More than half of research and development organizations have now integrated AI tools into their workflows. And yet, according to benchmark data published by Wiley’s Knowledge Hub, R&D waste hasn’t meaningfully declined. The 2026 R&D Benchmark Report finds that inefficiency remains stubbornly embedded in how organizations discover, validate, and act on research — and AI adoption alone isn’t the fix anyone hoped it would be. For anyone watching how AI risk engineering plays out at the organizational level, this report is a sobering reality check.
The report, titled “Waste, AI, and the Race to Market,” surveyed R&D professionals across industries and found that duplicated research efforts, siloed knowledge, and slow literature discovery are still costing organizations significant time and money. Teams are deploying AI — but they’re deploying it on top of broken processes, not instead of them.
The Duplication Problem Nobody Wants to Admit
One of the report’s sharpest findings is around redundant research. According to Wiley’s data, a significant portion of R&D teams report unknowingly repeating experiments or literature reviews that had already been completed internally elsewhere in their organization. The culprit is fragmented knowledge infrastructure — research lives in individual inboxes, disconnected databases, and departmental silos that AI search tools can’t fully bridge when the underlying data is never properly ingested or tagged in the first place.
This is the core irony of the moment: companies are spending on AI-powered research assistants while their foundational data hygiene remains poor. If the corpus an AI tool draws from is incomplete or inconsistently structured, the output will reflect that. Garbage in, garbage out — a principle that turns out to be as relevant in cutting-edge R&D as it ever was in legacy enterprise software. The report suggests organizations need to fix knowledge architecture before layering intelligence on top of it, not after.
Speed Pressure Is Making the Problem Worse
The “race to market” framing in the report’s title isn’t decorative. Wiley’s benchmark data points to competitive speed pressure as a primary driver of waste — teams are rushing validation cycles and skipping systematic literature reviews because the organizational incentive is to ship, not to be thorough. AI tools are being used to accelerate that rush rather than to build more rigorous discovery pipelines. The result is faster iteration on potentially flawed premises.
The financial stakes are real. When R&D cycles produce results that were already known internally, or when teams proceed without full visibility into prior work, the sunk costs compound fast — in researcher hours, in materials, and in delayed product timelines. The report stops short of a single industry-wide dollar figure for this waste, but the pattern it describes maps onto a structural problem that exists across pharma, materials science, software, and manufacturing R&D alike. Wiley’s findings suggest that organizations scoring highest on research efficiency share one trait: they’ve built deliberate knowledge-sharing systems, not just deployed tools.
What Actually Moves the Needle
The report doesn’t just diagnose — it points toward what separates high-efficiency R&D teams from the rest. Organizations that perform well on waste reduction metrics tend to invest in centralized research repositories, enforce consistent metadata standards, and treat literature discovery as a formal workflow stage rather than an informal habit. AI tools deployed within that kind of structured environment show measurable gains. Deployed without it, they largely just speed up the same dysfunctional patterns.
There’s a parallel here to how enterprises have struggled with data governance in other domains. As Future Wire has covered in reporting on AI model limits, the gap between what a capable model can do and what it actually delivers inside an organization is almost always an infrastructure and process story, not a technology capability story. Wiley’s 2026 benchmark makes the same argument for R&D specifically: the tools exist, the bottleneck is organizational, and fixing it requires deliberate investment in the unglamorous work of knowledge management before the AI layer can deliver on its promise.
