Deloitte's Center for Controllership found that 63% of finance leaders have fully deployed AI somewhere in their function this year. Fewer than a quarter say it's delivering measurable value so far. That gap has less to do with the technology than with where it gets pointed first.
The fintech tasks to automate before anything else are the narrow, repetitive ones that sit between systems and rarely touch a customer directly: the invoice queue, the reconciliation spreadsheet, the expense report nobody wants to audit by hand.
Sorting the FinTech Tasks to Automate From the Ones to Wait On
Randstad's research on finance operations points to five processes worth automating first, because each one is rules-based enough that a machine can run it without making a judgment call:
- Invoice processing and data entry
- Multi-way purchase order matching
- Expense report auditing
- Bank and account reconciliation
- Dunning and collections follow-up
Randstad's own Workmonitor 2026 survey found that 75% of finance professionals say AI frees them up to take on more fulfilling work, once it's handling this layer instead of them.
None of these five need a model that reasons about a customer's intent or a market's direction. They need a system that reads a document, checks it against a rule, and flags the exception. That's a much smaller problem than most fintechs start with, and it's why it clears first.
Our AI & Automation team gets asked to take on exactly this layer before anything customer-facing, because it's the fastest way to prove the investment actually did something.
Why So Much AI Spend Isn't Paying Off Yet
The adoption numbers explain the value gap. CFO Dive reported that in Deloitte's 2026 Finance Trends survey, 54% of finance leaders are prioritizing AI agent integration this year, while just 14% have fully integrated an agent into a specific finance function.
Most of the 63% who've technically "deployed" AI are running pilots, not production. A tool sitting in a sandbox doesn't reconcile a single account, whatever the deployment dashboard reports.
We've seen the same pattern outside fintech. Mapping what an ecommerce AI workflow actually looks like for a retail client showed the automation that stuck was the one that owned a single process end to end, not the one spread thin across five.
The same survey found 52% of finance leaders naming data quality and access as a separate priority this year. An automation layered onto messy source data just moves the error somewhere harder to spot, so that cleanup tends to come before the automation, not after it.
Start Narrow, Then Let the Numbers Justify the Next Step
Photo by Visual Tag Mx on Pexels
A broader 2026 industry outlook argues that legacy systems across financial services are giving way to AI acting as the execution engine, not a feature bolted onto the old workflow. That shift plays out over years. The reconciliation queue does not.
Pick the one task costing the most staff hours today. Automate it completely, measure what changed, and use that number to make the case for the next one. It's the same discipline we bring to building the internal tools finance teams actually run on, instead of a pilot nobody adopts.
A healthcare client took the same narrow-first approach with intake instead of invoices. Our case study on what response times show found that automating one bottleneck, rather than the whole process at once, was what moved the number that mattered.
Fintech and finance teams chasing the same result don't need a bigger AI budget first. They need one process, automated completely, before a second one gets added to the list.
Cover photo by Annushka Ahuja on Pexels
