New Delhi, Feb. 16 -- As enterprise finance teams move from cost control to capital efficiency, Hyderabad-based spend management firm Zaggle is positioning artificial intelligence (AI) as more than a back-office automation tool. The company says it is embedding AI deep into spend management and payments workflows to help CFOs cut leakages, tighten compliance and improve working capital discipline.
"At Zaggle, we are using AI not just to automate processes but to fundamentally improve how enterprises control, predict and optimise spend," said Avinash Godkhindi, MD & CEO, Zaggle.
Unlike traditional expense tools that generate static reports, Zaggle's platform applies AI-driven analytics across employee expenses, vendor payments, rewards and fuel spends to surface what Godkhindi calls "actionable insights." This includes anomaly detection to flag potential leakages, pattern recognition to identify inefficient spend behaviours and predictive models that help finance teams forecast budgets and cash flows more accurately.
The shift, he argues, is from reactive reconciliation to real-time and predictive decision-making. "We help CFOs reduce unauthorized spends, improve compliance and increase ROI on every rupee spent. The outcome is tangible-lower leakage, faster closures and better working capital discipline," he said.
Fixing fragmented workflows
Enterprise expense and rewards workflows remain riddled with inefficiencies. Fragmented systems, manual approvals and retrospective audits often mean policy enforcement kicks in only after the money has been spent.
"The biggest inefficiencies stem from delayed visibility and static rules," Godkhindi said. "Many organisations still rely on retrospective audits, which leads to leakage and delayed decision-making."
AI, he said, can address a significant portion of this friction. In expense management, algorithms can flag outliers, predict budget overruns and automate reconciliation. In rewards programmes, AI can optimise attribution, personalise incentives and track redemption effectiveness. While human oversight remains critical, the goal is to move from compliance-driven processes to outcome-driven spend management, where intelligence is embedded directly into workflows.
Scaling AI beyond pilots
A persistent challenge in enterprise tech is scaling AI from pilot projects to production systems. Godkhindi believes many initiatives falter because they are treated as standalone experiments rather than embedded capabilities.
"What has worked for us is integrating AI directly into core spend and payment workflows where value is immediate and measurable," he said. Instead of abstract modelling, Zaggle focuses on clear financial outcomes such as leakage reduction and faster month-end closures.
The company introduces AI incrementally-starting with decision support and automated controls before expanding into predictive insights as data quality improves. Clean data pipelines, strong governance and seamless ERP integrations are key to driving trust and adoption, he added. Build versus buy: A hybrid AI stack
On the technology front, Zaggle follows a hybrid approach. Domain-specific capabilities-such as spend intelligence, policy enforcement and compliance logic-are built in-house. These models are trained on contextual enterprise spend data, forming the company's core differentiation. At the same time, it integrates best-in-class third-party models for horizontal capabilities such as language processing and document intelligence.
"The balance lies in owning the intelligence layer that drives decisions while leveraging external innovation to accelerate time to market," Godkhindi said.
CFO scrutiny reshapes product roadmap
With CFOs tightening technology budgets, buying behaviour is shifting from feature-led adoption to outcome-led investments. Enterprises are prioritising consolidated platforms that deliver measurable ROI quickly and reduce the total cost of ownership.
"There is far greater scrutiny on payback periods and integration effort," Godkhindi said. "Relevance comes from solving core problems at scale, not from incremental features."
This is shaping Zaggle's roadmap toward deeper platform unification, faster time-to-value and AI-driven analytics that demonstrate clear financial impact-particularly in leakage reduction and compliance automation.
API-first, compliance by design
Interoperability is becoming critical as enterprises modernize finance stacks. Zaggle has adopted an API-first architecture to integrate with ERPs, HRMS platforms and banking systems, enabling real-time data sync and automated reconciliations.
Regulation, particularly in payments and fintech, also influences architectural decisions. "Compliance cannot be an overlay; it has to be embedded by design," Godkhindi said. The platform incorporates modular architecture, data segregation, secure APIs and role-based access controls, alongside automated audit trails and real-time monitoring.
The intelligence layer for enterprise finance
Looking three to five years ahead, Godkhindi sees AI transforming finance from a transaction-centric to an intelligence-led model. "Finance teams will move beyond tracking spends to continuously predicting outcomes, preventing risks and optimising capital allocation in real time," he said, adding that Zaggle's ambition is to serve as the intelligence layer across expenses, payables, rewards and benefits-embedding AI into everyday finance decisions while maintaining governance and compliance.
Published by HT Digital Content Services with permission from TechCircle.