Industry Outlook 2026: Biopharma Braces for Economic Upheaval While Eyeing AI-Driven Transformation (Part 1)

*Full transcript available below

The biopharmaceutical and bioprocessing industries enter 2026 navigating a dual challenge: mounting political and economic uncertainty and the disruptive potential of AI, notes Eric Langer, president and managing partner of BioPlan Associates, in an interview with BioPharm International®. Despite headwinds, industry fundamentals remain “very robust and strong,” according to Langer, who has tracked sector trends for 35 years.

Recent and pending US tariffs, along with wider geopolitical tensions, are slowing investment and complicating long-term planning, he notes. In the middle of these disruptions, companies face difficulty modeling supply chain scenarios while key policies may soon be reversed. A potential US Supreme Court decision to overturn tariffs, for instance, could rapidly reshape sourcing and manufacturing strategies, Langer emphasizes. For now, many organizations are “just putting on the brakes,” he says, creating pent-up demand that may be released once conditions stabilize.

“AI is really moving very rapidly across the board in biopharmaceutical manufacturing,” Langer adds.

He further points out that, even as firms tread cautiously on capital decisions, they are exploring AI across the value chain—from discovery and process biochemistry to downstream, clinical, and commercial-scale manufacturing. Early priority areas include continuous bioprocessing, quality management systems, and supply chain logistics, where predictive modeling and optimization could reduce risk and cost.

How will AI transform biomanufacturing and bioprocessing?

According to Langer, near-term adoption of AI is expected in regulatory and documentation workflows, which have traditionally been labor-intensive and error-prone. Opportunities include AI-generated standard operating procedures (SOPs), automated deviation categorization, SOP version tracking, and AI-assisted analytical protocols. Automating batch-release documentation, validation, and inspection could free skilled staff for higher-value tasks while strengthening compliance, Langer highlights.

The industry’s ability to capture these gains will depend on workforce readiness, Langer explains, which includes retraining staff to manage AI-enabled systems and integrating these tools within a tightly regulated environment. As economic and political uncertainty delays major investments, many organizations are using this period to evaluate AI pilots, refine data strategies, and prepare for broader deployment once the macroeconomic climate becomes clearer, he states.

Transcript

Editor's note: This transcript is a direct, unedited rendering of the original audio/video content. It may contain errors, informal language, or omissions as spoken in the original recording.

Speaker 1

Thanks very much for the opportunity to talk with you about some of the issues and trends that are affecting the biopharmaceutical and bioprocessing industry today, obviously, we're experiencing a lot of shifts. Even post covid, we're seeing a lot of changes in the way that the industry is responding to the both the investment and political climate. Bio plan has been doing analysis of this industry for 35 years, and this is an opportunity for us to see how today's Economics and Political events might be compared to others over the last, actually, three decades. So I think we're in an interesting position to be able to compare where we are today and where we might be going. My role here is the managing partner at bio plan associates, is to make sure that the data that we collect is relevant to the biopharmaceutical industry

that's a really good question, and it's obviously very timely. The industry today is a very robust and strong based on what's been experienced over the last number of decades. How it responds to the current economic slowdown is not significantly different from how it's responded to others. When we look at today, though, the political uncertainties are being developed and created by some of the tariff issues that we experienced, as well as some of the geopolitical trends and shifts that we're seeing in global disruptions. Normally, what that creates economic risk creates investment slowdown. It doesn't mean people are stopping. In fact, that often we've seen that when we see these economic slowdowns in the past, what happens is you end up creating pent up demand for this growth that will occur after the economy or the climate changes and reverts to a more stable situation, but to the point the impact of the 2025 US tariffs, we're seeing a slowdown in the way that the the industry is responding to those tariffs, the the inability to predict and the requirement for more Time and the inability to create the flexibility to adjust means people are just putting on brakes. Here's an example. The US Supreme Court may overturn tariffs coming up shortly. If this happens, how will that affect supply chain strategies? Again, this uncertainty is really just creating an investment caution situation in climate so climate

AI is really moving very rapidly across the board in biopharmaceutical manufacturing, it's relatively difficult to identify where AI is actually being implemented, partly because this is a a regulated environment, but also, because it's so new and complex, there are areas in pharma, as well as biopharma, where AI actually can enhance the overall process. These range from everything from discovery and biologic to process biochemistry, downstream, clinical scale, commercial scale applications, continuous bioprocessing, quality management systems. These are some of the areas where we will see adoption. I don't know how much adoption we're going to be seeing today, but within two years, clearly some of these are going to start to emerge. Regulatory and documentation, automation is most definitely on the on the books. Validation areas, inspection areas are definitely involved, everything from drug product and fill and finish all the way to supply chain logistics, all of these are in play. The reason for that is the potential impact that AI has in bio processing over the coming years. Include areas such as simplifying SOPs in document writing that's an exceptionally labor intensive process. Ai generated documentation and deviation categorization, or even SOP version tracking. All of this stuff sounds pretty arcane, but it's really critical, and AI can most definitely support that, analytical protocols, AI assisted document generation, simple as that process analytical technologies or other in process analysis, all of this needs to be addressed. Quality analytics, process optimization, predictive modeling, all of this can and will be implemented at some point. Question is, how is it going to be implemented, and do we have the staff capable of taking the current methodology and moving from, say, the manual batch release documentation to automation of batch release documentation.