Volatile Markets Cost Fabricators Speed, Not Price

Fabricators lose margin to slow repricing, not high material costs; the fix is connected pricing, sourcing, and production data.

Adapting to the speed of changes driving today’s market instability is a major challenge for manufacturers.

According to the National Association of Manufacturers’ Q2 2026 Manufacturers’ Outlook Survey, 83.1% of manufacturers cited rising raw material costs as a top business challenge, while 71.8% cited trade uncertainty. Data from the Bureau of Labor Statistics indicate that producer prices for steel mill products and aluminum mill shapes increased 23.4% and 27.3%, respectively, over the 12 months ending in August 2026—illustrating how significantly the cost of critical inputs can move amid shifting trade, energy, supply chain, and geopolitical conditions. When asked what they expect to see with raw material prices and other input costs, manufacturers predicted an increase of 5.8%. This is the highest rate of increase since Q2 2022 when the inflation rate was between 8% and 9%, and they anticipate a 5.8% increase over the next 12 months, up from the 4.1% increase projected just one quarter earlier.

Many fabricators are treating these fluctuations in prices as a cost problem; it isn’t.

In a market where supply costs change on a weekly basis, companies that lose margins aren’t losing on price. They are losing on response speed.

The Leak Is in the Lag

In today’s market, fabricators quote off a material cost that is stale by the time a purchase order can be generated. Quote windows close before the deals close. Surcharges fluctuate as mill pricing changes. These constant disruptions wreak havoc on fabricators’ ability to deliver high-mix, low-volume quoting, material pass-through, and scrap and remnant exposure. The hard truth is this: You can’t out-negotiate a sudden, external cost increase.

So, what can you do? Out-execute a competitor that continues to reprice manually to market changes.

The AI Confidence Trap

In July 2025, MIT’s Project NANDA released a report, The GenAI Divide: State of AI in Business 2025, which found that despite enterprise investments of $30–40 billion, 95% of generative AI projects yield no measurable business returns. This stands in stark contrast to the confidence most businesses have that their data is ready for AI.

Recent research from Infor found that while confidence in AI adoption is high, decision-makers are still navigating the operational challenges required to make AI work at scale. This reflects a broader shift in how organizations are thinking about AI, moving from isolated experimentation toward embedding intelligence into core business processes. While roughly 70% of businesses in the U.S. and 74% in U.K. report having the capability to manage AI implementation, a meaningful share of organizations still faces structural barriers, highlighting a disconnect between readiness and real-world execution.

“Many fabricators are treating these fluctuations in prices as a cost problem; it isn’t.”

Businesses consistently point to data, integration, and system limitations as core barriers, suggesting that the ability to operationalize AI is constrained less by access to technology and more by the environments in which it is deployed. At the same time, the pace of AI innovation is increasing investment complexity, as organizations must continuously invest in new tools and employee training while ensuring those efforts translate into measurable value.

For fabricators, layering AI on top of disconnected pricing, inventory, and production data doesn’t fix the lag problem that exists—it merely inherits it.

What Fast Looks Like

The gap between leaders and late-comers isn’t caused by AI budgets, but by whether or not data is connected. When costing, sourcing, and production run on connected data, surcharges triggered by external market forces reprice the open quote automatically, keeping the job on track. If a margin breach occurs, threshold monitoring catches and corrects it.

Connected data is a discipline: every function that touches a quote, from raw material procurement to shop floor scheduling, draws from the same live source of truth instead of a patchwork of spreadsheets, siloed databases, and manual updates. Two key principles separate fabricators who reprice in hours from those who reconcile for weeks.

Contextualized data beats centralized data. Raw information sitting in a shared database doesn’t help a quoting team if it isn’t tied to the right customer, product line, or plant environment. Oberg Industries, a precision stamping and machining manufacturer founded in 1948, addressed this by building a data environment that adapts to the different product lines and customer requirements across its operations, rather than forcing every job through a single rigid template. The lesson for the broader industry: connection without context still produces slow, manual work; the value comes from data that arrives already framed for the decision it needs to support.

“The gap between leaders and late-comers isn’t caused by AI budgets, but by whether or not data is connected.”

Visibility across locations must be consistent, not just available. Multi-site fabricators frequently accumulate different pricing logic, surcharge triggers, and reporting standards at each facility, especially after acquisitions. Ellwood Group, a manufacturer of engineered steel components for the mining and drilling industries, has scaled through acquisition while maintaining a single, consistent view of operations across its locations. That consistency is what allows a corporate team to catch a margin breach at one plant as quickly as at another, instead of discovering it weeks later during a monthly roll-up.

Business at the Speed of Change

For the foreseeable future, cost changes driven by external forces are not going to go away—and they certainly won’t stabilize on a fabricator’s timeline. To succeed (and survive), manufacturers and fabricators need to respond at the speed of change. Fixing the data foundation—and creating connected data—is more important than any single AI investment. The companies who treat speed as their margin strategy will be the ones who can protect their margins when the next major market disruption takes hold. M

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