After AI agent pilots underperformed: Resetting supply chain automation for operational impact

At recent industry conferences, AI agents are often presented as if meaningful autonomy in supply chain planning and execution is already commonplace. Demos show agents rebalancing inventory, re-planning production, or resolving exceptions in near real time. In private conversations with supply chain leaders, however, a different picture emerges. Many organizations have invested in AI agent pilots, yet few have seen those systems consistently influence decisions once conditions become volatile.

Arturo Torres Arpi Acero

That early enthusiasm was understandable. Labor constraints, demand volatility, and supplier unreliability have placed constant pressure on S&OP and IBP cycles. AI agents promised faster sensing, quicker re-planning, and fewer manual handoffs between planning and execution teams.

What many organizations encountered instead were pilots that generated recommendations but failed to change outcomes. When demand spikes hit mid-cycle, suppliers missed commit dates or production schedules slipped, and planners and operators reverted to spreadsheets, emails, and judgment calls. The agent remained visible, but no longer decisive.

This gap was not simply a matter of immature technology. It exposed a more fundamental issue. Many AI agent initiatives launched without clear definitions of which decisions the agent owned, under what operating conditions, and with what tolerance for uncertainty.

Why AI agent pilots underperformed in practice

Across planning and execution functions, underperformance followed a consistent pattern. The failure was rarely model accuracy. It was decision design.

Assumption one: Full autonomy was the natural destination.

Many pilots were justified with a belief that agents would eventually replace human decision-making across an end-to-end process. In practice, supply chain decisions rarely behave that cleanly. Inventory positioning, supplier allocation, and production scheduling all involve tradeoffs across cost, service level, and risk that shift daily as constraints change.

The pilots that showed promise focused on narrower decision points. Examples included prioritizing which purchase orders to expedite when capacity tightened or flagging inventory imbalances across distribution centers early enough for intervention. These were not retreats from autonomy. They were realistic definitions of value.

Assumption two: AI agents would behave like ERP systems.

ERP transactions are deterministic. A purchase order created with the same inputs produces the same result every time. Many leaders expected AI agent recommendations to meet that same standard of consistency.

Agentic systems do not operate that way. They reason probabilistically. Two similar demand signals can produce different re-planning recommendations depending on confidence scores, data freshness, or constraint weighting. That variability becomes an operational risk if agent outputs are treated like system-of-record transactions.

This dynamic helps explain why so many pilots stalled. A recent report highlighted by Fortune, citing research from MIT, found that 95% of generative AI pilots have failed to deliver measurable P&L impact. That figure should not be read as evidence that AI agents do not work. It reflects how difficult it is to operationalize probabilistic systems without clear decision ownership, governance, and trust thresholds in place. 

Assumption three: One agent could manage multi-objective decisions.

Another recurring pattern was the attempt to deploy a single agent that could forecast demand, allocate inventory, negotiate suppliers, schedule production, and manage exceptions. In reality, these decisions operate on different time horizons, draw from different data sets, and carry different financial and customer risks.

Without clear boundaries, the agent became an ambiguity engine. It performed well in demonstrations but plateaued once exposed to real execution pressure.

What these failures revealed about decision design

What stalled most pilots was not intelligence, but unclear decision ownership. Teams struggled to answer practical questions:

• Which re-planning decisions can the agent execute automatically?
• When does expediting require human approval?
• What confidence threshold triggers escalation during a demand shock?

Without explicit answers, AI agents remained advisory tools rather than operational ones. They explained potential outcomes, but they did not reliably drive action when conditions deteriorated.

This is why many pilots appeared successful early, then quietly faded from daily use.

From autonomy myths to practical maturity

A more grounded approach is now taking shape. Instead of treating autonomy as a binary state, leaders are adopting a maturity mindset. In their book Agentic Artificial Intelligence, Pascal Bornet and James Wirtz describe a progression of agentic capability in which most systems today operate at intermediate levels, with higher autonomy achievable only in narrow, well-controlled domains.

An analogy to autonomous driving helps clarify the sequencing. Most vehicles today can handle highways reliably but still require human intervention in complex conditions. Supply chains are no different.

Many organizations attempted to operate in all conditions before proving reliability in specific ones. The reset has been to deploy agents in constrained environments where data quality, decision rules, and failure modes are understood.

What supply chain leaders are actually changing now

Organizations that are making progress are changing how they design and fund agentic systems.

Data validation becomes the first gate.

AI agents depend on consistent inputs across ERP, WMS, TMS, and supplier systems. Master data accuracy, lead time variability, and latency between planning outputs and execution reality matter more than model selection. Leaders are increasingly unwilling to scale agents whose recommendations cannot be audited back to stable data.

Guardrails and governance move upfront.

Rather than adding controls after trust is lost, teams now define approval thresholds for actions like PO release or expediting before agents go live. Confidence scoring, fallback logic, and escalation paths are designed alongside the model.

Multi-agent architectures replace monolithic designs.

Instead of one general agent, work is decomposed into specialized agents. One agent may handle purchase order creation timing. Another focuses on exception triage. A third supports inventory rebalancing across distribution centers. An orchestrator coordinates these agents, but autonomy remains bounded by decision type and risk exposure.

Execution is sequenced deliberately.

Stabilizing data, clarifying decision rights, validating recommendations, and only then automating execution has become the dominant pattern. Agents are scaled where failure modes are understood, not where ambition is highest.

Executive lessons before funding the next AI agent initiative

• Fund decisions, not technology. Define the specific decision, time window, data inputs, and acceptable error before selecting models.
• Treat trust as a design requirement. Probabilistic systems require governance, validation, and escalation logic from the start.
• Aim for constrained autonomy. Task-level agents tied to real workflows scale faster and safer than end-to-end automation.
• Invest in data consistency as the multiplier. Unified operational data determines whether agents create value or noise.
• Decompose before orchestrating. Specialized agents with clear boundaries are easier to validate and operate under pressure.

The underperformance many leaders experienced was not a dead end. It was a signal. Supply chains punish ambiguity, and AI agents surface it quickly. The next cycle of investment will reward organizations that design for decision trust, not theoretical autonomy.

About the author

Arturo Torres Arpi Acero is the founder and CEO of Ventagium, a supply chain analytics consultancy trusted by U.S. manufacturers, logistics providers, and consumer brands navigating disruption and operational complexity.

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