The question is no longer whether research “matures” enough to reach the market, but how we design systems capable of detecting opportunities, accelerating decisions, and reducing friction on the path from paper to product.
But the true change is not in ‘having agents,’ but in orchestrating them: integrating them into real processes with reliability, traceability, and strategic criteria. Because in technology transfer —and even more so in deep-tech— speed without control is not an advantage: it is a risk.
The new paradigm of transfer orchestration
The traditional transfer model —with specialized offices (TTOs/KTOs) managing intellectual property, contracts, and alliances— remains essential. What changes with agentic AI is the ‘how’: we are moving from systems that provide occasional help to systems that work continuously, connecting weak signals with decisions.
1) Proactive detection of opportunities
Agents integrated into innovation flows can track scientific literature, patents, and market signals to identify emerging technologies and application spaces. This approach aligns with a broader trend: AI is entering the ‘industrial core’ (manufacturing, logistics, operations) as a lever for productivity, not just as a conversational layer.
2) Faster (and more systematic) feasibility assessment
Instead of relying on ad-hoc analysis, agents can help structure scenarios: market hypotheses, regulatory risks, competition, and adoption paths. Note: this does not eliminate uncertainty; it makes it manageable if combined with human supervision and clear criteria.
3) Hybrid teams with constant translation
In pharma, for example, AI tools are already being used to accelerate critical operations (site selection, recruitment, regulatory documentation), reducing time and costs in information-intensive tasks.
This type of adoption is driving the creation of teams where R&D, clinical, regulatory, and business departments share a ‘bridge language’ supported by AI —not replacing the expert, but reducing friction between silos.
Regarding the leap to ‘end-to-end production’ in logistics and operations: there are forecasts anticipating a strong advancement of agentic AI in 2026, but it is advisable to treat it as a trend/projection, not as a universal fact.
Implementation challenges: trust, ethics, and governance
The adoption of agents in technology transfer is not merely a technical shift; it is an institutional one. In this arena, Europe is establishing a clear “before and after” through its regulatory and strategic framework.
1) Governance and accountability
In practical terms: the challenge is not just “using agents,” but answering uncomfortable (and necessary) questions:
- Who is responsible if an agent gives a bad recommendation?
- How is its reasoning audited?
What records and evidence remain?
2) Quality of knowledge (ground truth)
Agents amplify what they consume. If the foundation is weak, the system scales errors with efficiency. Competitive advantage is starting to look less like ‘having exclusive data’ and more like ‘having robust processes to validate, audit, and improve the quality of what the agent uses’.
3) Cultural alignment: academia vs. industry
AI can translate, summarize, and accelerate, but it does not eliminate differences in incentives: publication vs. market, exploration vs. return, long timelines vs. urgency. In practice, AI does not replace the human: it makes it more visible where alignment is lacking… and forces us to design it.
Conclusion: the imperative is not to have agents, but to design their adoption
Agentic AI can turn transfer into a more continuous system, more sensitive to signals and less dependent on individual heroism. But its real value will come from the adoption strategy: governance, quality, traceability, and hybrid teams.
“In this context, the scale of investment also speaks: Gartner forecasts that global AI spending will reach $2.52 trillion in 2026.
That means the discussion stops being futuristic: it is operational. The future is not just about managing what we know, but about designing systems capable of learning what we do not yet know —without losing control, trust, or responsibility.
