CFOs from Biogen and Johnson & Johnson told finance professionals at the Life Sciences Accounting & Reporting Congress in Philadelphia that AI is already cutting drug development timelines and improving forecasting, but its value hinges on strategic deployment, strong governance, and clean data - not blind adoption.
Robin Kramer, CFO at Biogen, pushed back against the idea that AI is overhyped. "I would say it's not hyped," she said. "If you are strategic in how you're thinking about how to deploy it in the organization, from a finance perspective, my preference is to find whoever has the best technology platform for what I'm looking to solve." For Biogen, that means using established platforms like Coupa for contract-to-pay processes and relying on their native AI capabilities as they mature, rather than building custom tools from scratch.
Strategic deployment beats permanent pilots
Custom AI work at Biogen is concentrated in areas like forecasting and trending, where the company is automating what had been a manual analyst's job. Kramer acknowledged a common trap: companies stuck in endless testing. Biogen created a dedicated team within IT to govern AI deployment, set policies, and push initiatives through a managed pipeline. "We're trying to do this in a way where we're not in permanent pilot zone, where you're not yielding benefits from the deployment," she said.
Salvatore Giovine, CFO of Johnson & Johnson's Innovative Medicine North America, echoed that AI's potential is genuine. "I don't think it's a hype. I think it's real," he said. His team is integrating AI into commercial functions like demand planning and forecasting, and he sees strong upside - but also areas where enthusiasm has outpaced financial discipline.
"I think we've kind of come out of the last two to three years where it's just AI, just put AI in. We have to AI, AI, AI," Giovine said. "I agree, but there's areas where I think the return profile is actually potentially poor as well."
Time-to-market advantage
In biopharma, the real payoff often comes from speed, not cost savings. Kramer described how AI-assisted medical writing cut significant time from preparing regulatory submission packages, shaving weeks off readout dates. "The sooner you can get the drug to market, it's going to have an impact on your top line," she said. "Maybe you leapfrog somebody else from a timing perspective."
Master data: the unglamorous prerequisite
Both executives pointed to master data management as the foundation that determines whether AI delivers. Giovine recalled a former controller describing it as "just about the least sexy thing we can do as finance professionals. However, if we don't do some of that, there's a real sort of rate limiter in terms of what we can do." Johnson & Johnson is consolidating systems into a single enterprise resource planning framework through its "Signify" program, a move Giovine believes will remove barriers and allow AI to accelerate.
"You have to invest in parallel, and particularly you have to build competencies and skill sets in the organization in parallel," he said. "You can't get to that place over the next couple of years and just be completely flat-footed." For finance leaders building those competencies, the AI Learning Path for CFOs offers structured guidance on weaving governance and strategy into AI adoption.
Augment, not replace
When asked whether AI replaces finance professionals, Kramer was direct: "I would say leverage, not replacement." She pointed out that Biogen requires mandatory human review of all AI-generated information for ethics and accuracy. "I view it as another enabler for us to do our jobs more effectively and better."
Giovine agreed but noted that some displacement is likely in highly routine, rules-based tasks. His advice to early-career professionals: "Build the skillsets around decision support, data analysis, data synthesis, and then learn the tools that can augment the human steps to get to a better outcome."
Why this matters for finance professionals
The message from two biopharma CFOs is that AI in finance isn't a shortcut. Real gains come after the unglamorous work of cleaning data, setting governance rules, and defining clear use cases with measurable returns. For CFOs and their teams, the near-term priority is not chasing the latest model, but ensuring the data foundation, review processes, and internal skills are in place - so that when AI scales, it actually sticks.
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