What Spend Management Vendors Announce, and Why Their Customers Actually Leave

Two independent datasets, assembled separately, point in opposite directions.

On the vendor side, IndustryLens tracked 240 announcements over the last 90 days. Of those, 77 are AI-flavoured, they mention AI, agent, agentic, autonomous, copilot, LLM, Joule, MCP or intelligence. That is 32.1% of everything the category said publicly in a quarter. [Corrected 2026-09-07: original estimate (24.4%, 58 of 238) was unverified; recomputed using the keyword methodology stated above. Corpus measurement: spend_mgmt_ai_share_keyword_2026_09_07.] By contrast, only a small minority of announcements were framed around support or usability, with most of the remainder covering general product updates.

On the buyer side, 371 switching mentions in the review corpus carry a stated reason for the move (corpus measurement: spend_mgmt_switching_reasons_total_2026_10_05). Reading those 371 stated reasons at the time this report was built, automation or AI was almost never cited; usability or a dated interface was the single most commonly cited reason, and support problems were cited often as well, with both themes recurring steadily in reviews written in 2026, not only in older ones. This category read was a one-time qualitative classification made when the report was built. It is not stored in a form IndustryLens can reproduce, so the picture above is described in kind rather than repeated as precise counts or percentages.

Put simply: the theme the category invests most of its announcement volume in, AI, is barely mentioned by buyers explaining why they left. The themes buyers name most often, usability and support, receive comparatively little announcement volume.

The asymmetry is the finding. The theme the category invests the most announcement volume in is the one buyers almost never name when explaining a move. The theme buyers name most is the one that receives the least announcement volume.

Two cautions before this is over-read. First, announcements and switching reasons are not the same kind of object: a vendor may improve an interface without announcing it, and roadmap work often precedes public messaging by quarters. Second, an AI capability could well be driving purchases without buyers describing it in those words, reviewers write in the language of their own frustration, not in category taxonomy. What the data supports is narrower and still substantial: in the stated language of people who moved, AI is not the reason, and interface quality is.

It is also worth noting that the usability and support themes are not a legacy artefact: a substantial share of the mentions behind both are from reviews written in 2026 itself, not only from older reviews. The complaint set is current.

Reading the 371 stated reasons at report-build time, usability or a dated interface was the most frequently cited theme by a clear margin, support was second, and feature depth, price, integrations and multi-country requirements followed at more modest and roughly comparable levels. Automation or AI was cited only rarely. A large share of the 371 reasons did not fit neatly into any of these themes and were left as other or uncategorised, a residual large enough that this split should be read as a qualitative impression of the corpus, not a precise taxonomy. (The total of 371 stated reasons is a stored, reproducible figure: corpus measurement spend_mgmt_switching_reasons_total_2026_10_05. The category split itself was a one-time classification made when the report was built and was not retained as data IndustryLens can re-run, so no specific counts or percentages are given for it here.)

Within that reading, the ranking was unambiguous: usability led by a wide margin, support was a clear second, and price was named distinctly less often than interface quality, notable in a category where discounting is a routine competitive tactic.

The verbatim comments show why the usability and support themes overlap so heavily in practice. Reviewers rarely cite one in isolation; they describe a system that is hard to use and a support function that does not resolve the resulting problems. Each quote below links to the individual public review it came from.

"Concur support failed to deal with our requests and their platform was outdated. Furthermore, it was really important to us to find an expense platform where the end users can submit their expenses with ease."

Enterprise reviewer, moving to Navan, G2, 8 July 2026

"We had many connection issues with Concur and their support team was not helpful"

Mid-market reviewer, moving to Tipalti, G2, 22 June 2026

"Concur does not have a clear policy setup for expenses and approvals, and routing items back to employees is a very manual process. Also, integration with Sage Intacct has to be done through a third-party application"

Enterprise reviewer, moving to Navan, G2, 8 July 2026

"The UI and travel catalog in our previous solution were poor, and it no longer suited the needs of a multi-country company."

Mid-market reviewer, moving to Navan, G2, 8 July 2026

The third quote is instructive because the reviewer describes a manual, high-friction workflow (precisely the problem category AI messaging targets) without reaching for the vocabulary of automation. The gap above may be partly a gap in language, not only in substance.

Reviewers who named a specific prior product define a directional picture of displacement within this corpus, not a market-share or revenue statement.

SAP Concur stands out as the vendor most frequently named as the one reviewers left, across enterprise, mid-market and small-business segments alike, and a meaningful share of those mentions are from reviews written in 2026. The most frequently named destination for SAP Concur switchers is Navan, with Ramp, Brex and Tipalti also named as destinations by a smaller number of reviewers. Expensify is the next most frequently named origin, with reviewers naming a mix of destinations including Mesh Payments, Navan, Brex, Payhawk and Ramp. Payhawk is the most frequently named destination among reviewers describing a move from European-origin tools such as Spendesk and Kloo.

Inbound mentions to SAP Concur in the same corpus are comparatively few, and are spread thinly across a mix of point tools and adjacent categories, including spreadsheets, rather than concentrated in one source.

Stated plainly: within this dataset, SAP Concur draws named switchers from a wide mix of point tools and adjacent categories, not solely from spreadsheets, while several different vendors draw named switchers from it. That is a directional observation about review text, not a market-share or revenue statement, and the methodology note sets out why the two must not be conflated.

Three implications follow from the data, and only these three.

The stated reason set is stable and current. Usability and support are not residual complaints from an older cohort: a substantial share of the mentions behind both themes come from reviews written in 2026, in the same corpus that runs to 2 August 2026. The pattern is being renewed in the most recent reviews available, not just inherited from older ones.

Displacement is currently concentrated. SAP Concur is named as the departure point far more often than any other vendor in this corpus, with Navan the most frequently named destination for those switchers and Expensify the other repeatedly named origin. If those two nodes continue to generate outbound mentions at the observed rate, the near-term competitive story in this corpus will remain a story about a small number of origins rather than a broad reshuffle.

Announcement mix and stated demand are not converging. With roughly a third of tracked announcements AI-flavoured (32.1%, corpus measurement spend_mgmt_ai_share_keyword_2026_09_07) against AI being rarely cited as a stated switching reason, and usability being the most frequently cited switching reason against a comparatively small share of announcement volume framed around usability, there is no evidence in this quarter's tracked announcements of the messaging mix moving toward the themes buyers name. Whether that gap narrows is measurable on the announcement side, and IndustryLens will re-run that count.

The review corpus comprises 3,486 reviews of 13 tracked vendors, collected from G2, Capterra and Trustpilot, with the earliest review from 2020 and the latest dated 2 August 2026. Per-vendor review counts range from 371 (Navan) to 56 (Bill). Within the corpus there are 405 switching mentions in total, of which 371 carry a stated reason (corpus measurement: spend_mgmt_switching_reasons_total_2026_10_05); only those 371 are referenced in this report's discussion of stated reasons. The announcement dataset comprises 240 vendor announcements tracked over the last 90 days; the AI share of that dataset (32.1%, corpus measurement: spend_mgmt_ai_share_keyword_2026_09_07) was classified precisely by keyword.

• This is self-reported data from people who chose to write a review. Reviewers who have recently switched are more motivated to write than satisfied incumbent users, which biases the corpus toward switching narratives.
• A switching mention is not a churn rate. These counts measure how often a product is named in review text as the one a reviewer left. They say nothing about accounts lost, revenue affected, or share of any vendor base.
• Review volumes differ by vendor, so raw mention counts partly reflect how much each vendor's users write. Average ratings range from 4.84 (Ramp) to 2.13 (Bill, on only 56 reviews), the latter sample is small enough that the average should not be treated as a stable signal.
• The reason-category breakdown in this report is qualitative, not a stored metric. Beyond the 371 total (which is stored and reproducible), the split of those 371 reasons into themes such as usability, support, feature depth, price, integrations, multi-country requirements, AI/automation and other was a one-time classification made when the report was built. It was not retained in a form IndustryLens can re-run, so this report describes that split qualitatively (which themes were common, rare, or recurring in 2026) rather than citing specific counts or percentages for it.
• Announcement classification is keyword-based, and was run precisely only for the AI share (32.1%, stored). It captures messaging emphasis rather than underlying engineering investment.