TLDR: SAN FRANCISCOâIBM Consulting Americas leader Neil Dhar says weak leadership and process failures turn AI pilots into costly science experiments. CFOs must demand auditable ROI and track measurable business process gains.
Key Takeaways:
- Companies chased early AI fear of missing out with siloed proof of concepts and costs, then faced pressure to prove returns.
- Neil Dhar blames AI stalls on top leaders lacking a tech mindset and teams skipping end to end process redesign before deploying AI.
- He argues CFOs should steward assets by requiring auditable data and targeting 2.5x to 3x returns, with revenue harder to measure than productivity.
- Survey context adds a sharp gap: 80 percent of executives expect significant revenue from AI by 2030, yet only 24 percent know the source.
The AI hype phase is giving way to boardroom math, and Dhar is basically saying the lab coat only works when the workflow pays the bill. CFOs are being asked to turn promises into receipts, not slides.
The AI hype phase is giving way to boardroom math, and Dhar is basically saying the lab coat only works when the workflow pays the bill. CFOs are being asked to turn promises into receipts, not slides.
Q&A
If revenue is hardest to underwrite, what metric shifts should CFOs require first?
Start with productivity gains and measurable workflow time savings, then build credible paths from those gains to revenue levers like capacity, quality, and cycle time.
Why do siloed proof of concepts keep resurfacing even after companies fund them once?
Because teams can show quick demos without forcing end to end process change, making pilots easier politically than restructuring how work actually happens.
What changes when leadership lacks a tech mindset, beyond slower decisions?
Teams often optimize for AI feasibility instead of business feasibility, so models get built around existing processes rather than redesigning processes to capture value.
What happens after CFOs demand auditable data and 2.5x to 3x return targets?
Budgets will likely concentrate on fewer, better scoped initiatives tied to specific workstreams, while underperforming pilots get cut earlier.
How can companies translate measured employee and customer satisfaction into financial outcomes without guessing?
Tie satisfaction signals to specific operational changes, then track intermediate outcomes such as retention, churn reduction, service cost per case, and throughput before projecting revenue.
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