Business and technology leader Victoria Savio says the transformation programs that fail rarely do so where anyone is watching. “Most transformation programs are not lost in the strategy deck,” she says. “They’re lost in the gap between the people who run the business and the people who build the solution.” Everyone leaves the room in agreement, and then the requirements get lost in transit, the timeline slips, and the outcome no longer resembles the idea. Programs are governed by milestones that produce something visible, whether an approved strategy, signed-off requirements, a funded build, or a launch, while the handoff between them produces nothing that anyone can present.
Savio has spent more than 15 years in financial services driving sales enablement, data, and AI transformation for asset management. She has owned product teams across more than 15 applications, grown a global team spanning the US and India from 2 people to 20, and launched a multimillion-dollar intelligence and data platform.
The Business Product Owner as Translator
The two sides of a transformation program speak different languages. Business speaks in process, while technology speaks in systems, and Savio warns that without someone bridging them, “the requirements get lost in transit.” Her answer is the business product owner. That person sits with the business, understands what the work looks like, and carries the intent through delivery rather than handing it off at a checkpoint.
A requirements document captures a decision at a single moment and cannot answer the dozens of questions that surface once a build is underway. Someone who watched the work happen can answer them, and someone reading a specification cannot. Savio credits the role with a specific outcome. It is how teams like hers support hundreds of users with solutions those users will adopt, which is a different measure from delivering what was specified.
Data Product Ownership Before Ambition
Savio identifies a pattern common to every stalled project she has encountered. The process was mapped but the data underneath it was not trusted. The sequencing failure has an obvious cause: process mapping produces a deliverable that a steering committee can review, while data governance produces nothing to present. Her approach starts with introducing data product ownership, which entails assigning accountability for a dataset the way an organization assigns accountability for an application. That ownership comes with governance, key performance indicators, and monitoring so the quality of that data is measured continuously rather than assumed.
That foundation determines what the program can support later. It is what makes automation, analytics, and AI land instead of drift, since each of those depends entirely on the reliability of what sits beneath it. A program built on untrusted data proceeds cleanly through design and delivery, then fails at the moment someone has to act on what the system reports.
Sequencing for Adoption Rather Than Launch
The final gap opens after the visible work concludes. “A go-live is a date,” Savio says. “Adoption is the result.” Too many teams staff for the build and then disappear after launch, leaving users with an unfamiliar system and nobody who understands it. Savio’s rule is to stay agile past go-live and keep the team staffed for what happens after launch rather than only for reaching it.
The staffing decision reveals what an organization is actually measuring. A program that releases its people at launch has defined success as delivery, whatever the business case claimed, and the adoption numbers arrive months later to settle the question.
Savio has watched this handoff make or break more transformations than any strategy deck. The gap between business and technology exists in every program and appears on no plan, which is why it stays unowned until someone claims it. Her advice it to build it on purpose. To learn more, connect with Victoria Savio on LinkedIn.
