![[Adobe FIrefly]](https://www.drugdiscoverytrends.com/wp-content/uploads/2024/04/Firefly-Visualize-a-photo-realistic-HD-pharmaceutical-laboratory-image-with-concentrated-workers-in_-e1712103436381-300x195.png)
[Adobe FIrefly]
“In terms of AI adoption, almost all of the companies we’ve been talking to about cloud are also discussing data and AI in some shape or fashion,” said Ram Viswanathan, CTO of AI at Rackspace.
So much interest, so little (AI) talent
Research from a year ago indicated significant interest in generative AI (genAI) across industries. The Generative AI Executive Survey from Capgemini April 2023 found that generative was a board-level topic of discussion at 98% of pharma and healthcare organizations. Across industries, just under three-quarters of respondents felt the benefits of the technology outweighed its risks.
Regardless of budget cuts, AI talent remains scarce, which remains a critical ingredient for mature AI deployments. A total of 86% of respondents in the Rackspace survey said they had attempted to recruit people with AI/ML skills over the past year.
Ram Viswanathan
“Talent is definitely one of the current barriers. There is a war for good talent,” Viswanathan said. The talent shortage has two aspects with the first related to topics such as data normalization and data science. The second aspect refers to applying “AI from a domain perspective, such as healthcare or discovering new drugs,” he added.
The AI talent gap: It’s not just pharma’s problem
The topic isn’t unique to pharma. Across industries, a total of 58% of respondents named lack of capability/talent to manage data effectively as an issue.
But that doesn’t mean companies aren’t seeing progress. Already, 44% of pharma IT respondents credit AI with providing “substantial” benefits.
On the technological front, Viswanathan stressed the significance of data management to lay the groundwork for more mature AI projects. “You need to first get your data act together. That’s your first step.”
AI projects tend to fall into three buckets: experimenting, incubating and industrializing. “Depending on where they are in the journey and their data readiness, the productivity gains they’re seeing can be internal or customer-facing,” Viswanathan said.
The AI maturity journey: From productivity to business model reinvention
“In terms of maturity for using AI in real production, we see three areas: optimizing internal productivity, enhancing customer experience, and using data and AI to develop new processes or business model reinvention,” Viswanathan said. The third bucket is “more advanced” and goes beyond just “generative AI” which has made waves over the past roughly 18 months given its democratization of everything from coding to content creation. “For most companies, including those in the pharma industry, the focus is on enhancing employee productivity,” Viswanathan said.
Leadership awareness is a core element, Viswanathan said. Leaders need to be tuned into the AI conversation and understand the urgency. Some executives put AI on “the back burner” as a result of financial or other pressures. Yet organizations that want to be on the leading edge and create a differentiator with AI are “ramping up on both reskilling and external hiring,” Viswanathan said. “So those two things — upskilling existing staff and bringing in outside talent — are both happening in parallel to address the AI talent gap.”
The AI arms race: Building moats and barriers
While adopting a technology rapidly requires a careful strategy, organizations that adopt AI more aggressively could be able to “use the technology as a moat,” Viswanathan said. “They can create barriers for others to come in, come up with new business models, new drugs, and new ways of connecting with the clients.”
But the path to gaining an advantage with an emerging quickly-moving technology like AI requires a concerted strategy that unfolds over phases. It requires talent. “Organizations are addressing this in a crawl, walk, run approach through retraining programs,” Viswanathan said. “Every organization we’ve spoken to has some kind of training initiative underway, though the quality varies. Externally, companies are also bidding for good talent. It will probably take a couple of cycles for organizations to be fully ready.”
Filed Under: Drug Discovery, machine learning and AI
