Transaction data could be the missing link to proving AI ROI in enterprises

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AI took enterprises by storm, with many opting for integration as fast as possible in fear of falling behind the more ambitious tech adopters. But speed alone isn’t always an advantage and as a result, 95% of enterprise pilot programs still failed to deliver measurable financial returns just last year.

Now that we’re a few years past the initial AI explosion, the pressure is on to prove true ROI from these projects.

Businesses have most frequently poured resources into AI tools aimed at boosting productivity and automating workflow in hopes to target the most universal, longest standing business goal: doing more with less. But what leaders should be doing is identifying where the technology can solve the biggest issues specific to today’s business climate. One of those issues is cash flow forecasting.

This year, 52% of American CFOs named cost management as their most worrisome internal concern. While a well-oiled cost management strategy remains critical for creating a strong financial cushion and remaining resilient, balancing fixed operations with constantly shifting real world variables is never easy.

As companies increasingly look for ways to remain nimble and improve decision-making, those that can leverage AI to forecast trends in cash flow demand, churn risk, and spending pattern shifts will find themselves on a quicker path to ROI. Doing so requires harnessing the right data, and these financial signals are hidden in the transaction layer.

While businesses have long mined transaction data for traditional analytics and reporting, it’s far under-utilized in AI strategies. There needs to be a shift from viewing these insights as archival records of past performance to real time indicators of what’s to come.

Making revenue forecasting more adaptive

There are several revenue indicators that lie within bottom funnel operations that AI has the ability of turning into actionable insights. From frequently adjusted terms within contract renewals to the average time it’s taking customers to finalize transactions, purchase signals like these can help AI systems make smarter predictions about demand or accounts receivable.

To provide a more granular view into the value of this data layer, let’s look at upgrade or renewal activity for example. Customer retention is a key element to maintaining predictable cash flow and is among the first to go during an economic shakeup.

Tracking accounts that consistently upgrade a product or service on time to see that they suddenly miss a milestone could immediately flag eventual churn risk. These deviations should also be compared across similar accounts to segment risk based on geography, product lines, or size and industry.

From there, leaders can act proactively with strategies like targeted discounts or incentive measures to encourage retention. Alternatively, accounts that are expanding faster than expected could provide predictions into other customers who might be ready for higher value offerings.

Identifying cues like those that often precede cancellations, along with delayed payments, reduced usage, or smaller order size for instance, can equip finance or leadership teams with rolling forecasts. Whereas on the other hand, monthly or quarterly forecasts typically only rely on historical averages and don’t provide the real time guidance needed for a quickly shifting marketplace.

This can manifest into a powerful decision-making engine. One that is dynamic enough to support flexible cost management strategies. Seeing where cash-flow is moving allows leaders to make more informed decisions.

For example, if it’s consistently being found that these customers are upgrading at slower rates then it may be a good indicator to preemptively reduce inventory or relax timelines for product development teams. In turn, leaders can avoid allocating too many resources to demand that may not end up materializing.

This also gives teams more flexibility to adjust spending and production before any cash flow pressure sets in.

An important element to keep in mind is that these purchasing behavior insights often sit across separate systems. While sales teams may have insights into average order values, only legal or finance may know how payment terms are changing across clients.

First mapping where all of these metrics currently live is critical to then unify them into one place for predictive models to cross-analyze everything against each other and make stronger recommendations.

Connecting to ROI directly

Many businesses have revolved their AI projects around generative AI for efficiency gains in producing content or developing software for instance. But not only are these task-level initiatives harder to prove a measurable impact from, in some cases they end up hurting productivity in the long run with added time spent reviewing and editing AI outputs.

When it comes to analyzing data for forecasting and predictions, AI has shown immense value and is tied to more tangible business outcomes. Now, forecasting real time insights tied directly to revenue can help leaders remain adaptable in an increasingly unpredictable economy, offering a fast track to ROI on these projects.

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