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Anshu Bansal turns PwC's 12% figure into a case for an AI control plane
Founder Anshu Bansal blames missing control planes for AI's unproven returns, citing PwC's finding that just 12% of CEOs report both cost and revenue gains. The survey counts the gap without explaining it, so a board choosing new tooling over a finance fix is acting on his argument.
The Board Room · Leadership desk
What happened
- A January 2026 PwC survey found only 12% of CEOs say AI has delivered both cost and revenue benefits for their organizations.
- Anshu Bansal, a startup founder writing in Forbes, says many companies treat that gap as a reporting failure and hand it to finance.
- He argues existing control planes are built to manage instances, containers, storage and networks, while AI's unit of production is useful work.
- In his account, one user request can now generate dozens of inference calls across multiple systems.
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Why it matters
- decision Handing AI returns to finance treats the gap as a reporting job; Bansal's diagnosis would put it with whoever runs the models, data and tools each request touches.
- constraint A control layer bought this quarter is a fixed design, and Bansal expects fixed AI configurations to turn wasteful within months as model prices and serving engines change.
- cost Procurement that compares models on token price can buy the more expensive option once rework is counted.
Most boards already get a precise AI spend figure. Bansal writes that technology leaders can usually produce one, and that most executives cannot then say which outcomes it bought, which workloads drove growth, or whether the same results were available for materially less [4]. PwC's finding means 88% of CEOs did not report both cost and revenue gains [3].
The finance-led response treats this as an allocation problem that better reporting can close [5]. Bansal puts the cause elsewhere. "I believe the more likely culprit is deploying a production dependency on an architecture that was never designed to be operated in this way," he wrote [6]. In his account, the tools most companies own can count activity without connecting it to a result. "Without that lineage, enterprises can measure AI activity but cannot explain or improve it," he wrote [9].
A finance chief could fairly answer that this is still reporting, only with more fields. Bansal's first remedy gives that objection some weight. The first of the four disciplines he proposes is a unit of production the business recognises, such as cost per resolved case, accepted code change or completed task [11]. "Tokens are a technical measure, not a business outcome," he wrote [10]. Choosing that unit is a definition finance can own. Recording it for every request needs the context he says operating AI requires: which model produced an answer, which prompts and retrieval steps shaped it, and whether a cheaper route would have met the same service level [8].
The two halves of the argument rest on different evidence. The 12% is a survey result, reported secondhand through the column [2]. The survey as cited counts the CEOs who see both gains; the explanation for the rest is Bansal's. He runs a stealth-mode startup in AI, cloud and cybersecurity [1], and the column does not say what it sells.
None of this has to be settled this week. Bansal writes that the problems "cannot be solved overnight" [13]. A company can pick its unit of production this quarter without buying anything. Next quarter, that unit tests the tooling. If existing monitoring can report cost per resolved case request by request, the operating layer Bansal describes is a configuration job. If it cannot, it is a purchase.
What to watch
- Whether PwC's full January 2026 survey reports why most CEOs miss one or both gains; that would test Bansal's architecture explanation directly.
- What Bansal's stealth-mode startup sells when it launches, and whether it is the kind of control plane his column argues for.
- Whether companies that adopt outcome units such as cost per resolved case show up among the CEOs reporting both gains in PwC's next survey.