AI Spending Is Rising. Leadership Is Not. The Real Gap Is How Work Gets Run

Recent research suggests a distinct tension. About one quarter of communications leaders plan to allocate more than 10% of their budget for AI, but 88% feel unprepared to lead an AI transformation (Boston Consulting Group, 2026; Dubner, 2026).

This is not a contradiction. It is a signal.

Expenditure is advancing more rapidly than the capability of leadership.

And that gap now is becoming operational. This is not a technology gap. This is a gap in the leadership system.

When we analyse the root causes, the trends are similar.

Some are structural. Most importantly, leaders are unable to design an operating model, often lack the necessary AI skills, and are unclear in determining ROI (Boston Consulting Group, 2026).

Some are human. Employees do not quite individually trust or embrace the tools, and leaders who mean well can’t navigate that transition with confidence (Hawkins, 2026).

Some are strategic. Much of the potential benefits associated with AI remain unrealised, as many teams continue to treat AI as merely an enhancement of productivity rather than a change in how organisations structure decisions, workflows and accountability (Telum Media, 2026)

To put it simply, most organisations are attempting to embed AI in their current frameworks.

But in terms of how AI works, it is not a tool that you can simply "add." It transforms how work is decided, undertaken and assessed.

Training does not bridge the gap

On the surface, the issue appears to be a training problem. But the problem is deeper. The majority of executive education still divides two things that should not be divided:

Strategy is taught as guidance and placement .Tools, use cases or technical exposure are what practice is taught as

That split has worked in the past. It does not work with AI. Since AI perfectly lies within the decision execution gap. It goes on to dictate how decisions are made, how work flows and how results are measured.

Leaders know the fundamental value of AI, but training separates strategy from execution — they do not really understand how to operate it within the enterprise. This gap is why confidence remains muted despite investment rising (Boston Consulting Group, 2026).

The gap actually lies elsewhere. The AI knowledge level is not the only place where a gap exists.

But it lies in three places that rarely come together in training.

First of all, decision design.

Leaders do not know where to put AI at work and leave people to act, nor how that boundary will move over time.

Second, execution coordination.

There is little understanding of how when AI is introduced changing work flows. What happens in the meeting room and after everyone has left it.

Thirdly, value and responsibility.

Many leaders struggle to explain the usefulness of AI, how to measure it, and who is responsible for the results of partially automated decisions. (Telum Media, 2026)

These are not technical problems. They are management problems.

What would be needed instead?

If the aim is to close that gap in readiness, then teaching must change quite pragmatically. It should move from "seeing AI" to "operating AI" within a company. That means three shifts.

First, leaders have to practice deciding how the systems that make decisions are designed rather than just talking about strategy.

They have to think about how the introduction of AI will really change lower-level processes, not only the use case level but also which tool each operator uses.

And they have to work out how to translate AI into something people can measure of everyday value rather than as a way to forecast percentage increases in economic activity.

This kind of training is more like a model than words.

It forces choices. It lifts barriers. It makes leaders commit themselves.

Because after all, transformation by AI is not just knowing more. It is deciding differently under uncertainty and with new forms of capacity.

The gap actually lies elsewhere The AI knowledge level is not the only place where a gap exists. But it lies in three places that rarely come together in training.

First of all, decision design.

Leaders do not know where to put AI at work and leave people to act, nor how that boundary will move over time.

Second, execution coordination.

There is little understanding of how AI changes workflows when it is introduced. What happens in the meeting room and after everyone has left it.

Thirdly, value and responsibility.

Many leaders cannot explain why AI is useful, how to measure it, or who takes responsibility for the results of partially automated decisions. (Telum Media, 2026)

These are not technical problems. They are management problems.

What would be needed instead

If the aim is to close that gap in readiness, then teaching must change quite pragmatically. It should move from "seeing AI" to "operating AI" within a company. That means three shifts.

First, leaders have to practice figuring out how the systems that make decisions are designed rather than just talking about strategy.

They have to think about how the introduction of AI will really change lower-level processes, not only the use case level but also which tool each operator uses.

And they have to work out how to translate AI into something people can measure of everyday value rather than as a way to forecast percentage increases in economic activity.

This kind of training is more like a model than words. It forces choices. It lifts barriers. It makes leaders commit themselves. Because after all, transformation by AI is not just knowing more. It is decided differently under uncertainty and with new forms of capacity.

References

Boston Consulting Group (2026) – GenAI Adoption in Corporate Affairs & Communications- https://www.bcg.com/assets/2026/genai-adoption-in-corporate-affairs-and-communications.pdf

Dubner, R. (2026) – Corporate Comms Is Playing Catch Up on AI. A Few Leaders Are Showing the Way. https://www.bcg.com/publications/2026/corporate-comms-catch-up-on-ai-leaders-show-the-way

Hawkins, E. (2026) – AI’s adoption gap is becoming a communications problem https://www.axios.com/2026/03/26/ai-adoption-gap-employees-comms

 elum Media (2026) – Study Highlight: (Gen)AI Adoption in Corporate Affairs & Communications https://insights.telummedia.com/pr-news/study-highlight-genai-adoption-in-corporate-affairs-communications