Corning Data survey finds 43% of manufacturers see industrial AI as transformational

Corning Data’s Manufacturing Sentiment Report finds rising confidence in industrial AI, with 43% of surveyed participants calling its impact transformational and more than 80% budgeting 1% to 2.5% of sales for innovation. The report also flags execution gaps, with most manufacturers saying progress is slow and key risks tied to supply chains, decision speed and organizational readiness. Why it matters: - Industrial AI is moving from experiment to operating strategy for many manufacturers. - The report suggests companies that can scale AI and innovation faster may gain efficiency, stronger margins and better customer response. - The findings also show a gap between belief in AI’s value and the ability to execute at scale. What happened: - Corning Data released a Manufacturing Sentiment Report on June 17, 2026, based on a survey of manufacturing participants. - Forty-three percent of participants described industrial AI’s impact as transformational. - The remaining 57% split into three groups: 29% saw AI as important but not a primary growth driver, 15% viewed it as a defensive strategy to avoid falling behind, and 9% said it was largely experimental. - National Association of Manufacturers data show 40% of members now believe in AI’s potential, up from 10% two years ago. - One consultant questioned whether the industry’s real leaders are the companies embracing AI or the companies ignoring it. The details: - More than 65% of participants said greater efficiency is the top motivation for AI and innovation. - More than 50% cited efficiency and productivity, followed by decision-making speed and quality, as the biggest drivers. - Manufacturers said the biggest barriers are costs, funding prioritization and willingness to change operating models. - More than 80% said they budget 1% to 2.5% of sales revenue for innovation. - About 80% said innovation initiatives are making progress, though half said progress is slow but steady. - Jon Walczak, chief architect at Corning Data, said innovation requires planning, commitment, patience and flexibility. - Nathan Peterson, founder of Vedera Modular in Denver, used AI coding tools to build custom software for real-time labor balancing across assembly stations. - Peterson said the software has already doubled factory efficiency and could raise labor utilization efficiency by up to 55%. - Peterson said the goal is to make housing more attainable by lowering construction costs. Between the lines: - The report points to a manufacturing sector that sees AI as useful but not yet universally essential. - Supply-chain disruption remains a central concern because pandemic-era uncertainty and geopolitical events still affect planning. - Decision speed ranked as the second-highest extinction warning sign, underscoring how fast-moving operations can be a competitive edge. - Consultants highlighted sales pipeline health, repeat orders and margin on sales as metrics manufacturers should monitor closely. - The report suggests culture and workforce readiness may matter as much as technology, especially where AI literacy and upskilling are required. - Paige Ricci, managing director at Baker Newman Noyes, said companies should think about how AI supports the business model rather than treat it as a massive technology project. What’s next: - More manufacturers are likely to move from pilots to production as confidence in AI grows. - Companies that can show effectiveness within 6 to 12 months appear better positioned to justify broader deployment. - The report suggests organizations that improve AI literacy, change management and cross-functional execution will be more likely to scale successfully. The bottom line: - Manufacturers see real upside in industrial AI, but the winners may be the companies that can turn experimentation into repeatable, companywide execution.