R&D teams deploy AI for execution, miss early-stage decision support
Organizations are deploying AI at scale but missing a critical opportunity: most implementations focus on execution tasks like data analysis rather than early-stage decision support where they could prevent costly failures. The report reveals that over a third of R&D budgets vanish on projects that never ship, with half of teams losing more than $1M per failed initiative during development. The gap exposes a strategic misalignment in how enterprises apply AI, suggesting that shifting AI investment upstream to feasibility assessment and ideation could dramatically improve capital efficiency and reduce downstream waste.
Modelwire context
Analyst takeThe report doesn't just quantify R&D waste; it identifies a specific misallocation: AI is being deployed downstream (execution) rather than upstream (decision-making), suggesting that the real ROI lever remains untapped. This isn't about AI capability gaps but about where in the workflow organizations are choosing to apply it.
This connects directly to the June startup launch backed by Marc Benioff (TechCrunch, August 3rd). June targets the gap between model capability and operational readiness, but this IEEE report suggests the gap runs deeper: organizations are operationally ready to deploy AI, they're just deploying it in the wrong phase of work. The security access control finding from IBM (August 3rd) reinforces this pattern: enterprises are struggling with foundational infrastructure decisions, not model selection. Meanwhile, the coding agents research (The Decoder, August 1st) shows that even when AI can accelerate execution tasks, it still requires domain experts to validate correctness. Taken together, these stories suggest enterprises are investing in AI for tasks where human judgment remains essential, while starving the earlier stages where AI could prevent costly decisions entirely.
If organizations that shift AI investment upstream to feasibility assessment report measurable reductions in failed project costs within the next 12-18 months, that validates the thesis. Watch whether Benioff-backed June or similar deployment-focused startups begin marketing feasibility assessment and early-stage decision support as core offerings, rather than just integration tooling. That product pivot would signal market recognition of this capital allocation gap.
Coverage we drew on
This analysis is generated by Modelwire’s editorial layer from our archive and the summary above. It is not a substitute for the original reporting. How we write it.
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Modelwire summarizes, we don’t republish. IEEE Spectrum - AI originally reported this story as “Why R&D Waste Persists Despite Widespread AI Adoption”. The full content lives on content.knowledgehub.wiley.com. If you’re a publisher and want a different summarization policy for your work, see our takedown page.