AI has become remarkably good at making organizations move faster and more efficiently. The harder question is whether companies know where they should be going. As artificial intelligence moves deeper into everyday work, the competitive divide is beginning to shift from access to technology toward the quality of the decisions surrounding it.

Gallup reports that 52% of US employees now use AI in their roles at least a few times a year, while 30% use it several times a week or more. The technology is already in the workplace. Strategy is what determines whether it matters.

The productivity opportunity is substantial. BCG’s 2026 AI at Work research found that 74% of frontline employees are now regular AI users, up 23 percentage points from the previous year, while 42% of regular frontline users report saving at least eight hours a week.

Yet 66% receive limited or no guidance about what to do with that time, and more than half do not redirect it toward strategic work. The problem is becoming less about whether AI can accelerate a task and more about whether an organization has decided what the newly available time should accomplish.

“Technology can accelerate an ill-defined strategy just as efficiently as a good one,” says Claudia Harvey.

An organization can automate research, generate marketing material, analyze customer data, or shorten a production cycle, but none of those capabilities establishes which customers should be pursued, which problems deserve investment, or which commercial outcome the business is trying to achieve. Deloitte’s 2026 research reinforces the gap, finding that 59% of organizations take a technology-focused approach to AI, while only 6% of leaders report making progress in intentionally designing human-AI interactions.

The human consequences of getting that sequence wrong are becoming increasingly visible. Today, 65% of organizations believe their culture needs to change significantly because of AI, while 42% of workers report that their organizations rarely evaluate AI’s impact on people. Governance, in that context, involves determining how work changes, where accountability sits, how decisions are made, and how people understand their role as technology takes over increasingly complex tasks.

Harvey approaches that challenge first as a change management consultant and strategist through On the Verge Transformation Consulting, with AI forming part of a broader transformation toolkit. Her experience spans executive leadership, entrepreneurship, business growth, and change management, informing a philosophy that places the business objective ahead of the technology selected to pursue it.

Her perspective is particularly relevant as organizations attempt to convert the extraordinary speed of AI into meaningful operational progress.

According to Harvey, the starting point is not the latest model, platform, or automation capability. To successfully manage change, an organization must define its goals, measure progress, and pinpoint exactly where updates are needed. It must also evaluate how these changes affect customers and employees, using the right tools and strategies to reverse-engineer the desired outcome.

She notes that it’s important to communicate and align the employees towards the common destination and set out measurable steps to manage the change. Only then can leaders determine whether AI is the appropriate tool. She believes this sequence challenges one of the most persistent habits of technology adoption: choosing the tool first and searching for a reason to use it afterward.

“Faster does not automatically mean better,” Harvey argues. “AI can provide information, automation, analysis, and incredible speed. But someone still needs to provide the strategy, judgment, relationships, values, and leadership.”Her distinction places AI as a powerful means of accelerating work whose direction has already been established.

The same logic applies inside established businesses. Harvey points to sales and marketing as an area where AI can dramatically compress timelines by creating a marketing funnel, researching prospects, building lists, and supporting outbound activity. However, technology cannot make strategic decisions: it cannot determine which prospects to pursue, how to position an offering, what relationships to build, or how the value proposition will impact the brand. AI can assist employee training, yet Harvey insists that employee communication around the brand is key to success.

The human role consequently moves rather than disappears. AI can process information and accelerate execution, while leaders remain responsible for deciding what deserves to be done, how it should be done, and where human judgment and interaction must remain in control.

Trust in the technology itself also requires discipline. Harvey cautions against treating AI-generated information as inherently reliable, emphasizing verification and fact-checking as part of responsible adoption, especially as AI hallucination persists. The same discipline applies to proprietary information, employee use, and organizational policy. Companies need enough understanding of the technology to establish sensible boundaries, while employees need enough guidance to understand how those boundaries apply to their daily work. Many companies still maintain that AI technology will not be used by their employees because they fear unknown consequences.

As access to increasingly capable tools becomes widespread, the technology itself becomes a less durable source of differentiation. The advantage moves toward organizations that understand their objectives, redesign work intelligently, and use AI selectively to accelerate the journey. Technology, Harvey posits, can change the pace of business, but strategy is absolutely necessary to determine the direction.