Did IDC’s 20% Command-and-Control Prediction Hold?

In October 2024, IDC FutureScape: Worldwide Future of Work 2025 Predictions forecast that by 2026, organizations relying exclusively on command-and-control leadership would see a 20% drop in profitability because of a lack of AI innovation and adaptability. Two years later, Amy Loomis, Group Vice President, Workplace Solutions at IDC, has tested the idea behind it against IDC’s own survey data.

The survey measured AI returns, not profit, so it does not confirm the 20% figure directly. What it does show is a clear gap in how leaders and laggards approach AI, and it starts with what they do with the time AI frees up.

Why did IDC predict a 20% drop for command-and-control leadership?

Loomis defines command and control as strategic decisions made by senior management with little or no input from workers, a fully top-down model. IDC made the prediction because more than half of the companies investing in generative AI at the time said it was disrupting their business. Treating AI like a standard technology rollout, she says, would bring surprises.

The more successful companies took a different path. They gave employees a say through pilots and experimentation, and they collected feedback, which let adoption move faster.

Did the prediction hold?

Two years on, Loomis wanted to see whether that pattern showed up in the data. She put the prediction to a test by splitting IDC’s Future of Work survey by the ROI organizations reported on their AI investments. About 15% of respondents were leaders, with an ROI of 50% or higher (174 of roughly 1,145). Another 38% were achievers, working their way up, and 47% were laggards, with no ROI or no tracking yet.

Loomis is careful about what the split shows. Command-and-control organizations are not simply unprofitable, she says. Chasing the low-hanging fruit that can be made more efficient at the outset gives them false momentum, and they can “win the battle and lose the war” if no one looks at the long-term impact on the organization.

What separates AI leaders from laggards?

The gap between the groups starts with what organizations do once AI makes them more efficient. Loomis explains that leaders asked what to do with the free time that efficiency creates. They could have raised the bottom line or cut headcount, and laggards predominantly focused on profitability and efficiency. Although she says profitability and innovation do not have to be in opposition, leaders redirected the time into new work practices and more innovative products.

That emphasis extended to people. Leaders invested across the whole life cycle, including entry-level employees, so every level was upskilled with the same tools. She points out that agentic work requires the whole workforce, and the survey gaps she describes show how far apart the groups are:

A balanced approach that includes every level of the organization: 45% of leaders versus 25% of organizations seeing no ROI.
A fully operational center of excellence with collective governance: 63% of leaders versus 15% of laggards.
78% of leaders could point to a concrete measure of the relationship between employee experience and customer experience over the past 12 months, compared with 29% of laggards.
Leaders were also more than 10 percentage points likelier than laggards to be building new roles for agentic AI.

The metrics they track point the same direction. Productivity scores ranked near the top for organizations working toward high profitability. Leaders ranked lowest on that measure and highest on skills proficiency and teaming behavior, which Loomis reads as a long view that aligns the people side of the equation with the technology side.

Why does stronger governance bring more AI freedom?

The center of excellence gap points to a pattern: companies with the most formal AI oversight also give AI the most freedom. Loomis describes a balance: strict guardrails cover deterministic functions, such as preventing identity fraud and prompt injection. Inside those limits, leaders leave space for innovation. They pair human or AI governance with rising levels of AI autonomy.

When trusted people and processes sit alongside IT guardrails, she says, organizations can give AI more autonomous purview in a gated, guarded way and innovate at scale. That takes calibration and repeated testing, because hallucinations are real and managers vary in quality. It also depends on collecting the right data to measure. IT holds a primary role in that governance, she adds, alongside collective decision-making across functions.

Will command-and-control models keep declining?

Loomis does not expect command and control to fade in a direct, linear way as AI spreads. Boards are pressing leaders for results and bottom-line growth, and she describes the mood as full speed ahead. She expects spurts of command and control to complete specific tasks or initiatives.

Over time, she is fairly confident, a more moderated, federated model will become the key to success. Organizations that show they can grow at speed and scale by investing in people and governance with a long-term view will, she expects, set new standards of leadership. Those standards could be adopted much as command and control became the standard for the industrial era.

What should leaders watch next?

The emphasis on measuring the right things carries into her 2027 research. Loomis notes that her FutureScape 2027: Human-Agent Workforce Strategy predictions focus on the how of the shift. Organizations will need to measure, monitor, and manage the relationship between human workers and agents, which she calls a cornerstone of 2027 planning.

She also points to how people learn and share skills, and how new entrants prepare for agentic ways of working, which she calls a work in progress. Loomis describes the work as balancing AI as a tool with AI as a component of how work gets done and how success is measured. Tokenization is on the list too: weighing the whole investment against the whole return from these tools.

The takeaway: pair your AI strategy with an organizational plan

Those 2027 questions build on what leaders are already doing. Loomis adds that every technical adoption strategy needs an organizational parallel, and in the survey, leaders paired the two. They were likelier to create new roles and redesign human and AI roles in the same exercise, and they retrained and upskilled far more than others.

Leaders also connected functions. They asked how those functions link, such as internal ERP data and external CRM data, or accounting and HCM applications. AI suits those interconnected workflows well, Loomis says, so a practical starting point is to map where functions that often operate in isolation depend on each other, alongside a plan for the roles and skills involved.