Artificial intelligence is no longer an emerging technology in food and beverage—it is becoming ubiquitous. As AI accelerates research, formulation, quality assurance, and supply-chain decision-making, it is also compressing the timelines that once defined intellectual property strategy.
That tension sat at the centre of CFIN’s recent webinar on Intellectual Property in the Age of AI. The session brought together perspectives from academia, legal practice, and industry, featuring Steve De Brabandere and Dr. Maria G. Corradini (University of Guelph), Lorelei Graham (Bennett Jones LLP), and Harjeet Bajaj (Savormetrics).
Together, the panel examined how food innovators should adapt their IP thinking as AI becomes embedded across the sector—and what now determines competitive advantage when iteration cycles are measured in months, not years.
Below are some of the highlights that emerged from the discussion.
Four Key Takeaways
1) The IP clock is moving faster
One of the most consistent signals from the panel was the shrinking window of IP relevance. Where companies once assumed multi-year advantages from novel processes or technologies, AI-driven iteration is now narrowing those gaps dramatically.
This shift does not mean IP no longer matters. It means timing, execution, and integration matter more than ever. Patents filed too late, or disconnected from operational reality, offer limited protection in fast-moving markets. Conversely, companies that combine speed, governance, and clear commercialization pathways are better positioned to extract value before competitors catch up.
2) Data governance is now an IP strategy
Across the discussion, data emerged as both the most valuable and most vulnerable asset in AI-enabled innovation.
Panelists highlighted persistent gaps in how food companies manage:
In many cases, these risks are not technological—they are organizational. Agreements that pre-date AI adoption often fail to specify how data can be reused, commercialized, or retained once models are trained. In an environment where AI systems continuously learn, those ambiguities can quickly translate into lost advantage.
The implication is that data governance is no longer a compliance exercise. It is a core component of IP protection.
Patents, trade secrets, licensing structures, and contractual controls each serve different purposes—and none are sufficient on their own. In practice, effective IP strategies increasingly align protection mechanisms with how value is actually created and defended in the business.
For many food companies, that means focusing less on owning algorithms—which are rapidly commoditizing—and more on controlling application context, operational know-how, and customer-specific learning.
From product quality to food safety to legal defensibility, panelists stressed the importance of validation, oversight, and accountability. AI can compress timelines and expand analytical capacity, but it does not replace responsibility—particularly in a sector where trust, safety, and reputation are foundational.
Many of the themes explored in the webinar are also examined in greater detail in CFIN’s recent whitepaper, Artificial Intelligence and the Future of Canada’s Food Sector, which outlines how AI is already reshaping productivity, security, and competitiveness across food safety, supply chains, automation, product development, and food waste reduction.