How Corporate Treasury Teams Can Shift To Strategic Work Without Adding Headcount

Treasury teams are about to be handed more time than they have had in years. Most of them have no plan for what to do with it.

The pressure to adopt AI in finance is now coming from above. CFOs are funding automation faster than treasury teams are ready to absorb it, and the work that used to fill an analyst's week, the file pulls, the normalization, the formatting, is the first to compress. The hours come back whether the team has decided what to do with them or not.

That decision matters more than the automation itself. The article below lays out a workload audit framework treasury teams can run this quarter to figure out which categories of work fill their days now, which categories should fill their days next, and how to use that distinction to win the budget conversation.

How treasury time gets spent today

The headline numbers are telling. According to data covered by CTMfile and produced by Strategic Treasurer with TD Bank, treasury teams using spreadsheets spend an average of 1.8 hours per day on manual and operational tasks. Teams operating on a treasury platform spend 1.3 hours. Over a year, the spreadsheet teams give up an extra 130 hours, more than three full work weeks, to operational drag. A separate TD Bank survey conducted at the 2025 AFP conference found that nearly 80% of treasury departments still rely on manual or fragmented processes.

Cutting those hours down is the obvious play. The strategic play comes second, and it lives inside the plan a treasury team builds for the hours that come back. Most teams have not built that plan.

The three categories of treasury work

Every task on a treasury team's plate falls into one of three categories. Naming them matters, because most teams treat all three as the same kind of work.

Execution work is the assembly layer. Pulling files from bank portals, normalizing data structures, reconciling categories, building summaries, and formatting outputs for leadership. None of this work generates insight on its own. All of it is necessary because the underlying data needs translation before anyone can act on it.

Execution work also carries a hidden risk concentration. The institutional knowledge of how each bank's data flows, how categories map across vendors, and how anomalies should be handled lives inside an analyst's head and spreadsheets. When the analyst turns over, the knowledge leaves with them.

Maintenance work keeps the system functional. Pricing tables get loaded and then go stale. Accounts open, close, and shift category structures. New services appear on fee schedules. Codes need to be mapped to the right categories. Most teams treat maintenance as a one-time setup activity, which is why most teams run their analyses on data that has quietly degraded for months. 

A useful gut check is to ask when the last review of pricing tables happened, when the last dormant account audit happened, and when fee categories were last reconciled against contracted services. Answers longer than a quarter for any of those questions usually signal that the data underneath the team's analyses has drifted away from reality.

Judgment work moves the business forward. Interpreting variance, making allocation decisions, holding banking partners accountable, contributing forecasting inputs to FP&A, surfacing risk to leadership, and deciding what to do with excess balances. Every other category of work supports this one.

Judgment work is also the category leadership notices. A CFO who sees treasury bringing forward a recommendation on yield, a structured comparison of banking relationships, or a forward-looking liquidity scenario perceives treasury as a strategic function. A CFO who sees treasury delivering accurate but undifferentiated monthly reporting perceives treasury as an operational function. The category mix becomes the perception.

For teams thinking about how to make that perception shift visible to leadership, the 8 treasury KPIs that demonstrate value to leadership is a useful companion read.

Why the ratio matters more than the headcount question

Treasury leaders tend to frame workload pressure as a hiring question. The team is stretched, the request is to bring in another analyst, and the budget either allows it or does not. That framing skips the more useful question.

Misallocated hours create most of the workload pressure treasury leaders feel. A team running at 70% execution, 20% maintenance, and 10% judgment produces less strategic value than the same team running at 30%, 30%, and 40%. Hiring distributes the same allocation across more bodies. It does not change the ratio.

There is a second-order effect. A treasury team perceived as operational rarely gets the budget to hire judgment-layer talent. The team gets approved for another analyst, not a strategist. The new hire enters the same execution-heavy environment, and the ratio holds. The teams getting noticed in 2026 are the ones who shifted their ratio before they shifted their headcount.

What the numbers say about the gap

Crisil Coalition Greenwich's June 2025 study of more than 100 corporate treasurers found that roughly half of large companies have not yet deployed AI in their treasury departments at all. Among those who have, progress is generally limited to process automation rather than judgment-layer work.

The CFO view runs in the opposite direction. Deloitte's Q4 2025 CFO Signals Survey found that 87% of CFOs expect AI to be extremely or very important to their finance department's operations in 2026, and 54% identify integrating AI agents into finance as a transformation priority. Only 21% of active AI users report clear, measurable value, and only 14% have fully integrated AI agents into the finance function. Gartner's 2025 Finance AI Survey found 59% of finance functions reporting active AI use, and PwC's 2025 Global Treasury Survey adds that more than 70% of treasury teams remain in experimental or early development phases.

CFOs are buying AI faster than treasury teams are ready to absorb it. Teams who run the workload audit first arrive at the AI evaluation with a clear answer to the question every CFO is asking, which is which specific hours the team wants automated and what the team plans to do with the recovered capacity. 

A workload audit treasury teams can run this quarter

The audit does not require a vendor, a budget cycle, or a new tool. It requires honest answers to five questions.

First, list every analysis your team knows should run on a recurring schedule but does not. Bank fee analysis is the canonical example, and most teams carry five to ten others. Dormant account reviews, unwanted service audits, earnings credit rate optimization, category-level pricing audits, and intercompany transfer reviews all tend to live on the same shelf. Writing the list down surfaces work the team has been carrying mentally for years without ever committing to action.

Second, mark which of those analyses your team already has the data to support. This number is almost always higher than the team assumes. The blocker is rarely the data itself. The blocker is the cost of organizing and interpreting it. Categorize each item as data-ready, data-partial, or data-blocked. The ratio of data-ready to data-blocked items shows the team's available automation runway.

Third, draft what a monthly cadence would look like once those analyses run on a schedule. Even on paper, the exercise reveals which judgment-layer questions your team would surface once execution work stopped consuming the calendar. The cadence draft also gives the team a concrete artifact to bring to leadership when the budget conversation comes up.

Fourth, identify one banking relationship where a recurring, structured report could change the conversation. Plan to share that report quarterly, regardless of whether the underlying analysis is fully automated yet. Banking partners respond to structured data the same way every other counterparty does, and many treasury teams discover that asking the bank questions backed by their own data shifts the negotiation posture immediately.

Fifth, write down the strategic questions you would ask once production work stopped consuming the schedule. That list becomes the team's judgment-layer backlog and the team's strategic narrative when the CFO asks what treasury is working on beyond reporting.

Common objections

The first objection is time. An audit feels like adding work to a team that is already stretched. In practice, a team that spends one week defining its ratio and drafting a cadence usually recovers that week several times over within the next quarter, because the team stops repeating decisions about what work is urgent.

The second objection is that automation tools have not been bought yet, so auditing a state that cannot change feels premature. The audit is what justifies the tool conversation. Treasury leaders who walk into vendor evaluations with a clear picture of which categories of work they want to compress, and which they want to expand, evaluate vendors more rigorously and negotiate harder. Teams who skip the audit tend to buy tools that automate work they should not be doing in the first place.

What sits on the other side of the audit

Treasury teams who have run this kind of audit tend to surface the same set of opportunities. Pricing contract hygiene becomes a recurring discipline rather than a one-time event. Yield decisions on excess balances move from quarterly hand-waving to monthly review. Bank fee data starts feeding into cash forecasting models rather than living in a separate file. 

Conversations with banking partners shift from reactive to proactive. The treasury team becomes a contributor to FP&A, procurement, and occasionally legal, because the data the team sits on is data those functions want and rarely have clean access to. None of this requires a specific tool. All of it requires the team to have decided, in advance, what the reclaimed time is for.

The data foundation sets the ratio ceiling

A workload audit produces a plan. Executing the plan requires the data layer underneath the treasury function to be structured, normalized, and trustworthy. This is where most internal AI initiatives stall. Industry coverage of treasury AI outcomes consistently points to the same failure mode, where teams skip data readiness and jump straight to the model. The result is faster output that nobody trusts.

Trovata sees the data foundation as the leverage point. Centralizing, normalizing, and orchestrating financial data across banks creates the conditions for a judgment-heavy treasury team. 

Whether a team eventually runs AI agents, internal scripts, partner tools, or analysts working with cleaner spreadsheets, the cleaner the data layer, the higher the share of judgment work the team can sustainably take on. Teams who run the audit first, fix the data foundation second, and adopt automation third will move faster than teams who reverse the order. The order is the strategy.

For a practitioner conversation on how a 27-year account analysis veteran is thinking about this shift inside his own workflow, watch the full replay here.