Thermo Fisher's Kane Says AI Is Reshaping How CDMOs Add Value

For Anil Kane, PhD, the question facing small biotech companies isn't just which CDMO can run the work, but which one can make their asset worth more by the time it's done.

"I think creating value and enhancing the value of their assets is something that a small biotech company should be looking at while selecting a CDMO player," says Kane, global head of technical and scientific affairs within the Pharma Services Group at Thermo Fisher Scientific. He shared that view, along with a look at how the company is weaving artificial intelligence (AI) into its CDMO workflows, in a conversation with out sister publication BioPharm International at the 2026 BIO International Convention (BIO 2026, June 22–25 in San Diego).

Thermo Fisher has built strategic partnerships to support AI integration across its service lines, Kane says, pairing outside expertise with in-house machine learning algorithms and predictive models trained on proprietary datasets the company has accumulated over years of development work. Those AI applications now touch preclinical and clinical phases, quality management systems, supply chain operations, and contract processing, essentially the full breadth of CDMO work, from early development through commercialization.

So what should a biotech actually weigh when picking a CDMO for AI-enabled drug development? Kane points to regulatory experience, a demonstrated quality track record, and scientific depth across modalities. He's particularly bullish on the advantage of working with a single organization that can carry a program across small molecules, biologics, messenger RNA, and viral vectors through the full development and commercialization spectrum, rather than stitching together multiple specialized partners.

On where AI itself stands, Kane says it's already producing measurable speed and efficiency gains at several development stages, though he's candid that some applications still need time to mature. The near-term emphasis, he says, has been on what he calls the "low-hanging fruit": more accessible use cases first, with deeper integrations to follow. For biotechs weighing out-licensing strategies in particular, he adds, a strong CDMO partner can move the needle directly on asset value.