AI in Manufacturing Adoption Rates: Deploying vs Piloting

By Reliable Media

AI in Manufacturing Adoption Rates

Search "AI in manufacturing adoption" and you'll get a wall of confident percentages. One says 77% of manufacturers have implemented AI. Another says 95%. Nationally representative government data puts the share of manufacturers using AI at all well below either number.

These numbers don't answer the same question, so they shouldn't be read as competing measures of the same thing.

The useful question is narrower than "how many manufacturers use AI." It's this: how many have AI running in production, versus how many are still piloting, experimenting, or just planning to spend. Those are three different stages, and headline coverage frequently blurs them.

Here's what holds up when you separate deployment from intent.

What these numbers measure

The word "adoption" carries at least four definitions across the surveys people quote, and they sit at very different bars.

The loosest is planned investment: "we've invested in AI/ML or plan to within five years." Rockwell Automation's number lives here. The next rung is any use: "we used AI somewhere in the business." The US Census Bureau and McKinsey measure versions of this. Higher still is production deployment: "we've deployed AI at the facility or network level." Deloitte provides the closest facility/network-level comparison. The strictest is measurable financial return: "the pilot moved P&L." MIT's widely shared figure sits there.

A manufacturer can be a "yes" on the first definition and a "no" on the last one at the same time. That's not a contradiction. It's the distance between buying a tool and running it in production.

Sample frames matter just as much. Nationally representative government data covers smaller firms that are often underrepresented in consulting surveys. Vendor and consulting panels skew toward large manufacturers with the budget to deploy, so their numbers run higher.

The Reliable Confidence Score

Source or Claim Figure Reliable Confidence What It Really Means
US Census Bureau, BTOS (2026 AI supplement) In late 2025, ~18% of US firms reported using AI in any business function (32% employment-weighted); manufacturing reported below that national average. The older "in producing goods or services" wording ran near 10% nationally in fall 2025. HighNationally representative Drawn from a frame of ~1.2M businesses and weighted to the national firm population. No vendor stake. Measures whether a firm uses AI at all, not depth or production scale. By firm count, most manufacturers don't yet report using AI. The conservative anchor.
Deloitte 2025 Smart Manufacturing and Operations Survey 29% are using AI/ML at the facility or network level; 24% have deployed generative AI at that scale. 23% are piloting AI/ML; 38% are piloting generative AI. MediumLarge-firm sample 600 executives, self-reported, fielded Aug–Sept 2024. Sample is very large manufacturers only ($500M+ revenue, 1,000+ employees), so it runs well ahead of the broad population. The clearest facility/network-use-versus-pilot comparison we found. Deloitte labels the 29% as "using," not "deployed"; only the 24% generative-AI figure is called deployed.
McKinsey Global Survey, State of AI 2025 ~88% of organizations use AI in at least one function; about one-third have begun scaling, nearly two-thirds have not. MediumCross-industry Strong multi-year survey, but cross-industry, not manufacturing-specific. Captures the gap between using AI somewhere and scaling it across the enterprise.
Rockwell Automation, State of Smart Manufacturing 2025 95% of manufacturers have invested in, or plan to invest in, AI/ML over the next five years. LowInvestment intent 1,560 respondents, run by a company that sells the technology. A legitimate read on investment appetite, weak evidence for production: it bundles current spend with five-year intent and doesn't establish what's running on the floor.
MIT Project NANDA, The GenAI Divide 2025 Only ~5% of integrated generative-AI pilots reach measurable P&L impact ("95% fail"). LowP&L, not deployment Self-described as preliminary and directional. Built on interviews with 52 organizations, 153 senior leaders surveyed at four conferences, and 300+ public initiatives. Cross-industry, not manufacturing. Measures financial return, not whether a pilot deployed. Routinely misread as a pilot death rate.
Rootstock 2025 State of AI in Manufacturing Survey "77% of manufacturers have implemented AI to some extent" (up from 70% in 2023). LowVendor-commissioned Rootstock is an ERP vendor; survey of 369 manufacturers with 100+ employees across the US, UK, and Canada, fielded via Researchscape in late 2024. Traceable and competently run, but vendor-commissioned, broadly worded ("to some extent"), and not population-representative. A snapshot of a vendor's survey sample, not a deployment rate.

The Big Takeaway

Put the rows side by side and a consistent picture shows up.

Nationally representative US data puts manufacturing AI use, in any business function, below the all-industry average, and the all-industry average itself sits around 18%. Among the very large manufacturers Deloitte surveyed, 29% reported using AI/ML at the facility or network level and 23% were piloting it; for generative AI, 24% reported deployment and 38% were piloting. Higher figures like 77% and 95% use broader definitions or fold in future investment, and they shouldn't be read as plant-floor deployment rates.

So the honest framing of "deploying vs piloting" is this. Production deployment in manufacturing is a minority position even among the largest firms. Across the full population, government data put AI use lower still, though that measure captures any use, not production deployment. Piloting accounts for a substantial share, particularly for generative AI, where piloting (38%) runs well ahead of deployment (24%). For conventional AI/ML the two are closer, with facility-level use (29%) slightly above piloting (23%).

Broad definitions produce impressive adoption numbers. Measures tied to facility-level use, deployment, or financial impact paint a far more restrained picture.

Why the numbers disagree so much

Five things drive the spread, and definition is the biggest.

First, what counts as adoption. Invested-or-plan-to, used-it-somewhere, deployed-at-scale, and moved-P&L are four different questions. Surveys rarely lead with which one they asked.

Second, who got surveyed. A nationally representative frame includes small shops; a consulting or vendor panel leans toward big manufacturers that deploy more. Same question, different population, different answer.

Third, who's asking. A government statistician has no stake in the result. A technology vendor surveying "will you invest in AI" has an obvious one.

Fourth, AI/ML versus generative AI. These get merged constantly. Generative AI piloting runs well ahead of generative AI deployment, so a survey that folds both together can land anywhere depending on the mix.

Fifth, recency and question wording. The Census Bureau rewrote its core AI question in November 2025, moving from "in producing goods or services" to "in any business function." That single change roughly doubled the national headline rate, from about 10% to about 18%, and broke the time series. A 2024 number and a 2026 number from the "same" survey may not be comparable. The 10% was a national figure tied to the old wording, not a measure of plant-floor deployment.

How to use these numbers safely

Cite the definition and the date with every figure. "29% using AI/ML at the facility level (Deloitte, fielded 2024, very large manufacturers)" is defensible. "AI adoption in manufacturing is 95%" is not, because it hides the intent-versus-deployment gap and the vendor sample behind it.

Keep the three stages separate in your own reporting: deployed in production, piloting, and planning to invest. If a source won't tell you which one it measured, downgrade it.

For population-level claims about how many manufacturers use AI, lean on nationally representative data like the Census BTOS. For the deploy-versus-pilot split, a large consulting survey like Deloitte's is reasonable, as long as you carry the large-firm skew and the fielding date as caveats.

Treat "implemented to some extent" vendor numbers as broad any-use indicators, not deployment measures. They're the least useful figures in the category for answering the production question.

Where teams go wrong

The most common error is quoting Rockwell's 95% as if manufacturers have AI in production. That figure measures invested-or-plan-to over five years, and it doesn't establish production deployment either way.

The second is reading MIT's 95% as a pilot failure rate, or as a manufacturing statistic. It's cross-industry, it measures P&L impact rather than deployment, the sample is small, and the authors call it directional.

The third is treating "uses AI somewhere" as "AI in production." A finance team running a chatbot is a real AI user and tells you nothing about the shop floor.

The fourth is quoting the Rootstock 77% as a deployment rate. "Implemented to some extent" among a vendor's survey sample of large firms is a different measure entirely.

The fifth is stitching numbers from different surveys into a fake trend line. A 2023 production-only figure and a 2026 any-function figure aren't two points on the same curve.

Methodology

We pulled the specific deploy-versus-pilot splits where surveys publish them, anchored the population floor to nationally representative government data, and rated each source on a single question: how much should you trust this as evidence of AI running in production in manufacturing.

That question combines two things on purpose. A survey can be competently run and accurately quoted yet still rate Low, because it answers a different question. Rockwell's 95% is a legitimate investment-intent result and a poor deployment proxy, so it rates Low here without any implication that the survey was done badly. The same logic puts Rootstock's traceable, vendor-sponsored 77% in the Low tier.

Vendor marketing blogs were not used as load-bearing citations. Where a widely repeated figure came from a vendor-commissioned survey, we named the sponsor, the sample, and the wording rather than launder it into a clean statistic.

The Reliable Confidence Score rates confidence in the claim, not always in a number. A government estimate that manufacturing trails the national average is a High-confidence claim even though AI adoption itself keeps moving. These are survey-driven numbers in a fast-moving category, so treat any single figure as a snapshot with a definition and a date attached, not a settled benchmark.

The Short Version

Manufacturing AI adoption depends entirely on what you're counting. Nationally representative government data (Census BTOS) puts manufacturing AI use, in any business function, below the ~18% all-industry average. Among the very large manufacturers Deloitte surveyed, 29% reported using AI/ML at the facility or network level and 23% were piloting it; for generative AI, 24% reported deployment and 38% were piloting. Higher figures like 77% (Rootstock) and 95% (Rockwell) use broader definitions or include future investment and shouldn't be read as plant-floor deployment rates. Cite the definition and the date, or the number means nothing.

Sources

Frequently Asked Questions

What share of manufacturers have deployed AI versus just piloting it?

It depends on the survey's definition. Deloitte's 2025 Smart Manufacturing and Operations Survey found 29% of (very large) manufacturers using AI/ML at the facility or network level and 24% having deployed generative AI at that scale, while 23% were piloting AI/ML and 38% were piloting generative AI. Nationally representative US Census Bureau data (BTOS) puts manufacturing AI use, in any business function, below the roughly 18% all-industry average, so production deployment across the full population is lower.

How many US manufacturers use AI, according to government data?

The US Census Bureau's Business Trends and Outlook Survey (BTOS) found about 18% of US firms used AI in any business function in late 2025 (32% employment-weighted), with manufacturing reporting below that national average. Under the survey's older 'in producing goods or services' wording, the national rate ran near 10% in fall 2025. These measure whether a firm uses AI at all, not production-scale deployment.

Is it true that 95% of manufacturers are using AI?

No. The 95% figure comes from Rockwell Automation's 2025 State of Smart Manufacturing report and refers to manufacturers that have invested in, or plan to invest in, AI/ML over the next five years. It measures investment intent, not production deployment, and Rockwell is a vendor that sells the technology.

Where does the '95% of AI projects fail' statistic come from?

It comes from MIT Project NANDA's July 2025 report, The GenAI Divide: State of AI in Business 2025, which found only about 5% of integrated generative-AI pilots reached measurable P&L impact. The report is cross-industry rather than manufacturing-specific, draws on 52 organization interviews, 153 senior-leader surveys, and 300+ public initiatives, and the authors describe the findings as preliminary and directional. It measures financial return, not whether a pilot was deployed.

What is the source of the '77% of manufacturers have implemented AI' claim?

That figure comes from Rootstock Software's 2025 State of AI in Manufacturing Survey, a vendor-commissioned poll of 369 manufacturers with 100+ employees across the US, UK, and Canada, conducted by Researchscape in late 2024. It reports 77% having implemented AI 'to some extent,' a broad definition that bundles experimentation with deployment, so it should not be read as a production deployment rate.

Why do AI manufacturing adoption numbers vary so much?

Mostly because surveys measure different things: planned investment (Rockwell), any business use (Census, McKinsey), facility-level deployment (Deloitte), or measurable financial return (MIT Project NANDA). Sample frames differ too, with vendor and consulting panels skewing toward large manufacturers. The US Census Bureau also changed its core AI question in November 2025, which roughly doubled the national headline rate from about 10% to about 18% and broke the time series.

How much of manufacturing generative AI is in production versus pilots?

Deloitte's 2025 Smart Manufacturing and Operations Survey found 24% of (very large) manufacturers had deployed generative AI at the facility or network level while 38% were piloting it, so piloting runs ahead of deployment for generative AI. For conventional AI/ML the two are closer, with 29% reporting facility-level use and 23% piloting.