Corning Data Industry Report Explores Sentiment Surrounding Motivations, Risks of Extinction and Ability to Scale AI-Driven Innovations

RALEIGH, NC, UNITED STATES, June 17, 2026 /EINPresswire.com/ -- Qualifying the power and potential of industrial AI, innovation and technology for manufacturing firms across the globe is like acknowledging whether the glass is half full or half empty; it all depends on perspective.

According to a Manufacturing Sentiment Report produced by Corning Data, 43% of participants in a survey describe the impact as transformational. The remaining 57% of participants were split between important but not viewed as a primary growth driver (29%), a defensive strategy to avoid falling behind (15%), and largely experimental (9%).

National Association of Manufacturers (NAM) statistics support the increasing magnitude of AI and innovation’s role in industrial manufacturing. Today, as manufacturers embrace AI and move into production-based deployment, 40% of its members believe in its potential. Two years ago, only 10% recognized its potential impact.

One industry consultant, however, played devil’s advocate and asked, “With almost 60% not considering industrial AI vital to their business, the bigger question may be who are the industry thought leaders—those ignoring the distraction of AI or those running towards it?”

Additional takeaways from Corning Data’s Sentiment Report include:
• Efficiency and productivity, followed by decision making speed and quality are the greatest motivating factors, said more than 50% of participants.
• Costs, funding prioritization, and the willingness to change operating models are the issues most likely to stall progress and long-term success.
• Supply chain impact and speed of decision making rated the highest of seven “warning signs” of extinction, but all were rated a moderate risk with a score over 3.3.
• More than 80% of manufacturers say they budget 1-2.5% of sales revenue for innovation.
• Transformation doesn’t occur overnight. Approximately 80% say initiatives are making progress; but half said progress is slow but steady.

“When they work as intended, innovation and technology drive solutions, solve problems and create opportunities in business,” said John Walczak, Chief Architect, Corning Data. “But it doesn’t happen magically overnight. Finding solutions require planning, commitment, patience, and flexibility.”

The Motivation of Efficiency
More than 65% of participants named greater efficiency as the top motivation for leveraging AI and innovation. Among the ways manufacturers are looking to be more efficient, and enhance their financial position, include eliminating invisible waste (resources tied up in excess inventory or inefficiencies during machine changeovers), overcoming primary bottlenecks caused by configuration complexity, and quoting delays.

“Companies today regardless their size, scope, or products manufactured look for innovative ways to harness the potential of industrial AI and technology,” said Rick Scearbo, Chief Revenue Officer, Corning Data. “They’re investing in partnerships designed to make them more efficient and create opportunities.”
Report participant Nathan Peterson, founder of Vedera Modular, a modular homebuilder in Denver, Colo., and his team developed custom software using AI coding tools to solve "variability," his definition of “the primary enemy of assembly lines.” The software performs real-time labor balancing to move workers between assembly stations based on specific design requirements. The software has already doubled factory efficiency. Peterson estimates it could increase labor utilization efficiency by up to 55%.

“The ultimate goal of our tailored innovation is to make housing more attainable by creating a competitive advantage through genuine cost savings rather than relying on commoditized and homogenized products,” Peterson said. “The efficiencies we create can trickle down, lowering construction costs and making home ownership more attainable.”

Beware of Extinction
Participants rated seven warning signs of extinction on a risk scale from 1 (low) to 5 (high). The average risk of the seven warning signs was 3.38, clearly a moderate level risk. Supply chain impact was rated the top warning sign, 3.65, based on the degree of uncertainty that has existed since the pandemic and further exacerbated by geopolitical events. Embracing the adage “time is money,” a very close second was the speed of decision-making, 3.61.

Consultants suggested that one of the most critical metrics to follow, or ignore at your own peril, is the sales pipeline, including repeat orders from existing customers. Sales teams seek to break through and establish new accounts, but they can’t overlook the sales activity patterns of existing clients.

“They will likely tell you more about your product—its capabilities, pricing, and effectiveness, than winning over a prospective new client,” Jonathan Gross, Managing Director, Pemeco, said. “Manufacturers need to ask themselves, ‘How can I serve my customers better?’
Another key metric is margin on sales—the breakdown of the costs of production and the price being charged for the product. A picture that goes above and beyond the numbers also requires understanding the forces impacting costs and sales prices—like supply chain issues.

Execution
According to the survey, just over 80% of participants said innovation initiatives progress steadily (albeit sometimes slowly), and 31% said it moves quickly from pilot to scale. Most companies, 92%, said the timing for determining effectiveness of innovative new tools, is one year or less. More than half determine effectiveness within six months.

However, determining effectiveness and integrating innovative tools and systems companywide can be arduous and expensive. The mindset of the organization may be the most significant hurdle in identifying and adopting innovative tools and systems—not the technology itself. The transition often is more about changing culture from reactive to proactive behavior than it is about new technology. At the same time, "workforce readiness" and "AI literacy" are essential through structured upskilling or the initiative is destined to fail.

“Take the pressure off; remove the stressor of thinking of this as this massive technology initiative,” Paige Ricci, Managing Director, Baker Newman Noyes advised. “Look at it more from a perspective of how to use and leverage your business model to support AI."
Internally, the answers to questions such as, “How do we get even better at what we're good at?” and “How do we go from good to great," can be a guide for investigating, testing and integrating innovation.

Michael Dillon
Corning Data
+1 518-542-8717
mdillon@corningdata.com
Visit us on social media:
LinkedIn

Legal Disclaimer:

EIN Presswire provides this news content "as is" without warranty of any kind. We do not accept any responsibility or liability for the accuracy, content, images, videos, licenses, completeness, legality, or reliability of the information contained in this article. If you have any complaints or copyright issues related to this article, kindly contact the author above.