What changed
Therabody, the wellness technology company behind Theragun and a portfolio of recovery devices sold in more than 60 countries, has finished consolidating its financial operations onto a single Oracle NetSuite instance. Before the migration, the company ran separate financial systems by region across North America, Europe, and Asia Pacific, each with its own chart of accounts, currency handling, and reconciliation process. That structure is common for companies that scaled internationally through acquisitions or regional buildouts, and it is exactly the kind of sprawl that quietly inflates technology spend without anyone deciding to spend more.
The result, according to the company, is a 45 percent reduction in technology costs. That figure did not come from a single feature or a headcount cut. It came from retiring the duplicate infrastructure, licenses, and integration work that regional systems required to talk to each other and to corporate reporting. Chief Technology Officer Yash Murali described the outcome directly: NetSuite helped the company bring its operations together with AI capabilities that simplify and accelerate workflows. The consolidation was the precondition for that automation, not a side effect of it.
The mechanics of the savings
NetSuite OneWorld is built specifically for this problem: it manages multiple currencies, local tax rules, and regional reporting requirements inside a single consolidated ledger rather than through bolted-together regional instances. For a company selling physical devices across dozens of tax jurisdictions, that collapses what used to be parallel finance stacks into one system with one source of truth. The reconciliation work that previously consumed finance team hours every month, matching numbers across systems that were never designed to agree cleanly, largely disappears when there is only one ledger to reconcile.
The order management side tells a similar story. Therabody's Advanced Order Management module now automates processing for thousands of orders daily during peak demand periods, work that previously required manual intervention spread across separate regional systems with different capacity and different failure modes. Consolidating onto one platform did not just cut licensing costs, it removed the operational fragility of running peak-season order volume through systems that were never load-tested together as a single pipeline.
Why the AI layer needed consolidation first
Therabody added an AI Connector Service that links external AI models to NetSuite data using the Model Context Protocol, enabling workflow automation across finance and operations. This is the part of the story most companies get backwards. They try to bolt AI automation onto fragmented regional systems and get inconsistent, low-trust results, because the AI is reasoning over data that disagrees with itself depending on which regional instance it queries. Therabody's AI layer only became genuinely useful after the underlying data was unified into a single ledger.
That sequencing matters for any CIO currently weighing an AI investment against an ERP consolidation project. Automation vendors will happily sell agentic tooling that sits on top of whatever data mess already exists. The Therabody case is a data point for doing it the other way: consolidate the system of record first, then layer automation on top of a single, trustworthy data source. Skipping the first step is how companies end up paying for AI capabilities that produce outputs nobody in finance actually trusts enough to act on.
The scale problem consolidation actually solves
Running finance across separate regional systems carries an ongoing tax on every process that touches more than one region: currency conversion for intercompany transactions, tax reporting that has to be manually mapped between systems with different chart-of-accounts logic, and month-end close cycles that stretch because finance teams are reconciling numbers across platforms that were never built to agree. Companies tend to underestimate this tax because it shows up as headcount and overtime rather than a line item labeled software cost, which makes it easy to defer fixing.
Therabody's scale, over 60 countries and thousands of orders processed daily during peak periods, makes the case unusually clean. A company operating at that footprint has to run finance on spreadsheets bridging systems until it consolidates, and the 45 percent savings figure reflects what happens when that bridging work simply disappears. Smaller multinationals running two or three regional instances will see smaller percentages, though the mechanism, retiring duplicate infrastructure and the manual work required to keep it synchronized, scales down proportionally.
What this means for a fragmented enterprise
Most mid-size to large enterprises carry some version of Therabody's starting point: regional or business-unit ERP instances accumulated through growth, acquisition, or historical IT decisions nobody wants to revisit. The 45 percent figure is a useful anchor for building the business case internally, because it reframes ERP consolidation as a cost-reduction project with a measurable target rather than an open-ended modernization initiative that finance will always deprioritize against feature work.
The order in which Therabody executed also matters as a template: consolidate the ledger, automate the transactional workload that consolidation makes possible, then add AI on top of clean, unified data. A CIO building the case for a similar project should lead with the infrastructure elimination math, not the AI story, because that is where the actual savings showed up here and where finance leadership will find the argument most concrete.
The sequencing risk most transformations get wrong
The common failure mode is inverting Therabody's order: buying an AI or automation product first, on the promise that it will paper over fragmented systems, and only discovering during implementation that the AI has nothing reliable to reason over. That failure is expensive twice, once for the AI tooling that underdelivers and again for the consolidation project the company still has to do afterward, now with less budget goodwill left to fund it.
Treat ERP consolidation as the prerequisite line item on any AI transformation roadmap, not a parallel workstream competing for the same budget cycle. If your finance systems are still fragmented by region or business unit, that is the project to fund before the next AI pilot goes to committee, because every automation layered on top of fragmented data inherits that fragmentation's unreliability, and no amount of model quality fixes a data problem sitting underneath it. Therabody's sequencing is the template worth copying, in that specific order.
