Adoption stops at Level 1
Two thirds of automated workflows remain at draft-and-review a year in. Not because accuracy is inadequate, but because nobody with authority signed off on a policy having consequences.
Research · 2026 edition
What finance teams actually adopted rather than what they said they would. Drawn from 23 live deployments and 41 rescue engagements between 2024 and 2026 — a small sample, stated as such, from a vendor with an obvious interest in the conclusions.
The written edition with methodology, per-workflow data, and the anonymised deployment profiles.
Six findings
Two thirds of automated workflows remain at draft-and-review a year in. Not because accuracy is inadequate, but because nobody with authority signed off on a policy having consequences.
Every deployment we observed began with accounts payable, regardless of industry. It is half the manual volume and the fastest to a high straight-through rate.
Approval rates climb toward 99% and time-per-item falls below four seconds within roughly two months, which is the point at which review has become a formality.
Security and audit review typically starts after selection rather than during. Where it starts during, the deployment is measurably smoother.
Deployments on charts above 400 accounts took roughly twice as long to reach a stable straight-through rate as those under 250.
Of 23 deployments, none reduced finance headcount in the first year. All redeployed capacity into close acceleration, collections, and analysis that was previously never done.
Adoption
The distribution is heavily weighted to Level 3 with low authority — agents initiating work and drafting, humans committing nearly all of it.
A bill arrives; an agent codes it and routes it.
The work starts without a person. Requires an authority model, an audit trail, and a policy engine — which is why it cannot be retrofitted onto a screen-driven system.
Accuracy
Failure
From the 41 rescue engagements. Automation does not change this distribution — it inherits it, which is why readiness matters more than capability.
Nobody reduced headcount. Across 23 deployments, in the first year, not one finance team got smaller — despite most business cases including a headcount assumption and despite straight-through rates reaching the high eighties on the largest workflow.
What happened instead was consistent: close duration fell, collections finally received attention, and analysis that had been permanently deferred started happening. In four cases the team grew, because the company grew and finance stopped being the constraint.
We do not think this means the savings are illusory. It means the capacity is real and gets spent on work that was previously not being done — which is a better outcome and a harder one to put in a spreadsheet. If your business case depends on removing people, the honest thing is to say that is a management decision rather than an automatic consequence.
The most actionable finding is that two thirds of workflows remain at draft-and-review a year in, and the cause is almost never accuracy. In interviews the recurring reason was that nobody was willing to be the person who authorised a policy that would post transactions automatically.
Where deployments did move to Level 2, one factor was present in almost every case: a simulation showing what the policy would have done against historical data. That is a narrow and slightly self-serving finding — we build that feature — and it was strong enough in the interviews that omitting it would be dishonest.
This is not independent research. It is our customers, on our product, analysed by us, and we chose what to publish. The mitigations are that the sample size is stated everywhere, the workflows we perform worst on are included, and the findings that are inconvenient — no headcount reduction, adoption stalling at Level 1 — are the ones we led with.
Questions
Full methodology, per-workflow data, and anonymised deployment profiles.