Gregory Shahnovsky is CEO of Modcon Systems, specializing in process analytics and AI-driven optimization for complex industrial operations.gettyThe industrial AI market is moving rapidly from the ambitions of startups to capital allocation. Schneider Electric recently agreed to acquire industrial data and AI company Cognite for $3.1 billion, while Emerson completed its acquisition of the remaining AspenTech shares in a $7.2 billion transaction. Forbes has also highlighted a wider pattern of acquisitions and partnerships involving Siemens, ABB, Honeywell and Rockwell Automation.These deals tell us something important. Industrial AI is no longer treated as a side project for data science teams. The major automation companies increasingly see software, industrial data and AI-based optimization as part of the core industrial technology stack.But industrial AI should not be confused with traditional process control. Based on my 40 years of experience in process control and optimization, I see AI as an additional layer in the automation hierarchy, not a replacement for the systems already running the plant.Traditional Control Keeps The Plant StableProcess control and optimization have evolved over many decades. At the basic level, a PID controller has a clear job. If pressure, temperature, flow or level moves away from its set point, the controller adjusts a valve, heater, pump or another manipulated variable.Advanced process control (APC) and model predictive control (MPC) consider several interacting variables simultaneously and predict how the process will respond and operate within defined constraints. Commercial MPC applications have been used in process industries since the 1970s.Above APC, real-time optimization is often used to calculate economically attractive operating targets, while planning and scheduling operate over still longer timescales.In simple terms, regulatory control keeps the process stable; APC coordinates it; and optimization determines where it should operate. Industrial AI adds another capability to this hierarchy.Where Traditional Optimization Becomes DifficultConventional optimization relies heavily on mathematical process models. This works well when the process can be described precisely without making the model impractical to maintain or solve online. However, real plants are rarely so cooperative.A crude distillation unit, for example, may be affected by crude composition, furnace performance, column conditions, exchanger fouling, utility costs, product values and downstream constraints. The relationships are nonlinear and they change over time. A simple model may be fast but insufficiently accurate. A rigorous model may represent the physics very well but become difficult to maintain or computationally demanding.Machine learning offers another route. It can identify useful relationships directly from large volumes of historical and real-time operating data. Industrial AI is commonly defined around this combination of machine learning, sensor data, connected equipment and operational decision-making. That can be extremely useful, but it creates a new problem.A Plant Is Not Simply A DatasetIndustrial AI operates in the physical world. A compressor has a real operating envelope. A distillation column has hydraulic and thermodynamic limits. A reactor has temperature and pressure constraints. Chemical reactions continue to obey thermodynamics regardless of what a statistical model predicts.Plant data are imperfect as well. Laboratory measurements may arrive hours after the conditions that produced the sample have changed. Field instrumentation and sensors drift. Process analyzers fail or their readings are not correlated to laboratory results.A purely data-driven model can therefore discover a mathematically strong relationship that makes little engineering sense. This is why useful industrial AI needs more than algorithms. It needs process context, engineering constraints, data validation and preferably some connection to physical models.Recent work in industrial analytics increasingly points toward hybrid modeling for exactly this reason: combining first-principles knowledge with data-driven models rather than treating them as competing approaches.Control And AI Answer Different QuestionsThe simplest distinction is this: Traditional process control asks, “How do I keep the process at its target?” Industrial AI can ask, “Is this still the right target?”Imagine an APC system perfectly controlling a refinery unit. Temperatures are stable, product qualities are on specification, and the controller is doing exactly what it was designed to do.But perhaps the crude feed has changed or the downstream unit may now be limiting production. An exchanger may have lost efficiency, and optimum operating point may therefore have shifted. There is nothing wrong with the controller. It may simply be controlling the plant extremely well toward yesterday’s optimum.Industrial AI can evaluate the wider operating context and recommend a different target. The existing control system can then do what it already does very well: move the plant toward that target while respecting its constraints.AI Should Normally Sit Above Trusted ControlThis is why simply replacing established DCS or APC systems with AI is usually the wrong objective. A more practical architecture is to introduce AI initially as a decision-support and optimization layer. It can analyze current conditions, predict future behavior and recommend changes. Engineers and operators can compare those recommendations with actual plant performance before allowing tighter integration.This gradual approach is less dramatic than announcing an autonomous refinery and also is more credible. Industrial plants tend to trust technology after it has survived real operating conditions, including the untidy ones.Better Data May Matter More Than Better AlgorithmsThere is another limitation that deserves more attention. Plants measure temperature, pressure, flow and level very well. These variables tell us how the equipment is operating. They do not always tell us what material is actually being processed.​Real-time analytical measurements can therefore add considerable value to industrial AI. Combining chemical composition and physical properties data with conventional process measurements gives the model a much better representation of the actual process state.Industrial AI Is An Evolution Of AutomationRecent acquisitions in the industrial software market suggest that major automation companies expect AI to become deeply embedded in industrial operations.Traditional control is strongest at the “How”—how to control the process. APC helps define the “What”—what should be optimized. Industrial AI adds the “Why”—why we need to optimize it under current operational and economic conditions.The future of industrial automation is therefore not about AI replacing process control, but about each technology doing the job it is best suited to.Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?