Webinar Recap: From Reactive to Autonomous — The Agentic Shift in Supply Chain Risk Management

Summary

In our Future.Now webinar, From Reactive to Autonomous: The Agentic Shift in Supply Chain Risk Management, we explored how organizations are redefining supply chain risk management (SCRM) by moving beyond reactive response models toward autonomous, agent-driven decision-making. As disruptions grow in frequency and complexity, traditional SCRM approaches centered on visibility and escalation are no longer enough. A new model is emerging, one that detects risk early, evaluates trade-offs in context, and executes mitigation actions at scale.

We also examined how agentic decision intelligence supports the full decision lifecycle, from explicit modeling and orchestration to execution, monitoring, and governance. As AI agents increasingly augment and automate enterprise decisions, the focus shifts to building trust, ensuring accountability, and enabling coordinated responses across procurement, logistics, planning, and finance. The result is a more proactive, resilient, and continuously learning approach to managing supply chain risk.

Key Takeaways

• Disruption is now the operating baseline, not the exception.
  Supply chain volatility continues to increase in both frequency and scale. Recent data shows that most organizations experienced disruption in the past year, and the true challenge is not only occurrence but propagation. A single issue can ripple across suppliers, logistics, production, inventory, service levels, and revenue targets, amplifying downstream impact.
• Visibility alone has reached its limits.
  While control towers and dashboards have improved awareness, they do not resolve the execution gap. Seeing a disruption does not mitigate it. Traditional approaches still rely heavily on manual coordination, spreadsheet analysis, and cross-functional escalation. The result is delayed response, inconsistent decision-making, and limited scalability under pressure.
• Enterprise AI must move from recommendation to accountable action.
  In operational environments such as supply chain and finance, it is no longer sufficient for AI to suggest options. Decisions must be explicitly modeled, orchestrated across systems, executed reliably, monitored continuously, and governed with traceability. Trust, auditability, and constraint-based execution are essential for enterprise adoption.
• Agent teams enable cross-functional orchestration at scale.
  Autonomous AI agents can collaborate across procurement, logistics, planning, and finance within unified workflows. Rather than operating in silos, agents assess trade-offs, evaluate alternatives, coordinate responses, and execute updates across systems in minutes. This reduces latency and improves consistency across the decision lifecycle.
• Governance and trust architecture determine long-term success.
  As AI agents augment or automate more business decisions, governance becomes critical. Industry forecasts indicate that a significant portion of decisions will be agent-augmented within the next few years. At the same time, ungoverned AI usage introduces financial and reputational risk. Structured decision modeling and clear accountability are required to ensure safe, enterprise-ready autonomy.
• Every disruption can strengthen the system.
  Agentic systems do more than respond in real time. They monitor outcomes and incorporate learning into future decisions. Over time, this closed-loop approach improves speed, resilience, and accuracy. Risk management becomes proactive rather than reactive, with each event contributing to a more intelligent supply chain.

Speakers

Rob Wolfe, Client Partner, Aera Technology, Chief Revenue Officer, Aera Technology Rob is a client partner at Aera Technology with deep experience in supply chain and operations transformation. Rob previously delivered control tower solutions across industries during his time at Deloitte. At Aera, he advises customers on value realization, roadmap planning, and decision intelligence strategy, helping organizations implement agent-driven capabilities that deliver measurable business impact.

John Coffey, Solution Engineer, Aera Technology, SVP, Data Science & AI at Aera Technology John is a solution engineer at Aera Technology with more than a decade of experience in AI, decision intelligence, and enterprise automation. John has led major customer implementations, including Aera’s first end-to-end agent-orchestrated deployment at scale. Prior to Aera, he worked in AI SaaS and spent seven years at EY delivering technology and process intelligence solutions.

Full Recording

Q&A

At the end of the presentation, we held a short session dedicated to answering attendees’ questions. Below are several of the key questions and answers.

Q: How does Aera's approach to supply chain risk management differ from traditional supply chain risk management (SCRM) platforms?

A: Traditional SCRM platforms tend to focus primarily on detection and alerts. They surface potential risks, but they often stop short of driving resolution. Aera extends beyond detection by identifying risk, evaluating alternatives, and autonomously executing mitigation actions within clearly defined guardrails.

Rather than relying on siloed tools that require manual coordination, Aera deploys agent teams that collaborate in real time across functions. These agents close the loop by writing decisions directly back into ERP systems, whether that means adjusting purchase orders, reallocating inventory, or triggering supplier notifications. Each disruption becomes a learning signal, enabling the system to continuously improve the quality, speed, and consistency of future responses.

Q: How is Aera’s agentic decision intelligence different from what others are doing in the agentic space?

A: Many agentic platforms offer general-purpose AI agents that can perform broad tasks. In contrast, Aera delivers agents that are purpose-built for enterprise decision-making, where governance, traceability, and operational constraints are essential.

This differentiation is achieved through several foundational design principles. Agents operate within certified data and logic through the Aera Decision Data Model™, which establishes a trusted and governed environment. Agency levels are configurable, ranging from deterministic, rule-based execution to advanced multi-model reasoning depending on the decision context. Agents are also composable into structured workflows as decision nodes rather than functioning as standalone bots.

Because of this architecture, every action remains explainable and auditable, and each decision is directly tied to measurable business outcomes. This enables structured, end-to-end decision orchestration within the guardrails required for enterprise-scale adoption.

Q: What types of supply chain risks can Aera agents handle autonomously versus those requiring human oversight?

A: Aera agents can operate fully autonomously in well-defined, policy-bound scenarios where constraints and thresholds are clearly established.

Fully autonomous scenarios include:

• Routine supplier delays when qualified alternate sources are available
• Standard logistics rerouting within approved cost and service thresholds
• Inventory rebalancing within established safety stock limits
• Forecast-driven production adjustments that remain within policy guidelines

Other situations benefit from human oversight, particularly when decisions extend beyond predefined boundaries or require strategic judgment.

Human-in-the-loop scenarios include:

• High-value decisions or those that exceed defined thresholds
• Novel disruptions with limited historical precedent
• Strategic sourcing changes that impact long-term contracts
• Multi-layered disruption cascades that require executive judgment

In these cases, agents support analysis and recommendations while keeping humans engaged for review and final approval.

Q: How quickly can organizations see results with Aera's agentic approach to SCRM?

A: Aera delivers rapid time to value through its composable, agent-based architecture. Initial agents can go live in approximately 8 to 12 weeks, which is significantly faster than the 6 to 9 months often required for traditional implementations.

Value begins to accumulate incrementally as each agent is deployed. Rather than waiting for a large, monolithic rollout, organizations realize measurable improvements in stages. Over time, benefits compound as the system learns from disruptions and operational outcomes. The result is measurable impact within weeks, not years, along with continuous improvement in decision quality, speed, and resilience.

Q: How long did it take to develop this "team of agents"?

A: Building a complete skill, from initial design through go-live in production, typically takes 12 to 16 weeks. Within that same timeframe, the development of individual agents and coordinated agent teams can occur in days or weeks.

Because the architecture is composable, new agents can be developed and integrated within the broader skill as needed. This allows organizations to evolve their agent teams progressively rather than building everything at once, accelerating both deployment timelines and long-term scalability.