How AI Is Accelerating Innovation In Research And Development

Innovation is increasingly intertwined with advancements in Artificial Intelligence. Across industries from healthcare to manufacturing, AI reinvents processes, improves efficiencies, and drives progress. According to a recent Prosper Insights & Analytics survey, 44.1% of people using AI are using it to regularly conduct research. In Research and Development (R&D), AI has the potential to serve as the foundation for the iterative process, fostering more efficient technology development and enabling quicker go-to-market.

Traditional R&D processes are time-consuming and resource intensive. And it’s no secret that consumers aren’t particularly patient. The challenge to keep up with consumer preferences is immense. And constant iteration on existing products means not only major cost to a company, but also huge amounts of time – some of which is spent innovating, but too much of which is spent doing the pre-work: research, determination of patent availability, etc.

With its ability to process vast amounts of data at unprecedented speeds, AI can significantly accelerate research timelines. Whether it's analyzing genetic sequences in drug discovery or simulating complex scenarios in engineering, AI-driven algorithms can sift through mountains of data, identify patterns, and generate insights much faster than human researchers alone.

Jeffrey Tiong is the CEO of Patsnap, the global IP technology company that just reached $100M ARR, with a large language model (LLM) built specifically for IP and R&D professionals. Tiong says that properly implemented AI enhances and improves the R&D innovation and product development process. AI can accelerate the quantity and quality of novel ideas that are successfully launched and commercialized. “With friction and rote workflows eliminated, I see that unlocking new levels of creativity and human ingenuity more broadly,” Tiong said. “Happier R&D scientists, engineers, etc. are a boon to business. And they will become even more valuable to organizations going forward.”

According to Boston Consulting Group’s 2023 Global Innovation Survey, 1,000+ company leaders ranked innovation, R&D, and product development among their Top 10 company priorities. Furthermore, companies that deployed AI saw five times the number of ideas generated, with significant gains in ideas validated and incubated.

Tiong said that when companies and teams are developing new technologies and products, they face extensive legal searches for prior art (which determines whether a new invention is patent-able) and Freedom to Operate (which determines whether one company is infringing on other companies’ intellectual property rights).

Tiong and his team have seen firsthand how customers benefit from AI. They’re harnessing the power of machine learning to empower human ingenuity, while simultaneously improving collaboration within the IP department. After validating the novelty of an idea (and maybe more importantly, eliminating blind spots in novelty checks) using AI tools, teams can use AI to submit their ideas to IP: building detailed, generative AI-assisted invention disclosures for their IP teams. According to Tiong, Patsnap’s LLM is “specifically trained on proprietary, market-leading innovation data. Thanks to domain-specific training, this means R&D teams get answers that are accurate, reliable (with references linked), and far less prone to hallucinations than compared to generic LLMs like ChatGPT and Google Gemini.”

“Over the past decade, the average cost to bring a new innovation to market has increased by 67%. Rising costs of development and dwindling returns on R&D hinder potential innovation breakthroughs because the process has become an expensive and challenging game,” Tiong said in an interview. “AI can remove this friction through more effective, efficient workflows — especially when it comes to de-risking R&D bets, gaining confidence on where the biggest innovation opportunities are, and deciding how to maintain an edge in a particular industry.”

With AI, R&D teams can make data-driven decisions, thereby reducing risks and increasing the likelihood of success. Whether it's identifying promising research avenues or optimizing experimental parameters, AI can guide researchers towards more informed choices. And ultimately, keep consumers happy: according to a recent Prosper Insights & Analytics survey, 34.5% of Gen-Z consumers would rather speak with an AI chat program over a person when online shopping.

Tiong added, “When innovators can use AI to search, review, and validate their invention, plus gain a complete picture of the technological and competitive landscape with automatic alerts, R&D teams are freed up to spend time and energy on what they do best — invent — with better partnership and collaboration with IP to protect inventions at scale.” He added, “R&D teams use AI to eliminate blind spots in novelty checks and see exactly how an idea compares with previous inventions — without cumbersome tasks in their workflow.”

As with all AI tools, AI models in R&D require ethical evaluation: thinking through security, privacy, data protection, and unintended consequences. In order to ensure that researchers and stakeholders are kept apprised of decision-making processes, transparency is key. Tiong noted that it’s essential that AI models in R&D (as in all industries) address bias and discrimination in AI algorithms to ensure safety and fairness.

AI holds immense potential to drive development and innovation in R&D. By leveraging AI's capabilities to accelerate research processes, enhance decision-making, facilitate predictive modeling, enable cross-disciplinary collaboration, and optimize resource allocation, R&D efforts can be propelled towards greater efficiency and effectiveness. “With the breakneck pace of AI breakthroughs, there’s really no predicting how massive a role AI could play in actually generating invention and how the patent system adapts in tandem," said Tiong. “R&D teams that leverage AI to support innovation will achieve outsized business impact; those that fail to adopt AI technologies will be left in the dust.”