The Turning Point Most Transformations Miss

Hari Sonnenahalli is a thought leader and seasoned enterprise architect at NTT Data Business Solutions (NDBS).

​A global manufacturing company had just completed a rigorous vendor selection process for a multimillion-dollar transformation. The leadership team was aligned, the road map was ambitious, and the chosen solution stack included AI-driven planning, a modern ERP core and a scalable cloud platform. The program had everything that defines a strong start: executive sponsorship, funding approval and a sense of urgency to move fast. By all conventional measures, this was a transformation set up for success.

Six months into the project, the stakeholder meeting room felt different. The conversations had shifted from vision to frustration. Timelines were slipping, integration challenges were escalating and the business teams were beginning to question the value of what was being built. The technology itself wasn’t failing; most components were being delivered as designed. However, the program was losing momentum in ways that were difficult to explain.

I’ve seen this pattern more times than I can count over the past two decades: projects that begin with confidence and alignment often lose direction midway. This is because intent was never fully established. The clarity of intent should always precede the technology. In most cases, the urge to leverage technology to get ahead of the digital race doesn’t go as planned, and this was no exception.

In this case, the turning point came during a routine workshop when a senior business leader asked a simple question: “What exact decision will this AI model improve for my team on a daily basis?”

The room went silent. What followed was ambiguity, as different stakeholders had different answers. Some spoke about forecast accuracy, others about inventory reduction and a few about improving customer responsiveness.

These were all valid objectives, but none of them clearly defined the decision the system was meant to improve. That was the moment the real issue surfaced. The organization had aligned on technology but not on intent.

From that point forward, every challenge became harder. Data models were debated endlessly because there was no agreement on what “good” looked like. Integration priorities shifted because different teams optimized for different outcomes. Even successful pilot results failed to scale because they were validated against inconsistent expectations.

The program didn’t derail overnight. It drifted slowly, quietly and expensively. What made this situation particularly instructive was that nothing about it was unusual. It reflected a broader pattern I’ve observed across industries. Organizations are moving faster than ever to adopt advanced technologies, but many are doing so without establishing the clarity required to translate those investments into value.

Technology, by its very nature, amplifies what already exists. If the underlying intent is clear, it accelerates outcomes. If the intent is ambiguous, it amplifies confusion.

In the months that followed, the leadership team made a deliberate decision to pause—not to slow down the transformation but to realign it. This wasn’t an easy choice. Pausing a high-visibility program requires conviction, especially when significant investments have already been made. However, it was necessary.

The reset began with a series of structured discussions focused on one question: “What are the critical decisions that define success for this business?” For demand planning, the answer was the ability to decide, with confidence, how much to produce and where to position inventory under uncertainty. For supply chain operations, it was the ability to respond to disruptions without compromising service levels or cost targets.

Once decisions were clearly defined, everything else began to align. Data requirements became more precise because they were tied to specific decision inputs. Process flows were simplified because they were designed to support decision making, not just system transactions. Technology components were reevaluated based on their ability to improve those decisions rather than their individual capabilities.

Within a few months, the same program that had been struggling began to show measurable progress. Not because new tools were introduced, but because the existing ones were now aligned to a clear purpose. Conversations shifted from “What are we building?” to “What decisions are we improving?”