AI in the R&D Process: Why Billions Disappear into Projects That Never Reach Market

The Paradoxical Problem of Digitalized R&D: More AI, but No Better Outcomes

Artificial intelligence has long since arrived in the research and development departments of global companies. AI-driven data analysis, automated modeling, machine learning for materials development – the tools are in place and their use is widespread. And yet a recent benchmark report, presented by IEEE Spectrum and Wiley in collaboration with innovation intelligence provider Patsnap, paints a sobering picture: Despite massive AI adoption, companies continue to lose enormous portions of their R&D budgets to projects that never reach the market.

The study, based on responses from more than 200 senior R&D professionals across North America, the UK, and Europe, delivers concrete figures on a problem that is often downplayed in strategy papers: the structural failure of innovation projects – not in spite of, but in part because of, the way AI is being used today.

The Hardest Numbers: Where R&D Budgets Are Really Being Lost

The report's findings are unambiguous and should command attention at every executive level:

• More than a third of all organizations surveyed report spending between 25 and 40 percent of their total R&D budget on projects that never reach market maturity.
• Nearly half of respondents estimate the loss on each canceled late-stage project at over one million US dollars – per project, it should be noted.
• The most common cause: critical insights into market viability, competitive landscape, and patent status reach decision-makers too late – often only after significant resources have already been committed.

These figures are not a marginal phenomenon confined to individual industries. The study covers nine industrial sectors – from life sciences and materials science to software engineering. The problem is systemic in nature.

The Blind Spot: AI for Execution Instead of Decision-Making

Here lies the central contradiction that the report exposes – and one that is also well known from the practice of process automation: Companies primarily use AI for operational execution tasks, such as data analysis, simulation, or automated reporting. What remains largely untouched, however, are the strategic decisions: Which projects actually deserve investment? When should an initiative be stopped? Which technology areas offer genuine differentiation potential?

These "go/no-go" decisions in the stage-gate process are still made predominantly without adequate AI support – on the basis of fragmented data, inconsistent information sources, and competitive analyses that are all too often obtained too late.

"AI that only accelerates execution while maintaining the wrong direction saves no resources – it burns through them faster."

Dr. Maik Bunzel, founder and CEO of mabucon.eu, observes this pattern in his consulting practice as well: many organizations initially implement AI tools where success appears easiest to measure – in clearly defined subtasks. The genuinely value-creating application, namely supporting complex investment and prioritization decisions, is frequently left out of the equation.

When AI delivers the greatest leverage: Timing is everything

One of the report's most important insights concerns not only the "what" but the "when" of AI adoption. The data clearly shows: the greatest value leverage lies in the early phase of the innovation process – during ideation and feasibility analysis, before significant resources are committed.

In concrete terms, this means: early access to patent intelligence, competitive landscapes, white-space analyses, and market data can prevent poor decisions that later prove to be multimillion-dollar dead ends. Those who wait until a project has reached the testing phase before seeking external intelligence are acting reactively rather than proactively.

• Patent Intelligence: Early identification of IP risks and freedom-to-operate issues prevents costly course corrections in the late stage.
• Competitive Intelligence: Continuous monitoring of the competitive landscape identifies overlaps and differentiation opportunities before internal development capacity becomes tied up.
• White Space Analysis: The systematic identification of unoccupied innovation spaces enables more targeted resource allocation with a higher probability of success.

Structural problem: Fragmented data and sluggish processes

The report also identifies the organizational causes that hinder better AI adoption. Teams struggle with fragmented data sources that provide no consistent basis for decision-making. Added to this are lengthy internal approval processes that slow the flow of information – and all this in an environment where development cycles are growing ever shorter and markets are changing faster than ever.

There is also a cultural dimension: decisions in R&D processes are frequently shaped by subjective assessments, internal power structures, and so-called "sunk cost" thinking. Projects are continued because a great deal has already been invested – not because the success signals are positive. AI-assisted decision support can provide an objective counterweight here, provided it is integrated early enough and with sufficient data depth.

Implications for companies: Where action is needed

For companies that seriously want to improve their R&D efficiency, the report identifies clear areas requiring action:

• Realign AI strategy: The focus must shift from pure execution support to strategic decision support. This requires different system architectures, different data sources, and different use cases.
• Prioritize early phases: Investments in innovation intelligence pay off above all when they flow into the ideation and feasibility phases – not just at the stage-gate review after intensive development work.
• Break down data silos: An integrated data foundation that brings together patent, market, and competitive intelligence is a fundamental prerequisite for data-driven R&D decisions.
• Redesign processes: Existing stage-gate processes should be examined to identify where structured data intelligence can be incorporated – and which manual steps can be automated or accelerated.

The real competitive advantage lies in decision quality

What the 2026 R&D Benchmark Report ultimately makes clear is a fundamental shift in the understanding of AI value creation. It is not the organization that deploys AI most broadly that wins the innovation race – but the one that deploys AI where it has the greatest leverage: at the critical decision points of the innovation process.

Dr. Maik Bunzel, founder and managing director of mabucon.eu, puts it in a nutshell: intelligent automation does not mean moving through more processes faster. It means tackling the right processes in the first place – supported by a data foundation that grounds strategic decisions and prevents misallocations before they occur.

For companies in research-intensive industries, the findings of such benchmark data should serve as a clear signal: The next major efficiency gain lies not in faster execution, but in better decision intelligence. Those who leave this opportunity untapped will be unable to close the competitive gap against more data-driven organizations – despite all their AI investments.