{"id":180808,"date":"2026-08-02T19:03:33","date_gmt":"2026-08-02T19:03:33","guid":{"rendered":"https:\/\/ktromedia.com\/?p=180808"},"modified":"2026-08-02T19:03:33","modified_gmt":"2026-08-02T19:03:33","slug":"how-tracelink-is-putting-ai-to-work-inside-the-supply-chain","status":"publish","type":"post","link":"https:\/\/ktromedia.com\/?p=180808","title":{"rendered":"How TraceLink Is Putting AI to Work Inside the Supply Chain"},"content":{"rendered":"
\n

Somewhere in a pharmaceutical supply chain right now, a purchase order is sitting in a queue. Someone has emailed a trading partner asking for an update. Someone else is reconciling numbers between two systems that do not talk to each other. The exception will eventually get resolved, but not before the delay has already moved downstream. In life sciences, that is not just an operational problem. It can mean a patient waits longer for a treatment they need<\/strong>.<\/p>\n

This is the problem that Shabbir Dahod<\/a> has been trying to solve since 2009. As the co-founder and CEO of TraceLink<\/a>, he has spent seventeen years building a system that links pharmaceutical companies, manufacturers, distributors, and logistics providers on one network. Today, this network is used by more than 315,000 organizations.<\/p>\n

Most artificial intelligence tools used in supply chains are bolt-ons working outside of the operations and provide suggestions. A person still has to take action based on those suggestions. TraceLink\u2019s approach is different. The company uses AI agents that work inside live supply chain processes. These agents have jobs, follow rules, and keep a record of all their actions. They make decisions alongside human teams. These agents are called OPUS Agents. They do not just send notifications. They take action. An agent can check a purchase order, approve it, partially fulfill it, reject it, or flag it for a human to review. All of this happens within the system that the rest of the team uses.<\/p>\n

We talked to Shabbir Dahod to learn more about how this works in real life. We wanted to understand what it takes to use AI responsibly in one of the most regulated industries in the world. We also wanted to know what mistakes companies make when they start using AI in their supply chain operations.<\/p>\n

The Person Behind the Company<\/strong><\/h2>\n

1. TraceLink was founded in 2009 with a focus on digitalizing the life sciences supply chain. Seventeen years later, the company is deploying AI agents that execute supply chain decisions in real time. At what point did AI move from being a future consideration to the actual center of the company’s strategy?<\/strong><\/p>\n

The center of our strategy has always been the same: building the network, information foundation, and operating model needed to help life sciences companies coordinate operational work across supply chains and ensure patients receive the medicines they need safely, reliably, and on time.<\/p>\n

For more than seventeen years\u2014long before anyone was talking about agentic AI\u2014we have been linking companies, business transactions, supply chain processes, and real-time operational data across the world’s largest life sciences network, creating a Digital Twin of the Supply Network that reflects what is happening across trading partners in real time.\u00a0<\/p>\n

What has changed is that AI has become capable of acting on that foundation. This is what we now define as the Agentic Supply Chain Operating Model. It is not a departure from our strategy; it is the next stage of it. The model combines human expertise, trusted operational context, Agentic Control Towers, Agentic Business Processes, and governed OPUS Agents so supply chain work can be sensed, prioritized, coordinated, and performed across trading partners with greater speed, consistency, and accountability.<\/p>\n

2. Before TraceLink, Shabbir held leadership roles at Microsoft and Asymetrix, working on collaboration, knowledge management, and enterprise software. How did that experience shape the thinking behind building AI systems that work reliably inside large, complex organizations?<\/strong><\/p>\n

My experience taught me that technology succeeds when it adapts to the way enterprises operate, not the other way around. Especially in regulated industries, organizations run on governance, accountability, security, permissions, collaboration, and well-defined business processes. If a technology cannot operate within those realities, it may be interesting, but it will struggle to deliver lasting business value.<\/p>\n

That lesson is highly relevant to AI. Many AI systems today are designed primarily to generate information. Enterprise AI must go further. For an agent to work, it needs clear objectives, defined responsibilities, authorized boundaries, and auditability. That philosophy is reflected in OPUS Agents. We designed them as governed digital teammates that operate within real business processes, using real-time operational context and the same controls that organizations expect from any trusted participant in the supply chain.\u00a0<\/p>\n

The Problem and Why It Has Not Been Solved<\/strong><\/h2>\n

3. When a pharmaceutical supply chain breaks down, what does that usually look like in practice? Is it delayed orders, missing data, manual reconciliation, partner follow-ups, or something else?<\/strong><\/p>\n

Supply chain breakdowns rarely start as dramatic events. They usually begin with something that looks routine: a purchase order that has not been acknowledged, a missing shipment update, an inventory mismatch, or a team waiting for a trading partner to respond. Individually, these issues may seem minor. But when they occur across disconnected companies, systems, and processes, they create delays, uncertainty, and operational risk that can ripple across the supply chain.<\/p>\n

The deeper issue is that much of this work is still coordinated manually. More than 70% of life sciences transactions still involve emails or PDFs, and 40% to 60% of supply chain exceptions are handled manually.\u00a0<\/p>\n

As a result, teams spend valuable time chasing information, reconciling data, and following up with partners instead of resolving issues quickly and consistently. Over time, those delays can reduce productivity, increase operating cost, weaken resilience, and affect production schedules, service levels, inventory turns, working capital, revenue, and ultimately product availability for patients. That is why the industry needs to move from fragmented, manual coordination to an Agentic Supply Chain Operating Model, where supply chain work can be continuously managed across the network with greater speed, context, and control.<\/p>\n

4. Life sciences supply chains run across dozens of partners, each operating with different systems, data formats, and levels of digitalization. What does that fragmentation look like from the inside, and why has coordinating across all of them proven so difficult?<\/strong><\/p>\n

Fragmentation is the natural byproduct of the evolution of supply chains. Critical operational work happens across manufacturers, suppliers, contract manufacturers, logistics providers, distributors, pharmacies, and healthcare organizations, each with its own systems, processes, and data standards. As a result, most supply chain information exists outside any single enterprise. Our research indicates that 60% to 80% of supply chain data resides beyond enterprise systems, while fewer than 25% of pharmaceutical companies have visibility beyond portions of their top-tier partner network.<\/p>\n

Historically, the industry has addressed this complexity through point-to-point integrations, portals, spreadsheets, emails, and custom processes. While those approaches can solve individual problems, they create a growing burden of implementation, maintenance, and ongoing support as partner networks evolve. TraceLink takes a fundamentally different approach with a shared business network where companies integrate once and digitalize transactions and processes beyond enterprise boundaries. The result is a more durable, cost-effective digital foundation that can adapt as business requirements change and support the next generation of agentic supply chain operations.<\/p>\n

5. Other AI tools already claim to help with supply chain operations. What did that generation of tools get wrong, and why was it not enough?<\/strong><\/p>\n

Bolt-on AI tools were for information gathering via chat interfaces. However, the supply chain operating model consists of humans that need to work on business transactions within a business process in order to manage the end-to-end flow of products and services. Humans must reconcile information, respond to exceptions, collaborate across functions and the supply network.<\/p>\n

The next phase is not simply better recommendations. It is moving from insight to governed agentic execution: AI that can operate within trusted business processes, use real-time operational context, follow defined rules and permissions, and work alongside human teams with auditability and control. That is where the real value of AI emerges \u2014 when insight, context, governance, and action come together.<\/p>\n

The Solution and How It Works<\/strong><\/h2>\n

6. If an AI system sees a problem with a purchase order, what is the difference between telling a person what to do and actually resolving part of the issue itself?<\/strong><\/p>\n

The difference is that the agent is not operating as a standalone assistant. It is participating in an Agentic Business Process with defined objectives, rules, permissions, and human oversight.\u00a0<\/p>\n

Most AI systems can flag that a purchase order has a problem. For example, the requested ship date cannot be met because available inventory is short, a contracted substitute product exists, and the order is for a high-priority customer. A traditional AI assistant might summarize the issue and recommend next steps: check inventory, review the contract, contact the supplier, propose a partial shipment, and notify customer service. The person still has to interpret the recommendation, move between systems, coordinate with partners, and perform approved work.\u00a0<\/p>\n

An OPUS Agent goes further by performing approved work within defined boundaries. In that same scenario, it could validate the customer priority, confirm substitute eligibility, reserve available inventory, split the order, update the promised delivery date, generate the supplier or customer communication, and route only the exception for human approval if policy thresholds are exceeded. It\u2019s the difference between knowing and doing.\u00a0<\/p>\n

7. The commerce order evaluation agent is one of the clearest examples of OPUS Agents in action. Can Shabbir walk through what actually happens, from the moment a purchase order arrives to the point where the agent approves it, partially fulfills it, rejects it, or passes it to a human reviewer?<\/strong><\/p>\n

When a purchase order arrives, the agent first looks at the order in context. It checks whether the requested products are available, whether there are any product or fulfillment constraints, what commitments have been made to the customer, what the contract allows, and whether there are already related issues in progress. In other words, it is not just reading the purchase order as a document. It is evaluating what should happen next based on the real operating conditions around that order and its representation within the Digital Twin of the Supply Network.<\/p>\n

From there, the agent follows the rules and objectives set by the business to decide whether the order can be approved, partially fulfilled, rejected, or needs human review. If the answer is clear and within the boundaries the business has approved, the agent can carry out the action and record what happened. If the situation is uncertain, risky, or requires judgment, it passes the issue to a person with the relevant context and reasoning. That is the important distinction: the agent is not simply recommending what someone should do next\u2014it is enabling human-agent collaboration where the agent helps perform the work and people remain in control where judgment is required.<\/p>\n

8. OPUS Agents are built using what TraceLink calls the IOTDR framework, where an agent’s behavior is defined by its Intent, Objective, Tasks, Decisions, and Rules. How does a business user who is not a developer actually build one of those agent profiles? And what prevents them from configuring it poorly before it runs inside a live process?<\/strong><\/p>\n

A business user is not programming an agent in the traditional sense. They are defining a role. Just as you would define the responsibilities, authority, objectives, and policies for a new employee, you define the Intent, Objective, Tasks, Decisions, and Rules that govern how an agent operates. The IOTDR framework provides a structured way to translate business knowledge into agent behavior without requiring users to write code.<\/p>\n

Just as important, the platform is designed to prevent organizations from treating agents as unrestricted AI systems. Agents operate within defined roles, permissions, and decision boundaries, applying specialized digital expertise to specific supply chain work. Their actions are governed, monitored, and auditable, and organizations can begin with narrowly scoped use cases before expanding responsibilities over time. In regulated industries, trust is earned through control and accountability. Instead of giving agents unlimited autonomy, the goal is to create governed digital teammates that can operate inside the Agentic Operating Model with clear intent, measurable objectives, defined responsibilities, authorized boundaries, and full auditability.\u00a0<\/p>\n

9. At the core of OPUS Agents is what TraceLink describes as a metadata-driven network architecture, one that structures business objects such as purchase orders, invoices, and inventory records in a way that allows language models to reason over them and make governed decisions. Why is that architectural layer necessary, and what breaks without it?<\/strong><\/p>\n

A language model can understand words, but supply chain work requires understanding business context. A purchase order is not simply a document. It is connected to products, inventory, suppliers, shipments, contracts, customer commitments, exceptions, quality requirements, compliance obligations, and partner activity. If an AI system only sees text, it may generate a plausible answer, but it cannot reliably determine what action should be taken, whether the data is complete, or whether the action complies with business rules.<\/p>\n

That is why the architecture matters. TraceLink\u2019s Integrate-Once\u2122 Agentic Business Network links companies, systems, trading partners, business transactions, and processes. That creates trusted operational context and a Digital Twin of the Supply Network. Agentic Control Towers then bring together the knowledge, intelligence, analytics, reasoning, and visibility needed for humans and OPUS Agents to monitor, decide, and act. Agentic Business Processes provide the governed environment where the work is actually performed.<\/p>\n

Without that foundation, AI can still be useful as an assistant. With it, AI can become part of a governed operating model for performing supply chain work across company boundaries.<\/p>\n

10. The OPUS platform connects more than 315,000 authenticated network entities. How important is that existing network to making AI agents work in practice? Could OPUS Agents function on a smaller or less connected data foundation?<\/strong><\/p>\n

The more important question is whether an agent can create meaningful business value across the supply chain. Most supply chain work is cross-company work. A manufacturer depends on suppliers. A distributor depends on manufacturers. Logistics providers, pharmacies, and healthcare organizations all contribute information and perform work that affects the outcome. If an agent only has visibility into one partner, its ability to understand and influence what is happening across the broader supply chain is naturally limited.<\/p>\n

That is what makes TraceLink\u2019s Integrate-Once\u2122 Agentic Business Network so important. It provides the governed multienterprise foundation for linking companies, trading partners, business transactions, product movement, process signals, and operational events. TraceLink\u2019s network represents a digital twin of more than 315,000 businesses with more than 339,000 links across end-to-end supply chain orchestrations.<\/p>\n

An AI copilot may be able to automate tasks within one enterprise application. But it cannot easily recreate the trusted relationships, permissions, data flows, operational context, and shared process foundation required to perform governed work across companies.<\/p>\n

Governance, Trust, and Real Adoption<\/strong><\/h2>\n

11. How do you decide what an agent is allowed to handle on its own and what still needs a human decision? Can customers adjust those limits over time?<\/strong><\/p>\n

We think about agent autonomy the same way organizations think about delegation. You would not give a new employee unlimited authority on day one, and the same principle applies to agents. What an agent is allowed to handle should depend on the risk, complexity, and consequences of the task. A low-risk, rules-based activity may be appropriate for automation, while a decision involving ambiguity, financial exposure, customer impact, or regulatory risk may require human review.<\/p>\n

Autonomy exists on a spectrum that customers should be able to configure and adjust over time. They may choose to start with agents that surface issues and prepare recommended actions, then move toward approval-based work, and eventually allow independent action in areas where the rules are clear and outcomes are measurable. Successful adoption will come from treating autonomy as something that is governed, monitored, and earned over time. That is the essence of human-agent collaboration: agents can take on more work as confidence grows, while humans remain in control of the decisions that require judgment.<\/p>\n

12. An agent that rejects a purchase order is making a real business decision with real financial consequences. If that agent makes an error, who is accountable? And how does the audit trail reconstruct exactly what happened and why the agent made the call it did?<\/strong><\/p>\n

Accountability does not shift from the organization to the agent. Organizations remain responsible for the processes they operate, the policies they establish, and the decisions they authorize. OPUS Agents participate within those governed processes; they do not operate as independent actors outside them.<\/p>\n

That is why TraceLink captures both an audit trail and a progress log. The audit trail records every operation performed on the business object\u2014what action was taken, when it occurred, and by whom or which authorized agent. The progress log captures how the work was completed, documenting the sequence of tasks, decisions, approvals, and agent actions that led to the outcome. Together, they provide complete operational transparency so organizations can reconstruct what happened, understand why it happened, verify that policies were followed, and continuously improve how work is performed.<\/p>\n

13. GxP compliance is a legal and regulatory requirement in life sciences, not a preference. What does the audit trail actually capture in practice, and how does it hold up under a formal inspection or regulatory review?<\/strong><\/p>\n

In a regulated environment, an audit trail has to show more than the fact that something happened. It needs to show who or what took the action, when it happened, what information was used, what rules or approvals applied, and why the action was allowed. For an agent-driven process, that means keeping a clear record of the business objective, the data the agent evaluated, the policies that governed the action, the outcome, and whether the agent stayed within the boundaries set by the company.<\/p>\n

That becomes especially important with AI. If an inspector asks why a purchase order was rejected, a shipment exception was escalated, or a transaction was approved, the organization needs more than a timestamp and a result. It needs a clear, reviewable record showing that the process was controlled, compliant, and properly governed from start to finish.\u00a0<\/p>\n

14. What should companies do now to prepare for agentic supply chain execution?<\/strong><\/p>\n

Companies should think about agentic execution as the next stage of supply chain digitalization, not as a standalone AI initiative. The first step is to digitalize business transactions, collaborative processes, and operational data across the end-to-end supply network, creating a trusted operational foundation that spans enterprise systems and trading partners. From there, organizations establish the intelligence and governance needed for agentic execution by defining the business rules, roles, permissions, approvals, and standard operating procedures that govern how work should be performed. With that foundation in place, they can configure company-specific Agentic Business Processes that combine people and specialized OPUS Agents around a shared operating model.<\/p>\n

Rather than deploying general-purpose AI assistants, organizations should start with a high-volume business process where every business transaction, collaborative process, or product movement becomes the starting point for coordinated work. OPUS Agents evaluate context, recommend actions, perform approved work, and collaborate with people according to customer-defined business rules and policies. As organizations gain experience, they can continuously refine those Agentic Business Processes, expand them to additional supply chain operations, and scale agentic execution across the enterprise\u2014without custom development.<\/p>\n

The Hard Questions and the Road Ahead<\/strong><\/h2>\n

15. If OPUS Agents can handle work that people used to do manually, what does that mean for those teams? Is this about giving them more capacity, or about needing fewer people over time?<\/strong><\/p>\n

Supply chain teams are being asked to manage more complexity with greater speed and precision than ever before. They are coordinating across more partners, responding to more exceptions, and operating under higher service expectations \u2014 often with processes that still depend heavily on manual follow-up. OPUS Agents can help by taking on repetitive, time-sensitive, and rules-driven work, supporting always-on operations and creating an elastic workforce that gives human teams more capacity to focus on the work that requires experience and judgment.<\/p>\n

Human teams can spend less time chasing status updates, reconciling information, or managing routine exceptions. Instead, they can focus on the work that actually benefits from human expertise: making judgment calls when tradeoffs are unclear, working directly with customers and partners, addressing the root causes of recurring issues, and improving how the supply chain responds to disruption.<\/p>\n

The larger opportunity is to elevate the role of human teams. Agents can support continuous work, but people remain essential for judgment, accountability, strategy, innovation, and relationship-driven decisions.<\/p>\n

16. You\u2019ve seen several major technology adoption cycles over your career. What feels different about this moment with AI agents, and what should life sciences leaders keep in mind as they move from experimentation to real operational use?<\/strong><\/p>\n

What feels different about this moment is that AI is moving from analyzing work to performing work. That raises the stakes. In earlier technology cycles, the challenge was often adoption: getting people to use a new system or process. With agents, the challenge is also about trust, governance, and deciding where digital teammates can safely perform work.\u00a0<\/p>\n

For life sciences leaders, the key is to stay focused on business outcomes while building confidence and readiness step by step. The companies that succeed will not be the ones that chase the most ambitious AI vision on day one. They will be the ones that identify valuable operational problems, establish trusted transaction and process context, put governance in place, prove measurable business impact, and then expand responsibly. That is how AI agents move from experimentation to the Agentic Supply Chain Operating Model.<\/p>\n

17. When a customer deploys OPUS Agents, what should they actually measure to know whether it is working? TraceLink’s materials point to OTIF improvement, stockout reduction, and faster exception resolution as goals. Which of those tends to show results first?<\/strong><\/p>\n

Companies should think about success as a progression through an Agentic Supply Chain Maturity Model. The first stage is operational productivity. Organizations should measure how quickly exceptions are resolved, how much manual coordination is eliminated, how consistently business processes are executed, and whether teams can manage more transactions and complexity without adding headcount. Those are the earliest indicators that Agentic Business Processes are improving the way work gets done.<\/p>\n

As organizations mature, those productivity gains should translate into measurable business performance. Faster, more consistent execution improves service levels and OTIF performance, reduces inventory and stockouts, strengthens working capital, and ultimately supports revenue growth and profitability. That’s the progression organizations should expect: productivity enables stronger operational performance, operational performance improves financial performance, and financial performance creates competitive advantage. The organizations that benefit most won’t simply automate individual tasks\u2014they’ll continuously optimize how work is performed across the end-to-end supply network.<\/p>\n

18. Three years from now, what does a life sciences company running OPUS Agents look like operationally, compared to a competitor that has not made that move?<\/strong><\/p>\n

Three years from now, a life sciences company that has adopted the Agentic Supply Chain Operating Model should look very different from one that has not made that move. Human teams and governed OPUS Agents will support always-on operations, giving teams the real-time context they need to sense issues earlier, prioritize work faster, coordinate across partners, and take governed action before disruptions cascade.<\/p>\n

Compared to a competitor relying on fragmented systems, manual follow-up, delayed reporting, and isolated AI tools, that company should be able to run supply chain operations with greater speed, consistency, and resilience. The business impact can show up in higher productivity, better service levels, improved inventory performance, stronger working capital, lower cost, stronger compliance and quality, greater resilience, and improved revenue performance.<\/p>\n

But the larger impact is societal. Healthcare supply chains are among the most important forms of critical infrastructure because they help ensure patients have access to safe and authentic medicines when they need them. The companies that successfully adopt this new operating model will help build a more intelligent, linked, and resilient healthcare ecosystem\u2014one that is better equipped to protect product availability, strengthen trust, respond to disruption, and ultimately better serve patients, public health, and society as a whole.<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

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