The margin squeeze in the process industries is real. Rising input costs, volatile energy prices, aging infrastructure, and stricter sustainability demands are putting unprecedented pressure on operations. Traditional methods alone no longer suffice. AI optimization is emerging as a practical, proven solution. Closed Loop AI Optimization (AIO), a methodology validated by ARC Advisory Group, is enabling refiners, chemical manufacturers, and metals producers to unlock measurable gains in yield, efficiency, and uptime. In this article, you’ll discover seven field-tested AI optimization strategies that reduce waste, increase throughput, and improve margins—without requiring new capital investment. Strategy 1: Maximizing Yield In Real Time Untapped potential yield optimization is leaking money every day. Operators make adjustments based on experience and periodic reviews, but they can’t process hundreds of variables at once or adjust instantly when conditions change. Those tiny inefficiencies can add up to big losses. AI optimization (AIO) solutions, powered by reinforcement learning, monitor live process data like temperature, pressure, and composition. They make real-time setpoint adjustments to improve yield while staying within safety and equipment constraints. These systems spot opportunities in complexity that human operators simply can’t see. The results of capturing those tiny inefficiencies are real improvements: higher throughput, less off-spec production, reduced waste, better energy efficiency, and higher margins. Strategy 2: Reducing Energy Costs Energy is one of the most significant operating costs in the process industries—and managing it is far from simple. Demand shifts throughout the day. Energy prices fluctuate by the hour. Equipment performance degrades over time. And raw material quality isn’t always consistent. Each of these factors affects how much energy a plant consumes and how efficiently it runs. Yet many plants still rely on control systems that operate on fixed parameters. These systems don’t adjust to changing conditions in real time. So when the process drifts or external factors shift, they either overcorrect too late—or not at all. The result: wasted energy and lost margin. Closed-loop AI optimization (AIO) changes this. By using live operational data, AIO continuously monitors plant performance, identifies inefficiencies, and automatically fine-tunes control settings. It analyzes a wide range of inputs—temperature gradients, pressure levels, equipment behavior, energy pricing, and production rates—to make real-time decisions that reduce energy consumption without compromising output. The result is twofold: immediate cost savings and reduced CO₂ emissions. In many plants, energy savings alone pay back the investment in under three months—before factoring in any sustainability upside. Strategy 3: Optimizing the Entire System—Not Just Individual Units Single-unit optimization is common in the process industries because it’s easy to manage and measure. But when you focus on one unit, you’re chasing a local optimum, and you may just miss the global optimum. Take a diesel product pool in a refinery for example. You could optimize diesel flash independently on your atmos tower, vac tower, hydrotreaters and coker and wind up with giveaway when the pool is blended because you weren’t seeing the bigger picture.  Closed Loop AI Optimization (AIO) changes this by coordinating multiple units simultaneously. Rather than treating each unit in isolation, AIO enables system-level optimization—balancing competing objectives across the entire operation. Rather than each team chasing their own profitability, they now have the awareness and ability to work together towards chasing the best outcome for the site. AIO identifies the optimal operating point that aligns all units, maximizing overall plant margins instead of local gains. This success depends on breaking down data silos. A unified data architecture is essential. By integrating data flows across formerly isolated systems, AIO can operate with full visibility, treating the plant as a coordinated whole rather than a collection of competing units. Strategy 4: Cutting Catalyst Costs While Maximizing Feed Conversion Catalyst deactivation and feedstock variability erode margins through declining conversion rates and premature replacements. Many operations rely on fixed schedules or react to performance drops—both approaches result in avoidable losses. AI Optimization continuously monitors catalyst activity and feed composition, dynamically adjusting temperature, pressure, and flow rates to maintain optimal conversion as conditions change. The impact is significant. In refining and chemicals, even modest gains in catalyst efficiency can generate millions in annual savings. AIO can also forecast ideal replacement timing, avoiding costly early swaps or extended, inefficient runs. With AIO managing the full catalyst lifecycle—from activation to timely replacement—your investment delivers maximum return, even as feedstock quality fluctuates in real time. Strategy 5: Slashing Unplanned Downtime With Predictive Control A single unplanned shutdown can cost millions in lost production, emergency repairs, and supply chain disruptions. Historical run-to-failure maintenance strategies have been purely reactive, which exaggerate each of these consequences.. AI-powered predictive control changes this by detecting issues before they escalate. These systems continuously monitor equipment via sensors and analyze historical failure patterns to forecast when intervention is needed—often days or weeks before alarms would typically trigger. This early warning enables a proactive approach, timely part procurement, and scheduled maintenance—avoiding the chaos and cost of emergency repairs. AI optimization with predictive control goes beyond monitoring against simple thresholds. It analyzes subtle changes in vibration, temperature, pressure, and other parameters to create detailed equipment health profiles. This allows it to predict drift and failure modes before they become process deviations. Real-time anomaly detection compares current conditions with normal patterns, spotting gradual or complex deviations that human operators might miss. The business impact spans three key areas: reduced unplanned downtime increases production and revenue, maintenance costs drop through proactive planning, and equipment life extends through early issue resolution and optimized operation. Strategy 6: Bridging Critical Knowledge Gaps A wave of retirements is draining plants of decades-deep expertise just as operations grow more complex. The resulting knowledge gap threatens safety, reliability, and margins. Closed Loop AI Optimization (AIO) fills this void. By mining historical data and live operating conditions, the system delivers real-time recommendations, then automatically acting once operator trust is established. Think of it as your most seasoned operator on call 24/7, guiding decisions without overruling human judgment. Instead of reacting after an alarm trip, crews now receive early warnings and clear preventive steps. Each suggestion is backed by patterns that AIO has learned from thousands of similar scenarios—insight even veteran staff would struggle to recall instantly. Operators stay in control, but with sharper context and less guesswork. When operators aren’t spending brain cycles on how to maximize margin, they can focus more energy on process safety and skill-building amongst both new and seasoned employees. In this way, AIO doesn’t replace expertise; it preserves and amplifies it for the next generation. Strategy 7: Turning Market Volatility Into Profit Opportunities Many process facilities rely on static production targets that remain unchanged for weeks or months—missing out on daily commodity market shifts that could significantly impact margins. Closed Loop AI Optimization (AIO) changes this by dynamically adjusting production rates and product specs in response to real-time market signals. It continuously monitors commodity prices, demand forecasts, energy costs, and other variables—then optimizes operations to capture margin opportunities as conditions evolve. These AIO models blend live market data with historical trends and your facility’s unique production constraints. To make this work, your operational and information technology systems must be tightly integrated. AIO gives control systems insight into market intelligence—allowing fluctuations on commodity exchanges to influence the optimization strategy in your control room. With this setup, you can confidently shift production toward higher-margin outputs as economics dictate—turning your plant into an agile, profit-optimized operation. Why Imubit’s Industrial AI Platform Outperforms Point Solutions Imubit leads the Closed Loop AI Optimization market offering unique capabilities that link advanced reinforcement learning techniques and legacy process control infrastructure. With over 100 deployments in energy, chemicals, and metals, Imubit’s platform offers: Payback periods under 90 days Throughput gains of 1–3% Energy savings of 5–10% Unlike vendors providing standalone tools, Imubit delivers multiple optimization strategies in a single, integrated system. The platform’s continuous learning creates compounding benefits—each optimization builds on the others, evolving into a smarter, more efficient ecosystem tailored to your processes and goals. The Path Forward AI Optimization is no longer optional—plants that delay adoption risk falling behind competitors already benefiting from measurable gains. Whether your focus is energy efficiency, yield improvement, uptime, or market responsiveness, AIO is the key lever to boost margins in today’s challenging environment. Explore how Imubit’s Closed Loop AI Optimization solution can drive rapid, sustained margin improvements for your facility by scheduling your complementary AIO assessment today.

Today’s manufacturers face rising costs, shrinking margins, and increasing pressure to deliver high-quality products quickly. Traditional methods of running plant operations—built on static systems and periodic reporting—no longer suffice. In this environment, advanced manufacturing data analytics offers a smarter path forward. By applying analytics and AI to real-time plant data, leading manufacturers are not just improving efficiency—they’re preventing problems before they start. According to McKinsey, operators that have applied AI in industrial processing plants have reported a 10 to 15 percent increase in production and a 4 to 5 percent increase in EBITA. This shift from reactive problem-solving to proactive optimization helps companies minimize downtime, improve efficiency, and save significant expenses. What Is Manufacturing Data Analytics? Manufacturing data analytics involves collecting, processing, and analyzing data from various sources—such as sensors, equipment logs, and enterprise systems—to improve decision-making and performance on the plant floor. Broadly, analytics in manufacturing falls into four categories: Descriptive Analytics: What happened? Diagnostic Analytics: Why did it happen? Predictive Analytics: What is likely to happen? Prescriptive Analytics: What should we do about it? Most manufacturers start with descriptive and diagnostic analytics to get a clear view of operations. But real value comes from predictive and prescriptive analytics that can anticipate issues and recommend actions in real time. From Data Overload to Actionable Intelligence: The Evolution of Manufacturing Analytics Manufacturing data analytics has evolved significantly. It moved from traditional retrospective analysis of isolated historical data to modern real-time analytics using cloud computing, IoT, and AI. This evolution enables manufacturers to predict outcomes and act proactively rather than reactively. This shift helps move manufacturing from solving problems after they occur to preventing them with early warnings and recommended interventions. As the core of Industry 4.0, manufacturing analytics links physical and digital environments, enabling simulations of operations to optimize performance without disrupting production. Key benefits include: Proactive optimization to reduce downtime Instant quality issue detection Demand forecasting for inventory optimization Continuous process improvement Developing expertise in manufacturing data analytics is essential for maintaining competitiveness and ensuring long-term operational success. How Manufacturing Data Analytics Transforms Production Manufacturing data analytics is transforming industrial process plants worldwide. Here’s how these tools are making a real difference in day-to-day operations. Predictive Analytics Power Proactive Optimization Unexpected operating conditions, such as equipment failures and quality upsets, can cost process manufacturers thousands of dollars per hour. Predictive analytics leverage sensor data and historical patterns to anticipate operating scenarios and allow ample time for proactive adjustments to optimize for quality or runtime. This reduces unplanned downtime, slashes maintenance costs by 30%, and extends asset life—all while improving overall equipment effectiveness (OEE). Simulating Operations for Process Excellence Simulating operations involves creating virtual models of physical assets or systems. By mirroring real-time conditions, it allows manufacturers to simulate different scenarios, test adjustments, and predict outcomes before making changes on the plant floor. Whether testing a new production schedule or tweaking machine settings, these simulations provide an offline playground for experimentation, reducing the risk of disruption while speeding up innovation. Production Optimization: Doing More with Less The integration of IoT and sensor data with advanced manufacturing data analytics provides manufacturers with comprehensive operational visibility. Analytics can identify inefficiencies across the production line—from process bottlenecks to quality constraints. Manufacturers can fine-tune operations to boost throughput without capital expenditure by analyzing multivariate relationships between critical process variables. The result is higher yield, better product quality, and more agile production. Building Resilient Supply Chains Through Analytics Global supply chains are more volatile than ever. Analytics enables real-time visibility into supplier performance, inventory levels, and demand forecasts. This helps manufacturers quickly adapt to disruptions, optimize inventory, and reduce lead times. Advanced analytics also supports demand sensing and dynamic routing, ensuring products get to the right place at the right time. Green Operations: Aligning Efficiency with Responsibility Manufacturing data analytics has become a powerful tool for sustainability, helping manufacturers track and reduce their environmental impact. Companies use analytics to monitor and reduce energy use across facilities, maximize resource utilization and minimize waste, track and report environmental metrics accurately. These data-driven sustainability efforts often pay for themselves through cost savings. For instance, analyzing energy consumption patterns can reveal opportunities to shift production to off-peak hours or invest in more efficient equipment. The Bottom-Line Impact of Manufacturing Analytics Data-driven manufacturing delivers clear financial and operational advantages. Real-time insights help lower costs by reducing waste, energy use, and unplanned downtime. Productivity increases as optimized workflows enable higher output with fewer delays. Quality improves through root-cause analysis and continuous monitoring, reducing off-spec and reprocessing. Teams make faster, more informed decisions by relying on timely data instead of inconsistent experience. Most importantly, manufacturers gain greater agility to respond quickly to market shifts and disruptions. With measurable gains across multiple KPIs, data analytics is becoming a strategic imperative for manufacturers looking to stay competitive. Building the Right Foundations: Data + Context Before manufacturers can harness the full power of AI and advanced analytics, they must first build a strong foundation. Just like a machine can’t run without stable power or clean inputs, AI needs high-quality, well-structured data—and context—to deliver reliable outcomes. Contextualization: More Than Just Data Points Raw data without context is just noise. AI needs to understand not only what a number is, but where it came from, when it was recorded, and what it represents in the physical world. For example, a temperature spike means little unless it’s tied to the relevant equipment and location of the sensor in the plant, the environmental conditions at that moment, and operator interventions or process changes that happened nearby. Contextualization transforms isolated readings into a cohesive picture of operations, enabling AI models to detect real patterns and anomalies. Data Quality: Clean, Consistent, and Real-Time Inaccurate, inconsistent, or delayed data can derail even the most sophisticated AI system. Manufacturing environments often struggle with noisy sensor data, inconsistent data formats across legacy systems, time delays and process dynamics that camouflage process correlations. Establishing automated data validation, cleansing pipelines, and standardized formats ensures the AI is learning from trusted, real-time data streams, not corrupted or stale inputs. System Integration: Bridging OT and IT Manufacturers typically operate a mix of systems—SCADA, MES, ERP, historians, and LIMS—all generating valuable insights. But these systems often operate in silos, making end-to-end visibility difficult. Effective analytics requires bi-directional integration between: Operational technology (OT) like historian sensor data and lab data Information technology (IT) such as ERP, CRM, and cloud data lakes Integrated systems provide a single version of truth, enabling AI models to learn from the full context—production conditions, material inputs, maintenance logs, and market demand. Data Analytics Maturity Models and Readiness Every manufacturer’s journey toward analytics and AI is different. Some have rich data environments but lack skilled personnel. Others have advanced control systems but disconnected data. That’s where analytics maturity models come in. These models help manufacturers benchmark their current capabilities, understand where they are in the transformation journey, and prioritize investments for the greatest impact. Key Maturity Dimensions Data Infrastructure & Governance: Is your data stored in a structured, governed environment—or scattered across spreadsheets and servers? Real-Time Data Usage: Can your teams access up-to-the-minute data to make decisions, or are you relying on outdated reports? Cross-Functional Collaboration: Are analytics initiatives isolated in IT or aligned with operations, quality, and leadership? Culture of Data-Driven Decision Making: Do frontline teams trust data insights and use them daily, or do gut instincts still drive key decisions? Benefits of Maturity Mapping Understanding your maturity level helps uncover key challenges—such as siloed systems, outdated tools, or skill gaps—that may be holding you back. It also enables the creation of a phased roadmap with realistic short-term wins and clear long-term goals, ensuring analytics efforts stay aligned with real business outcomes. This structured approach minimizes risk and builds stakeholder confidence through steady, measurable progress. Overcoming Obstacles in Manufacturing Analytics Implementation Despite its benefits, analytics adoption in manufacturing is rarely straightforward. It involves navigating technical and operational challenges that can slow progress or derail initiatives entirely. Siloed Systems and Data Fragmentation Most manufacturers operate with multiple platforms—ERP, MES, CRM—that don’t talk to each other. This fragmentation prevents the creation of unified insights. Poor Data Quality Incomplete, outdated, or erroneous data undermines analytics outcomes. Garbage in, garbage out. Legacy Systems and Infrastructure Gaps Older machines or proprietary platforms may lack modern connectivity, making data extraction difficult or expensive. Manual, Time-Consuming Reporting Many teams still rely on spreadsheets and monthly reports, which are slow and prone to error. This delays action. Cybersecurity Concerns Integrating analytics with operational networks increases the attack surface, requiring robust cybersecurity planning. Skills Gaps and Resistance Operators may distrust or resist analytics-driven recommendations. Data science teams may lack process knowledge. Without mutual understanding, adoption stalls. The Solution: A Holistic Strategy Manufacturers must approach analytics transformation with a blended strategy that includes technology enablement (modern tools and platforms), organizational change management (training, communication, leadership support), and upskilling and collaboration (bridging IT and OT skillsets). Transformation doesn’t succeed through tools alone—it requires people to trust and embrace data and technology as part of their daily decision-making. Setting Up Your Manufacturing Analytics for Success Once manufacturers commit to using analytics, how they implement it makes all the difference. Success is not about adopting the most advanced AI—it’s about solving real business problems in a structured and scalable way. Connect Analytics to Your Business Priorities Instead of chasing general “optimization,” link analytics initiatives to concrete goals like reducing non-prime or reprocessing, improving first-pass yield, minimizing energy consumption, and optimizing maintenance scheduling to limit downtime. By aligning with business KPIs, analytics projects can deliver measurable impact and build internal credibility. Build Your Cross-Functional Analytics Dream Team Successful implementation of manufacturing data analytics depends on broad organizational engagement. Cross-functional collaboration is key to ensuring initiatives are practical, relevant, and aligned with business goals. Start by forming a multidisciplinary data team that includes members from operations, process engineering, planning, controls, management, and IT. This ensures diverse perspectives and shared ownership from the outset. Regular cross-functional review sessions help maintain strategic alignment, allowing teams to share insights, validate progress, and adjust priorities as needed. These sessions keep everyone focused on delivering measurable outcomes. It’s also important to develop a shared terminology framework. Clear and consistent language improves communication and reduces misunderstandings between technical and non-technical teams. With this collaborative approach, analytics efforts are more likely to solve real operational challenges and deliver impact across the business. Create a Data Foundation That Grows With You Developing effective manufacturing data analytics capabilities is a continuous journey. Start by creating a single, shared view of the plant using one unified data model. This helps eliminate conflicting insights and ensures everyone is working with the same version of the truth. Establish strong data governance standards to maintain accuracy, consistency, and trust across teams. High-quality data is essential for reliable analytics. Automate data collection wherever possible to reduce manual errors and speed up insight generation. This frees up time for more strategic tasks. Continuously refine your analytics capabilities based on user feedback and evolving business needs. This ensures your systems remain relevant and valuable over time. To support these efforts, modern integration technologies are essential. Use API management platforms to connect systems quickly, without relying on heavy custom development. Implement IoT gateways to gather real-time data from production equipment and sensors across the plant. For critical processes that require rapid insights, consider edge computing. This enables time-sensitive analysis close to the source of data. Together, these practices help manufacturers overcome integration challenges and build a strong foundation for scalable data analytics. With this in place, it’s easier to move from pilot programs to full-scale deployments—without disruption. Making the Shift from Reactive to Proactive Operations The shift from reactive problem-solving to proactive, data-driven optimization is more than just a technology upgrade—it’s a mindset change. It calls for manufacturers to rethink how they approach daily operations and long-term strategy. To start, data silos must be broken down, allowing for a unified view of operations. Analytics should move beyond reporting what happened to explaining why issues occur, enabling teams to take meaningful action. Proactive manufacturers use data to anticipate and prevent problems, rather than simply reacting to them. This shift requires that AI and analytics efforts are clearly aligned with business goals, ensuring measurable impact. Empowering teams with trusted, real-time insights allows them to act confidently and quickly. As manufacturing continues to evolve at a rapid pace, companies that embrace this approach will be better equipped to lead, while others may struggle to keep up. The future of industrial operations lies in predictive capabilities and continuous optimization. Those who invest now in building a solid data foundation will gain a sustainable competitive advantage. Real Results: What Manufacturers Are Achieving with Imubit Leading manufacturers across sectors are using Imubit’s solution to drive step-change improvements that traditional process control tools can’t match. Across industries—from refining and chemicals to metals and cement—Imubit customers  are seeing transformational outcomes: More efficient operations with improved throughput, better yields, and less waste. Reduced energy intensity and emissions, supporting sustainability goals without sacrificing performance. Greater product consistency and quality, with fewer off-spec batches and costly downgrades. Stronger process control and stability, even in complex, nonlinear systems where traditional models struggle. Empowered operations teams, who are better equipped to make high-quality decisions thanks to intuitive AI support. Rapid implementation and early wins that help build confidence and momentum across the organization. Sustained financial and operational impact, delivered continuously through real-time optimization. These are not isolated wins—they’re repeatable, scalable outcomes being achieved every day by process manufacturers embracing industrial AI. Start Building a Proactive Plant with Imubit Imubit helps leading manufacturers go beyond dashboards and reports. Our Optimizing Brain Solution unlocks true optimization by translating complex operational data into real-time action—empowering your team to solve problems before they start. Request a demo and see how Imubit can help you close the skills gap, preserve institutional knowledge, and improve margins across your plant.

The manufacturing skills gap is one of the most pressing challenges facing industrial operations today. By 2028, Deloitte predicts 2.4 million unfilled jobs—threatening up to $1 trillion in lost output. Three forces are driving this growing talent shortage: A Wave of Retirements: Experienced professionals are exiting the workforce, taking decades of knowledge with them. High Turnover: About 21% of Millennials report switching jobs within the last year—a stark contrast to previous generations like the Baby Boomers and Gen X. Technology Outpacing Adoption: As new systems are introduced, the skills required to operate them continue to evolve and upskilling is required for successful adoption. This leaves manufacturers in a difficult position. Even as AI and automation technologies promise performance gains, many plants lack the skilled personnel needed to fully implement or manage them. Without the right people and the right skillsets, even the most advanced tools can fall short. Retirements Are Widening the Manufacturing Skills Gap The wave of retirements is draining the industry of vital expertise. For decades, these professionals have fine-tuned complex processes through hands-on experience. But this knowledge is at risk. Labor participation in manufacturing has been declining for over 20 years. Recruitment struggles and outdated perceptions about industrial work only make matters worse. Without effective knowledge transfer strategies, manufacturers face: Lower productivity from less experienced operators Risk of resurfacing historical operating struggles Reduced innovation due to a loss of deep process understanding Preserving and passing on expert knowledge is no longer optional. It’s essential to remain competitive. AI-Powered Training: Rethinking Operator and Engineer Onboarding AI is transforming how new front-line employees learn complex industrial systems. Experience that was once acquired over the course of a decades-long career is now being developed rapidly through simulation environments. Advanced AI tools are being used to create these simulation environments replicating real world scenarios specific to a particular unit within a specific plant. These simulations offer advantages like: Safe, hands-on training without operational risk Faster onboarding and shorter learning curves Interactive environments that make optimization more intuitive Unlike traditional training methods—manuals, static videos, or passive shadowing—simulation-based learning immerses operators in real-time decision-making. This approach turns theoretical knowledge into practical experience, accelerating the time it takes for new hires to become effective contributors. Breaking Down Silos: How AI Democratizes Expertise Industrial companies are broadening the way they think about AI. What was once used as a point solution to specific use cases (e.g. predictive maintenance) is becoming a way of thinking and working to unlock and scale operational knowledge across the organization.  Closed Loop AI Optimization (AIO) is a concrete example of AI driving a fundamental shift in the way companies think about running their production facilities. It models complex process relationships by learning directly from years of historical operating data—without relying on rigid simulation assumptions. This AI doesn’t just replicate past decisions; it learns how expert operators would respond under hundreds of millions of possible conditions. This approach creates a living system of institutional knowledge—one that’s continuously updated and accessible to all team members. Operators and engineers can now test what-if scenarios, explore disturbances, and validate how the AI controller would respond—all in a risk-free, offline environment. This builds trust, accelerates onboarding, and reduces reliance on a few seasoned experts. At the same time, emerging generative AI technologies are making insights from complex process models more accessible. They interpret data-driven outputs and controller behavior in plain, context-aware language. When combined with Closed Loop AI Optimization, generative AI can translate the “why” behind decisions—making advanced process control understandable to a broader audience, regardless of technical background. Together, these AI systems break down silos between disciplines and experience levels—giving everyone the tools to contribute to smarter, faster, and safer decisions. Preserving Tribal Knowledge Through AI One of the biggest risks of the skills gap is the loss of tribal knowledge—insights learned over years of operating a specific plant or process. AI systems trained on historical operational data can capture this expertise. These models often identify patterns or correlations once known only to veteran operators. By embedding this knowledge into the system, manufacturers ensure that critical insights are retained and accessible to new personnel. This not only safeguards performance consistency but also empowers new operators to question outdated assumptions and discover better ways of doing things. Earning Operator Trust in AI Tools On paper, leveraging AI to close the manufacturing skills gap seems like a clear win. But in reality, adoption is often slow. One of the biggest barriers? Trust. In industries where even small errors can lead to millions in lost margins—or compromise safety and reliability—operators are understandably cautious about handing decision-making power to AI. This is especially true for experienced operators who’ve built deep, intuitive knowledge over decades on the plant floor. To build trust, process manufacturers must take deliberate steps: Engage operators early in testing and training Communicate transparently about the role of AI Provide hands-on learning opportunities Big West Oil did just that. They involved their operators in feedback loops, making them active participants in AI model development. This collaborative approach allowed veteran personnel to experience the system’s capabilities firsthand and contribute valuable insights to its development process, fostering a sense of ownership and partnership rather than imposition. Another key success factor was explainability. When operators understood how AI made its decisions, they were more likely to trust it. As operators witnessed the AI system successfully optimizing complex processes and preventing potential operational issues through closed loop AI optimization, their confidence developed organically. By focusing on augmentation—not replacement—Big West Oil helped their teams see AI as a partner that enhances their skills, not a threat to their jobs. Reshaping Workforce Development for the Next Generation AI is not just solving today’s skills gap—it’s reshaping how industrial teams are trained and developed. This shift couldn’t come at a more crucial time as we face projected labor shortages across manufacturing sectors. These advancements deliver tangible results. Boston Consulting Group research shows AI can help reduce manufacturing costs by 14%. These gains help offset labor shortages while improving ROI on workforce development efforts. These benefits extend across the heavy process industries, including mining and cement manufacturing. The manufacturers who thrive will be those who view AI not just as process automation, but as a powerful ally in building the highly skilled workforce required for tomorrow’s industrial landscape. AI Will Empower, Not Replace in the Manufacturing Sector The skills gap is not just about hiring—it’s about knowledge. And the real power of AI lies in enabling people to do more with that knowledge. The value of AI in process optimization isn’t in replacing people—it’s in unlocking their full potential. It accelerates learning, supports better decision-making, and enhances performance at every level. Plants that recognize AI as a people-first tool—not just an automation solution—will be the ones that close the skills gap and build a competitive workforce for the future. Explore What’s Possible with Imubit Leading manufacturers are looking to technology providers for guidance on leveraging AI effectively to address both business and workforce challenges. Imubit partners with industry-leading organizations implementing their Closed Loop AI Optimization (AIO) solution to bolster their bottom line through real-time process optimization and upskilling their workforce in the process.  Imubit’s Industrial AI Platform: Transforms institutional knowledge into a dynamic model that mirrors how your plant truly behaves Enables AI-powered simulation environments, allowing new operators to safely explore how the controller responds to disturbances Builds operator confidence and trust, with explainable recommendations and outcomes they can understand and validate Supports cross-functional teams—from control engineers to operations—without needing deep APC/RTO expertise Shortens time-to-value, with implementation timelines under 6 months versus 18–24 months for traditional tools By making expert-level decision logic visible, accessible, and testable, Imubit bridges the gap between experienced operators and the next generation—helping teams grow stronger, faster. Ready to explore how Closed Loop AIO can help your team learn and perform better? Schedule a demo to see it in action.

The process industry companies who get most out of technology investment won’t be those who chase the buzzword of the week, but rather those who put AI technology to work where it counts, improving margins, reliability, and control. Imubit CEO Gil Cohen spent some time unpacking this in Control Global magazine—exploring the role of Controllable AI, reinforcement learning, and even GenAI in industrial plant modeling. It’s time to shift the narrative from possibility to real, plant-specific solutions. To do so, operating companies are embracing AI that’s transparent, aligned with economics, and adaptable to site-specific complexity. Why Controllable AI Matters Now Considerations for process teams as AI becomes part of daily operations. 1. Trust > Black Boxes Engineers don’t just accept black box models, they want to tinker and understand why decisions are made. Controllable AI is about giving users visibility into how decisions are made, and the ability to adjust them with confidence. 2. First Principles Still Matter The best AI doesn’t ignore your years of engineering experience, it builds on it. Combining plant data, reinforcement learning, and your employees domain knowledge creates a system that’s both smart and grounded in reality. 3. Plant-Wide Optimization Is Becoming Practical New AI architectures make it possible to stitch together unit models into plant-wide frameworks. This is resulting in better coordination across teams and fewer tradeoffs between throughput, energy, and margin. 4. GenAI Can Help… But Only If It’s Grounded in Plant Context Large Language Models (LLMs) aren’t plug-and-play for process operations. But when tailored to plant data and embedded in decision workflows, they can reduce onboarding time, improve troubleshooting, and level up team capabilities. 5. It’s All About Empowering Your Experts This next wave of industrial AI isn’t automating people out of the loop. Instead, it’s putting them at the center of it, with better tools to make faster, smarter decisions. Read the Control Global article: The next decade of industrial process modeling.

Large language models (LLM) and generative AI (genAI) are proving increasingly strategic for enterprises across all industries. However, there will be certain impacts of this revolution on the modeling frameworks of industrial companies in the next decade.

By Gil Cohen, CEO of Imubit Earlier this month, Imubit brought together over 40 refining professionals from 13 companies across 6 countries for two energizing Transcend events in Athens and Munich. These weren’t just knowledge-sharing sessions — they were deep, honest conversations about the operational challenges facing the industry today, and how AI is being embraced not just as a tool, but as a catalyst for transformation. From safety and reliability, to economic optimization and workforce reskilling, the themes were clear — and the appetite for innovation was strong. We were proud to be joined by teams from OMV, Clariant, Rosneft, HelleniQ, MOL, MOH, BP, and others — each bringing perspective, experience, and sharp questions to the table. Here’s what a few attendees had to say: “Glad to see the mathematical foundations behind Imubit AI.” – OMV “Fantastic overview of process control optimization techniques.” – Clariant “A day full of valuable exchanges and deeper understanding of AI applications.” – Rosneft In the session “From coffee to crude: democratizing model building,” Imbuit Business Consultant, Dennis Rohe, gave us a hands-on taste of how accessible it can be to build models and derive decisions from raw data—whether you’re optimizing espresso or a conversion unit.   While meeting with refining leaders during our Transcend EU events, I asked a simple but important question: What are your top concerns? The first thing that came up — short term, day to day — is safety. Long term, it was survival. Will my company be around in 10 years, given the unpredictable macroeconomic environment and the company and the plant’s competitive position as a business? Tracing back from the existential question of survival, several key themes come up in conversations, and are evident in recent surveys as well: Reliability – Unplanned outages are a plague, especially in aging western refineries. Most unplanned outages are not formally reported, making it challenging to draw industry-wide data. Mckinsey writes about it here. Economic Optimization – When the plant runs safely and reliably, the priority shifts to optimization and dealing with the multi-year margin pressure and crude source dynamics. Workforce reskilling – Valued but not prioritized. 92% of executives appreciate the reskilling and training of their workforce, but due to a lack of urgency compared to safety, reliability, and optimization, this area is generally not prioritized, and only 29% of companies are investing in retraining. Survey is here. While the adoption of AI is increasingly paramount to the longevity and prosperity of every business, only 15% of Oil & Gas companies use AI in live, day-to-day operations. Only 3% report highly integrated use of AI. Several potential reasons came up for this lack of integration of AI into the daily refinery operations: Data privacy and cybersecurity Reliability of AI models and their trustworthiness Understandability of model outputs Bandwidth of plant engineers and operators – Keeping the plant running is a full-time job. Overload in company initiatives, where every problem solved with AI requires a multi-year initiative, with multidisciplinary support, governance structures and change management. What kind of AI can address these concerns and help refiners survive and thrive? Large Language Models (LLMs) are disrupting all industries and enabling small and large businesses from all sectors to accomplish more with their existing staff. Many roles are being redefined to leverage AI for automation of tasks which involve certain workflows and processing, while focusing human attention on areas that require a deep understanding of the environment and ability to come up with new ideas that the AI hasn’t seen before. While LLMs are proven to solve a wide range of problems, a different form of AI is required in addition to LLMs to solve these key problems. In order to trust AI models to make plant decisions, certain properties are required by refiners: The AI model must be trained on the plant’s specific dataset, or a known, documented, trusted dataset. Specifically, due to the safety-critical environment, plant engineers need to control every source of data fed into AI model training, and every datapoint within every data source. The plant process dynamics captured by the model must be transparent to the user, and also controlled by the user if desired. Modeling Democratization – everyone at the plant can evaluate, simulate, update and build models. Capture the true dynamics of the plant from the plant data and provide insights to all technical staff, not relying on human generated documentation and tribal knowledge, biased assumptions. Serve as single model for a specific area of the plant that represents the plant in such a way that is useful for all technical plant disciplines Single initiative that solves multiple problems, thereby reducing the need for engineers and operators to learn new tools, and reducing management and organizational burden of new initiatives, governance, change management.   Where Are You on the AIO Journey? If these challenges resonate with you, it may be time to evaluate where your operations stand on the path to truly integrated, refinery-grade AI Optimization. Request a complimentary AIO Readiness Assessment to understand how your plant can move beyond incremental improvements and start solving for reliability, optimization, and workforce enablement — with one trusted initiative. Schedule your AIO Assessment.    

As the industry most directly responsible for global development, the cement industry can’t afford to slow down. But as the industry currently responsible for 8% of global emissions, they can’t afford to grow emissions with production or even continue at the rate they’re at. In lieu of a complete process redesign, the industry is looking to emerging technologies to close the gap on their carbon reduction commitments.

In today’s fast-paced industrial processing landscape, staying ahead means embracing and running with new technologies. Understanding the nuances of AI could be a game-changer for both your organization’s performance and your career in industrial processing. Imubit is pleased to offer these webinars as a 3-course series designed to help professionals in the process industries better understand the theoretical concepts behind modern AI frameworks and their industry applications. As an engineer or operator, learning about the latest advancements in AI, such as Reinforcement Learning (RL), and how to harness it can provide job security as you leverage it to improve operational outcomes for your plant.

Due to an aging workforce, a shrinking talent pool, and increasingly complex industrial operations, the petrochemical industry faces a…