The Tool Desk
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What logistics AI could do beyond generating content
Garg describes a progression from AI that creates or interprets content toward systems that coordinate decisions. Over the next couple of years, he expects multi-agent systems to help coordinate inventory, freight capacity, customs clearance, and responses to disruptions across supply-chain ecosystems. This is a forecast, not evidence that those capabilities have already been deployed at his organization. CIO’s September 8, 2026 interview presents the direction as an emerging possibility.
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The distinction matters in logistics. A model that summarizes a document produces information; a coordinated workflow may use that information to flag a discrepancy, route an exception, or prompt a stakeholder to act. Garg’s vision is not simply more automation, but connected decision-making with human oversight. The interview does not specify which decisions would be delegated, what controls would govern each action, or what measurable results such systems have delivered.
Why document automation depends on the systems around AI
Shipping workflows generate bills of lading, customs manifests, invoices, and other documents that may need to be interpreted and checked against enterprise records. Garg says AI can read these materials, validate information across systems, identify exceptions, interact with stakeholders, and trigger workflows. In his account, the model is only one part of the capability: links to ERP systems, reliable master data, and governance are essential.
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That framing highlights a practical dependency. If records use inconsistent identifiers or the underlying process is unclear, an AI model may surface a problem without resolving it—or accelerate a faulty process. Garg’s recommendation is therefore to connect AI to enterprise data and workflow controls rather than treat a standalone model as a complete automation solution.
Which measures should show whether IT creates business value?
Garg argues that CIO performance should reflect customer and commercial outcomes as well as technical efficiency. He names these measures for IT:
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- Reduced customer onboarding time.
- Improved shipment visibility.
- Shorter exception-handling cycles.
- Higher automation rates.
- Customer retention.
- Faster quote-to-cash processes.
He also says his organization added a target around the percentage ROI for an AI project, but the interview provides neither the target value nor an achieved result. These are measures Garg says leaders should track, not published evidence that a specific improvement has occurred. His broader point is that technology adoption is not itself proof of value; leaders need a defined business outcome against which to assess investment.
Why cargo visibility is an enterprise effort
Garg presents cargo visibility as a customer-facing differentiator that cannot be delivered by IT alone. It requires coordination among IT, operations, sales, customer service, and finance. The intended benefits are more proactive communication, faster issue resolution, and greater transparency, but the interview does not quantify those effects.
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This approach makes visibility a cross-functional operating capability rather than merely a tracking screen. Data must be available to the teams that can interpret an exception and communicate with a customer; technology has to support that response across organizational boundaries.
How to balance shared governance with local operating needs
Garg’s proposed model centralizes the capabilities that benefit from consistency while leaving operational choices closer to the markets and teams they serve. The division is not “central control” versus “local freedom”; it is shared foundations with localized application.
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| Centralized across the enterprise | Shaped by regional and operational teams |
|---|---|
| AI platforms | Use cases suited to local operations |
| Governance and security | Adaptations for local regulations |
| Reusable data-science capabilities and shared data foundations | Tools responsive to trade lanes and customer requirements |
Under this arrangement, common safeguards and reusable capabilities can reduce fragmentation, while local teams identify where AI fits their workflows. The interview describes this as Garg’s recommended organizational pattern; it does not establish how widely or successfully the model has been implemented.
How to add AI to legacy systems without replacing everything
Garg recommends incremental modernization rather than a wholesale replacement of legacy applications. His preferred building blocks are API-first architectures, modular platforms, governed data layers, and AI that remains loosely coupled from older systems. The intended rationale is to make integration more flexible and limit the accumulation of technical debt; those are strategic aims, not independently verified outcomes in the interview.
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For leaders, the underlying choice is between trying to rebuild the whole stack before adopting AI and modernizing in smaller pieces that allow systems to exchange data through controlled interfaces. Garg favors the latter, while emphasizing that governance and data quality still need to be addressed.
What Garg says technology leaders should do first
Garg’s advice combines business discipline, responsible adoption, and organizational change:
- Define the value before choosing the technology. Specify the operational or commercial outcome and how it will be measured; do not treat AI adoption as a result in itself.
- Do not automate a defective process. Understand and improve the workflow before using AI to accelerate it.
- Build trust through governance and change management. Controls need to accompany implementation, and affected teams need support as workflows change.
- Work enterprise-wide, but proceed incrementally. Garg calls for executive buy-in and a broad view of the organization, delivered through manageable steps.
- Learn the business beyond IT. He urges technology professionals to understand customer expectations, revenue streams, operational challenges, and industry economics.
The principles are consistent with his central argument: the CIO’s role is not only to deploy systems, but to connect technology choices to outcomes the business and its customers can recognize.
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The CIO interview identifies Garg as CIO of Allcargo Global Limited. Earlier sources used a different role and organizational context: an Express Computer interview published February 14, 2025 and an Allcargo Group company publication dated February 24, 2025 described him as CIO of ECU Worldwide; ETInfra reported his appointment at ECU Worldwide on June 20, 2024. Those references are time-specific and should not be silently collapsed into a single current title.
The 2026 interview supplies strategic views, proposed metrics, and a forecast about multi-agent AI. It does not report a quantified business impact, a named ROI target, or proof that the anticipated coordinated systems are operating in production. Its strongest contribution is a leadership framework: connect AI to business measures, build on enterprise data and safeguards, and let operating teams adapt shared capabilities to local needs.
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