Industry executives and experts share their predictions for 2026. Read them in this 18th annual VMblog.com series exclusive.
By Jim Chappell, Global Head of AI at AVEVA
Radical collaboration between humans and AI will further transform industrial operations. But the big story won’t be about robots replacing human workers. Rather, we will see industrial intelligence grow to support strategic decisions from shop floor to top floor, as multiple types of AI, from language models to agents, are deployed in practical and measurable ways to support complex outcomes. We see three major trends in 2026:
Generative AI (Gen AI) stops being a sandbox initiative and starts paying the bills as companies apply its capabilities in new ways. The average industrial worker now knows that Gen AI and large language models (LLMs) understand its benefits in summarizing information, creating early drafts of project reports and closing analysis and insight gaps. Spending on LLMs more than doubled in the first half of 2025, $3.5 billion to $8.4 billion, and teams will begin to prioritize performance over production, requiring specific outcomes. Gen AI is being incorporated into technological platforms, business processes, workforce development and improving products and services, often with new revenue models, PWC reports, even as the technology delivers up to 30% productivity improvements for companies that smartly integrate Gen AI. Looking ahead, a substantial portion of enterprise applications will now embed task-specific AI agents, as the focus shifts from �does it work?’ to �how does it deliver measurable value?’ In other words, matching Gen AI, combined with AI agents, to specific operational pain points with clear ROI metrics will drive competitive gains.
Radical collaboration between multiple types of AI will deliver results that single models can’t match, like an F1 pit crew: Consumers and business users are now familiar with generative AI, although industry has been using other applications of the science such as predictive maintenance for decades. We’re now beginning to see how different AI specialties can collaborate seamlessly. Think of it as an AI ecosystem that stacks together foundational models such as LLMs provide breadth and scale and specialized applications such as the neural networks that support Vision AI or time series forecasting. Combining one or more of these together with Gen AI and agentic techniques makes it easier for frontline workers to make the most of industrial intelligence. Value will come from building the right stack, for example, outlier detection for constraint checking, predictive models to drive performance and Gen AI for natural language queries. So, companies will pair an industrial AI assistant with generative design AI to speed up industrial engineering. With open standards, such as Model Context Protocol and Agent2Agent for agentic interoperability, this composite AI will help move from theory to practice. For users, the results arrive seamlessly, but behind the front-end interface, multiple types of AI interact to keep assets running – much like a Formula 1 pit crew. AI is a science with multiple applications whose outcomes depend on how application, data quality and human interpretation. Business solutions must therefore rest on customized systems, where AI suggests and human experts validate.
AI begins to move up the decision chain from tactical to strategic beyond 2026: As AI is embedded into industrial workflows, its success in driving tactical gains, particularly in uncertain operating environments, will begin to open doors to strategic applications. Already, connected industrial ecosystems can unite players across the value chain, from shop floor to top floor, so frontline workers and executives have access to one single source of truth. Extending production systems to enterprise resource planning, market analysis and long-term optimization enables multivariate analysis within seconds instead of months. Next, teams will be able to model different business scenarios within a single boardroom meeting, or optimize plants for the lowest costs without months of evaluation, shortening time to market, compressing development schedules and accelerating revenue outcomes. Executives understand that AI rarely delivers value in isolation, according to a Deloitte survey, and with the CEO now the primary leader of the AI agenda at a tenth of organizations, more companies will depend on intelligent technologies to make faster, better informed judgments. The caveat? Strategic AI will depend on unified data, seamless interaction across the ecosystem, and conscious cultural change.
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ABOUT THE AUTHOR
With over 30 years of experience in the industrial software sector, Jim Chappell is currently AVEVA’s global vice president and head of Artificial Intelligence. Prior to this, he led the Asset Performance Management suite of products and related engineering/analytics services for Schneider Electric. He was also a founding partner and managing officer of InStep Software, a global leader in industrial AI-driven Predictive Analytics and Big Data software, which was acquired by Schneider Electric in 2014.
Jim holds a B.S. in Nuclear Engineering from Rensselaer Polytechnic Institute in Troy, NY, a M.S. in Nuclear Engineering from the Naval Nuclear Power School in Orlando, FL, and a M.B.A. from Chaminade University in Honolulu, Hawaii. In addition, he graduated from the Civil Engineer Corps Officer’s School in Port Hueneme, CA. He also held a top-secret clearance while an officer in the U.S. Nuclear Navy.
