AI's real bottleneck isn't task speed — it's defining the outcome
A TechRadar Pro opinion piece argues that AI's value bottleneck sits upstream of the task, in deciding what to build, why, and which process actually drives business impact — not in how fast a task gets done. It cites Harvard Business Review's 2026 term "micro-productivity trap," warning that optimizing individual tasks yields isolated gains that never compound into business results. The author also points to PwC's 2026 AI Performance study, which found 74% of AI's economic value is captured by just 20% of organizations, and to Gartner's warning that over 40% of agentic AI projects will be cancelled by the end of 2027 because organizations fail to define the outcome they are building toward; the proposed remedy is "fusion teams" that pair domain experts with technical staff.
It pushes teams to measure AI by business outcomes rather than task throughput, so they avoid speed-only gains that never translate into value.