Manufacturers have long understood the promise of AI’s potential, especially for improving product quality, growing employee productivity, and reducing errors on the plant floor. Many manufacturers are looking at AI pragmatically to see if they’re able to prove measurable value – and repeat it at scale.

The investment in AI and emerging technology is there: Deloitte’s 2026 Manufacturing Industry Outlook cites that 80% of manufacturing executives plan to allocate at least 20% of their improvement budgets to smart manufacturing technologies, including automation, advanced analytics, cloud platforms, and agentic AI. The reasons are not abstract. In a market defined by tight margins and volatility, manufacturers are looking for ways to boost competitiveness and productivity, make faster decisions and become more resilient in how they plan, run, and recover operations.

The pilot to ROI disconnect

We often see teams launch pilots that look promising in isolation but when the work meets the realities of shift handoffs, legacy processes, data gaps, and production risk, the ROI never comes. In those moments, the issue is rarely the tech itself or the specific model used. More often, it is that the organization is not set up to measure improvement clearly, prioritize the right problems or embed AI into day-to-day execution.

Deloitte’s 2026 State of AI in the Enterprise report shows this is a common pattern across industries. Workforce access to approved AI tools expanded 50% in one year, but fewer than 60% of those with access use AI regularly – an adoption gap that limits value. And although agentic AI use is expected to increase sharply within two years, only one in five companies has mature oversight, which matters when autonomy meets operational risk.

The implication for manufacturing is straightforward: the primary hurdle is no longer getting a pilot off the ground. It’s operationalizing AI, i.e., turning models into decisions, decisions into actions, and actions into sustained performance improvement with governance that can keep up. Ultimately, manufacturers must do the work of identifying the best use cases for their business and creating a culture where AI can thrive in daily routines.

Three ways to unlock AI’s value

In our work with clients, we’ve seen manufacturers break through the ROI barrier by getting three things right:

1. Treat data readiness as a must-have foundation. Data quality, accessibility, governance, and context (aka the “why” behind a signal, think assets, products, recipes, shift conditions) are what make AI usable. Big strides often come from targeted data improvements tied to a priority problem such as standardizing downtime codes on a constraint line, tightening traceability around a costly defect family, or aligning energy data to production segments. Teams act with confidence when they know reliable, contextually verified information is powering decision making.

2. Baseline performance before pilots to plan for scale from the start. AI can only prove incremental value if the current state is clearly defined, so establishing operational KPIs early is key. First, capture a baseline period that reflects normal variability, and set success thresholds in advance. In manufacturing, that often means focusing on metrics leaders already track and teams can influence like cycle time, scrap and rework, and unplanned downtime. Then prioritize use cases that are feasible with your available data and directly tied to decisions that move those KPIs. Those early wins should be treated as the foundation for compounding gains – from there you can build reusable capabilities that make each subsequent deployment not only faster, but also customizable for different sites with different system landscapes.

3. Don’t underestimate the importance of shifting workflow culture. AI only provides lasting value if the people running operations feel confident using it in their daily work. The journey of building change into daily routines is often underestimated by manufacturing leaders, leading to a mismatch of expectations coming from the top and real employee experiences. It’s important to recognize that change can be hard – just because you make AI tools available doesn’t mean you can ignore the training and hands-on skill building necessary for success. Developing AI implementation plans that feel worker-led rather than top-down mandated can build a culture of eager participation.

Turning AI investment into sustained growth

As investment around AI grows so do expectations for ROI. The manufacturers that pull ahead will be the ones that stay disciplined on value: prioritize data foundations first, choose use cases that matter, measure against clear baselines, embed AI into how work gets done, and scale systematically with oversight. Putting these principles first is how leaders can move past AI experimentation and start seeing long-term gains.

It’s just as important to treat ROI as a continuous operating discipline that cascades across teams. The organizations that sustain gains are explicit about how frontline teams are trained and supported to act on insights. Cross-functional collaboration between operations, IT, data, engineering, and risk is what makes scaling repeatable and maintains guardrails. With each deployment comes more insights – it’s important to build workforce skills to analyze and act on those insights so that the next use cases cost less, scale faster, and deliver value more predictably.

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