Pharma regulatory workloads are now outpacing company growth, fuelling investment in AI-boosted regulatory process automation. ArisGlobal’s director of product management’s Agnes Cwienczek, reports on these and other top-level findings from a new Censuswide survey of senior life sciences regulatory professionals.
ArisGlobal
A new survey from Censuswide with 100 senior regulatory professionals in US pharma and biopharma organisations, confirms that regulatory obligations have soared over the last five years, with three in five (60%) saying the scale of increase is far beyond that of normal company growth - a trend set to continue over the next five years.
Specific regulatory process challenges include 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 data quality checks/assessing submission readiness.
A lack of qualified people was much less likely to be registered as a 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.
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.
Causes of hesitation
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what might be holding back initial or further investment in AI for Regulatory purposes, respondents most commonly cited outdated existing IT landscapes (45%); a belief that risks currently outweigh the benefits (44%); and inadequate availability/quality/consistency of data or content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 cent16% 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.
Regulatory AI aids will become paramount
To conclude, the survey asked about 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 address all of these challenges over time.