The difference between using GenAI and working alongside it  

R&D leaders entered 2026 with little doubt that generative AI would reshape how innovation gets done. The uncertainty now lies elsewhere: how much value is actually being created, and where?

Despite widespread investment and executive pressure to accelerate adoption, a clear majority of R&D leaders we surveyed say that their current level of GenAI integration is still not enough to achieve their objectives and that meaningful value creation has proved hard to achieve.

Our research suggests that part of the problem lies in how organizations think about GenAI in the first place. Using GenAI within existing processes may improve productivity at the margins, but it rarely delivers significant value. The organizations reporting the strongest results have taken a different path. They have started treating GenAI less like a tool and more like a new member of the R&D team.

More AI, same workflows

The most common approach to GenAI adoption has been relatively straightforward: teams receive access to tools, employees experiment with use cases and managers look for opportunities to save time and improve outcomes.

The problem is that the surrounding work often remains unchanged while functions scatter their technology budgets across multiple GenAI applications.

Our research found that nearly 50% of R&D leaders remain uncertain about how to leverage GenAI. At the same time, 64% report that employees still carry essentially the same responsibilities that they had before GenAI entered the picture.

Those numbers hint at a bigger problem. In most organizations, GenAI has been added to existing workflows without challenging the bigger assumptions about how work gets done, and by whom. We call this the GenAI augmentation approach.

The digital colleague difference

If GenAI is becoming ubiquitous, what exactly is its role?

An alternative to treating GenAI as a new technological complement to current workflows is what we have identified as the digital colleague approach. The phrase may sound unusual at first, but it forces leadership teams to answer a question that many have avoided: if GenAI is becoming ubiquitous, what exactly is its role?

Organizations routinely define goals, responsibilities and performance expectations for employees. Few have done the same for GenAI.

According to Gartner’s research, that distinction carries real consequences.

Organizations adopting the digital colleague approach were:

The organizations reporting the strongest results have made changes that go well beyond using GenAI to complete routine tasks more quickly. First, they have rigorously examined how R&D teams do their work and assessed where GenAI could provide the most significant capability advantages and impact if deployed. Then, they formalize the role GenAI will play in contributing alongside subject-matter experts.

In practice, that may mean assigning GenAI a specific role in the innovation process much like a human team member. As one example, a digital colleague could act as a research assistant responsible for conducting literature reviews, synthesizing research findings or generating competitive intelligence reports. Human experts remain responsible for determining whether the outputs are relevant and how to act on them.

Let humans and AI play to their respective strengths

One reason the digital colleague model has produced better results is that it starts with a realistic view of what GenAI does well and where it falls short.

GenAI is particularly strong when work requires speed, scale and pattern recognition. It can process enormous volumes of information, surface unexpected connections and help teams explore a broader range of possibilities than would otherwise be practical.

Humans retain clear advantages in areas that depend on judgment and context. R&D staff understand organizational priorities and stakeholder dynamics. They recognize the implications of decisions that extend beyond available data. They ultimately also carry the responsibility for R&D’s actions and outcomes.

That distinction has important implications for where R&D leaders focus their AI investments, and how roles for human staff will evolve.

Only 23% of employees say their manager has discussed with them how their roles will change as GenAI becomes embedded in their work.

Only 23% of employees say their manager has discussed with them how their roles will change as GenAI becomes embedded in their work. This information gap creates confusion about what new skills and responsibilities employees must adopt to succeed and may also fan concerns about eventual job displacement. R&D leaders should co-design the new roles with employees to account for changes introduced by the integration of GenAI and ensure buy-in among teams.

The next opportunity

Many organizations have spread their AI investments across a range of tools and a variety of R&D activities. We argue that big opportunities may lie early in the innovation cycle. Our research points to front-end innovation, e.g., the search for new ideas, intellectual property and market differentiation as areas where GenAI can most easily amplify value today.

That said, R&D teams under pressure to accelerate innovation should carefully assess where GenAI can deliver the greatest value for their specific needs. This requires evaluating the technology’s unique strengths and its potential impact on business outcomes, so teams can define the most effective role for GenAI within their organizations.

The R&D organizations reporting stronger outcomes today have taken time to determine where GenAI would have the greatest impact and have clearly articulated its role in R&D work. This process is often slower and more difficult than immediately seeking quick efficiency wins, but our research suggests it results in much stronger value gains for the R&D function and the people who work alongside GenAI.

Lauren Maiya is a Senior Director Analyst in Gartner’s R&D research practice. Gartner’s R&D practice develops insights, tools and guidance for heads of R&D. Lauren’s primary areas of expertise span a broad range of R&D leadership imperatives, including R&D strategy and operating models, innovation culture, R&D portfolio strategy and management and R&D workforce management.

The views expressed in this article are those of the author and do not necessarily reflect those of R&D World.