Allcargo Logistics CIO Sanjay Khiyani on cutting finance close time by 30%

The logistics company is consolidating finance and procurement on a common platform after a business merger, while expanding AI use cases across bookings, transshipment hubs and customer service.

Allcargo Logistics, an integrated logistics company offering express distribution and consultative logistics services across India, has cut the time taken to close its financial books by 30% after moving its finance operations to Oracle Fusion Cloud Applications. The move follows a business restructuring and is part of a wider effort to standardise processes and build a foundation for AI adoption.

The company has brought more than 4,000 users and over 700 operating units onto an integrated platform, replacing a fragmented financial reporting process that involved multiple systems and manual intervention. The implementation has also automated procurement processes, improved financial visibility and strengthened controls, according to the company.

The transformation comes as Allcargo Logistics brings its Express Distribution and Consultative Logistics businesses under a common operating structure. For Sanjay Khiyani, chief information officer at Allcargo Logistics, the immediate challenge was to unify financial operations while allowing the company's core operational systems to become more agile.

“The key aspect that we were missing was the speed of change,” Khiyani said in an interview. He pointed to the growing difficulty of keeping a heavily customised enterprise resource planning (ERP) system aligned with evolving technology and industry practices.

Consolidating fragmented financial systems

Allcargo's technology challenge had developed over several years. Its legacy enterprise management system, GEMS, had evolved into an all-encompassing platform handling operational processes alongside finance and accounting, human resources and customer relationship management.

While this arrangement had supported the business through changes such as the introduction of the goods and services tax, it had also increased the complexity of maintaining and upgrading the system.

The merger added another layer of complexity. The Express business and Consultative Logistics had different systems and ways of working, making it difficult for senior management to obtain a unified view of the company's financial position.

Finance and accounting processes were spread across the operational ERP, a separate procurement platform and Oracle E-Business Suite, which was being used to consolidate financial information and prepare management reports. Data moved between these systems, with manual work required to produce consolidated statements.

“We had to look at three systems,” Khiyani said, describing the challenge faced by the finance leadership and senior management.

The company chose Oracle Fusion Cloud Applications for finance to consolidate these processes and make more detailed financial information available through a common platform. The objective was not merely to replace legacy software, but to separate non-core functions from the operational backbone and standardise finance across the businesses.

The shift has reduced manual activities and shortened the financial closing cycle. The process previously took roughly three weeks, Khiyani said. A 30% reduction has freed up corresponding staff bandwidth for higher-value activities, while giving the chief executive, chief financial officer and finance controllers more timely visibility into the company's financial health.

Oracle Fusion Cloud Procurement has also helped standardise purchasing processes and provide a consistent view of procurement activity across distributed operations.

AI moves from back-office to logistics ops

Beyond financial systems, Allcargo is applying AI to specific operational bottlenecks rather than investing in the technology without clearly identified business use cases.

One example is an Android application launched in July-August for booking partners who collect consignments and record bookings in the field. Previously, these employees had to enter information from customer invoices manually on mobile devices.

The application now uses AI to extract information from scanned invoices and populate booking details, reducing data entry and the time required to complete a booking. Faster processing can help field teams handle more consignments, Khiyani said.

The company has also embedded AI into its mid-mile operations, where consignments move between locations through its transshipment network. A control tower monitors these movements, while AI-enabled capabilities support dynamic bay allocation and vehicle visibility at transshipment hubs. The information helps hub managers plan operations and improve productivity.

These deployments form part of a broader effort to establish cloud infrastructure and modernise enterprise applications before expanding automation further. Allcargo has moved its infrastructure to the cloud, shifted finance and accounting to Oracle Fusion and moved customer relationship management to Salesforce.

Cybersecurity oversight spans IT and operations

As logistics operations become more connected, Allcargo is relying on a group-level cybersecurity framework to manage risks across its technology environment. Khiyani said the group has established centres of excellence (CoEs) for infrastructure, customer relationship management, cybersecurity and AI and data science, enabling it to pool expertise and apply common practices across businesses.

The security team uses AI-enabled tools to monitor threats continuously and conducts regular penetration testing and vulnerability scans to identify potential gaps. The group has also finalised an AI policy and introduced guardrails to flag or hold potentially risky activities. Technology decisions are reviewed by security and technology specialists alongside business and financial considerations, Khiyani said.

Agentic AI on the roadmap

The next phase involves agentic AI, with the company exploring applications that can undertake tasks currently handled by employees.

In customer service, AI agents could respond to email queries, handle booking requests and address routine customer enquiries, allowing human agents to focus on more complex interactions. Khiyani said the company is targeting the launch of customer-service capabilities within the current financial year.

On the finance side, Allcargo is exploring agentic applications with Oracle in areas such as procurement and billing. The existing implementation has already introduced automation for financial statement visibility and anomaly detection.

The company is also working with partners to explore an agentic layer on Oracle Cloud Infrastructure for its operational systems.

However, the expansion of AI is being accompanied by an emphasis on governance and cybersecurity. Allcargo uses group-level centres of excellence for functions including infrastructure, customer relationship management, AI and data science, and security. Its security teams use monitoring tools with AI capabilities, alongside penetration testing and scanning, to identify potential vulnerabilities.

The group has also established an AI policy and guardrails to flag potentially risky activities. Technology decisions are assessed for security considerations as well as business value and financial implications, Khiyani said.

Customer certainty remains the priority

Over the next two to three years, Allcargo intends to use its technology investments to improve operational predictability and customer experience. Khiyani said the focus was on freeing employees from routine work so they could deliver more value, while giving operations teams better foresight.

For a logistics provider, he said, customers ultimately expect their goods to be delivered with certainty and promptness. The company is seeking to improve service levels against industry benchmarks while making delivery performance more predictable.

The approach reflects a shift from treating digital transformation as a series of technology deployments to using a common data and applications foundation to improve financial control, operational efficiency and service reliability. The next test will be whether planned agentic AI deployments can translate those foundations into measurable improvements in productivity and customer outcomes.