Regulatory workloads now far exceed the pace of company growth, but assigning more people to the tasks isn’t an option, which in turn is driving up investment in AI-powered regulatory process automation. That is the finding of a new Censuswide survey of US pharma/biopharma senior regulatory professionals. Yet mindsets around AI in a regulatory context have some catching up to do to really propel adoption. ArisGlobal’s Renato Rjavec reports.
In a new survey conducted by Censuswide with 100 senior regulatory professionals in US pharma and biopharma organisations, 97% said they had seen their regulatory obligations soar over the last five years, with three in five (60%) putting the increase beyond what would be expected as the result of company growth. Expectations are that this growth in work throughput will continue over the next five years: 95% indicated this would be the case; 41% predicting next increases would be significant.
The various regulatory process challenges are numerous, including excessive time spent producing submissions/dossiers; maintaining labelling compliance; inputting data/documents into IT systems; verifying submission correctness/completeness; performing regulatory impact assessments; and locating data or documents in existing IT systems. More than a quarter of the research base indicated each of these issues. Further barriers to efficiency include responding to agency queries; inadequacy of current IT systems; and time lost to administrative tasks (such as data quality checks, and assessing submission readiness).
Interestingly, a lack of qualified people was least likely to be registered as a serious challenge or concern, suggesting that preferred strategies do not involve allocating more people to processing regulatory workloads. Rather, pharma and biopharma regulatory functions are looking to smarter use of technology to ease the impact of their rising workloads, while ideally enhancing the output and impact of existing processes.
The case for AI in a regulatory context
By use case, almost all respondents could see direct potential for AI in transforming labelling compliance and deviations maintenance; capturing, searching, filtering the latest regulatory requirements; automating the intake of Health Authority interactions; automating regulated content translations for different markets; automating the authoring of responses to Health Authority queries; suggesting improvements to submissions/dossiers; performing regulatory impact assessments; authoring submission documents; automating document summarization; and generating entire regulatory submissions.
Although specialist AI tools and applications for targeted regulatory use cases are only now coming to market, over a third (35%) of respondents claimed to be using AI for regulatory purposes in some form already, while 42% plan to invest in the next 18 months. A further 15% are looking at a timeframe beyond that, but do also have plans to roll out AI within the regulatory function.
While no respondents were ignoring AI entirely, 6% were not yet convinced by the technology’s potential for regulatory purposes and had no current plans to invest in AI.
Barriers to adoption
Asked what might be holding back initial or further investment in AI for Regulatory purposes, respondents most commonly cited outdated existing IT landscapes (45%); a belief that risks currently outweigh the benefits (44%); and inadequate availability/quality/consistency of data or content resources to derive the value from AI (42%).
In addition, 39% of respondents felt the technology remained too immature/unproven; similarly, that the tools do not exist today to address their particular regulatory pain points. Sixteen per cent blamed a lack of trust in AI currently. This was ahead of budget challenges: only 15% named a lack of budget as a barrier to AI investment.
The research also identified the factors most likely to convert interest and inertia into active projects. Here, respondents most commonly cited the discovery that their competitors are using the technology (41%); soaring workloads/continued resource pressures (40%); advances with the technology/its being more mature and proven (36%); the availability of specific tools geared to the tasks regulatory teams find most challenging or expensive (35%); and relevant IT systems becoming easier and more affordable to deploy (33%).
Beyond those drivers, 31% said updating their upgrades to existing IT set-ups (making it possible to use AI reliably) would prompt investment. Endorsement or recommendation of AI by regulators would inspire investment also for just under a third of respondents.
Financial constraints do not feature
Budget constraints did not appear to be a particular barrier to investment plans: just 18% indicated that the availability of new budget would unlock AI investment.
That budget constraints are not a major barrier is encouraging, because hesitancy linked to “a lack of confidence to deploy” is surmountable and readily addressable now. AI technology, including Generative AI (GenAI) is maturing and advancing at an accelerating pace, and specialist applications for target use cases in a life sciences regulatory context are being actively developed and piloted today, showcasing what is possible. This is in keeping with Gartner’s prediction that, by 2027, more than 50% of the GenAI models used by enterprises will be specific to either an industry or business function, up from just 1% in 2023[1].
Regulatory AI aids will become increasingly essential
Finally, the research sought a sense of respondents’ expectations of AI in a regulatory context over the longer term. Almost half (48%) of respondents agreed that, in time, AI would transform a lot of routine regulatory work and considerably streamline processes. Over 2 in 5 (43%) felt AI would drive up accuracy and quality in the information they produce for regulators and patients. Almost 2 in 5 (39%) respondents believed AI would be critical to the regulatory function’s ability to keep pace with market demands. And over a third (35%) of respondents agreed that AI would save a lot of time and money.
This supports the finding above that pain points are multiple and diverse, and suggests that ideally an investment in the right AI capability should ultimately enable all of these to be targeted.
The full research report is available to download at https://www.arisglobal.com/resources/regulatory-industry-survey/
[1] 3 Bold and Actionable Predictions for the Future of GenAI, Gartner, April 2024: https://www.gartner.com/en/articles/3-bold-and-actionable-predictions-for-the-future-of-genai
