AI for manufacturers: From experimentation to capital expenditure

Whether operating in the United States or in Europe, manufacturers increasingly see investment in artificial intelligence as a core driver of future productivity, profitability and competitiveness rather than a discretionary innovation spend. While adoption remains uneven, the shift is clear: AI is moving from small-scale pilots to routine capital expenditure embedded within wider digital and operational strategies.

Most U.S. middle market manufacturing companies are already using AI and plan to increase their AI investments. Last year, 87% of manufacturing organizations were using generative AI tools, according to RSM’s 2025 survey of AI use in the middle market. However, 64% of those respondents agreed that generative AI has been harder to implement than expected.

Globally, manufacturing executives overwhelmingly rank AI among their top three strategic priorities, according to data from a 2025 Bloomberg survey. Expectations for outcomes are equally ambitious, with respondents anticipating 6%−10% sales growth and comparable profit uplift over the next two to three years because of AI adoption. Yet fewer than half of current industrial offerings are AI enabled, highlighting a clear gap between strategic ambition and operational delivery.

For many manufacturers, AI is now competing directly with traditional capital investments (e.g., equipment, facilities) for funding, increasing the importance of disciplined capital allocation. Resources shifting toward AI may lead to increased maintenance needs, more frequent repairs and higher levels of operational downtime. To mitigate these risks, manufacturers can leverage digital twin models to simulate operations, enabling more accurate prediction of equipment issues, better planning for maintenance and improved resilience across the production process.

How manufacturers are funding AI investment

In the United States, AI’s shift from experimentation to part of essential capital expenditure is less a result of policy than of structural labor constraints and productivity imperatives across the middle market. Companies are increasingly evaluating AI alongside traditional capital investments, and spending is rising significantly among upper middle market companies that have more access to capital.

Funding AI investment remains a central challenge for manufacturers. In the UK, private sector investment levels have fallen year over year since 2021 and remain lower than those of other G7 economies, putting pressure on businesses to prioritize capital carefully. Against this backdrop, government investment is shown to deliver significantly higher long-term returns than private investment alone, reinforcing the role public funding can play in stimulating private capital.

Policy announcements bear this out; the UK government’s AI Growth Zones program, announced in 2026 and expected to attract over $10.8 billion in private investment, illustrates how public expenditure can de-risk and accelerate adoption of AI infrastructure. For manufacturers, these programs provide access to funding and shared capabilities that would be difficult to build independently.

UK data shows that manufacturers currently allocate a relatively small share of their AI budgets to hyperscalers (12%) and AI-native providers (3%), with spending skewed instead toward IT services and systems integration, according to data from Bloomberg Intelligence. This suggests that the manufacturing sector isn’t as far along in AI adoption as sectors such as technology or financial services.

Similarly, the U.S. Census reports that current AI use in the information, finance, insurance and services sectors remains higher than in the manufacturing sector.

AI budgets are rising—but with disciplined return thresholds

Despite these constraints, manufacturers’ technology budgets are rising. In the U.S., over three-quarters of respondents in RSM’s 2025 survey said they have a budget for AI investments, and 88% of that group expected their AI budget to increase in the coming fiscal year. However, as middle market businesses become more complex, so do their investment decisions—and the scrutiny of returns intensifies.

This heightened complexity and scrutiny signal a clear shift: AI is no longer treated as a speculative bet, but as a utility or a type of infrastructure. Companies are funding automation, analytics and digital platforms much like they do machinery. However, relatively conservative internal rate of return (IRR) hurdles risk undervaluing the transformational upside AI can deliver if businesses focus on near-term efficiency gains rather than longer-term business model change.

In the UK, AI and technology spending across manufacturing is expected to increase by 6%–10% on average in 2026, according to Bloomberg Intelligence. At the same time, board-level return expectations remain modest. Weighted average IRR thresholds for AI projects sit at around 12%–13%, according to Bloomberg Intelligence.

AI adoption challenges: Execution, integration and widening divides

Even with rising investment, execution remains a major issue. Common challenges include a lack of in-house expertise, lack of a clear AI strategy, difficulty identifying use cases, difficulty selecting the right AI technology, and the cost of AI tools and implementation.

The uneven pace of adoption risks creating a widening productivity gap. Businesses able to fund and execute AI investment are likely to pull further ahead, while those constrained by capital or capability fall behind. Crucially, AI is widely seen as augmenting rather than replacing labor, with businesses expecting faster development cycles and a shift toward higher-value roles rather than job displacement.

AI opportunity: Key considerations for manufacturers

While less than half of respondents to a Bloomberg survey across all industries (not just manufacturing) expect direct price increases from AI features today, more than 90% expect AI-driven product upgrades. Around 82% believe these upgrades will accelerate equipment replacement cycles as customers invest earlier to access new functionality. This creates a powerful indirect pricing lever to boost aftermarket revenue and strengthen customer stickiness.

To seize on the current opportunity of AI and not fall behind, manufacturers should consider:

• Strengthening data foundations to improve data quality, accessibility and integration across legacy and operational systems
• Focusing on a defined set of high‑value AI use cases that align directly to core business objectives and near‑term operational impact
• Upskilling the existing workforce so employees can effectively adopt, operate and continuously improve AI‑enabled workflows
• Embedding AI into day‑to‑day operations, ensuring solutions are integrated into existing processes rather than deployed as stand-alone tools

These approaches can help manufacturers close the gap between strategic AI ambition and operational delivery—and ensure their expectations for returns are grounded in reality.