Brief
The debate sits on a data set both sides can cite honestly, because the numbers describe different things. McKinsey's State of AI global survey, published August 25, 2026 and based on 1,719 executives across 97 nations, found that 37% of respondents report AI has contributed positively to their organization's EBIT — essentially unchanged from 2025 — while only 6% qualify as 'AI high performers,' defined as attributing at least 5% of EBIT to AI with significant reported value, also flat year over year. Meanwhile 80% of respondents said AI has improved their individual productivity. That 80-versus-37-versus-6 spread is the entire argument in three numbers: capability and individual usefulness are not disputed; enterprise-level earnings attribution is.
Separately, MIT's Project NANDA GenAI Divide report, based on analysis of 300 AI deployments, 52 executive interviews, and a 153-leader survey, found that 95% of generative AI pilots delivered no measurable P&L impact, while only 5% of integrated systems created significant value — a preliminary, non-peer-reviewed finding whose starkest claims rest specifically on the 52 interviews rather than audited financials. Enterprise generative AI spend tripled in a single year, rising from roughly $11.5 billion in 2024 to about $37 billion in 2025 per Menlo Ventures' survey of US enterprise decision-makers, while hyperscaler AI infrastructure capex has climbed into the hundreds of billions for 2026 across Microsoft, Amazon, Alphabet, and Meta. The dollars flowing in are large and rising; the dollars flowing back out as attributable earnings, per McKinsey's own instrument, have not moved.
Against that backdrop sit specific, named case studies that vendors and some enterprises use to argue ROI is real today: Klarna's February 2024 announcement that its OpenAI-powered assistant handled roughly two-thirds of customer service chats in its first month, equivalent to the workload of about 700 agents, and JPMorgan's reported use of AI across hundreds of production use cases. But Klarna's own subsequent history complicates the case for using it as unqualified proof: in May 2025 the company began rehiring human agents after its CEO told Bloomberg that evaluating the shift primarily on cost had produced 'lower quality,' and by 2026 Klarna had settled into a hybrid model routing routine queries to AI and complex or premium interactions to humans. The case shows AI can absorb high-volume, bounded work; it does not show that full-scale AI replacement reliably protects earnings without new costs in quality and re-hiring, a distinction the current debate frequently elides.
McKinsey's 2026 data offers a mechanism for why the split exists: cost reductions from AI concentrate in supply-chain management, service operations, and manufacturing, and revenue gains concentrate in marketing, sales, and product development — narrow, bounded use cases with a measurable output — while broad enterprise chatbot and copilot rollouts tend to produce individual time savings that do not visibly flow to the income statement. The single factor McKinsey ties most strongly to becoming a high performer is workflow redesign: nearly three-quarters of high performers report having fundamentally redesigned workflows around AI, versus about a quarter of everyone else. That finding reframes the entire proposition: the question is not whether AI can generate earnings, since a real subset of organizations demonstrably does, but whether 'investment' alone — capital deployed, tools purchased, pilots launched — is a reliable predictor of earnings return, and the 2026 survey data says it is not.
What It Turns On (4)
Does 'productivity' at the individual or task level reliably convert into 'earnings' at the enterprise level, or does saved time simply stay at the worker's desk without being captured by the organization?
This is the empirical crux the entire debate turns on: McKinsey's own data shows an 80%-to-37%-to-6% cascade from individual productivity to any EBIT impact to significant EBIT impact, and resolving why that cascade narrows so sharply would settle whether current investment levels are justified or premature.
Is workflow redesign a prerequisite for earnings return, or merely correlated with the kind of company that would have succeeded with AI anyway?
If redesign is causal, the 94% not seeing earnings impact have an identifiable, actionable fix; if redesign is just a marker of already-disciplined, well-resourced organizations, then most companies may be structurally unable to replicate the 6%'s results regardless of how they deploy AI.
Should vendor-reported and single-company case studies (Klarna, JPMorgan, agentic-adopter surveys claiming 171% average ROI) be weighted as evidence of realized returns, or discounted as unaudited, self-selected, and pre-reversal snapshots?
Much of the case for the proposition rests on figures originating from the companies or vendors being evaluated rather than from independent audited financials, and the Klarna case shows an initially celebrated figure was later described by its own source as a forward-looking estimate rather than a realized saving.
Is the flat 6%/37% share evidence of a structural ceiling on enterprise AI earnings impact, or a lagging indicator that has not yet caught up to a genuinely accelerating deployment curve?
The scaling-of-agents share among large companies rose sharply year over year even as the EBIT-impact share stayed flat, so whether earnings attribution is a leading or lagging measure changes whether current investment levels are rational bets on a future payoff or capital being spent ahead of any evidence it will return.
The claims behind this analysis, each with its verification status — including what is contested, unverified, or could not be established.
What each grade meansA real, identifiable subset of enterprises is already converting AI investment into measurable earnings impact, and the share attributing any EBIT impact to AI is not trivial.
McKinsey's 2026 survey found that only 37% of respondents said AI has contributed to their organization's earnings before interest and taxes (EBIT), a figure McKinsey says is essentially unchanged from the prior year's survey, and among that group, AI high performers—respondents who attribute an EBIT impact of 5 percent or more to AI use and say their organizations have seen 'significant' value from AI use—account for just 6 percent of survey respondents, unchanged from 2025.
The gap between high performers and everyone else is explained by a specific, replicable practice — workflow redesign — not by unrepeatable luck or company size, meaning the return is achievable by design rather than accidental.
McKinsey reports that about three-quarters of AI high performers have fundamentally redesigned their workflows around AI, versus roughly a quarter of everyone else, and they are far more likely to pursue growth and innovation rather than pure cost-cutting, and are about twice as likely to have strong senior-leadership ownership of AI strategy and clear metrics for measuring its impact.
Agentic AI deployments specifically — as distinct from broad copilot and chatbot rollouts — show a materially higher first-year ROI rate, indicating the technology can deliver returns when deployed as an end-to-end workflow replacement rather than an assistant layered onto existing work.
Reporting citing McKinsey data states that among agentic AI early adopters, 88% report ROI within the first year, against 74% for gen-AI users overall, and separately that 40% of $1B-plus revenue companies are now scaling AI agents, up sharply from 27% a year earlier — vendor- and survey-adjacent figures that should be read as directional rather than audited.
Named enterprise deployments demonstrate concrete, functional-level financial effects even where enterprise-wide EBIT attribution has not yet been established, showing the return exists at the business-unit level and will likely aggregate over time.
McKinsey's own survey data show that respondents most often report cost reductions from AI in supply chain management, service operations, and manufacturing — the narrow category of use cases where AI is doing a defined, bounded job with a measurable output — while revenue gains show up in marketing and sales and product development, and separately even respondents who don't report enterprise-level EBIT impact do say their organizations are seeing financial impact from specific business functions' use of AI.
The dominant, survey-confirmed reality is that AI investment has not moved enterprise earnings at all for the large majority of organizations, and this share has been flat for a full year despite a sharp rise in spending and deployment.
McKinsey's 2026 data shows 31% report some EBIT impact but below the high-performer threshold; the remaining 63% report no measurable enterprise earnings impact at all, even as they expand AI deployment and report strong individual productivity gains, and the 6% high-performer share is unchanged from last year — a figure flat since 2025 despite the sharp rise in enterprise AI budgets over the same period.
Independent field research beyond McKinsey's self-reported survey data finds an even starker failure rate at the pilot level, suggesting the earnings gap is not a McKinsey artifact but converges with data from a separate methodology.
MIT's Project NANDA GenAI Divide report, based on analysis of 300 AI deployments, 52 executive interviews, and a 153-leader survey, found that 95% of generative AI pilots delivered no measurable P&L impact, while only 5% of integrated systems created significant value — a preliminary, non-peer-reviewed finding whose starkest claim rests specifically on the 52 interviews the report itself flags as directionally accurate rather than official company reporting.
◑ CONTESTEDcase against
Individual productivity gains are real and widely reported, but they are demonstrably not the same thing as organizational earnings, and the size of that gap is the core evidence against the proposition.
McKinsey's 2026 survey found that 80% of respondents said AI has improved their individual productivity, and 50% said it helps them make better decisions," yet "only 37% of respondents said AI has contributed to their organization's earnings before interest and taxes, with reporting characterizing this as the finding that organizations' conviction in AI is growing faster than the immediate financial returns they can attribute to it.
Even celebrated, publicly cited case studies of AI replacing enterprise workflows have required costly walk-backs when quality and customer experience deteriorated, undercutting the reliability of ROI claims built on early, self-reported vendor figures.
Klarna's CEO told Bloomberg in May 2025 that as cost unfortunately seems to have been a too predominant evaluation factor when organizing this, what you end up having is lower quality, and independent analysis of the case notes that the $40 million figure wasn't an audited cost saving; it was described as a projected 'profit improvement to Klarna in 2024,' a forward-looking estimate at the time of announcement," and the "700 full-time agents' figure was a productivity equivalence claim, not a headcount action.