The AI Value Gap Isn't a Technology Problem. It's a Management Development Design Problem.

A recent Wall Street Journal article surfaced a striking disconnect: many executives believe AI is making work dramatically more efficient, while employees report little to no meaningful time savings. In a Section survey cited by the WSJ, two-thirds of nonmanagement employees said AI saves them less than two hours per week (or no time at all), while more than 40% of executives reported saving more than eight hours per week.

This gap is more than a perception issue. It’s an early warning signal that many organizations are missing a critical component in management development: a practical architecture for adoption—where management behavior, day-to-day workflow, and technology reinforce each other.

Why executives feel the AI upside (and employees don’t)

The WSJ findings point to something leaders rarely say out loud: AI tends to benefit different layers of the organization in different ways.

Executives spend a large portion of their time synthesizing information, drafting communications, preparing talking points, scanning updates, and making sense of ambiguity. Those tasks are highly “AI-friendly.” When AI accelerates these activities, the gains can feel immediate and substantial.

Employees, meanwhile, often operate closer to the constraints of execution: system dependencies, handoffs, compliance requirements, data quality issues, customer exceptions, and cross-functional coordination. In these contexts, AI can be helpful—but it also introduces new work: validating outputs, correcting context, and applying judgment. Workday research has described this as an “AI tax,” where time saved is partially offset by review and rework.

In other words, the organization can experience AI as both “everywhere” and underwhelming at the same time: leaders see speed at the top, while employees feel friction in the middle of the work.

The management development gap hiding inside the AI gap

Most management development programs still over-index on one domain: people management.

We’ve spent the last decade building capability in listening, coaching, emotional intelligence, and feedback. These skills matter—and without them, teams lose direction, engagement erodes, and performance suffers.

But in today’s environment, people skills are no longer sufficient. Managers are being asked to deliver results in a faster, more fluid, more technology-mediated workplace. That means managers must develop in three distinct areas:

1. People managers — building trust, clarity, coaching, and accountability
2. Process managers — designing the operating rhythm of execution
3. Technology managers — using tools (including AI) to improve how work happens

This People–Process–Technology (PPT) framework is useful because it explains why the AI value gap persists. When manager development focuses primarily on interpersonal competence, organizations create managers who can communicate well—but who may not be prepared to redesign work, reduce friction, and integrate new tools in ways that actually produce productivity.

The key question is not whether managers can learn AI. It’s whether managers can translate new capabilities into work systems that deliver outcomes—without alienating, confusing, or overwhelming the people doing the work.

When the answer is “not reliably,” you get the symptoms we’re seeing now: executives believe they’ve unlocked value, while employees experience uncertainty, rework, and “extra work” disguised as innovation.

What managers need to master in process and technology

If people skills are the foundation, process and technology are the multiplier. They are also the missing links in many management development programs.

Process: managers as designers of execution

Managers need practical fluency in the routines that make work move. This includes:

• Performance management cadence: weekly one-on-ones, observation/coaching moments, mid-year and year-end performance conversations, and development planning (their own and their team’s)
• Meeting design and facilitation: not just running meetings, but choosing the right meeting type, clarifying outcomes, and ensuring decisions and follow-through
• Project and workflow management: aligning roles, dependencies, sequencing, escalation paths, and handoffs
• High-stakes communication: framing decisions, synthesizing input, and creating shared clarity under time pressure

These are not “extra skills.” They are the mechanics of managerial leverage.

Technology: managers as accelerators of workflow

Managers also need to be competent stewards of the tools that shape modern collaboration. Importantly, this is not about teaching people how to navigate enterprise software. It’s about using technology to reduce friction and improve thinking.

That includes:

• AI as a thinking partner: accelerating drafts, synthesizing inputs, preparing decision briefs, generating options, and improving clarity—while maintaining human judgment and accountability
• Live collaboration tools (e.g., Miro) to improve co-creation and speed alignment
• Continuity tools (e.g., Microsoft Loop) to reduce meeting-to-meeting drift and keep decisions, artifacts, and actions connected
• Team “operating systems”: simple norms and templates embedded in the tools people already use (Teams/M365) so good management practices are easier to execute than to ignore

Tools will continue to evolve. The manager’s job is not to become an IT department. It’s to make smart choices about how tools will be adopted and integrated into real work—so the technology creates lift rather than noise.

The real takeaway for executives

The executive/employee AI disconnect is telling you something valuable: your organization is adopting tools faster than it is adopting the management system required to convert tools into outcomes.

When management development is redesigned as a People–Process–Technology architecture—with transfer embedded into workflow—AI becomes less of a cultural argument and more of a practical capability multiplier.

If you want managers who are genuinely prepared for the AI-enabled workplace, don’t start by asking, “Are we using AI?”

Start by asking: “Have we built the management operating system that turns new capacity into better decisions, better execution, and better outcomes?”