Manufacturers Must Build an AI Control Tower for Margin and Reliability

Manufacturers are shifting AI from experimental pilots to reliable operational performance, anchored by strong governance, clear measurement, and rigorous accountability.

By Kelly Schindler, Head of Manufacturing, Grant Thornton Advisors

Key Takeaways

Manufacturers are evolving AI from curiosity to core operating system, treating it with the same rigor as safety, quality, and risk controls. The focus is on governance, real‑time measurement, escalation protocols, and accountability. AI now informs procurement, scheduling, maintenance, quality, and production decisions that directly impact margin and customer experience. The most advanced firms link AI outputs to tangible business results and proactively prepare for drift, failures, or operational disruptions.

Key Insights

• AI is deployed in high‑stakes operational settings that demand robust governance to prevent margin, uptime, quality, or customer commitment errors.
• Efficiency gains are common, but lasting advantage hinges on measurable business outcomes.
• Governance is shifting from a compliance checkbox to an operational imperative tied to accountability and risk mitigation.
• AI strategies must prioritize margin‑driving decisions rather than chasing competitors or vendor pressure.
• The next evolution of manufacturing AI will be defined by control, measurement, and disciplined operations.

From Pilots to Operational Advantage

While many manufacturers are experimenting with AI, scaling it across functions is the real challenge. Grant Thornton’s 2026 AI Impact Survey shows that 48% of manufacturers are still piloting AI, yet only 10% have fully embedded it into operations. Across all industries, 49% have scaled AI, but manufacturers lag at 39%.

Without scale, AI remains a siloed initiative. A predictive maintenance model in one plant offers little competitive edge compared with an integrated system that links scheduling, supplier performance, maintenance intervals, and delivery commitments across multiple sites.

Manufacturing leaders already excel at stress‑testing operational systems; that discipline must now extend to AI. Building systems that boost throughput, cut scrap, preserve uptime, and strengthen financial performance under real conditions is essential.

Operations: Highest Upside, Highest Risk

Manufacturing is deploying AI in its operational core faster than any other sector. The survey found that 62% of manufacturers identify operations as the most critical area for AI focus.

AI is driving production scheduling, predictive maintenance, quality control, safety, procurement, and supply‑chain coordination—factors that shape output, cost structure, service levels, and margin daily.

When AI improves scheduling, cuts downtime, or flags defects early, the upside is substantial. However, model drift, data degradation, or unclear escalation paths can quickly erode that value.

For example, an autonomous quality inspection system needs governance to maintain accurate detection thresholds as production conditions evolve. Predictive maintenance must verify that interventions reduce downtime without creating unnecessary work. AI‑informed procurement must ensure supplier allocations align with cost, quality, and risk priorities.

Operational AI accelerates decision speed and scale, amplifying the need for accountability.

Efficiency Gains Are Now Standard, the Real Opportunity Lies Ahead

Manufacturers report tangible efficiency improvements: 64% say AI has increased efficiency. Yet only 14% report accelerated innovation—17 points below the industry average. No manufacturing respondent noted significant revenue lift, and 47% saw only modest revenue gains.

These findings reveal that many firms have boosted activity levels without yet transforming business performance.

As AI adoption matures, basic efficiency will become baseline capability. The real differentiation will come from tying AI to margin‑driving decisions—procurement optimization around supplier risk, scheduling that accounts for energy costs, quality improvements that reduce scrap, and maintenance strategies that maximize uptime and asset life.

Companies that link AI directly to these operational and financial levers will separate themselves from those achieving isolated productivity gains.

Governance: From Compliance to Core Operation

Manufacturing already implements detailed controls for safety, quality, continuity, and operational risk. AI demands the same level of rigor.

The survey found only 7% of manufacturers have a tested AI‑specific incident response playbook. Simultaneously, 50% of leaders say formalizing an AI strategy or governance framework is the most critical change needed in the next six months. Merely 14% feel fully prepared to tackle AI‑related privacy and security challenges, while 57% cite compliance uncertainty as a top barrier to scaling AI, and 54% view compliance uncertainty as their main concern around agentic AI.

Kelly Schindler notes, “Manufacturers are deploying AI where failure has the highest impact, yet most have not rehearsed what happens when it goes wrong.” “The question isn’t whether AI belongs in operations; it’s how we will know, who owns recovery, and what evidence we have.”

Governance should be an operational discipline, not a bureaucratic overlay. Clear ownership, escalation pathways, audit‑ready evidence, testing standards, and monitoring processes are essential to confirm AI systems perform as intended.

Strategy Should Drive Margin, Not Competitive Position

Many leaders feel pressured to accelerate AI investment because competitors are moving quickly. The survey shows 45% of manufacturers are driven by competitor actions, yet only 42% have formal AI governance policies (vs. 52% industry average).

Investment without governance discipline can lead to fragmented deployment, inconsistent accountability, and unclear value.

Manufacturing boards report AI investment approval rates of 79%, but only 42% have established governance. Strategy should be the primary driver of ROI, beginning with the operating model itself. Leaders should identify decisions that most affect throughput, quality, uptime, procurement performance, and margin, and prioritize AI deployment around those decisions.

AI is not required in every process—only where operational and financial leverage is highest and governance can support measurable outcomes.

Those who prove they can trust, govern, and connect AI to tangible results will gain lasting advantage.

FAQs

Where should manufacturers focus AI first?

Prioritize AI in operational areas that directly influence margin, uptime, quality, safety, procurement, scheduling, and service performance.

What does an AI control plan include?

An AI control plan comprises governance policies, incident response procedures, escalation paths, monitoring standards, testing protocols, and accountability for operational outcomes.

Why tie AI strategy to margin?

Margin‑focused strategies target the operational decisions that most impact profitability, throughput, quality, and customer performance.

About the Author:
Kelly Schindler is the Head of the Manufacturing Industry and an Audit Partner at Grant Thornton’s St. Louis office. She oversees the growth and operations of the firm’s manufacturing practice, covering technology, assurance, tax, and consulting services. Kelly frequently travels with domestic and international manufacturing clients, delivering industry insights, identifying solutions, and fostering networks of best practices.

www.grantthornton.com