Enterprise AI Adoption Requires Connected Leadership Steering

How leaders working together with a simple Map → Change → Learn loop can turn scattered AI experiments into living, organisation-wide capabilities.

Most leadership teams are built for vertical responsibility. Finance leaders look after finance, tech leaders run tech, product leaders focus on product. When something big and cross-cutting comes along, especially something transformative for the organisation, the usual fix is to set up a steering group or working committee, drop in a few cross-functional meetings, and hope the pieces fit together in the end.

But Enterprise AI doesn’t work like that.

The pace is faster, the terrain is shifting, and adoption rarely starts in neat, centrally planned programmes. Echoing we theme we touched on last week, making the underlying technology available to people is just the beginning - the real value will be created in its application. It might bubble up in hotspots from individual experimentation, team-level automation, or a department trialling a new AI-powered tool. Some use cases will be officially sanctioned; others could emerge outside formal guidelines. All of them generate valuable learning; but in most organisations, they remain disconnected.

The challenge is that no single function can see the whole picture. What’s working in one corner may never be known in another. Hard-won lessons are lost, good ideas fail to spread, and pockets of innovation stall before they have a chance to influence the wider group.

To make AI adoption work at the leadership level, we need something different: a form of lightweight, distributed coordination. A way for leaders across functions to share context, surface what’s working (and what’s not), and build a shared organisational memory without slowing the work down or creating yet another bureaucratic layer. Leadership as a connected network, not a set of isolated silos.

This form of coordination creates a living map of AI activity, links innovation hotspots into a coherent network, and accelerates progress. It’s the difference between a scattering of pilots that fade away and an evolving, organisation-wide capability that gets sharper with every cycle.

From Digital Leadership Groups to AI-Era Coordination

A key technique we have used over the years in transformation projects is the establishment of Digital Leadership Groups (DLGs) to provide cross-functional coordination of all technology capabilities that are bought, built or rented. The principle is simple: bring together a cross-section of leaders and emerging talent, give them shared visibility of digital initiatives, and make them collectively responsible for steering progress guided by a shared roadmap and strategy.

DLGs work because they replace one-way reporting with peer-to-peer connection. They create a forum for exchanging practical experience, aligning on standards, and spotting opportunities that would never have emerged inside a silo. And over time, they build their own culture of mutual support and shared problem-solving.

In enterprise AI, the same need for connected leadership remains, but the context is faster, messier, and harder to track. AI adoption doesn’t move in neatly planned phases. It happens in bursts:

• An individual experimenting with a prompt library in one function

• A small team automating a recurring task

• A department trialling an AI-powered decision tool

• A central platform team deploying enterprise-wide AI infrastructure

Without a way to knit these efforts together, they risk becoming an archipelago of disconnected islands of experimentation. And without gathering all the lessons learned, we cannot improve our context management and reinforcement learning across the organisation.

We are therefore evolving the DLG playbook to become a distributed coordination system that links these islands of practice into a real-time learning network by:

• Embedding shared context into the flow of leadership work

• Linking emerging “hotspots” of experimentation

• Building a living memory of what’s been tried, learned, and achieved

Where DLGs are about steering digital transformation, the focus here is on accelerating AI adoption by connecting leadership intelligence across the organisation.

Lightweight System Design

The power of distributed leadership lies in its ability to connect people without bogging them down in process. For AI adoption, the coordination system needs to be fast, simple, and embedded in the way leaders already work.

The goal is to enable shared context, collective memory, and peer-to-peer coordination without adding a new layer of bureaucracy. Where possible, the system should surface patterns and connections automatically, using AI to capture and link insights so leaders don’t need to stop their work to contribute. It’s one part of a wider AI adoption framework, alongside leadership operating systems and distributed change models.

1. Build a Shared Space, Not a New Committee

• Create a digital workspace - this could be a private AI-enabled wiki, a shared Teams/Slack channel, or a central prompt library - where leaders can post updates, capture learnings, and flag opportunities.

• Use AI to auto-summarise updates, catalogue them and surface connections between related work.

2. Keep Updates Short and Frequent

• Encourage leaders to share brief, in-flow updates rather than waiting for formal reports.

• AI can handle formatting, tagging, and linking, so leaders just focus on sharing the substance.

3. Map the Hotspots

• Use AI-assisted surveys or interviews to quickly identify where AI experimentation is already happening, both official and unofficial.

• Create a visual map so everyone can see where activity is concentrated and where there are gaps.

4. Capture and Reuse Patterns

• When an experiment works, turn it into a repeatable pattern: what was done, how, and under what conditions.

• Store these in the shared space so others can adapt them without starting from scratch.

5. Make Coordination an ‘in-the-Flow’ activity

• Pair regular leadership rhythms (weekly or monthly check-ins) with AI-generated summaries of activity since the last meeting.

• Ensure every conversation starts with a shared, up-to-date view of the whole system, not just individual silos.

This design keeps the system light on ceremony and heavy on visibility. Leaders don’t have to change the way they work; they just plug into a network that makes their work visible to others, and vice versa.

Embedding the Map → Change → Learn Loop

In an AI-enabled coordination system, leaders don’t just share insights, they effectively map, change, and learn together. This continuous loop keeps the coordination lightweight yet generative.

• Map: Establish a shared picture of AI activity and emerging capabilities cross the organisation: where experimentation is happening, what use cases are emerging, who’s involved, and what’s working or failing. This live “map” of hotspots ensures leadership remains aware without waiting for formal reporting, and includes individual, unofficial uses where people are finding value on their own. Over time, the loop connects these hotspots into a coordinated, organisation-wide capability.

• Change: Acting on that map, leaders make informed decisions, launching pilots, surfacing successful prompts or workflows, sharing effective playbooks across teams, and adapting tactics intelligently in real time.

• Learn: Every outcome - new insight, failed experiment, repeatable pattern - gets captured into the shared memory. AI summarises what’s been tried, what we’ve learned, and recommends what to do next. Then the map updates, and the cycle continues.

This loop elevates coordination from static meetings into a dynamic, strategic competence. It turns leadership into a learning system, with AI helping to structure what emerges naturally.

The Map → Change → Learn pattern aligns seamlessly with the three pillars we've outlined:

• Mapping reinforces shared context.

• Acting builds collective memory.

• Learning together strengthens peer-to-peer coordination.

Shifting leadership in this way, away from rigid committees and toward living coordination in the flow of work, is powerful precisely because it's practical, intuitive, and resilient.

So how do you turn this lightweight coordination system into a living, self-improving capability? We’ve boiled it down to a three-step leadership loop - simple enough to fit in a slide, powerful enough to rewire how leaders see, decide, and learn together.

Let’s explore.