Building an AI strategy to leapfrog tech debt in supply chain execution

Summary: Legacy systems slow progress when execution decisions need to happen in minutes, not weeks. The fastest path forward is not replacing everything, but orchestrating intelligence across what already exists to drive trust and measurable outcomes. 

Supply chains are absorbing shocks from every direction: geopolitics and tariffs, demand swings, omnichannel commerce and the relentless pace of technology change. 

The winners in this environment, will be those who treat supply chain execution as a strategic capability, by connecting systems end-to-end, enabling real-time decisions and building adaptability into day-to-day operations to create resilience. This is the concept of intelligent supply chain execution (ISCE).

In this post, we explore how organizations can overcome one of the biggest barriers to AI adoption - technology debt - and how to leapfrog legacy constraints to create scalability and long-term resilience.

Why technology debt hits execution hardest

Supply chain execution is widely viewed as being behind the technology curve: constrained by legacy stacks, brittle integrations, dated user interfaces and slow adoption of innovation.

While planning and management functions can re-schedule or re-design over weeks, execution must decide in real time how to move today’s order through disruptions like reroutes, substitutions, missed pickups, border delays. 

Deterministic software struggles when context is scattered across systems and tribal knowledge lives in heads and chat threads. 

The result: brittle handoffs, manual workarounds and rising cost-to-serve – just when service expectations are at their peak.

Legacy systems limit agility when execution decisions need to happen in minutes, not weeks.

Re-defining the goal from automation to Intelligent Execution

Infios’s AI strategy leapfrogs tech debt by re-defining the target state. 

Intelligent supply chain execution (ISCE) means connected workflows powered by AI-native services, delivered as a platform and operated by a hybrid workforce of agents and people (human-in-the-loop model). 

Instead of adding more point solutions, Infios introduces an intelligence layer that listens to signals, understands events, reasons over data and orchestrates actions across OMS, WMS and TMS. This transforms static automation into adaptive execution systems that learn and respond in real time.

From AI strategy to practice: building modular, explainable systems

To help customers turn AI strategy into outcomes, Infios has designed an approach that is modular, human-aware and secure: 

• APIs become skills; agents do the work
  Infios leverages APIs in execution systems (OM, WM, TM) as modular “skills” that can be composed into end-to-end workflows. These agentic AI systems monitor signals, detect anomalies and take prescriptive actions; escalating to humans when reviews or approvals are needed. This creates a unified data and intelligence layer while avoiding “band-aid” integrations and one-off automations.
• Scale agents across use cases 
  Teams can create agents and operationalize business context at scale. Through intuitive development environments, operations teams – not just data scientists – can refine and train agents. Testing and QA prior to deployment ensure that actions are explainable and auditable. Transparency features let users review outputs and transcripts. This turns tribal knowledge into actionable intelligence - without months of manual rule creation.
• Hybrid operations by design 
  Human-in-the-loop checkpoints make decisions explainable and trustworthy. Supervisors can override, annotate, or approve actions, reinforcing models and codifying best practices -  without asking teams to trust a black box.
• Modular, secure and explainable 
  The architecture allows customers to start with high-impact use cases and integrate existing systems without rip-and-replace. Guardrails for safety, privacy and governance are built in from day one.

Infios turns AI strategy into execution by blending modular design, agentic intelligence and human oversight to drive adoption, trust and measurable outcomes.

Responsible AI as the foundation for trust and adoption

Enterprise AI success depends on governance, transparency and safety. Infios’s approach to Responsible AI aligns with global standards such as ISO/IEC 42001 and the EU AI Act.

Key elements include:

• Human-in-the-loop controls to keep decision authority transparent.
• Prompt-leak prevention and hallucination mitigation safeguards.
• An internal AI Council that manages intake, risk scoring and decision rights to ensure innovation and governance advance together.

Responsible AI is not a safeguard. It’s the foundation for trust, adoption and scale.