Manufacturing insights: 2026 AI Impact Survey Report

Manufacturers are piloting. Competitors are scaling.

Even though 48% of manufacturers are piloting AI, only 10% have fully integrated AI into operations, four points below the full-sample average. That distance between piloting and integrated performance is where the returns gap lives. Organizations with fully integrated AI are dramatically more likely to report revenue growth, accelerated innovation and higher-quality outputs. Manufacturers are running pilots while the performance evidence accumulates on the other side of a threshold most have not yet crossed. On the other end, only 39% said they are scaling AI across multiple functions — 10 points below the broader survey average of 49%.

AI is compressing the timeline that previous technology cycles stretched across a decade. Grant Thornton’s manufacturing practitioners see a consistent pattern: companies buy AI solutions and wait for their technology vendors to determine how to deploy them.

That is not a strategy.

Competitors who are building governance and data foundations now are creating advantages that compound quietly — until they show up in margin performance, customer outcomes and market position.

Many manufacturers are stuck at the piloting phase of AI instead of moving toward integration, because they lack governance infrastructure guided by leadership. The governance infrastructure can help them drive a more proactive AI strategy that moves ahead of competitors. The strategy that is currently driving their AI investments is a vendor-defined competitive reaction, rather than deliberate margin building. That means even the investments being made are pointed in the wrong direction.

Efficiency returns are real. They are not enough.

The 64% of manufacturers reporting increased efficiency are seeing genuine returns on deliberate operational investment. The problem is what has not followed. Only 14% of manufacturers report accelerated innovation — 17 points below the full-sample rate and the second-lowest figure of any sector.

Yet manufacturing leaders ranked a creativity and innovation mindset as the top leadership attribute for the AI era, at 48%, 12 points above the average. Innovative mindsets can lead to tangible solutions, like predictive quality control that catches defects before production, AI-driven design iterations that compress development cycles, dynamic supply chain reconfiguration that responds to real-time disruptions or predictive maintenance and asset management that move from scheduled downtime to condition-based intervention. Leaders need to accelerate innovation to deliver on the true potential of AI.

Plus, the efficiency gains that every competitor also captures will have a shelf life. Operational AI that informs decisions about throughput, procurement and production scheduling in real time is what changes margin outcomes before finance reports them. AI-informed procurement decisions can adjust supplier allocations based on real-time cost and risk data, showing up in margin that same quarter. A production scheduling model can optimize throughput against energy costs, with the financial impact traceable in the P&L. A quality control system can reduce scrap rates by a measurable percentage, directly attributable to an AI intervention.

When operational AI connects to financial outcomes in real time, manufacturers can prove what their AI is producing. That connection between operational decisions and financial performance is what the next phase of manufacturing AI advantage requires. Manufacturing COOs need to help drive the next phase of AI now, because today's efficiency gains are tomorrow's table stakes.

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Most manufacturers cannot prove their AI is governed

Fifty percent of manufacturing leaders said formalizing an AI strategy or governance framework is the single most important change their organization needs to make in the next six months, possibly because only 7% have a playbook that is defined and tested — the lowest rate of any sector in the survey. So, out of 100 manufacturing leaders surveyed, 93 are running AI in production environments, on factory floors, in supply chains or in quality systems, without having rehearsed what happens when something goes wrong.

In an industry that runs safety drills, tests backup generators, and rehearses crisis protocols for physical systems, the AI capabilities running alongside those systems have had no rehearsal.

That illustrates a stark contrast between how manufacturing treats physical risk preparedness and how it treats AI risk preparedness.

Just 14% of manufacturers feel their organization is extremely prepared to handle AI-related privacy and security challenges, the lowest rate of any industry in the survey, compared with a 40% overall average. When AI fails in an operations environment — a quality control error, a scheduling cascade, a procurement decision gone wrong — organizations without a tested response plan discover the cost in production, not in policy.

Regulatory uncertainty compounds the pressure. Fifty-seven percent cite compliance uncertainty as a top AI scaling barrier; 54% name it as their primary concern about agentic AI. Manufacturers with facilities across multiple states face inconsistent guidance with no federal standard in place. The manufacturers pulling ahead are building governance infrastructure now, because the evidence built before the pressure arrives is what creates the confidence to scale when it does. Waiting for external clarity is a decision with a measurable cost.

At the board level, manufacturing organizations are approving AI investments — 79% of respondents said their boards have done so. But fewer manufacturing boards are taking the next step: establishing formal AI governance policies (42%, compared with 52% across industries). Investment is moving faster than governance, which creates a compounding risk. Without clear controls, scaling an AI pilot does not just expand its potential — it expands its exposure.

Strategy drives returns. The strategy is mostly unbuilt.

Strategy is a conviction that most manufacturers say drives their AI returns, but competitive pressure is what drives their behavior. Most manufacturers are running the second while naming the first — and the performance distance that creates is already showing up in innovation returns.

A manufacturing AI strategy grounded in competitor observation produces investment in what peers are already doing, which is also what they have already done. Effective strategy in this sector answers specific questions: which decisions drive our margin structure, where does AI create outsized impact in our operating model and who owns accountability when AI influences an outcome.

Governance can help you define an AI strategy focused on solutions that meet your greatest needs. It can also help you control and scale those solutions. An autonomous quality inspection system can flag defects in real time, but it needs governance to monitor whether the system’s detection thresholds are still accurate as production conditions change.

A predictive maintenance agent that schedules interventions without human approval will need governance that tracks whether those interventions are actually preventing downtime or creating unnecessary stoppages. An AI-driven procurement system that adjusts supplier allocation autonomously needs governance that detects when the model's decisions drift from the parameters leadership set.

The manufacturers who have built answers to those questions are accumulating competitive position right now. The ones still mapping competitor moves are funding the wrong race.

The window is closing

Manufacturers who have captured efficiency returns from AI have a foundation. Grant Thornton’s 2026 AI Impact Survey shows that the organizations pulling ahead have used this foundation to build a proven infrastructure: tested incident response, data that connects operational decisions to margin outcomes in real time and strategy specific to their operating model. The competitive distance between those organizations and the ones still piloting is already visible in the data.

The path forward for manufacturers is not another pilot. It is not a wholesale technology overhaul, either. The executives in this survey have already demonstrated they can invest and experiment. What they need now is a more deliberate sequence: get the data foundation right, build internal capability alongside purchased tools, and put governance in place before scaling rather than after, while leadership is involved and supporting along the way.

These are not abstract recommendations. They are the practical work of making AI investments earn their return — and they look different in manufacturing than they do in financial services or technology. The operational realities are different. The regulatory pressures are different. The workforce dynamics are different.

Grant Thornton's AI and manufacturing specialists work with companies navigating exactly this transition — from pilot to proven, and from investment to impact. Not with hype, and not with sweeping transformation agendas. With practical, sequenced work grounded in how real manufacturing businesses actually run. Request a meeting to get your customized playbook today.

Methodology

Between Feb. 23 to March 18, 2026, Grant Thornton surveyed 950 business leaders, a group restricted to CFOs, CIOs/CITOs, COOs and VPs, department heads and directors who report directly to the C-suite. The manufacturing-specific subgroup comprises 100 respondents. Role-specific findings within the manufacturing subset of data are directional only.