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AI is moving from pilots into daily work, and the accelerated pace of AI disrupts the traditional operations. The leadership question is no longer whether people can access the tools. The question is whether the organization can absorb faster insight and turn it into better action.

That distinction matters.

Many organizations are scaling AI faster than they are redesigning work. Employees can produce analysis, summaries, recommendations, drafts, and comparisons with less effort. The visible speed of work increases. But the decisions that follow often still move through the same unclear ownership, alignment meetings, approval layers, and delayed customer feedback.

This is where the operating model becomes visible.

AI increases the speed of insight, but the operating model still determines how quickly people can act on it. If authority remains distant from the work, if teams lack the competence to resolve issues locally, or if feedback reaches decision-makers too late, faster analysis creates limited value.

In some cases, it increases organizational load.

More insight creates more decisions to absorb. More options create more alignment work. More visibility creates more escalation. The organization becomes better informed without becoming more adaptive.

This is why AI is now disrupting operating models.

Microsoft recently reported that Accenture is rolling out Microsoft 365 Copilot across roughly 743,000 employees, the largest enterprise Copilot deployment to date. In Accenture’s earlier 200,000-user cohort, Microsoft reported that 97% of employees said they completed routine tasks faster and 53% reported significant productivity and efficiency improvement. Reuters also covered the rollout as Microsoft’s largest Copilot enterprise deal, while noting broader market skepticism about whether AI investment is yet translating into measurable productivity gains.

That tension is important. AI can make tasks faster. It does not automatically make the business better.

The real bottleneck moves

When analysis was slow, many organizations could mistake information delay for organizational delay. Decisions took time because data was incomplete, reporting was manual, or preparation required effort. AI removes part of that explanation.

If insight now arrives faster and action still does not follow, the constraint becomes harder to ignore.

The bottleneck moves from information production to organizational absorption.

Absorption is the organization’s ability to convert insight into changed work, better decisions, customer response, and economic impact. It depends on how work is designed. It depends on where authority sits. It depends on whether teams have the knowledge and competence to act. It depends on whether customer feedback travels fast enough to shape decisions while those decisions still matter.

A service team may use AI to identify recurring customer issues faster. But if the team cannot change the underlying process, adjust the response model, or influence product and operations, the insight becomes another report.

A commercial team may use AI to detect demand shifts earlier. But if pricing, supply, finance, and customer commitments remain split across several approval paths, the decision still waits for alignment.

An operations team may use AI to improve planning quality. But if exception handling remains centralized, managers become arbitration points and cycle time does not improve.

The pattern is consistent. AI accelerates the signal. The operating model determines whether the signal becomes action.

Why this becomes a performance issue

The greatest risk is no longer that AI fails to produce useful output. The risk is that useful output travels through structures that were not designed to absorb it.

That creates a practical performance problem.

Teams see more issues but cannot resolve them. Managers receive more escalations. Functions spend more time aligning interpretations. Customer-facing employees explain decisions they did not shape. Leaders are pulled into operational arbitration instead of system design.

The organization becomes busier.

This is the hidden cost of scaling AI into a fragmented operating model. Faster analysis does not remove coordination cost. It can expose and increase it. The more insight the system produces, the more pressure it places on decision ownership, workflows, feedback loops, and team capability.

IBM’s 2026 CEO research reinforces this point. IBM reports that 83% of surveyed CEOs say AI success depends more on people’s adoption than technology. It also reports that organizations redesigning five core business areas are four times more likely to have delivered on business objectives.

The implication is clear. AI value depends on organizational redesign, not tool deployment alone.

McKinsey’s 2025 State of AI research points in the same direction. It identifies management practices across strategy, talent, operating model, technology, data, adoption, and scaling as essential to capturing AI value. Earlier McKinsey research also found that organizations are beginning to redesign workflows and create structures intended to generate future value from generative AI.

This is the emerging executive challenge. AI adoption is becoming broad. AI absorption remains uneven.

The organization must change with the work

AI changes how work gets done. That means the organization around the work must also change.

This does not mean every company needs a radical restructuring. It means leaders need to examine where AI changes the flow of work, decisions, and customer interaction.

• Where does AI create new insight?
• Who can act on that insight?
• What decisions still require escalation?
• Which teams have the competence to resolve issues locally?
• Where does customer feedback arrive too late?
• Which management layers are adding capability, and which are adding delay?

These are operating-model questions.

The strongest organizations will not only train employees to use AI. They will redesign the conditions under which employees use it. They will simplify decision ownership before scaling. They will reduce unnecessary handoffs. They will move knowledge, competence, and authority into the teams doing the work.

That last point is critical.

If teams are expected to act faster, they need more than tools. They need context. They need decision rights. They need access to relevant data. They need the competence to judge trade-offs. They need feedback from customers and operations quickly enough to learn.

Without that, AI creates a stronger signal into a weak system.

Leadership work shifts

AI changes the role of leadership because it changes where the constraint sits.

When information was scarce, leaders often added value by requesting analysis, comparing options, and making decisions after escalation. When information becomes more abundant, that model becomes less effective. The leadership bottleneck moves from access to insight toward design of the system.

Leaders need to focus less on arbitration and more on whether the operating model is improving.

• Are teams resolving more issues without escalation?
• Is time-to-action improving?
• Are decisions reopened less often?
• Is first-pass quality increasing?
• Is customer feedback reaching the teams that can act on it?
• Is cash impact visible?

These questions matter more than AI usage alone.

High adoption can coexist with weak performance. Employees may use AI frequently while decisions still wait. Teams may produce better work faster while customer issues remain unresolved. Reports may improve while cycle time does not.

That is why AI programs should not be judged only by licenses, prompts, usage, or enthusiasm. Those indicators show activity. They do not prove absorption.

The more important test is whether the organization becomes faster at learning and acting.

What to redesign first

The starting point is not a full operating-model transformation. It is a focused diagnosis.

First, identify where AI changes how work gets done. This means looking at real work, not abstract use cases. Where does AI change preparation, analysis, customer interaction, exception handling, planning, service response, or decision-making?

Second, simplify decision ownership before scaling. AI should not be added to workflows where ownership is already unclear. Faster insight will only expose the ambiguity faster. Leaders should remove overlapping mandates, clarify who can act, and reduce unnecessary escalation paths.

Third, move knowledge, competence, and authority into the teams doing the work. This is where many organizations will face the harder design choice. Some roles that previously existed to review, approve, or coordinate may need to become part of team capability. Managers may need to shift from being decision checkpoints to becoming capability builders, standards owners, or expert contributors.

Fourth, measure whether absorption is improving. Time-to-action and first-pass quality are useful starting points. So are reopened-decision rates, escalation frequency, customer feedback latency, cycle time, and cash impact.

These measures reveal whether AI is changing performance or only increasing activity.

The board question

Boards should also adjust their AI oversight.

The question is not only whether management has an AI strategy, a governance model, or a risk framework. Those remain necessary. But they are incomplete.

The board should ask whether AI is improving how the business operates.

• Where is AI reducing cycle time?
• Where is it improving first-pass quality?
• Where is it shortening customer feedback loops?
• Where is it reducing escalation?
• Where is it improving margin, revenue, working capital, or capital efficiency?
• Where is it creating more activity without measurable performance improvement?

This reframes AI from a technology investment to an operating-model test.

A company can have strong AI pilots and still have weak absorption. It can deploy tools widely and still leave decision rights unchanged. It can increase analysis speed while customer response remains slow. It can report productivity gains while the economic impact remains unclear.

The board’s role is to press for the link between AI adoption and business performance.

The strategic implication

AI does not remove the need for operating-model discipline. It increases it.

In coherent systems, AI can strengthen team capability, reduce information asymmetry, shorten analysis cycles, and accelerate learning. In fragmented systems, AI exposes unclear ownership, delayed feedback, repeated alignment work, and authority that sits too far from the work.

This is the reason AI is disruptive beyond technology.

It changes what organizations can see. It changes how quickly they can produce insight. It changes the pressure on teams, managers, and leaders to respond. But the economic value still depends on whether the organization can act.

The leadership responsibility is therefore structural.

Do not only scale the tool. Redesign the work around it. Simplify decision ownership. Move knowledge, competence, and authority into the teams doing the work. Measure whether insight becomes action faster and with better quality.

The organizations that benefit most from AI will not be the ones with the most activity. They will be the ones that absorb new insight into the way the business operates, serves customers, and makes money.

The leadership question is simple:

Where does AI expose operating model friction in your organization?

👉 If you want to increase your impact as you introduce AI, then let’s have a conversation.

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