By Jennifer Shine, Principal Solution Engineer at Imubit
We invite customers to present at internal Imubit meetings from time to time, so the teams working behind the scenes hear the end-user and leadership view. An executive sponsor at one of those sessions said, "We're in a place right now where technology innovation is outpacing adoption." The line stayed with many of us. New technology is exciting, and its impact still depends on whether people adopt it.
As companies bring AI into their workflows, there is a risk that can stall progress before any value arrives. We call it conversion without transformation.
It happens when a company adopts AI tools but keeps the mindset, processes and decision frameworks it had before. The technology changes, and the way people think and work does not. Then the team moves on to the next project. Without change management, and without leadership that encourages and invests in adoption, the projected return is at risk.
The efficiency trap
It is easy to use AI to automate the tasks a team already does: reports, content, faster workflows. That feels like progress, and in part it is. Efficiency on its own does not change outcomes, though.
When teams plug AI into old systems and processes, they tend to get the same results faster. Much of what the technology could do goes unused, and there is little room for new ways of working.
Getting past it
Going further than doing the same work faster means rethinking four things.
- Decision-making. Use AI to make better-informed and more predictive decisions, as well as faster ones.
- Domain engagement. Combine AI with domain expertise so the solution reflects real process knowledge and helps teams solve operating problems before they escalate. That grounding is what makes a recommendation something engineers and operators trust and act on.
- Collaboration. Give operations, engineering and commercial teams the same data and the same optimization targets on one secure platform, so they align on decisions instead of working from separate views of the plant.
- Leadership mindset. Move the question from "how do we do this faster?" to "how do we do this better?"
Domain expertise still matters
AI cannot and should not stand in for process knowledge, operating intuition or industry experience. It should add to the expertise of engineers, operators and decision-makers, so they make better decisions with more confidence.
Without domain context, AI outputs can drift away from how the unit actually runs. The improvements that follow tend to be superficial, and the unit gets no closer to its optimum.
Imubit builds and refines its technology with experienced process engineers and operations specialists, so it reflects how plants in refining, chemicals and polymers, cement, and mining and minerals behave. Customers do not have to do this alone. Imubit engineers work alongside site teams to fill skill gaps, interpret model behavior in the context of the unit and sustain the results.
What it looks like in practice
We have refined this approach to adoption across over 100 closed-loop controllers deployed worldwide. Closed-loop optimization with Deep Learning Process Control® (DLPC®) supports that change in four ways.
- Optimization in real time. Operating targets update continuously as plant conditions and market prices change. DLPC® writes those targets to the existing APC or DCS, inside limits set by plant engineers.
- A link between operations and commercial strategy. The economic optimization runs across unit and team boundaries, so technical teams work toward the same business objectives as planning and commercial.
- Models built with domain experts. A Deep Learning Predictor learns how the unit behaves from its own history, guided by first principles and the process knowledge of the engineers who run it, so decisions follow the operating logic of the plant.
- Learning that stays in the organization. Knowledge moves in both directions between the model and the operations team, and that learning becomes part of daily work.
The technology is where this starts. Most of the value comes from changing how operators, engineers and commercial teams work together and solve problems.
Why this matters now
Companies that treat AI as a plug-in efficiency tool risk falling behind those that use it to find better ways of working and to align operations with commercial goals. AI adoption is already happening at every level of most organizations, so how it is adopted makes the difference.
Are we using AI to change how we think, collaborate and grow, or just to do the same things faster?
Leaders who change how their teams work, and who respect the role of domain expertise, get more from AI than faster versions of the same tasks. Adopting the technology comes first. The return depends on the expertise behind the models and on how far the organization is willing to change.
