in “all” or “most” of their processes at this point. (By advanced automation, we mean the adoption of artificial intelligence (AI) and machine learning (ML).)

Q: What Does this Suggest to you?

A: It means that awareness of the opportunity for AI-based transformation of knowledge work and essential processes is high and growing, which is promising. Indeed, more than seven in ten respondents went on to express plans to expand business process automation over the next 18 months. The overarching trend is to move on from rudimentary, fairly crude process automation, toward something more material and a capability that is sophisticated, which tangibly alleviates the pressures on Safety and Regulatory professionals’ time.

Q: Can you Clarify the Difference?

A: Automation technology has come a long way from the early days of simple robotic process automation (RPA) solutions. These have harnessed optical character recognition and rules-based workflow to identify and manage standard documents and structured data in a fairly basic way. Today, thanks to intelligent, AI-powered automation, and the ability to identify and analyse all kinds of data, it’s become possible to distil all kinds of new actionable insights – irrespective of how colossal the volumes, or how diverse the range of sources. As AI and ‘deep learning’ solutions advance in line with the scale and sophistication of available data, life sciences R&D organisations are growing more ambitious in their ability to harness data and its insights in ever smarter ways. That could be to hone decision-making, expedite and remove cost from processes, and deliver important treatments to patients more efficiently and affordably.

Q: What has Held Pharma Companies Back Until Now, with all of this?

A: The challenge previously has been how to assess, interpret and reliably harness vast amounts of unstructured data (the kind that exists in documents, in emails or on paper, rather than in a searchable database) which represent immense potential value if only Safety, Quality, Regulatory and Clinical teams had the resources to process it all.

This is where the latest knowledge- based intelligent automation technologies (including machine translation and a growing range of artificial intelligence capabilities, such as Generative AI and deep learning) come in. These next-generation capabilities make it possible to distil reliable, actionable insights from across that wealth of data in all its forms. It was the pace of companies’ transitions to intelligent automation that we were particularly keen to identify in the 2024 ArisGlobal Industry Survey Report.