The innovation disconnect: Can artificial intelligence finally align academic discovery with industry needs?

Academic and industrial setups have lived in two different worlds since time immemorial. One world brainstorms idea and publishes papers while the other manufactures and ships products. Every few years, it is declared that these two worlds are about to merge and collaborate and every few years the gap turns out to grow wider than expected. We keep pondering over whether this problem is going to be fixed or will become newest excuse to talk about it again.

Most academic discoveries never move past early-stage research. This gap is often called the Valley of Death; the point where promising ideas fail to reach patients.[1,2] It does not always mean that the research idea or outcome was bad. Rather, sometimes, it is only because nobody translated it into something a company could manufacture or sell. Meanwhile, industry often works on those problems that pay the bills the ongoing quarter. Risky and speculative science does not fit neatly into a product roadmap. Hence, the two sides drift apart. Academic institutions reward publications whereas industry invests time and money into regulatory approval, scalability, and patient outcomes. This fundamental mismatch slows down the whole process of moving discoveries from the academia labs to the bedside.[3] This is not a new complaint. Tech transfer offices, industry-sponsored labs, etc., exist because our seniors somewhere tried to bridge this gap. However, results have been mixed at the best. The academic research base is strong in India with ample human resources and government aided institutions, backed by agencies such as the Department of Biotechnology and the Indian Council of Medical Research. However, only a few academic discoveries become approved therapies. This is due to weak translational infrastructure, late involvement of clinical pharmacology, and poor alignment with regulatory pathways.[4] One such example of an early collaboration is Chimeric Antigen Receptor (CAR)-T cell therapy. NexCAR19™, the first CAR-T product made in India, was possible due to collaboration between IIT Bombay, Tata Memorial Center, and ImmunoACT. It won regulatory approval and led a path to show that this kind of therapy is possible even with limited resources.[5] This shows the synergistic effect of academia and industry. Getting industry involved early means clinical pharmacology principles of dose optimization, pharmacokinetic-pharmacodynamic modeling, and good trial design, can be built in from the start, instead of being added later.[6,7]

Now bring artificial intelligence (AI) into the picture, and there is a real argument that something might actually be different this time. AI is good at reading everything faster humans might take a lot of time to read. It can scan thousands of papers, patents, and product roadmaps and spot where a lab’s discovery quietly matches a company’s unsolved problem. That kind of matching used to take a volume of person years of networking and luck in the recent past as well. A model can facilitate it in lesser amount of time and may lower the cost of translation as well. An academic paper full of jargons can be turned into a plain-language summary which may be actionable by a business team. A rough prototype idea can be turned into a simulation or a working demo faster than ever. The middle steps that used to require a whole team of specialists is getting cheaper with the use of AI-aided tools. AI changes the economics of risk. Companies avoid academic partnerships partly because the testing of hypothesis is slow and uncertain. If AI tools can quickly test whether an idea is even feasible by running simulations, generating early data and raising red flags; some of that uncertainty is caught earlier in time.[8] Ascertaining faster failure is useful in terms of fewer wasted years and money.

There are reasons to be sceptical before getting too excited. AI does not guarantee a culture change. It is only a tool to be used judiciously. The core reason academia and industry do not talk to each other is the lack of incentive for each of them in collaborating. It is not that they are totally negligent of information from other worlds. Professors get rewarded for publishing, not for productizing while companies get rewarded for quarterly numbers.

Even if AI perfectly matches a company’s need with a lab’s discovery, someone still has to negotiate Intellectual Property rights, funding, liability, and control. The human involvement is the bottleneck for this match making. Unguided AI-driven matching could push research toward whatever is easiest to summarize and commercialize. Some of the most valuable science may look useless for 20 years before it suddenly healthcare arena starts to understand its value. If AI systems are optimized to find quick wins for industry, they might quietly steer researchers away from the slow, strange ideas that turn out to matter most.

I believe that this collaboration model with AI can also work somewhere in between the hype and the skepticism. AI can almost certainly get better at surfacing connections, translating jargon, and speeding up early-stage testing. However, AI cannot fix incentives, trust, or long-term thinking. Those are human and institutional problems. If universities keep rewarding quantity in publishing over building, and companies keep rewarding this quarter over next decade, AI will just become a faster way of doing the same disconnected dance.

Maybe the more honest question should be “Are academia and industry actually willing to be aligned or do they just want a better excuse for staying apart?” Although AI can hand both sides a map, it cannot make them walk toward each other without their willingness.

Let us not become too optimistic or pessimistic for this problem too soon. Let us ponder whether this is a genuine turning point, or another wave of optimism that will run into the same old walls?