Elsevier Report Finds R&D Evidence Gaps
Elsevier report finds 63% of those who answered made an important R&D decision using data later found inaccurate or incomplete.
By Academic Writing Club Newsroom
Elsevier reports that 63% of those who answered say their organization has at some point made an important R&D decision using data or evidence later found to be inaccurate, incomplete or outdated. Its Corporate R&D of the Future report surveyed more than 1,000 R&D leaders, managers and researchers at R&D-led corporations. More than three quarters (76%) of respondents are confident in the quality of their data, while about half (51%) say their organization has had to make an important R&D decision before all the evidence was in. The report examines how R&D teams use data and evidence, where it comes from, how well it is connected, and how AI is being applied and trusted.
Evidence Access Barriers
While 78% have access to the third-party evidence they need, more than half (56%) of those still report at least one barrier. Across all respondents, the top barriers are scattered sources (42%), slow finding and synthesis (32%) and not knowing what evidence exists (30%).
Respondents in APAC (90%) are most likely to have access to third-party evidence, compared to 73% in North America and 77% in Europe.
AI Use And Trust
The survey found that 81% expect AI to fundamentally transform how R&D decisions are made over the next five years, yet under half (47%) say it is used regularly in their organization’s R&D function today. A further 40% say it is being piloted. Most respondents use AI across R&D activities, led by data analysis (71%), literature search (70%), and competitive intelligence (70%).
Of those answering, 48% use general-purpose AI tools such as ChatGPT or Copilot for literature search, against 23% who use specialist scientific AI tools. For data analysis and modeling, specialist tools are the most common choice.
Stuart Whayman, President, Corporate Markets at Elsevier, said:
“Confidence is not the same as accuracy. R&D teams are under pressure to decide faster, and often before all the evidence is in. Confidence keeps them moving, but it can't make up for evidence that is incomplete, outdated or unreliable. AI will raise the stakes, because it will amplify whatever evidence it is given, good or bad. The advantage will go to the organizations that can bring their evidence together and trace and verify what sits behind their decisions.”
Regional Views Of AI
Among R&D leaders and managers, 42 % in North America, 32 % in Europe and 23 % in APAC say evidence that is not robust or reliable enough reduces their confidence. Implementing AI at scale is seen as a significant challenge by 52 % in North America, 43 % in Europe and 28 % in APAC.
The report says 30 % in North America, 36 % in Europe and 61 % in APAC view AI-generated summaries or outputs as essential evidence. It also found that 43 % in North America, 62 % in Europe and 47 % in APAC would be more willing to use AI if it complies with laws and regulations.
What The Report Covers
The full report compares views by region, organization size and role, and sets out recommendations for R&D organizations.
