Artificial Intelligence (AI) is driving automation across industries and is quickly becoming a key engine for innovation. In fact, the World Economic Forum recently identified AI as a leading factor in labor market disruption over the next five years. This shift is expected to create new roles in big data analytics, cybersecurity, and business operations.
However, developing new tools and services to support this evolving workforce will require even more research and development (R&D). These activities themselves are being transformed as AI and machine learning (ML) are used to streamline processes.
So, how can companies use AI responsibly and effectively to optimize their R&D?
“AI is like a layer cake”
“The main challenge in R&D is always about speeding up innovation—but at what cost?” explains Suresh Joshi, Boast AI’s VP of Engineering, in a recent #InnovatorsLive session and podcast.
Suresh adds, “But also, how can you accommodate talent to enable all of that?”
As Suresh and Boast AI’s VP of Product, John Can Karayel, put it: “With AI and R&D, it’s like a layer cake.”
“You start with the data layer, then move to the insights layer, and finally the predictive layer. Right now, we’re seeing a complete transformation in how the industry can scale at the predictive layer,” Suresh explains. “This predictive layer will not only help you adjust course faster, but also forecast what’s coming next.”
As is often the case in our data-driven world, scaling up remains the biggest challenge to taking action.
John explains, “We’re dealing with more data than ever before, and as the volume grows, so does the complexity—taking into account internal and external factors, customer data, and more.”
In short: For companies to fully benefit from AI’s promise—gaining deeper insights and anticipating next steps—they first need to strengthen their own ‘data culture’ and capabilities.
“When you look at the level of data literacy and culture within organizations, and combine that with the growing volume and complexity of data, plus customers demanding faster insights, there are key trends emerging that business leaders need to watch.”
Spotting the AI Opportunity
Modernizing your entire product development process with AI is ambitious—it takes significant investment and a large team to pull off. (Depending on the project’s scope, this could even qualify as an R&D tax credit-eligible activity.)
A more practical first step is to identify one specific challenge that AI could help solve. Before you implement an AI solution, though, it’s important to understand what AI can—and can’t—do.
Want to learn how your team can harness AI for stronger R&D results? Download our ebook, How to Use AI for R&D.
