By 2028, AI-driven industrial plants will not only run autonomously, they will explain their decisions in real time. At the Honeywell User Group in The Hague, Honeywell Chief Product Officer Claudia Chandra and Adlib CEO Chris Huff described this shift as a move from data entry to data fluency, where operators ask questions in natural language and receive instant, contextual answers.
What Is Data Fluency in Manufacturing?
Data fluency in manufacturing is the ability of operators to ask AI systems questions in natural language and receive instant, contextual answers, rather than manually entering and reconciling data. At the Honeywell User Group in The Hague, Claudia Chandra and Chris Huff identified this shift as the defining change coming to industrial plants by 2028.
As Chandra noted, today’s process control systems can only adapt within strict boundaries defined by engineering models. But when AI and machine learning merge with those first principles, the plant gains judgment. It can learn from past patterns and adjust to new conditions without waiting for human recalibration.
How Does AI Change Production Decisions?
The go/no-go problem is the time operators lose at each shift start interpreting handwritten notes, inconsistent data, and incomplete reports before production can proceed. Adlib CEO Chris Huff identified this as one of the most overlooked inefficiencies in modern manufacturing, one that costs hours per week across plants worldwide.
In his words, “If the scribbles from an eight-hour shift can be translated into machine-readable insights, you can standardize the formats and get to a decision much sooner.” It’s not flashy—but it’s transformative.
Will AI Replace Plant Workers?
AI will not replace plant workers. Both Claudia Chandra of Honeywell and Chris Huff of Adlib agreed that AI’s role is to remove repetitive, transactional work so engineers and operators can focus on analysis, optimization, and decision-making. As Huff stated, “AI won’t replace an employee. But an employee using AI will replace one who isn’t.”
AI’s real role isn’t to replace people—it’s to remove repetition. By taking over the transactional side of work, it frees up engineers and operators to focus on analysis, optimization, and innovation.
What Will Industrial Plants Look Like in 2028?
By 2028, control room operators will function as conversational analysts, querying unified AI systems instead of searching emails and handwritten notes. Reliability engineers will run scenarios rather than crunch numbers manually. When something goes wrong, the plant will raise an alert, identify the cause, and recommend corrective action.
And when something goes wrong, the plant won’t just raise an alert—it will tell you why and what to do next.
As Huff summarized, “AI won’t replace an employee. But an employee using AI will replace one who isn’t.”
That’s not hype, that’s 2028.
This article was written based on a video interview at the Honeywell User Group in The Hague, with Claudia Chandra, Chief Product Officer at Honeywell, and Chris Huff, CEO of Adlib.
Sponsored by Adlib. Travel to the event was supported by Honeywell.
FAQ
1. What does “data fluency” mean for industrial plant operators?
Data fluency means operators can query AI systems in natural language and receive instant, contextual answers, rather than manually entering data into dashboards. Honeywell CPO Claudia Chandra described this at the Honeywell User Group in The Hague as the next generation of plant operation, where AI summarizes shift notes, generates logs automatically, and recommends corrective actions in real time.
2. How does AI solve the shift handover problem in manufacturing?
AI solves the shift handover problem by translating handwritten notes and inconsistent shift records into machine-readable insights that merge with live sensor data. Adlib CEO Chris Huff explained that digitizing this information removes a hidden bottleneck at the start of each shift, allowing go/no-go decisions to happen instantly and consistently instead of after lengthy manual interpretation.
3. What is the biggest barrier to AI adoption in industrial plants?
The biggest barrier to AI adoption in industrial plants is not the technology itself; it is change management. Chris Huff of Adlib stated at the Honeywell User Group that “AI is ready” but that plants must redesign workflows to support collaboration between human intuition and machine precision. Workers do not need to become data scientists, but they do need to become data fluent.
4. How does AI judgment differ from traditional process control systems?
Traditional process control systems can only adapt within strict boundaries set by engineering models. Honeywell CPO Claudia Chandra explained that when AI and machine learning merge with those first-principles models, the plant gains judgment: it can learn from past patterns and adjust to new conditions without waiting for human recalibration, making the plant capable of explaining its own decisions.
