For years, traditional automation has helped improve efficiency in supply chain operations. Yet in the complex, multi-partner networks of pharmaceutical and life sciences supply chains, legacy systems often fall short. Regulatory complexity, patient-critical products, serialized inventory, and multi-country compliance introduce exceptions and risks that traditional automation—or even general-purpose AI—cannot adequately manage. What is needed now is Artificial Expert Intelligence (AExI): domain-specific, deeply contextual intelligence designed for supply chain precision, accuracy, and safe decision support.
What’s Related
According to a recent IDC SC Survey, advanced analytics and AI continue to rank among the top near-term investment priorities for supply chain leaders, but the industry is now shifting from broad AI experimentation toward expert systems that deliver measurable value.
The limitations of traditional automation
Conventional automation works well for predictable, repetitive tasks, but it struggles in dynamic global supply networks where exceptions are the norm. General-purpose AI models face similar constraints. They lack domain specificity, understanding of partner-to-partner business rules, context needed to evaluate serialized events, and the precision and accuracy required for life-critical decisions.
In pharmaceutical supply chains, these gaps can have real consequences, from production bottlenecks to medication shortages that directly affect patient care. The Drug Supply Chain Security Act (DSCSA) further elevates the burden by demanding end-to-end traceability, serialization, and verification. Attempting to meet these requirements with general AI tools or brittle automation creates risk, not resilience.
Artificial expert intelligence
The next revolution in supply chain technology—Artificial Expert Intelligence (AExI)—moves beyond general-purpose intelligence toward expert digital specialists trained for specific, high-value supply chain functions.
Five core attributes define AExI systems:
Domain specificity with sub-agents: Each expert agent is built for a narrow, high-value operational domain—EPCIS validation, temperature-excursion analysis, exception triage, inbound receipt matching, and more. Sub-agents coordinate to support granular responsibilities across multi-enterprise ecosystems.
Better precision and accuracy of generative decisions: Because AExI agents reason over supply-chain–specific data models, serialized events, and operational context, they produce decisions with far greater accuracy, consistency, and trustworthiness.
Lower cost and faster reasoning: Domain-trained agents operate with dramatically lower token costs and significantly faster inference speeds than general AI, enabling real-time decision support at scale.
Specialists for fine-grained agentic responsibility: Each agent has a well-defined role, mirroring real-world organizational design. This ensures clear accountability, measurable impact, and safer, more intelligible automation.
Clear roles and responsibilities paired with humans: AExI strengthens, not replaces, human expertise. Every agent operates under structured human-to-agent oversight, escalating when needed and executing only within its defined guardrails.
Shabbir Dahod
The capabilities of AExI agents
AExI agents today operate within a clear and governed framework that enables safe, precise, and scalable orchestration across the end-to-end supply network:
Intent: Enhance human decision-making by autonomously monitoring, interpreting, and orchestrating multi-party processes with greater speed, accuracy, and precision.
Objectives: Maintain uninterrupted product flow, ensure continuous regulatory confidence, and elevate planning accuracy by eliminating information gaps and synthesising real-time signals.
Tasks: Detect and diagnose exceptions, validate serialized data and EPCIS events for DSCSA readiness, and aggregate partner inputs to generate forecast refinement recommendations.
Decisions: Determine when to escalate anomalies, trigger corrective actions, adjust planning assumptions, or block product movement based on data validity and network risk.
Rules: Apply canonical data definitions, shared metadata, network-wide validation logic, and human-defined guardrails that specify where agents act independently versus with human approval.
To operate safely and effectively, AExI agents require a connected data foundation spanning the global supply network, something no general-purpose AI model can achieve in isolation.
Governance by design: ensuring safety, compliance, and trust
Because AExI agents take action rather than just observe, governance must be embedded from the start. This includes:
Human-to-agent oversight defining goals, permissions, and escalation paths ● Role-based access control aligned with enterprise and partner agreements
Full traceability of every agent decision, including input data and alternatives considered
Outcome alignment to measurable supply chain KPIs such as OTIF, cycle time reduction, and inventory optimization
Treating agents like generic chatbots erodes accuracy, precision, and speed while inflating processing costs. Sustainable impact comes only when expert agents are domain-trained, network-aware, governed, and explainable.
The future
For pharmaceutical and life sciences supply chains, AExI unlocks visible, measurable advantages: fewer shortages and stockouts, stronger DSCSA compliance, network-wide exception resolution, faster partner collaboration, and real-time intelligence from serialized data.
Ultimately, agentic orchestration powered by Artificial Expert Intelligence represents a new operating model in which humans lead, expert agents assist, and the entire supply network becomes continuously smarter, faster, and more resilient.
Shabbir Dahod is CEO of TraceLink.
