Pharma dips toe into tech adoption, but is it deep enough?

Ben Hargreaves looks at the uptake of enabling technology by the pharma industry. A recent report highlights how broad the adoption has been, but also unpicks the limiting factors in getting the most from the tech.

The advance of technology is rapidly transforming the pharmaceutical industry. Artificial intelligence and machine learning dominate headlines, but other areas, such as R&D data platforms, robotics and automation, and connected laboratory instruments are arguably more important for day-to-day operations.

As the breadth of engagement with technology increases, the investments being made in parallel are also rising. According to McKinsey, in the past three years, the top five pharma companies have made more than 50 investments in digital health companies and assets; in a further survey, the company found that respondents leading in digital and analytics planned to grow spending by 10% to 15% over the next three years. In some respects, this pivot to greater investment is necessary because of the big tech companies that are quickly entering the space, holding the advantage of being set up to effectively utilise the reams of data produced in healthcare.

A recent report from Benchling aimed to provide an overview of pharma’s progress in the adoption of tech, and also to highlight the barriers to greater implementation and success. To deliver the insights, Benchling surveyed 300 respondents from various companies across the industry, with 59% being based in R&D roles, and 41% in IT, digital, or informatics.

Broad tech adoption

What is made clear by the report is that the pharma industry is actively engaged in deploying new technology to help meet the long-term challenges it faces. Of the respondents to Benchling’s survey, 70% said that their company have adopted an R&D data platform, with the largest companies reporting 79% adoption; 63% of those surveyed said that they employed robotics and automation; artificial intelligence and machine learning saw over half (59%) of companies report usage; and 53% reported that at least three out of five of their organisations’ instruments are directly connected to some type of software.

However, Software-as-a-Service (SaaS) utilisation was found to be on the lower-end of adoption, with just 18% of respondents stating the use of such software, which Benchling suggested indicated a high level of legacy systems still being in use across the industry.

According to the report, the high adoption of R&D platforms is due to the complexity and amount of data generated during the research process. As a result, 54% of respondents had built out their own custom R&D data platform, whilst 74% indicated that they were also using at least one other service provider. Of these additional service providers, Microsoft Azure was the most popular (57%), followed by Amazon Web Services (48%) and Google Cloud Platform (43%).

In terms of the adoption of other technology, a unifying factor is the tendency for the larger companies to be at the forefront – a logical state of affairs, given the difference in financial firepower. In the use of robotics and automation, more than twice as many larger companies are using this technology compared to smaller organisations. The same was also identified with the use of AI, where more than a quarter (27%) of large companies had adopted the tech, whilst only 14% of respondents from smaller companies said the same. This gap is likely to grow, with 55% of respondents from larger companies indicating that it would be very or extremely important to adopt the tech in the next 12 months, against 36% of respondents at smaller companies.

Gains to be made

The adoption of the various technologies is being done with a goal in mind: driving efficiencies across the business. Benchling broadened the areas of influence down to five categories: speed, success, scalability, quality, and cost. Of these potentials for improvement, the respondents rated quality and speed as the areas where gains could be achieved. Cost was considered to be important, though comparatively lower than the other factors mentioned, with 60% believing it to have a strong impact over the next 12 to 24 months.

Deloitte reported in 2022 that life sciences digital and analytics specialists had estimated that its use drove bottom-line improvement by between 5% and 15% in the previous five years. This resulted in Deloitte estimating an annual impact of digital technology’s use to be worth between $6 billion and $9 billion. However, the company estimated that the full application of digital solutions could be worth anywhere between $130 billion and $190 billion to the industry, suggesting major room for improvement.

Barriers to better use

Expanding upon the difficulties facing full application, Benchling highlighted the key challenges noted by respondents. This was broken down into three major issues to address: low availability of skilled talent; lack of built-for-science tech; and change management and cultural barriers.

The first of these is self-explanatory – as respondents noted that employing any of the technologies mentioned has been limited by the lack of skilled talent available for recruitment. On the second issue, which is focused on the lack of software built specifically for science, the respondents specifically noted that off-the-shelf tools for scientific AI not being available was one of the barriers faced. Lastly, a major challenge was an apparent disconnect between R&D and IT teams when it comes to organisation culture and change management. An example of this was found to be connected lab instruments, with the R&D teams four times more likely than IT to state that change management was a barrier to adoption.

More broadly, this was emblematic of a wider divergence in views on the adoption of enabling tech between R&D and IT teams. According to Benchling, IT teams consider the importance of investing in such technology as more important than their R&D counterparts. When asked about the importance of investing in tech, IT respondents consistently rated investing in all platforms in the next 12 months as very or extremely important. By comparison, on each platform mentioned, fewer than half of R&D team respondents agreed to the same premise.

The report authors conclude: “The findings are a clear call to action — we need to bring more skilled talent into the industry, both individuals who have a deep understanding of emerging tech and others who have a strong desire to build new, fit-for-purpose scientific software that can delight scientists and free up IT teams. Second, companies also need to take a logical step of fostering stronger alignment between their R&D and IT organisations — including rationalising divergent tech priorities and having an honest dialogue on the real barriers to adoption, such as organisational culture and change management.”