Adding AI to Industrial Process Solutions Without Replacing What Works

For decades, industrial process solutions meant investing in better equipment, tighter control loops, and more experienced operators. Distributed control systems, multivariable controllers, upgraded instrumentation, optimized heat exchanger networks: these investments delivered real value, and they still do. But process industry leaders are increasingly recognizing that the next layer of performance won’t come from replacing this infrastructure. It will come from making it smarter through AI.

The advanced process control (APC) systems that represent decades of engineering investment are quietly degrading. In some cases, fewer than 10% of implemented APCs remain active and maintained over time, according to McKinsey research. The technology itself isn’t the problem. Equipment ages, feedstocks vary, and process conditions shift beneath the surface. Over time, original tuning parameters become increasingly suboptimal. Traditional process control provides the foundational stability that front-line operations depend on daily. The question is whether layering AI optimization on top of that foundation creates genuine improvement or just adds complexity.

TL;DR: How AI Optimization Integrates with Existing Process Control

AI optimization adds adaptive capabilities above existing DCS and APC, preserving safety interlocks while addressing static model limitations.

Where Static Control Models Hit Their Limits

• APC models degrade as equipment ages and feedstocks shift, with no ability to self-correct
• Critical quality parameters depend on lab analysis with delays of 15 minutes to several hours
• Maintaining traditional APC requires scarce specialist expertise from a shrinking talent pool

How AI Adds What Static Systems Cannot

• AI models update continuously, reducing the need for manual retuning
• Soft sensors infer quality variables from process data, closing the gap between lab samples
• Plants begin in advisory mode, capturing value from day one before progressing toward closed loop

Here’s how these capabilities work in practice across industrial operations.

Where Static Control Models Hit Their Limits

Traditional process control systems encode decades of hard-won operational knowledge. Distributed control system (DCS) architectures provide the deterministic control loops that keep columns, reactors, and heat exchangers operating safely, while APC layers add multivariable coordination that single-loop controllers cannot achieve. This foundation works. But it has inherent limitations that become more consequential as operations grow more complex and margins tighten.

The core constraint is that these systems are static by design, confronting inherently dynamic operations. A model tuned for one catalyst state and one feedstock composition can drift significantly over just a few months of operation, and the manual retuning required to correct that drift demands expertise most plants can’t dedicate consistently. Several specific limitations compound this problem.

Model degradation without self-correction. First-principles and empirical models assume conditions that no longer exist. Without continuous adaptation, accuracy erodes with every equipment change and feedstock shift. The result is a widening gap between what the control system thinks is happening and what’s occurring in real process conditions.

Nonlinear dynamics that outpace linear models. Real processes exhibit nonlinear behavior that linearized models capture only within narrow operating ranges. Push beyond those ranges and predictions diverge from observed behavior, especially when rapid disturbances hit faster than the controller can respond. This combination is why plants often operate conservatively: staying well within known bounds rather than pursuing the tighter specifications that would yield better margins.

Unmeasured variables requiring inference. Critical quality parameters often require laboratory analysis with delays ranging from 15 minutes to several hours, forcing control systems to operate reactively rather than proactively. During that waiting period, the process continues running under conditions that may already be suboptimal, with no mechanism to correct course until results return.

Data quality gaps that compound over time. Even plants with well-maintained data infrastructure face constraints around sensor drift, miscalibrated instruments, and gaps in data system coverage. Traditional controllers have no mechanism to identify or compensate for degraded data quality, meaning the models they rely on may be built on measurements that no longer reflect actual process conditions.

Expert dependency creating bottlenecks. Traditional APC requires deep expertise for development, tuning, and maintenance. That constraint is particularly acute as specialized control engineers remain scarce and the knowledge needed to maintain these systems concentrates in a shrinking pool of experienced professionals.

None of these limitations reflect poor engineering. They reflect the design assumptions of systems built for a different era of operational complexity.

How AI Adds What Static Systems Cannot

AI optimization operates as a layer above existing DCS and APC, writing setpoint adjustments through the same infrastructure operators already trust while maintaining all safety interlocks and operator override capabilities. Columns, reactors, and compressors continue operating through the same control systems they always have. The difference is that the setpoints those systems receive are now continuously optimized based on current conditions rather than assumptions from the last tuning cycle.

What changes, specifically, is how the AI model handles the constraints outlined above.

Continuous learning replaces periodic retuning. AI models can update continuously as conditions change, reducing the need for manual retuning and steady-state conditions. As equipment ages and feedstock properties shift, the model adapts in parallel rather than falling progressively out of alignment with current process behavior. Periodic review and validation are still recommended, but the burden of maintaining accuracy shifts substantially.

Simultaneous multivariable optimization. Traditional APC optimizes within and sometimes across process units, but each controller works within its own scope. AI-powered process control coordinates across hundreds or thousands of variables at once, identifying interactions that would take an engineer weeks to map manually. That means trade-offs between throughput, energy, and product quality can be optimized against real-time economics rather than static assumptions.

Real-time inference and data quality compensation. AI-powered soft sensors continuously estimate quality parameters from available process data. This enables tighter control without waiting for laboratory results. These same models can identify when sensor readings drift or instruments fall out of calibration, flagging data quality issues that would otherwise silently degrade control performance. The result: both the delay between lab samples and the uncertainty around measurement reliability shift from blind spots into managed variables.

Broader access to optimization. Instead of requiring a controls specialist to configure and maintain the optimization model, operators and engineers can work with it directly: adjusting objectives, setting constraints, and evaluating scenarios through interfaces designed for plant teams. This broadens meaningful engagement with optimization from a handful of specialists to potentially dozens of people across a site.

The same McKinsey research that documented APC degradation also documents the upside: facilities implementing AI optimization have reported 10–15% production increases and 4–5% EBITA improvements in some cases. These reflect measured outcomes from plants that have moved beyond pilot programs to production deployment.

What Integration Actually Looks Like

The critical question for any optimization layer isn’t whether it can improve performance. It’s whether implementation requires tearing apart infrastructure that already works. The short answer: it doesn’t.

The dominant approach uses modular, vendor-neutral architecture: secure edge gateways, data collection systems as primary integration points, and open industrial standards like OPC UA that enable interoperability across mixed-vendor environments. The AI layer reads from existing sensors and writes to existing control systems. No new field instrumentation is required, and existing safety systems remain untouched.

Implementation typically follows a phased approach that delivers value at each stage. Plants begin in advisory mode, where the AI model provides recommendations that operators evaluate against their own experience. This phase isn’t a waiting room for closed loop. It delivers standalone value: enhanced visibility into process behavior, what-if analysis for competing objectives, and consistent decision support across shifts. Planning and economics teams can run production scenarios, evaluate pricing trade-offs, and update linear-program vectors more frequently than the annual cycles most plants rely on. Operators gain a second perspective that reduces variability between crews, even before any automated control actions are introduced.

As confidence builds, plants can progress toward closed loop operation with human oversight and fallback mechanisms, and eventually to autonomous optimization within operator-defined boundaries. Each stage delivers measurable returns, so the pace of progression reflects organizational readiness rather than a delay before benefits begin.

What This Means for Operations Teams

BCG research suggests that roughly 70% of challenges in AI projects stem from people and process issues, not technical ones. The technology may integrate cleanly, but the workforce dimension determines whether AI delivers its full potential or stalls after a promising pilot.

When AI handles the data-intensive work of optimizing setpoints across a unit, operators can spend less time reacting to alarms and monitoring trends and more time interpreting what the AI is recommending and why. That shift matters: experienced operators notice things no model can, from the sound of a compressor under unusual load to the pattern of an upset that looks routine to the control system but isn’t. Process engineers who spent hours maintaining model accuracy can redirect that expertise toward identifying new optimization opportunities.

The cross-functional dimension matters just as much. When maintenance, operations, and planning teams all reference the same AI model, they gain shared visibility into how their decisions affect each other. A maintenance timing decision that previously seemed isolated now has visible throughput and quality implications, and vice versa. That transparency doesn’t shift decision authority, but it does shift the quality of coordination across teams.

Because most AI adoption barriers are organizational rather than technical, experienced operators become more valuable, not less. They possess decades of tacit knowledge about edge cases, safety considerations, and operational context that AI inherently lacks. The combination of that judgment with AI’s ability to continuously process and optimize across variables is where the real performance improvements come from. Building workforce readiness alongside technical deployment isn’t optional; it’s where organizations capture the majority of the value.

How Imubit Delivers This Integration

For process industry leaders looking to add AI optimization to their existing control infrastructure, Imubit’s Closed Loop AI Optimization solution is purpose-built for this integration. The technology takes a data-first approach: learning directly from actual plant data to build dynamic process models that adapt as conditions change, then writing optimal setpoints in real time through existing DCS and APC systems. Plants typically begin in advisory mode, where operators and engineers evaluate recommendations and build confidence, before progressing toward closed loop optimization at their own pace. With 90+ successful applications deployed across process industries, the solution has driven measurable improvements in margins, throughput, and energy efficiency.

Get a Plant Assessment to discover how AI optimization can integrate with your existing control infrastructure and unlock performance your current systems cannot achieve alone.

Frequently Asked Questions

How does AI optimization fit alongside other industrial process solutions like equipment upgrades and DCS modernization?

AI optimization complements physical infrastructure investments rather than competing with them. Equipment upgrades improve the mechanical capability of the plant, while AI optimization ensures those assets operate at their full potential by continuously adjusting setpoints based on current conditions. Plants that combine infrastructure investment with AI optimization capture value from both the equipment and the intelligence layer operating above it.

What risks should operations teams consider when adding AI to existing control systems?

The primary risks are organizational, not technical. AI optimization integrates through standard industrial protocols without modifying existing safety systems, so the technical risk profile is low. The more significant risks involve insufficient change management, unrealistic timelines for operator adoption, and underinvesting in the training that builds team confidence in AI-driven recommendations. Starting in advisory mode mitigates these by giving teams time to validate the system before granting it control authority.

What data is needed to start integrating AI with existing control systems?

Effective integration requires historical process data from plant data systems covering temperature, pressure, flow, and quality measurements across representative operating conditions. While richer datasets sharpen results, plants can begin with existing plant data and improve data quality iteratively as the system identifies gaps and calibration opportunities. Perfectly structured data is not a prerequisite for getting started.