Survey: R&D budget waste persists despite AI adoption surge
IEEE Spectrum and Wiley have published a landing page promoting "The 2026 R&D Benchmark Report," a sponsored industry survey backed by Patsnap, a Singapore-founded patent and innovation intelligence company. The report draws on responses from more than 200 senior R&D professionals across North America, the UK, and Europe, spanning nine industries. Its headline findings, as summarized on the page: more than one-third of organizations spend 25-40% of their R&D budget on projects that never reach commercialization, and nearly half of respondents estimate losing more than $1 million every time a late-stage project is canceled. The report also states that AI adoption inside these organizations has surged, but most still apply it primarily to execution work rather than to the strategic decisions that determine which projects get funded in the first place. Its central argument is that earlier access to competitive, market, and patent intelligence has the greatest payoff during ideation and feasibility, before significant resources are committed, and that teams instead tend to work with fragmented data sources, slow approval processes, and insights that arrive too late to change investment calls. The page itself is a gated lead-generation form: it lists these bullet-point findings and invites visitors to download the full report, but does not publish the underlying methodology, survey dates, or a country-by-country or industry-by-industry breakdown. Patsnap, founded in 2007, says it is used by more than 15,000 enterprises, law firms, and research institutions for AI-driven patent and innovation analytics; its own product is the kind of "earlier intelligence" tool the report's recommendations point toward.
Key facts
- The report is based on a survey of more than 200 senior R&D professionals across North America, the UK, and Europe, spanning nine industries
- More than one-third of organizations spend 25-40% of their R&D budget on projects that never reach market
- Nearly half of respondents estimate losing more than $1 million each time a late-stage project is canceled
- AI adoption among these teams has surged, but is used mainly for execution rather than for strategic investment decisions
- The report argues earlier competitive, market, and patent intelligence has its biggest impact during ideation and feasibility, before resources are committed
Why it matters
The survey's headline claim is that AI adoption inside R&D organizations has not fixed the core waste problem: most teams still use AI for execution rather than for the upstream decisions about which projects to fund. If accurate, that means budget losses from late-stage cancellations, on the report's own estimate over $1 million each for nearly half of respondents, are continuing at scale even as AI tooling spreads through the same organizations.
Who it affects
The target audience is R&D and innovation leadership, plus the IP and patent-intelligence teams whose work the report says gets consulted too late. The report frames its findings around nine unnamed industries and three regions (North America, the UK, and Europe), without breaking out results by sector or country.
How to use it
The findings on this page are a marketing summary; the full report sits behind a registration-gated download ('LOOK INSIDE' / 'reserve your complimentary copy') on the Wiley Knowledge Hub site. No price is stated because the report is offered free in exchange for contact details, a standard lead-generation structure for a vendor-sponsored study.
How solid is it
The report is presented by IEEE Spectrum and Wiley but sponsored by Patsnap, a company that sells the kind of competitive and patent intelligence software the report recommends adopting earlier. No named authors or researchers are credited, no survey dates are given beyond the report's 2026 title year, and the landing page provides no methodology detail or per-industry breakdown, only the aggregate figures.
Risks and caveats
This is sponsored content built to promote a commercial product, not an independent academic or journalistic study, and the reader has no way to verify the survey design, sample selection, or response rate from what is published here. The specific figures (the 25-40% range, the $1 million loss estimate) are quoted without the underlying data, and no concrete company examples of the waste described are given.