A growing R&D divide threatens to leave organizations adrift as leaders accelerate.
Most R&D organizations would never make major product or technology decisions without first understanding what is happening around them. They continuously monitor emerging technologies, scientific discoveries, customer needs, competitors, startups, patents, and market signals because those insights shape where they invest, what they build, and how they compete.
But while organizations continuously scan the external environment, that discipline is applied far less consistently to understanding how the R&D function and other R&D organizations are changing. That perspective tends to be gathered through periodic benchmarking exercises, typically before a strategy refresh or transformation initiative.
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Those exercises remain valuable, but today’s environment is changing too quickly to rely on point-in-time benchmarking alone. Between one benchmarking exercise and the next, technologies mature, operating models evolve, AI capabilities advance, and new leading practices emerge. Benchmarking should therefore be informed by continuous awareness of the R&D landscape. That ongoing understanding provides the context needed to make benchmarking more relevant, actionable, and valuable.
The R&D function is changing as quickly as the innovations it develops
Continuous awareness is now a requirement because the R&D function itself is changing at a remarkable pace. AI is becoming embedded across the R&D lifecycle. Technologies continue to expand what can be simulated before physical experimentation begins. Organizations are redesigning operating models, adopting new collaborations, investing in different capabilities, and rethinking how R&D performance should be measured. All of this is happening while leaders face continued pressure to shorten development cycles, improve productivity, and balance near-term delivery with longer-term innovation.
A recent Everest Group survey of 260 senior R&D leaders reflects this evolution but also provides a clear insight into the investment trends and innovation strategies separating R&D trailblazers from the chasing pack. Contrasting investment priorities, capability gaps and collaboration models highlight a growing R&D divide.
That divergence is important because it suggests there is no longer a single model of leading practice. Organizations are responding to many of the same forces, but they are not responding in the same way. Understanding those differences is becoming just as valuable as understanding the trends themselves and is key to both defending a leadership position or attempting to cross the R&D divide. Surveying the whole landscape, to understand how competitors plan to develop their workforce, forge new collaborations, or apportion investment, can reveal which routes to improved R&D performance may be most effective, and which may be most challenging in light of important competitive context.
Competitive advantage increasingly depends on the R&D system itself
When people think about R&D competitiveness, they often think about products, intellectual property, or scientific breakthroughs. Those will always matter. However, increasingly, organizations are also competing on the effectiveness of the system that consistently produces innovation – from operating model to workforce strategy. As a result, organizations need to understand not only what their competitors are building, but also how leading R&D organizations are changing the way they innovate.
Viewed together, these findings tell a consistent story. Organizations have largely made the decision to invest in AI and digital technologies. What differentiates leaders from the rest of the pack is what happens after those investments are made. Some organizations are translating those investments into new capabilities, new ways of working, and stronger R&D outcomes, while others continue to struggle despite making similar investments.
That distinction has important implications for benchmarking. Traditional measures such as R&D investment, patent activity, product launches, and time-to-market are still important, but they no longer tell the whole story. Increasingly, leaders also need to understand AI maturity, organizational agility, workforce capabilities and strategies, collaboration models, governance, and digital foundations.
Continuous awareness should inform benchmarking – and both should inform strategy
Maintaining continuous awareness means understanding where investment priorities are shifting, which capabilities are emerging, how peer organizations are integrating AI, how operating models are changing, and how industry leaders are responding to new challenges. Periodic benchmarking then provides the structured opportunity to assess where an R&D organization stands, identify meaningful capability gaps, and determine where investment or organizational change is warranted.
The relationship between the two is important. Continuous awareness provides the context that makes benchmarking meaningful, while benchmarking provides the structured assessment needed to translate that understanding into strategic decisions.
No organization should try to adopt every emerging practice, nor should it react to every new trend. But organizations that prioritize a strategic relationship between continuous awareness and periodic benchmarking will be better positioned to make thoughtful decisions about how their R&D function should evolve.
In an environment where the pace of change continues to accelerate, developing this muscle is likely to prove as important as understanding the technologies shaping the next generation of products.
Interested in hearing more? Register for our September 24 Webinar, Essential Insights: How Leaders Stay Ahead of the R&D Divide – Everest Group Research Portal, which will share key findings from our inaugural R&D Trends & Priority Survey, with our author Jillian Walker joined by Research Director Vinay Venkatesan on the session.
If you’d like to continue this discussion further, please contact Jillian Walker ([email protected]).