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Celanese Leads the Pack When it Comes to Agentic AI

Published

2 ans ago

3 mars 2025

Celanese Leads The Pack When It Comes To Agentic Ai

Knowledge Graphs are emerging as an important tool for building advanced AI capabilities.

According to a survey by ARC Advisory Group, only 10% of industrial companies are ready to apply artificial intelligence/machine learning. The percentage of industrial companies broadly applying agentic AI and generative AI would be a small fraction of that number.

Celanese is an exception. ARC has been actively studying industrial AI for over two years. What Celanese has accomplished is the single best example ARC is aware of employing agentic AI and copilots at scale. Ibrahim Al Syed, the director of digital manufacturing at Celanese, was surprisingly forthcoming about how Celanese developed these capabilities at ARC Advisory Group’s 29th Annual ARC Industry Leadership Forum. He also spoke at the ARC forum in 2023, and this article is based on that presentation as well.

Agentic AI involves creating a system of interacting agents, each trained on a specific task or dataset. These agents can communicate, negotiate, and collaborate to solve complex problems. Agentic allows for much greater flexibility. Instead of relying solely on a single, monolithic AI model (based on a massive large language model), a company can orchestrate a team of specialized agents, each leveraging the best AI or mathematical technique for its specific task. generative AI is starting to be used as the orchestra director that weaves these agents together in a process flow and provides a uniform “co-pilot” style user interface.

The Celanese Supply Chain

Celanese Corporation (NYSE: CE), headquartered in Dallas, Texas, is a global chemical and specialty materials company with revenues of over $10 billion. The company operates in over 20 countries and has over 12,000 employees. The company has 55 manufacturing sites across the world. The company runs some plants, and some are operated by third parties.

The chemical industry has a complex supply chain. Their plants are very expensive. Maximizing factory throughput is critical. Further, multiple plants may be capable of making the same product, and figuring out which plant should produce the product based on the current supply and demand situation is not straightforward.

Chemical companies are extremely safety conscious. They must be. The risks associated with chemical manufacturing include the storage and transportation of raw materials, finished products, and waste. These hazards include, among other things, pipeline and storage tank leaks and ruptures, explosions and fires, and discharges or releases of toxic or hazardous substances. The occurrence of any of these events disrupts the global supply chain and can deeply impact profitability.

Building the Foundation

During COVID-19, Celanese began to think about the need for a digital transformation. Travel restrictions made it difficult to staff their plants. The ability to have a digital platform that supported workers who could help run their plants from remote locations was seen as highly desirable. Further, when they began thinking about a platform to detect and react to equipment anomalies, they realized those capabilities would support safety, better product quality, and production optimization. They realized the ROI associated with that could be massive.

At this point, they were not thinking about agentic AI, no company was, but the platform they put in place turned out to be perfect for agentic AI, and AI became a big goal in their digital transformation.

Celanese was also not looking to take humans out of the loop. Their guiding principle was human-centric digital design. For example, if an asset issue was detected, solving that issue could involve multiple applications used by multiple people, seeing different information, entering different data, bouncing emails and texts back and forth, and moving information from one place to another. “One event could create so much churn,” Mr. Al Syed explained.

A person at a manufacturing facility is involved in all kinds of processes, Mr. Al Syed elaborated. They prepare equipment for maintenance, do isolation (disconnect a piece of equipment from the flow of chemicals by closing valves), look at quality or reliability metrics, and do rounds. People-centered design focuses on distinct roles so that “every day we allow people to work at their maximum potential.”

“We needed to model the data in a way that we can do simple searching. Can I have an industrial Google at a manufacturing facility? Why is it so hard for our people to find information in the right context? We spent hours and hours looking for data, whether it was for audits, compliance, or just basic troubleshooting. This became an investment priority.”

In the past, if the business had a need, they would buy an application. Then, another application would be purchased, and another, and another. This ends in a spaghetti approach to data integration. Data does not move. Managing those applications becomes more challenging. Celanese recognized a need to decouple their data away from their applications. Data should be created once and move seamlessly, in real-time, to where needed. This architecture was necessary to create a unified experience for users.

Data must be modeled consistently across the organization. How data is created, used, and maintained must be standardized. Data governance is critical. This, Mr. Al Syed said, is key in moving from an incremental application ROI to bigger, more strategic forms of value creation.

Celanese chose the Data Fusion platform from Cognite as their industrial data platform. Increasingly what Cognite is offering is called a data fabric; in their case, it is a data fabric for plant-level data. Industrial data is voluminous. Celanese has 2.5 trillion records from 47 data sources in the Cognite platform. Industrial data is also more complex than enterprise data. Plant-level data includes time series data from sensors and machines, transactional data – like orders, and unstructured data from engineering drawings and pictures.

A data fabric speeds and simplifies access to data assets across the business. It accesses, transforms, and harmonizes data from multiple sources to make it usable and actionable for various business use cases.

Building Context

Celanese’s goal around human-centered design was to surface the correct data, with the proper context, to the right person, at the right time to make better decisions. But then people need to act. The ability to act on the information must be part of the workflow.

But getting the context right is a difficult problem. Contextualization is the process of identifying and representing relationships between data to mirror the relationships that exist between data elements in the physical world.

This is where knowledge graphs are being used. Cognite’s platform includes a knowledge graph. A knowledge graph creates relationships across previously siloed data sources. Knowledge graphs “weave” together a unified, seamless layer for data management and, by doing this, often uncover hidden patterns and relationships, patterns no human could detect. Answering a question like “Why has this piece of equipment gone down?” can require accessing many pieces of siloed data and then looking for relationships between the data sets and an event that has occurred. Knowledge graphs can find relationships that no human could uncover.

JO.AI, a Co-pilot for the Plant

JO.AI is the user interface. It is built on Generative AI. Generative AI is an artificial intelligence technology that can produce various types of content, including text, imagery, and audio. It turns out that the way to use GenAI is as an advanced user interface. At Celanese, JO.AI is the single point of interaction that facilitates workers in getting their work done.

JO.AI was built quickly. That was only possible because Celanese had the right foundation in place – they had cleaned their data, improved data governance, put in an industrial data fabric, and then used a knowledge graph to contextualize the data.

Mr. Al Syed demoed several use cases. They were beautiful demos. In one demo, a system detected an asset that was not performing right. A user is assigned to examine this. The user asked to see a piping and instrumentation diagram and then wanted to see the work orders for the vessel in question and the tag for that vessel. JO.AI provided these. The manager then assigned Fred the job of diagnosing the problem. The manager used JO.AI to create a work order for Fred. Fred then uses the interface to diagnose the issue. JO.AI asks, “Do you see this? Is this happening?” JO.AI even looked at a picture of the asset taken on Fred’s phone. Then based on the picture and the answers, JO.AI suggested that corrosion might be the problem. Fred agrees. Then a new updated work order was created to swap out the asset.

Radix, a consultant and system integration firm, helped to develop JO.AI. Pre-trained AI agents focused on specific use cases for specific user personas were created. Use cases focused on four key areas:

Optimized Operator Rounds: JO.AI provides insights that ensure operations teams are focusing their rounds on the proper checklists.

Data-Driven Checklist Management: The interface recommends the optimal frequency of checklist items, identifies areas with high-volume issues, and highlights deviations.

Balanced Workloads: JO.AI helps ensure the checklist workload is appropriate for each shift.

Streamlined Maintenance: The solution facilitates maintenance and work notification opportunities, recommends resource plans, and assists operators in writing work orders.

JO.AI was built in phases. First, it was piloted at one process unit at one plant. Then, gradually, the use cases expanded to over 40, and the number of plants using JO.AI increased to 50.

Mr. Al Syed did not talk in detail about the ROI, except to say it was significant and would continue to grow. He did give one example, their ability to do effective preventative maintenance did increase by 15%.

He also said JO.AI was not perfect. One key area of focus this year is to eliminate hallucinations. “It is better to have no answer than the wrong answer.”

Amazingly, this journey was accomplished in just three and a half years. In part, this was skill. Celanese focused first on building the right foundation. But there was also an element of luck. Cognite evolved from a robust industrial data platform to also being an AI platform just in time for Celanese to take advantage of it.

The post Celanese Leads the Pack When it Comes to Agentic AI appeared first on Logistics Viewpoints.

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The Systems-Engineered Logistics Operation: From Transformation Projects to Continuous Design

Published

18 heures ago

3 novembre 2026

For years, logistics transformation has been organized as a succession of projects. Implement the platform. Build the control tower. Automate the facility. Add the AI layer. The problem is that logistics no longer changes on a project cadence.

That environment is disappearing. The future logistics will need to be designed continuously. The Autonomous Logistics Network is one expression of this end state, but reaching it requires continuous design rather than a one-time transformation project.

Stop Thinking in Technology Stacks

Modern logistics networks are becoming integrated combinations of physical assets, software, data, optimization, AI, robotics, people, partners, and automated decisions. Calling this a technology stack understates what is happening.

It is an operating system. The important design questions are no longer confined to which applications a company owns. Leaders have to consider how decisions flow, how data maintain continuity across systems, how automation interacts with people, how models are governed, how exceptions escalate, and how the organization behaves when components fail.

That is systems engineering territory. The companies that manage this architecture deliberately will have an advantage over those that continue adding capabilities one project at a time.

Intelligence Is Moving to the Point of Action

Logistics technology is moving from systems of record toward systems of decision. Traditional applications captured transactions and supported workflows. Analytical systems added prediction and optimization. The emerging layer increasingly combines real-time signals, AI, optimization, agents, and execution connectivity.

The result is intelligence closer to the point of action. A shipment delay can be identified, its impact evaluated, alternatives generated, and a response executed with limited human intervention. Transportation systems can continuously re-evaluate routes, capacity, and exceptions instead of waiting for manual replanning. Warehouse and yard systems can adjust priorities as conditions change.

This will make logistics faster. It will also make architecture and governance more consequential.

When software only reported information, a bad output could be ignored. When software acts, the operating envelope matters.

Human Work Moves Up the Decision Stack

More automation does not eliminate the human role. It changes it.

People will spend less time collecting information, reconciling spreadsheets, monitoring routine transactions, and making repetitive bounded decisions. They will spend more time dealing with novel exceptions, cross-functional tradeoffs, strategic choices, carrier and customer relationships, system design, and governance.

That is a better use of human capability. It is also a harder job.

The future planner, transportation manager, warehouse leader, or logistics executive will need to understand not only operations but the automated systems influencing operations. They will need to know when to trust a recommendation, when to challenge it, and how a decision in one part of the system affects another. Logistics expertise will become more architectural.

Continuous Design Requires Continuous Validation

If the system changes continuously, validation must become continuous as well.

AI models drift. Business priorities change. automation rules evolve. New interfaces are added. Local process changes accumulate. External conditions move.

The organization cannot wait for the next transformation program to determine whether the operating system still meets its requirements. Performance, model behavior, exception patterns, overrides, decision latency, data quality, and failure modes will need ongoing monitoring.

This creates a closed lifecycle: design, deploy, observe, validate, improve, and redesign. Transformation becomes less of an event and more of a management discipline.

Resilience Becomes an Architectural Property

The future of resilience is also likely to move beyond static buffers. Inventory and redundancy will remain important, but digital systems will make logistics more capable of sensing, simulating, and responding dynamically.

A disruption can trigger scenario analysis. A digital twin can estimate consequences. Optimization can evaluate alternatives. AI can interpret unstructured signals. Automated decision systems can act inside approved boundaries.

This does not eliminate the need for physical resilience. It allows physical resilience and decision resilience to work together.

The strongest logistics networks will not simply carry more protection. They will understand their dependencies and reconfigure faster when conditions change.

Architecture Becomes a Competitive Capability

For years, companies differentiated logistics through network scale, facility and transportation assets, carrier leverage, operational excellence, service design, and technology investment. Architecture is becoming another source of advantage.

Two companies may use similar software and still achieve very different results because one has better process integration, clearer decision rights, stronger data, more effective automation boundaries, better failure recovery, and tighter governance. The advantage does not come from owning the component. It comes from how the components have been engineered to work together.

Logistics Management Becomes Systems Management

That may be the larger lesson of this series. Logistics networks are already systems of systems. The technology is making them more connected, more autonomous, and more adaptive. That complexity cannot be managed indefinitely through functional optimization and periodic implementation projects.

The discipline has to evolve.

Define requirements before selecting solutions. Design the operating model. Architect processes, data, and decisions together. Engineer technology into the system rather than around it. Validate the outcome. Govern change. Design for failure. Improve continuously.

The competitive advantage will not come from owning the same AI, automation, TMS, WMS, visibility, and execution technologies everybody else can buy. It will come from how coherently those components have been engineered to work together. The future of logistics management is not simply more digital, more autonomous, or more intelligent. It is more engineered.

Related Logistics Viewpoints research

Systems Engineering in Logistics
The New Architecture of Logistics
2026 Supply Chain Decision Intelligence Market Map
Digital Transformation in Logistics
Previous in this series: Design for Failure: Resilience, Risk, and Failure Modes

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Walmart and Symbotic: When Warehouse Automation Becomes a Network Strategy

Published

18 heures ago

3 novembre 2026

Warehouse automation has traditionally been discussed one facility at a time. A retailer installs an automated storage system, deploys mobile robots, adds robotic picking, or upgrades sortation. The business case is then measured against improvements within that particular building. Walmart and Symbotic are beginning to challenge that way of thinking.

On October 29, Walmart and Symbotic announced an expanded commercial agreement that will bring additional automation capabilities to 25 U.S. regional distribution centers. The agreement includes an initial commitment of approximately $6 billion, covering work over several years and multiple locations. Walmart says the initiative is intended to support upstream e-commerce inventory management, increase storage capacity, and improve the movement of products through its supply chain. Those numbers make the announcement noteworthy. But the more interesting story is what the scale of the commitment says about the next phase of warehouse automation.

From an Automation Project to an Operating Model

For years, warehouse automation investments were frequently justified around specific bottlenecks: reduced picker travel, increase storage density, improve sortation throughput, or offset labor requirements. Those are still important objectives. But Walmart is evaluating automation against something larger: the performance of an interconnected distribution network supporting stores, pickup and home delivery.

Rob Montgomery, executive vice president for Walmart U.S. Supply Chain Operations, tied the expanded agreement to improving service, growing capacity, simplifying work for associates and lowering costs. The company also expects the expansion to support broader assortment, greater speed and more reliable replenishment. That distinction matters.

Warehouse automation increasingly needs to be evaluated not simply by asking, “How much faster can this facility pick?” but by asking, “What does this investment change across the supply chain?” Does additional storage capacity improve inventory positioning? Does automation enable later ordering cutoffs? Can replenishment become more predictable? Can the same technology architecture be repeated across multiple facilities without redesigning the operation every time? Those questions take warehouse automation discussions much closer to business strategy.

Scale Is Becoming Part of the Business Case

The Walmart-Symbotic relationship is particularly interesting because this is not a new pilot. Reuters reported that Symbotic has been helping automate Walmart’s supply chain since 2017, while the latest agreement extends automation to another 25 regional distribution centers. There is another layer to the expansion as well. Supply Chain Dive reported that Walmart committed an additional $125 million toward research and development of racking technology and related picking, process-flow and inventory-management capabilities. That is important because it suggests the competitive discussion is moving beyond individual pieces of robotic hardware. Warehouse automation is increasingly about how storage, picking, inventory, material movement and software operate together.

Symbotic’s current portfolio illustrates that evolution. The company says its integrated offering includes BreakPack for each-picking and SymMicro for e-commerce micro-fulfillment, supported by proprietary software that uses AI to orchestrate product movement. The question for warehouse operators, therefore, becomes less about which robot performs an individual task and more about how multiple automation layers combine into a repeatable operating system.

Walmart Is Not the Only Signal

Developments elsewhere reinforce the same trend. Amazon reported in 2026 that it has deployed more than one million robots across its operations network. Its systems now span inventory handling, mobile robotics, robotic arms and packaging automation. The company also says its Sequoia system can identify and store inventory up to 75 percent faster at fulfillment centers.

Meanwhile, DHL Supply Chain and Locus Robotics reached another useful measure of scale in 2026: one billion warehouse picks using Locus AMRs. Locus says thousands of its robots now operate across more than 40 DHL-managed sites worldwide. These examples are different applications with different economics. That is precisely the point. The warehouse automation market is moving beyond the question of whether robotics can work. Leading operators are increasingly confronting the harder questions of where automation should be deployed, how quickly it can scale, how well it integrates with existing operations, and whether benefits survive replication across a network.

The Metrics That Matter After Go-Live

The risk in covering a $6 billion automation agreement is treating announced spending as though it were demonstrated return. It is not. The more valuable story will develop as the systems go live. The measures worth watching include throughput, storage utilization, replenishment performance, labor productivity, system availability, fulfillment speed, implementation cost and the ability to maintain performance during demand peaks. That distinction should matter to technology buyers. A compelling demonstration can show what automation can do. A network-scale deployment begins to show whether the technology can generate similar value repeatedly under different facility and operating conditions. For Walmart, that makes the 25 distribution centers as important as the $6 billion headline.

Takeaway

The next chapter of warehouse automation will not be defined simply by more robots. It will be defined by whether operators can turn automation into a repeatable, measurable and scalable operating capability.

Walmart’s expanded relationship with Symbotic provides an unusually large test of that idea. The announced objectives are ambitious: more capacity, faster product movement, better replenishment, broader assortment, simpler work and lower costs. Now comes the more important part: measuring what changes when those systems are operating across the network. For other warehouse operators considering automation, that may ultimately be the most useful lesson from Walmart. Do not evaluate the robot alone. Evaluate what the automation changes in the economics and performance of the supply chain around it.

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