At the 30th ARC Industry Leadership Forum in Orlando in February, the prominent themes focused on industrial uses of artificial intelligence (AI), and involved the evolving digital transformation. In exploring these broad topics, presenters explored workforce challenges, such as combating loss of plant “tribal knowledge” with a wave of retirements, as well as supply-chain challenges driven by geopolitical machinations. Broad agreement seemed to coalesce around the idea that changes in industrial automation and AI are occurring faster than at any time in the past, and the complexity is correspondingly higher. The situation makes collaboration among industry stakeholders increasingly critical.
Integration of AI
The integration of AI into traditional process control functions was among the main focuses of the event. Among the key questions facing process manufacturers are: how to develop the underlying data infrastructure necessary for effective use of AI tools; how to scale up AI beyond pilot projects and into widespread commercial operations; and how to integrate AI trust, transparency and security into process-control system designs from the outset.
Although the deployment and integration of AI was a major focus at the event, it was also placed within a wider digital transformation framework in which value-generation and business-performance outcomes are the ultimate objectives.
At a panel discussion sponsored by Schneider Electric (Rueil-Malmaison, France; www.se.com) at the ARC event, participants outlined the objectives of AI, and the necessary prerequisites for effective AI use. Jim Chappel, global head of AI at Aveva (Cambridge, U.K.; www.aveva.com), said AI tools should be aimed at bringing value to an operation, and achieving a return on investment (ROI), such as reducing downtime or identifying process issues early, preventing larger problems later, for example. AI use untethered to a business objective risks having the AI tool be a solution searching for a problem.
Mike Moody, automation and process control manager at BASF (Ludwigshafen, Germany; www.basf.com), said it is important to understand the impact of AI deployment on the plant floor, including the potential benefits of integrating AI technologies and the possible consequences of getting it wrong.
There seemed to be widespread consensus around the ideas that establishing a solid data foundation is required for successful deployment of AI technologies is critical to their success, as is the need to build trust and engagement with operations staff as AI tools are being integrated.
Two Keynote presentations focused on the potential for AI on improving operations, with a focus on supply-chain management. Chase Christensen, the chief information officer of manufacturing solutions provider Jabil (St. Petersburg, Fla.; www.jabil.com), talked about AI as a “force multiplier” that can help address workforce challenges. Ashin Parikh, senior VP for supply-chain at PepsiCo (Purchase, N.Y.; www.pepsico.com) talked about an ongoing shift for AI technologies, where AI is becoming a tool for decision-making, not just for insight generation.
The importance of AI literacy across the full workforce; the growing role of AI governance, the value-driven prioritization of AI use cases and cross-functional collaboration were also discussed.
Greg Gorbach, a vice president with event organizer ARC Advisory Group Inc. (Dedham, Mass.; www.arcweb.com), highlighted the most important aspects of industrial AI for those getting started in the process: strategy and governance, data fabric and people and culture. Gorbach also highlighted the progress being made toward connecting people with processes and empowering workers by using digital technologies
Digital transformation survey data
Gorbach presented recently collected survey data about digital transformation progress. The percentage of multi-industry respondents saying their organizations are employing little to no digitalization decreased from 17% in 2024 to just under 10% in 2025, while the percentage of respondents saying they are currently deriving insights from digital tools that are guiding optimization efforts increased from 12% in 2024 to 17% in 2025. The largest jump was seen in organizations applying AI (including machine learning) to automate and optimize their processes – in 2024, it was 11%, and it grew to 20% in 2025.
Meanwhile, the objectives driving business investment shifted in 2025. A pragmatic approach is common, with cost reduction now being the driving the most business investment, survey data suggest. Business expansion, growth and extension into new markets, which topped the list in 2024, slipped to third position. Sustainability and decarbonization was also a top motivator for business investment.
Regarding AI specifically, respondents were asked which primary business outcomes they are expecting to achieve through investments in industrial AI. Improving productivity of workers was the top response, with over 55% of respondents citing that as a desired outcome. Optimizing real-time production and asset performance was second, and automating complex business or operational workflows was third. Enhancing decision-making and accelerating R&D rounded out the top five desired outcomes for AI.
Gorbach also talked about a “digtal divide” when it comes to AI adoption. The data suggest a gap between pace-setters (13%) on AI adoption and laggards (32%). The other 65% fall into the spectrum in the middle.
Needs include a robust industrial data fabric; industrial AI infrastructure; and industrial models with agents; along with good AI governance policy and AI literacy among the workforce.
Shifting role of AI
At a lunch briefing during the ARC Forum event, Jay Allardyce, chief product officer at Hexagon AB spinoff company Octave (www.octave.com), discussed the “productivity paradox” — the phenomenon in which large investments in new digital transformation and industrial AI fail to produce the productivity gains envisioned.
Allardyce said that there remains a 70% failure rate for digital transformation spending in terms of ROI. “The biggest barrier to scaling AI is still culture,” he pointed out. Contextualizing data is among the keys to realizing real benefits for industrial AI, he said.
Octave saw a shifting role for industrial AI: one in which companies move away from using AI for information retrieval to viewing AI as a colleague. In the future, AI will become more of an orchestrator, Allardyce envisions.
Industrial AI software will be seen less as a tool and increasingly taking on the role of a teammate, he opined.
For more on the ARC Forum, specifically developments related to interoperability and open process automation standards, see Chem. Eng., March 2026, p. 4). https://www.chemengonline.com/editor-commentary-inflection-point-for-interoperability/
Scott Jenkins