Enterprise AI outcomes depend on the art of unlearning

There is no shortage of published thought leadership on the potential of artificial intelligence .

But what often gets overlooked is the gap between theoretical potential and the reality of deploying it successfully inside a global enterprise.

Gallup’s 2026 State of the Workplace report reveals a stark disconnect: while 65% of employees say AI has boosted their individual productivity , only 12% strongly agree the technology has actually transformed how work gets done in their organization.

Having led tech transformations from both sides — as an executive driving innovation at Canadian National Rail and as a partner accelerating AI outcomes for customers with Tenarai — I’ve learned that successful digital transformation thrives on people.

In the agentic era, the unlock for enterprise AI outcomes is empowering employees to unlearn the tools and habits they have relied on for years, helping them embrace new capabilities with confidence.

As the industry shifts from passive AI assistants to autonomous AI agents, traditional software delivery models fall short because they overlook the human-AI connection.

Bridging this gap requires elevating change management into a foundational pillar of enterprise AI transformation, aligning tech with the context and culture of the enterprise.

From assistants to active agents To understand why legacy engineering structures fall short on scaling AI outcomes, let’s look at how technology changes human workflows.

Generative AI serves primarily as a task-based drafting tool, keeping humans comfortably in the driver’s seat to review and approve.

Agentic AI, in contrast, is goal-driven, context-aware, and collaborative; acting more like an autonomous digital co-worker.

This shift is already reshaping complex enterprise operations, but its success hinges on people’s ability to adapt.

A recent data migration we led for a global logistics leader illustrates this challenge.

While moving its data pipeline from a legacy SAS architecture to the cloud presented significant engineering complexity, the greater challenge was actually guiding its long-tenured business analysts in unlearning many years of established workflows.

By pairing technical migration with hands-on change management initiatives, we helped these analysts understand the clear value and benefits of change.