kbj2qejf

A few trends stand out:

• Ops sits at “partially automated or worse” at 73.5%. The survey average is 56.8%. That’s the largest segment-to-average gap in the dataset.

• Adoption of modern document automation tools runs lower in Ops on every category that matters. Document automation and OCR sit at 24.5% (survey average 33.5%). RPA sits at 7.7% (survey average 14.5%).

• 1 in 7 Ops respondents say they don’t know which tools their team uses today, compared with 1 in 11 across the survey average. The awareness gap is the largest of any segment in the data.

The cost shows up as labor and silent failure. Ops teams are about a third more likely than the survey average to fall back to fully manual handling when automation breaks (21.3% vs. 16.1%), and almost twice as likely to report failures that go completely unnoticed until they cause downstream damage (3.7% vs. 2.1%).

The opportunity shows up in the same data. Ops reports the highest switching intent of any segment in our survey, and they see meaningful value faster than the average team when they automate. The barrier is starting.

The rest of this report unpacks why those divergences sit where they sit. Each chapter closes with a short list of moves Operations leaders can take based on the data.

For the full picture across all 310 respondents in our survey of finance, operations, and IT leaders, see the State of No-Code Document Automation 2026 main report:

1. Operations is the least tooled when it comes to document automation

Operations teams in document-intensive industries handle the widest range of document types in our survey. For instance, bills of lading from carriers, freight bills with frequently changing accessorial codes, or customs declarations that shift with each port. 

The work has more variety than most other functions, yet the tooling supporting it has the least depth.

Across our 310 respondents in finance, operations, and IT, Ops teams sit furthest from the automation that would help. They make up half the sample, run the messiest document load, and use the least specialized tooling.

The document automation maturity gap is the largest in the dataset: