The Spreadsheet Problem Is Actually a Decision-Making Problem
A CFO asks a straightforward question: “How much cash can we confidently deploy over the next 90 days?”
The answer requires three spreadsheets, multiple bank portals, an ERP export and several follow-up emails. By the time the consolidated position arrives, some assumptions have already changed.
This is not simply an Excel problem. It is a problem of fragmented information, delayed visibility and decisions waiting for data.
The Association for Financial Professionals’ 2026 Treasury Benchmarking Survey found that 49% of respondents considered cash and liquidity forecasting their most challenging treasury activity. Meanwhile, 30% identified AI and automation among their leading priorities.
The opportunity is clear. The execution model is less obvious.
What Agentic AI Changes in Treasury
Traditional automation follows predefined instructions. Agentic AI can coordinate multiple steps, use approved tools, evaluate incoming information and initiate actions within configured limits.
Consider a daily cash-positioning workflow.
An AI agent retrieves authorised bank balances, reconciles them against ERP records, identifies unexpected movements and prepares a liquidity summary. When figures breach predefined thresholds, it routes exceptions to the appropriate finance professional.
Another agent can evaluate collections patterns, payment schedules and historical forecasts to generate updated cash-flow scenarios.
These capabilities do not make treasury autonomous. They make financial information more continuously available.
The critical distinction is between preparing a recommendation and authorising a financial decision.
A model can identify a liquidity shortfall. It should not independently execute a material intercompany transfer or investment decision without appropriate authorisation.
Why Controls Must Come Before Autonomy
Treasury operates where operational errors can immediately become financial losses.
A duplicate payment, unreliable forecast or incorrect counterparty instruction can outweigh months of productivity gains.
The Bank for International Settlements’ guidance on AI governance identifies data confidentiality, model reliability and risk management as essential considerations for financial AI adoption.
For CFOs, this translates into practical controls: verified data connections, access restrictions, approval thresholds, audit trails and tested fallback procedures.
AI-generated forecasts must also be evaluated against actual outcomes. A confidently presented projection is not necessarily a reliable one.
The objective is not to remove human accountability. It is to give finance professionals better information before important decisions are made.
The India GCC Opportunity: Redesign the Function
For US-headquartered mid-market businesses, an India Global Capability Centre can provide more than additional finance processing capacity.
It can become an embedded finance capability responsible for forecasting operations, exception analysis, financial data quality and reporting improvements.
However, the roles must be designed accordingly.
Rather than building a team primarily to consolidate spreadsheets, companies can combine treasury analysts, FP&A professionals, data engineers and automation specialists around defined global outcomes.
The India team might own daily cash visibility and forecast performance, while global treasury retains responsibility for banking strategy, capital allocation and payment authorisation.
This requires clear decision rights between headquarters and India. Otherwise, AI merely accelerates the production of information that still waits for someone else to interpret it.
A Practical 90-Day Treasury AI Roadmap
A controlled implementation can begin without attempting to transform the entire finance function.
Days 1–30: Establish the baseline. Document bank feeds, reporting processes, reconciliation exceptions and forecast errors. Identify the activities consuming the most analyst time and verify whether the underlying data is dependable.
Days 31–60: Test in parallel. Introduce automated cash consolidation and exception identification in a controlled environment. Compare AI-assisted outputs with existing processes. Record discrepancies, human corrections and operational interruptions.
Days 61–90: Introduce supervised workflows. Deploy approved use cases with defined access privileges, monitoring and escalation ownership. Expand only after accuracy, reliability and security requirements have been met.
This approach aligns with the NIST AI Risk Management Framework, which emphasises managing risks throughout the AI lifecycle.
Measure Better Decisions, Not Just Faster Reports
CFOs should assess whether agentic AI improves the actual performance of treasury.
Useful indicators include forecast accuracy by time horizon, reconciliation completion time, unresolved exceptions, cash visibility, control incidents and analyst capacity redirected toward higher-value work.
A faster forecast that produces unreliable decisions is not progress.
Equally, hours released through automation should not automatically be reported as cash savings. Financial benefits depend on how that capacity is used.
The Next Treasury Model Is Human-Led and AI-Enabled
The strongest opportunity is to combine automated information flows with experienced financial judgment.
For organisations building or modernising an India GCC, this is an operating-model decision before it becomes a technology investment.
Enorbe works with finance leaders on GCC capability design, process ownership and governance architecture. If treasury transformation is part of your India expansion plans, those foundations are worth discussing before selecting the technology.
