Why your AI ROI may be hiding in transaction data

AI pilots are failing to pay off

Enterprises rushed into AI, but speed hasn’t translated into value. According to the article, 95% of enterprise pilot programs failed to deliver measurable financial returns last year.

A few years on from the initial AI boom, boards now expect proof of ROI, not just pilots and prototypes. The piece is authored by the co-founder and CEO of Invoice Home and argues that the missing link is how companies use transaction data.

From generic productivity to specific cash problems

Most organizations have funneled budget into tools that promise higher productivity or workflow automation. The goal has been the same for decades: do more with less.

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The author argues leaders should instead target today’s most pressing financial problems, with cash flow forecasting near the top of the list. In 2024, 52% of American CFOs named cost management as their most worrisome internal concern, highlighting the pressure to stay nimble while juggling fixed operations and volatile external conditions.

To get there, companies need better signals on:

• Cash flow demand
• Churn risk
• Shifts in spending patterns

Those signals sit in the transaction layer.

Treating transaction data as a forward signal

Businesses have mined transaction data for years, but mostly for traditional analytics and lagging reports. The article argues for a mindset shift: from viewing transactions as archival records of what happened to real-time indicators of what’s coming.

Bottom-funnel operations hide useful revenue indicators. Details such as:

• Frequently adjusted terms in contract renewals
• Average time customers take to finalize transactions

can become purchase signals that help systems predict demand or accounts receivable.

Churn and upgrade behavior as early warnings

Upgrade and renewal activity is a clear example. Customer retention is critical to predictable cash flow and is often one of the first casualties in an economic downturn.

Tracking accounts that reliably upgrade or renew on time, then suddenly miss a milestone, can flag early churn risk. Comparing these deviations across similar accounts by geography, product line, size, or industry helps finance teams segment risk.

From there, leaders can act before revenue slips, using targeted discounts or incentives to keep at-risk customers. On the flip side, accounts that expand faster than expected can hint at other customers likely ready for higher-value offerings.

Signals preceding cancellations—delayed payments, reduced usage, smaller order size—support rolling forecasts. That’s a sharp contrast with monthly or quarterly forecasts that lean on historical averages and lag behind a fast-moving market.

Turning forecasts into decisions

When fed into forecasting models, these transaction-level cues can turn into what the author describes as a dynamic decision-making engine. It underpins more flexible cost management strategies by revealing where cash flow is trending.

For instance, consistently slower upgrade rates might indicate the need to cut inventory ahead of time or ease product development timelines. That helps avoid over-investing in demand that never materializes and gives teams room to adjust spending before cash pressure hits.

One practical challenge: purchasing behavior data is scattered. Sales may own average order values, while legal or finance hold changes in payment terms. The article stresses that first mapping where these metrics live, then unifying them in one place, is critical so predictive models can cross-analyze and produce stronger recommendations.

Why this AI use case connects cleanly to ROI

Many enterprise projects still center on generative AI for content creation or software development. According to the author, these task-level efforts are hard to tie to measurable impact and can even drag on productivity when teams spend extra time reviewing and fixing machine outputs.

By contrast, using AI to analyze data for forecasting and predictions has already shown strong value and is linked to tangible business outcomes. Extending that to real-time, transaction-based insights directly tied to revenue gives leaders a clearer, faster route to ROI in an unpredictable economy.

The article notes it was produced as part of TechRadar Pro Perspectives. The views are those of the author, not necessarily TechRadar Pro or Future plc.