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Rajneesh Garg’s view of logistics AI is less about deploying a clever model than connecting data, systems, governance, and operating teams to business outcomes. In a September 8, 2026 interview with CIO, the CIO of Allcargo Global Limited argues that AI could progress from interpreting documents to coordinating supply-chain decisions—but treats that as a direction, not a result already achieved.
How does Garg expect logistics AI to evolve?
Garg describes a shift from generative AI that creates or interprets content toward systems that can coordinate decisions and actions. Over the next couple of years, he expects multi-agent AI to help coordinate inventory, freight capacity, customs clearance, and disruption management across supply-chain ecosystems. This is his forecast; the interview does not establish that such a system is deployed at Allcargo or that it has produced measurable results.
In this envisioned model, AI may help identify an issue and coordinate a response across connected workflows, while people retain oversight. The relevant change is therefore not simply a more capable model: it is a move from producing information to helping orchestrate decisions across operational boundaries.
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Why do document workflows depend on more than AI models?
Shipping documents such as bills of lading, customs manifests, and invoices can contain information that AI may interpret, validate against other systems, and use to flag exceptions. Garg also describes systems interacting with stakeholders and triggering workflows. Those steps depend on the surrounding technology and operating rules, not just document recognition.
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He emphasizes integration with enterprise resource planning (ERP) systems, reliable master data, and governance. A model working in isolation may extract or summarize information, but it cannot reliably support an end-to-end process unless the underlying records connect and exceptions have defined owners and actions. The practical implication is to assess the workflow, data quality, and controls alongside model capability.
What business outcomes should a logistics CIO measure?
Garg argues that IT success should be tied to business and customer outcomes rather than only backend efficiency. He names these possible measures:
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- Time required to onboard customers.
- Shipment visibility for customers and internal teams.
- Time needed to handle exceptions.
- Automation rates in operational processes.
- Customer retention.
- Quote-to-cash speed.
- Percentage return on investment (ROI) for an AI project.
The interview does not provide values for these measures, an ROI target, or evidence that any listed result has been achieved. They are measures Garg says leaders should consider—not published performance results.
Why does cargo visibility require cross-functional work?
Garg frames cargo visibility as a customer-facing differentiator that requires coordination among IT, operations, sales, customer service, and finance. Information is useful only if it can reach the people who can act on it and communicate with customers. He identifies proactive communication, faster issue resolution, and greater transparency as intended outcomes, but does not quantify their impact.
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This perspective makes visibility a shared operating capability rather than a dashboard project owned by IT alone. Teams need aligned processes for recognizing a disruption, deciding who responds, and providing customers with relevant updates.
How should AI governance balance shared controls and local needs?
Garg’s proposed model centralizes capabilities that benefit from consistency while leaving operational use-case choices close to the work. The interview’s framework can be summarized this way:
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| Shared at group level | Adapted by regional and operational teams |
|---|---|
| AI platforms, governance, security, and reusable data-science capabilities. | Use cases shaped by local regulations, trade lanes, customer requirements, and operational conditions. |
This is a suggested division of responsibilities, not a reported organizational assessment. Its logic is to avoid duplicating foundational controls while allowing teams closest to a market or process to adapt how tools are used. Central standards alone may not fit every context; unrestricted local experimentation, in turn, can make security, data handling, and reuse harder to manage.
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Garg recommends incremental modernization rather than replacing an entire application estate at once. His proposed ingredients are API-first architectures, modular platforms, governed data layers, and AI that remains loosely coupled from legacy systems. The goal is to add capabilities in stages and make integration more manageable; the interview presents reduced technical debt and flexibility as intended rationales, not independently verified outcomes.
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For a logistics organization, this approach makes the interfaces and data flows part of the AI plan. A useful sequence is to identify a business workflow, establish the data and system connections it needs, apply controls, and introduce automation in manageable increments. This avoids treating a model as a shortcut around a process or integration problem.
What leadership principles does Garg recommend?
Garg’s advice for technology leaders centers on three principles:
- Define business value. Specify the operational or customer problem and the measure that would indicate improvement before selecting technology.
- Build trust through governance and change management. Safeguards matter, but teams also need to understand and adopt changed workflows.
- Work at enterprise scale, implement incrementally. Executive buy-in and shared direction can coexist with staged delivery.
He cautions leaders not to automate a defective process: automation can accelerate existing problems rather than fix them. He also urges technology professionals to learn customer expectations, revenue streams, operational challenges, and industry economics so that technology choices reflect how the business works.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchWhat the interview establishes—and what it does not
The September 8, 2026 CIO interview presents Garg’s forecasts, operating principles, and proposed measures. It does not report quantified AI gains, a specific multi-agent deployment, or the achieved ROI of an AI project. Accordingly, the ideas are best read as a leadership framework for connecting logistics technology to business priorities, not as proof of a particular implementation’s performance.
The interview identifies Garg as CIO of Allcargo Global Limited. Earlier coverage used a different organization label: an Express Computer interview and an Allcargo Group publication in February 2025 described him as CIO of ECU Worldwide, while ETInfra reported his ECU Worldwide appointment in June 2024. These labels reflect the respective sources and dates and should not be silently treated as identical wording.
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