Treasury Intelligence Solutions (TIS), a cloud-based payments and cash management platform, published research on September 29, 2026 arguing that companies often underestimate what it costs to build AI-powered cash forecasting in-house. According to The Globe and Mail, the whitepaper, titled "Buy vs. Build: The Real Cost of Building AI-Powered Cash Forecasting In-House," compares the total cost of both approaches across three years. The biggest caveat is obvious: TIS sells the buy option, and the findings come from the company's own whitepaper rather than an independent study.
Still, the framing is worth unpacking, because the build-versus-buy question now reaches well beyond corporate treasury departments. Universities, school systems, and education technology teams face the same decision as AI tools spread through their operations. The tradeoffs TIS describes — control against maintenance, customization against staffing — apply almost anywhere a non-tech organization considers building its own AI capability. Readers following the broader debate can find more coverage in our education news section. This connects to our earlier piece, FAFSA for 2026-27 Opened in September, the Earliest Launch on Record.
What did the research actually find?
The whitepaper's central claim is that the initial build is only part of the investment. According to The Globe and Mail, TIS found that many organizations frequently underestimate the true financial, operational, and strategic costs of building and maintaining AI-powered forecasting solutions internally. The company also reported that internal builds took 9 to 24 months to deliver value, depending on the environment.
The research does not dismiss building altogether. TIS stated that building may make sense for some low-complexity organizations. The costs rise, it argued, as companies add entities, banking relationships, ERP systems, and geographies. Each layer of treasury technology brings additional development, integration, maintenance, and talent requirements.
Why does complexity drive the cost?
The logic is straightforward. A forecasting model is easy to demo and hard to sustain. It needs data feeds from every system it is supposed to see, and every new connection adds work. TIS's argument, as reported by The Globe and Mail, is that this work compounds: more entities mean more banking relationships, more ERP systems mean more integrations, and more geographies mean more edge cases.
That compounding effect is the part most organizations miss, according to the whitepaper. Teams budget for the build and assume the run will be cheap. TIS's position is that the run is where the real money goes — ongoing maintenance, retraining, and the specialist talent needed to keep the system accurate as the business changes.
Who is making the claim, and what is their stake?
TIS is not a neutral observer. The company describes itself as a leading cloud-based platform for payments and cash management, serving CFOs, treasurers, and finance teams since 2010, according to The Globe and Mail. Its business model depends on organizations choosing to buy. The whitepaper announcement also funnels readers toward a personalized cash forecasting demo.
Charles Bennett, TIS's Chief Product Officer, put the company's framing this way: "The question isn't whether organizations can build AI-powered cash forecasting capabilities. Many can." He added that the more important question is "whether maintaining that capability is the best use of treasury, finance, and IT resources over the long term." The quote is honest about the build being feasible; the argument is about opportunity cost, not capability. Readers following this should also see Spring 2026 Enrollment Hit 18.6 Million, Up 1 Percent Year Over Year.
That distinction matters for how much weight the findings can carry. A vendor's whitepaper is a marketing document with research attached, not the reverse. The 9-to-24-month build timeline and the cost-escalation claims are the company's own figures, and no independent methodology, sample description, or peer review accompanies the announcement. Readers should treat the numbers as TIS's claims, not established findings.
What can decision-makers take from it anyway?
Even from an interested party, the framework has usable parts. The whitepaper reportedly gives decision-makers a structure for evaluating the build-versus-buy tradeoff, and its core questions hold up regardless of who asks them:
- What does the system cost to maintain, not just to build?
- How many systems, entities, and geographies must it connect to?
- Who on staff will own it after launch, and what happens when they leave?
- How long until the investment delivers usable forecasts?
For education institutions weighing similar AI builds — enrollment forecasting, budget projections, student-success models — the same checklist applies. A district or university with simple needs may genuinely be better off building. A large system with many campuses and legacy systems should expect the maintenance bill to grow with every connection, exactly as TIS describes for corporate treasury.
What remains unknown?
The announcement leaves the important questions open. The whitepaper's sample — which organizations, how many, what sectors — is not described in the coverage. The three-year cost comparison is summarized but not published in the reporting. And because the source is a vendor with a product to sell, no independent replication exists. Anyone making a real build-or-buy decision should ask TIS for the methodology behind the figures, and should seek at least one source without a product in the market before treating the cost claims as settled. The evidence here is a starting point for the conversation, not the answer to it.
