Forecasts, Cancellations and the Labor Ledger: Sizing the Agent Economy

Thirty-seven percent of the 1,719 executives McKinsey surveyed between May 4 and June 8, 2026 attribute any EBIT impact to AI, 6% qualify as high performers with at least 5% of EBIT from AI, and 60% expect to raise AI investment next year, a divergence the firm’s Aug. 25, 2026 report summarized as conviction outrunning returns. That gap between belief and booked profit is the subject of this article, and it runs through three ledgers at once. Forecasts of the agentic AI market size for 2026 span $8.5 billion to $201.9 billion. Return surveys range from MIT’s finding that 95% of organizations have yet to see measurable P&L impact to McKinsey’s 37%. Labor data range from a 19% employment gap for the youngest workers in exposed occupations to an economy-wide occupational shift that Yale’s Budget Lab measures at roughly one percentage point above the pace of the early internet. Each ledger is dated below, with conflicting figures side by side.

Forecast Spread: The Agentic AI Market Size From $8.5 Billion to $201.9 Billion

Deloitte’s TMT Predictions 2026, released Nov. 18, 2025, put the standalone agentic AI market at $8.5 billion in 2026, $35 billion by 2030 in its base case and $45 billion if orchestration improves, and estimated that as many as 75% of companies may invest in agentic AI by the end of 2026. Gartner’s fourth-quarter 2025 forecast, read through Software Strategies Blog’s Feb. 16, 2026 summary of a paywalled document, put agentic AI spending at $201.9 billion for 2026, up 141%, reaching $752.7 billion in 2029 at a 119% compound rate, with agentic spend overtaking chatbot and assistant spend in 2027 as the latter peaks at $264.7 billion. The independent research houses cluster near Deloitte. A Feb. 26, 2026 roundup by the same blog recorded MarketsandMarkets at $7.06 billion for 2025 rising to $93.2 billion by 2032, Precedence Research at $7.55 billion rising to $199.05 billion by 2034 and Fortune Business Insights at $7.29 billion rising to $139.19 billion by 2034, and it called the 25× gap to Gartner a measurement problem: Gartner counts agentic capability embedded across software categories, while the others count software sold as agents.

Returns Ledger: AI Agents ROI From MIT’s 95% to McKinsey’s 6%

MIT’s NANDA initiative published “The GenAI Divide: State of AI in Business 2025” in August 2025, drawing on 300-plus public initiatives, 52 interviews and 153 senior-leader surveys fielded from January to June 2025; it found that 95% of organizations had measurable P&L returns of zero on $30 billion to $40 billion of enterprise generative AI spend, that about 5% of custom enterprise AI tools reached production, and that more than 90% of companies had workers using personal AI tools while 40% held official subscriptions. Self-reported productivity tells a different story. Deloitte’s “State of AI in the Enterprise 2026,” which surveyed 3,235 leaders in 24 countries in August and September 2025, found 66% reporting productivity or efficiency gains, 40% reporting cost reduction and 20% reporting revenue growth against 74% aspiring to it. McKinsey’s 2026 survey found 80% saying AI improved individual productivity and 50% saying it improved decision-making, against the 37% who attribute any EBIT impact and the 6% who qualify as high performers. The ordering is consistent across every survey: individual productivity first, cost second, revenue third, EBIT last. Returns exist at the desk and thin out on the way to the income statement.

Cancellation Rates: Gartner’s 40% and the 2027 Governance Deadline

Gartner said in a June 25, 2025 press release that more than 40% of agentic AI projects will be canceled by the end of 2027, citing escalating costs, business value that resists measurement and weak risk controls. Anushree Verma, a senior director analyst at Gartner, described most current projects in that release as “early stage experiments or proof of concepts driven by hype.” The same release counted about 130 vendors selling genuine agentic capability among the thousands marketing it, a practice Gartner labeled agent washing, and it reported a January 2025 poll of 3,412 webinar attendees in which 19% had made significant agentic investments, 42% had made conservative ones, 8% reported zero investment and 31% were waiting.

A second Gartner release, dated May 26, 2026, forecast that 40% of enterprises will demote or decommission autonomous agents by 2027 because of governance shortfalls, and it recommended tiered controls calibrated to each agent’s autonomy level. Two 40% figures thus bracket 2027: one for projects canceled on economics, one for agents demoted on governance. Both rest on a definitional base that Menlo Ventures measured on Dec. 9, 2025, when it found that 16% of enterprise deployments qualify as true agents and the rest run as fixed-sequence workflows. A workflow marketed as an agent carries agent-grade costs and controls with workflow-grade returns, which is the cancellation mechanism in one sentence.

Labor Ledger: Canaries in the Coal Mine and the 19% Gap

Stanford’s Digital Economy Lab revised “Canaries in the Coal Mine?” on Aug. 12, 2026 with ADP payroll data through June 2026; Erik Brynjolfsson, Bharat Chandar and Ruyu Chen report that employment of 22-to-25-year-olds in the most AI-exposed occupations sits 19% below where it would be had it tracked less-exposed peers, a gap that stood at 15% on July 2025 data and has widened steadily since August 2025. The effect arrives through hiring, which slowed, and it holds when technology firms are excluded and when interest rates and remote work are controlled for; the authors find the damage confined to the youngest cohort in exposed occupations, with economy-wide employment intact.

Goldman Sachs Research took the long view on Aug. 13, 2025. Joseph Briggs and Sarah Dong estimated that 6% to 7% of the US workforce could be displaced if AI is widely adopted, that 2.5% is at risk if current use cases were expanded economy-wide, that full adoption would lift labor productivity about 15% and add roughly half a percentage point to unemployment during the transition, and that 9.3% of US companies then used generative AI in production; “we remain skeptical that AI will lead to large employment reductions,” they wrote. Yale’s Budget Lab reached a similar conclusion on Oct. 1, 2025: since ChatGPT’s release in November 2022 the occupational mix has shifted about one percentage point faster than during early-2000s internet adoption, and measures of exposure, automation and augmentation show little relationship to changes in employment or unemployment.

METR’s randomized trial of July 10, 2025 supplies the productivity caveat: 16 experienced open-source developers working 246 issues in familiar repositories were 19% slower with AI tools, having expected a 24% speedup and still believing in a 20% gain afterward. Executives report the opposite direction at scale. Marc Benioff said on The Logan Bartlett Show, as reported by The Register on Sept. 2, 2025, that Salesforce cut customer-support headcount from about 9,000 to about 5,000 through AI agents. McKinsey’s survey adds the base rate: 39% of respondents expect AI-related workforce declines in the coming year, while 14% of organizations using AI report that AI contributed to a decline in the past year.

Measurement Problems: Denominators, Definitions and the True-Agent Share

Three denominators explain most of the disagreement. Market-size forecasts diverge by 24× because Deloitte counts software sold as agents while Gartner counts agentic capability wherever it is embedded, which sweeps in a share of every suite contract. Return surveys diverge because the unit of account moves from self-reported productivity (66% at Deloitte, 80% at McKinsey) to booked EBIT (37%) to material EBIT (6%), and because the MIT sample, drawn from initiatives in the first half of 2025, predates most agent deployments. Labor studies diverge because the cohort and the channel differ: Stanford isolates 22-to-25-year-olds in the most exposed occupations and finds hiring effects, while Goldman and Yale measure the whole workforce and find displacement in the low single digits with the transition still ahead.

The true-agent share ties the ledgers together. If 16% of deployments are agents by Menlo’s definition, then most of Gartner’s $201.9 billion measures workflows with an agentic label, most of the productivity gains in the surveys accrue to assistants and fixed pipelines, and the 19% hiring gap for young workers reflects automation of entry-level task bundles by tools that seldom qualify as autonomous. On that reading, the agent economy of 2026 is smaller than its forecasts and larger than its returns. The cancellations of 2027 will fall on the labeled workflows first.

What to Watch

Four dated events will move the ledgers. Gartner’s twin 2027 deadlines, more than 40% of projects canceled and 40% of enterprises demoting agents, become measurable claims once the firm publishes its 2027 retrospectives, and the cancellation figure will be the first forecast in this ledger to face an audit. McKinsey’s 2027 survey will show whether the 37% EBIT figure moves toward the 89% adoption figure or stays near the 6% high-performer figure. Stanford’s next Canaries revision will show whether the 19% gap for 22-to-25-year-olds keeps widening at the pace observed since August 2025 or flattens as the cohort ages into it. Deloitte’s 2027 TMT Predictions will reveal whether the standalone market tracked its $8.5 billion base case, and Software Strategies Blog’s roundups will record whether the independent houses converge on Gartner’s embedded definition or hold to their own. This ledger will be re-dated at each of those points.