Rajiv Gupta
July 2, 2026
A leading North American food and beverage company moved from SAP ECC to SAP S/4HANA. Eighteen months after go-live, an Axiamatic audit showed that most of its post-go-live issues had been detectable when they began.
A leading North American food and beverage company set out to modernize its ERP backbone. The objective was significant: consolidate SAP ECC operations into SAP S/4HANA across Canada. The program spanned more than 10 regional product configurations and a complex web of integrated systems, including Salesforce, ORTEC and ODM.
It was multi-year, multi-system and high stakes, and by most measures not unusual for an organization of this scale. What followed was not unusual either. The program completed and stabilization was reached. Then the real accounting began.
After go-live, the team found itself in a long recovery cycle that had become its own full-time workload. The numbers told the story:
The issues were not catastrophic in isolation. They were the kind of problems that surface in every large ERP program: integration mismatches, data conflicts, process exceptions with no clean system path, and edge cases that testing had not caught. Each one required investigation, workaround design, re-testing and stakeholder coordination. Across 27 critical issues and two releases, the cost in time, resources and organizational patience was substantial.
What the team did not yet know was how much of this had been preventable.
Most lessons-learned exercises are qualitative. Teams gather, discuss what went wrong and produce recommendations that rarely change the next program's outcome in any measurable way. This engagement was different.
The company brought in Axiamatic to run a structured retrospective risk and issue audit anchored in data, not recollection. Axiamatic's models ingested historical hypercare tickets, system logs and cross-system data flows from across the program lifecycle. They then traced each failure back to its origin.
The analysis covered the Order-to-Cash process, where post-stabilization issues were most concentrated. Four specialized AI models were applied:
The result was not a qualitative assessment. It was a structured, model-driven trace of 123 discrete issues back to their architectural, data and process origins.
The findings were precise. All 123 Order-to-Cash issues were mapped to a specific root-cause category, instead of being treated as an undifferentiated backlog of post-go-live problems.
A post-mortem that only lists what went wrong gives retrospective closure. A post-mortem that finds root causes, detects early signals and enables proactive action works as a safeguard against future failure. Axiamatic delivered the second kind, through a queryable, data-grounded Living Context Graph.
The company's leadership used the Living Context Graph for the next wave of rollouts. They delivered business outcomes with much higher transparency and predictability: on time, on budget and stable at first release. Institutional knowledge that had lived in tickets, workarounds and people's memory became structured, searchable and actionable.
The $3.1M identified in this analysis was not lost to mismanagement or lack of effort. The teams running this program were experienced, the systems integrators were competent, and the program delivered.
The costs were lost to a detection problem. The signals that would have identified these risks earlier existed in the program's own data: hypercare tickets, system logs, configuration records and cross-system data flows. What was missing was the capacity to read that evidence at speed and at scale, across 123 issue pathways at once.
That is not a failure of process or governance. It is a structural limitation of how transformation programs have always been run. Human teams cannot process the volume of signals a modern multi-system ERP program generates fast enough to act while issues are still in-phase and inexpensive to resolve. By the time a risk appears in a status report or an escalation, the window for low-cost intervention has usually closed.
Axiamatic is built to close that gap. The platform continuously ingests program artifacts, system logs, tickets and cross-functional data flows, and surfaces risk in real time while teams still have options. For organizations running S/4HANA, RISE with SAP or any multi-system ERP program, the question is not whether the signals are present. They always are. The question is whether anyone is reading them.
Axiamatic is the Transformation Assurance platform built for enterprise programs. The platform continuously analyzes transformation work across people, processes and systems to detect risk early, recommend and help drive role-specific actions, and preserve institutional knowledge across programs. Backed by Greylock Partners and Bessemer Venture Partners.
To learn more about the platform, visit axiamatic.com.
