By Alex Sorokin, Business Consulting Engineer, Imubit Across dozens of speakers at the 2025 World Petrochemical Conference (WPC), a key theme was repeated – the chemicals industry faces a period of uncertainty driven by geopolitics, trade conflicts, and supply chain volatility. However, plant operators cannot control those external factors. They must focus on what they can control: embracing innovation, driving operational excellence, and maximizing margins on existing assets. At WPC, industry leaders and analysts often pointed to AI as the enabling technology for achieving these goals. However, a critical question was left unanswered: how can AI help chemical plants become more resistant to uncertainty? AI is a Journey, Not a Short Cut Perhaps it’s not surprising that WPC did not offer a direct, unified answer – AI is not a single-point solution, but rather a set of tools, applications, and approaches. The possibilities are uncountable, and the enterprise transformation potential has not been matched by anything since Microsoft Excel was introduced a generation ago. But that also makes it hard to know where to start, how to show value from AI-enabled projects, and how to continue capturing this value persistently after a few weeks or months. One can see why as few as 1 in 10 digital transformation efforts succeed in traditional industries [1] – but the results are worthwhile, with early adopters outperforming peers by a factor of 2-3x [2, 3]. Learning Together With, and From, Our Customers At Imubit, we’ve observed that the most successful AI initiatives are those which empower cross-functional teams to immediately understand the benefits of new technology first-hand. Our customers, and the challenges they’ve faced with other solutions, have helped us ensure that the Imubit Industrial AI Platform does just that. One Chevron refinery used the platform to drive “conversations between multiple stakeholders that hadn’t been able to be done before, namely the planning group, operations and engineering,” accelerating the site’s planning cycle from weekly to real-time. At multiple Oxbow calcining sites, the platform has allowed operators, engineers, and leadership to “see immediate improvements [and] provided the basis … to expand into other improvements that [they] didn’t even know were possible.” By partnering with these customers and dozens of others in dynamic, elastic markets, we have developed industry-leading expertise in separating signal from noise and deriving concrete results from high-level goals – even as operations, economics, and the goals themselves evolve over time. As with any work process, those experiences have led to learnings and those learnings have been distilled into best practices. To tackle uncertainty, remain competitive, and succeed on the AI journey, follow these 3 proven best practices: 1. Break Out of Local, Single-Point Optimization Operating points across different areas must shift to meet common, system-wide goals. Drive to the optimal operating points plant-wide by using the right feeds, catalysts, and process conditions within process and environmental limits. Systemwide automation has been especially challenging to implement with traditional methods due to the number of constraints, scenarios, and people involved. An optimization solution must understand how interactions between different process units affect high-level, plant-wide goals and constraints. For example, if one process unit shuts down or has an unscheduled catalyst change, how should other units be operated to re-optimize the plant? In one such example, Imubit’s AI Optimization (AIO) solution dynamically adjusts multiple units’ feed rates and APC targets to compensate for run-limiting equipment fouling – and then automatically readjusts when equipment is cleaned. Elsewhere, our AIO models have challenged long-established process unit control strategies to unlock value plant-wide. For more details on this example, check out case study #2 in this Hydrocarbon Engineering article. The lesson learned is consistent across multiple customers, geographies, and industries: true value chain optimization requires greater coordination and flexibility than is possible with a series of disconnected single-point solutions.   2. Maximize Existing Assets to Their True Constraints Reduce giveaway, downtime, and suboptimal uptime by running to true underlying limits even as feeds vary, equipment fouls, and catalysts degrade. At the low points of the industry’s market cycles – such as the extended downturn ongoing since 2022 [4] – plants are simultaneously the most challenged to turn a profit and the most strapped for capital to unlock margins through equipment upgrades. This means that existing assets must be maximized further than ever before. But even the best human operators often run to conservative targets to avoid deactivating catalysts, degrading equipment, or violating poorly-understood limits. While existing automations can reduce the throughput or margin left on the table, significant value is left unrealized when the true underlying limits are poorly understood, nonlinear, or subject to long time dynamics. These challenges are a natural fit for AI. In several implementations on semi-batch delayed-coking units, Imubit’s AI solution has dynamically maximized the value of each batch, reducing sub-optimal asset utilization by over 20%. For a customer with two parallel mixed-feed steamcrackers, Imubit’s AI Optimizer is able to capture a 3% margin increase by adjusting feed rates to true coking limits and downstream constraints instead of historically assumed values. In these examples and in countless others, businesses that will survive and thrive tomorrow must use novel solutions such as AIO to make the most of the equipment they have today.   3. Make Technology a Force Multiplier Enhance the functional understanding and decision-making of process experts with no-code modeling capabilities and a suite of AI explainability tools. Organizational adoption is a major bottleneck to capturing value from automation and digitalization initiatives. This has been true for 20th-century technologies like APC – in some cases, less than 10% of APCs are tuned and maintained [5] – and businesses rightly fear that this will apply to 21st-century technologies, too. But one truly revolutionary aspect of AI is its accessibility across disciplines, which provides the ability to empower the entire workforce and democratize expertise. Imubit’s platform is designed with an intuitive point-and-click user interface to lower the barrier to entry of AI. With an hour of training, users with no AI expertise can build their own models, review AI insights, and instantly compare what-if scenarios. Some facilities integrate Imubit models into operator training programs as a safe environment to test moves and duel the AI optimizer. Others use model learnings to augment established workflows, from scheduling APC gains to updating LP vectors. Today, industry is more reliant on technology than ever before, but that technology needs willing and capable users. To capture sustained value in this environment, companies must invest in the right AI solutions – those designed from the ground up to be explainable and easy to use. AIO is shaking up the way companies think about process optimization and delivering the agility required to maximize profitability in times of uncertainty. For more on AIO and how it can transform your operations, visit imubit.com/aio.