What Actually Drives Change? Four Lessons from Organizational Transformation Experts

Organizations are pushing forward major shifts in AI adoption, productivity expectations, and operating models, yet outcomes continue to fall short of what leaders anticipate.

The gap is especially visible in generative AI. According to MIT Sloan, roughly 95 percent of enterprise GenAI pilots fail to deliver measurable business impact, even as 96 percent of C-suite leaders expect AI to increase productivity. Nearly half of employees using AI say they do not know how to achieve the gains leadership expects.

The pattern extends beyond AI. Harvard Business Review reports that only 12 percent of transformation programs produce lasting results.

Most organizations do not struggle to set strategy. They struggle to see where execution is weakening before the consequences show up in wasted investment, slower productivity gains, higher attrition costs, or missed business targets.

The signals are usually there, but they are scattered across teams, interpreted inconsistently, or dismissed as isolated issues. Manager inconsistency, rising workload, and uneven adoption often point to deeper execution friction.

Leaders need a clearer view of those patterns so they can separate root causes from surface noise and focus intervention where it will matter most.

Seramount’s organizational transformation experts see the same pattern across initiatives. What begins as a clear enterprise priority often loses consistency as it moves into manager decisions, workflow design, and day-to-day reinforcement.

1. If managers are reinforcing different priorities, alignment has not translated into execution.

Executive consensus does not guarantee organizational alignment. Outcomes depend on whether managers can translate that alignment into consistent execution.

“One of the clearest warning signs is when managers all say they support the transformation, but they describe it in completely different ways,” said Rumbi Petrozzello, Head of Assessment and Strategy at Seramount. “That usually means leadership has not defined the shift clearly enough for teams to execute it consistently.”

That ambiguity changes behavior. Teams allocate resources, sequence work, and reinforce priorities differently. What looked aligned at the top starts to fragment in practice, creating rework, uneven adoption, and conflicting local norms.

2. If new expectations are increasing pressure without changing priorities, tradeoffs have not been made explicit.

When leaders raise expectations around productivity, AI adoption, or collaboration while leaving legacy work largely intact, the organization is left with more to do but no clear basis for deciding what matters most.

“Managers are often expected to carry the shift without enough clarity on what changes first, what takes priority, and what can give,” said Zsofia Duarte, Director of Assessment and Strategy at Seramount. “Once those decisions are left to individual teams, execution starts to vary in ways leaders usually do not see right away.”

Teams then make those calls locally, according to their own pressures and assumptions. Over time, the transformation loses coherence because different parts of the organization are operating against different definitions of success.

3. If new tools are creating more activity but not better outcomes, the workflow has not changed.

Many organizations mistake adoption for transformation.

“A new tool does not change performance on its own,” said Cory Schneider, Director, Advisory at Seramount. “If the workflow around it stays the same, the technology gets inserted into an operating model that was never designed to capture its value.”

That pattern explains why so many AI rollouts generate visible activity without measurable gains. Teams experiment with prompts, review outputs, add controls, and spend more time coordinating around the tool while the underlying economics of the work stay largely the same. Usage increases while productivity does not.

Without redesigning how work moves across teams, where decisions sit, and which effort should be discontinued the tool adds complexity instead of reducing it.

4. If decisions are moving too slowly, structural friction is impeding execution.

Even when direction is clear and priorities are set, execution can slow materially when decision structures remain vague.

“When decision rights are vague, work starts moving to the wrong level of the organization,” Schneider said. “Teams escalate decisions they should be making themselves, and senior leaders get pulled into issues that should already have owners. That slows execution more than most organizations realize.”

How Execution Visibility Changes the Outcome

These warning signs rarely appear in isolation. They reinforce one another, which is why early execution visibility matters. It allows leaders to see where a transformation is losing coherence before those conditions harden into business impact.

In one recent Assess360 engagement, a global Fortune 500 company was in the middle of a complex digital transformation spanning regions, functions, and roles. New tools, training, communications, and operating expectations were already in place, but leadership still lacked a clear view of where execution was starting to weaken.

Seramount’s experts identified four conditions shaping execution risk: unclear role expectations, rising workload without clear tradeoffs, inconsistent communication across leadership layers, and sharp regional variation in reinforcement. Sixty-six percent of employees lacked clarity on how their role connected to the transformation, 58 percent reported increased workload, and 38 percent expressed no positive sentiment toward transformation communications. Engagement also varied significantly by region, ranging from 31 percent in North America to 65 percent in Australasia.

Those findings gave leaders a more precise basis for action. Decision rights and role expectations were clarified, duplicative effort was removed, decision paths were simplified, leadership messaging was standardized across regions, and manager expectations were aligned to reinforce adoption more consistently. With those conditions visible, leaders could intervene earlier and more precisely to keep the transformation on course.

Organizations that sustain transformation build the visibility and decision clarity needed to identify risk early and respond decisively.