Closing the last mile of treasury automation with an AI Assistant
Most treasury transformation programs automate the bulk of routine data work, yet a manual “last mile” remains. See how an AI Assistant can work across existing treasury systems, data, policies, and workflows to help teams spend more time deciding and less time assembling.
Treasury teams have expanded their role in business strategy, yet manual data work still consumes time that could otherwise go into analysis and decisions. An AI Assistant can address the remaining "last mile" of automation by working across existing systems, data, policies, and workflows in treasury’s own context. The same foundation can support forecasting, reporting, contract intelligence, and other use cases already running in production.
Most transformation projects already address the bulk of recurring data work. What remains is the manual work each automation cycle leaves behind — relatively small in volume, but significant in the strain it places on the team.
This is where expected ROI can quietly disappear: forecasts drift past two weeks, breaches surface at month-end instead of in the moment, and senior people spend their time assembling answers they should already have, even when the underlying platforms are doing what they were designed to do.
Closing the last mile with an AI Assistant
An intelligence layer sits across the systems treasury already uses and reasons over them in treasury’s own terms, including its entities, cash pools, policies, and definitions. On top of that layer, an AI Assistant works with the team using the same context, drawing on contracts, policies, and positions to provide plain-language, policy-grounded answers to treasury questions.
The Assistant is built around the way a treasury function already operates and works across the existing environment, so the team can use it without adding another platform to manage. More of the day can then go into making decisions, with less time spent assembling the information needed to make them.
Built around treasury business logic
The intelligence layer is grounded in the entity structure, cash pools, metric definitions, and workflows that shape treasury operations, giving AI the context it needs to reason against business logic instead of disconnected raw tables.
From there, it can orchestrate workflows and AI agents across TMS, ERP, banking platforms, and operational systems, while connecting treasury with AP, AR, FP&A, shared services, and other operational handoffs. The setup operates within the company’s enterprise architecture, security model, permissions, and governance framework, so the Assistant works within the same controls as the rest of the treasury environment.
What it already does in production
The following use cases come from live WaveAccess deployments and show how the same underlying approach can be applied across different parts of treasury work.
Forecasting and cash management
Forecasts are built on real transaction patterns instead of consolidated spreadsheets, allowing the treasurer to ask what shifted, what to adjust, and which customers are persistently late. Those questions can be handled in plain language without rebuilding pivot tables each time the team needs an answer.
Treasury reporting and board materials
Cash bridges, KPI packs, and management commentary can be drafted on demand, while last-minute board questions can be answered directly from the underlying data instead of rebuilding the deck the night before.
Contract intelligence for working capital and risk
The Assistant can read vendor contracts, credit agreements, and ISDAs for payment terms, hedging ratios, and covenant clauses, then check those terms against actual payments and bank data. This allows it to flag mismatches and identify early-warning covenant zones.
The intelligence layer behind the Assistant
The AI Assistant rests on an intelligence layer made up of three engineering components that can be built out or extended depending on what the treasury function already has.
Agents. Specialized AI supports individual workflows such as policy screening, liquidity queries, cash-flow variance, and contract intelligence, with each agent tuned and grounded in treasury context.
Integrations. Live connections to ERP, TMS, banks, market data, and policy documents bring the relevant information into one queryable layer.
Data foundation. Clean, structured treasury data provides the definitions the AI reasons against, including entities, cash pools, FX rules, and metric definitions.
When the scope is not yet clear, WaveAccess starts with an 8-week discovery to map it and determine which parts of the intelligence layer need to be built out or extended.
Why WaveAccess
WaveAccess combines treasury domain expertise with an established Data Science and engineering practice. The Data Science department was formed in 2015, while Tanya Kohen, CTP, brings 20+ years of treasury expertise to the finance practice.
Together, that expertise supports an AI Assistant that works across the systems, data, policies, and workflows already in place, giving the team a way to query and act on treasury context while preserving the architecture and controls around it.
