The Distribution Layer of AI

Artificial intelligence has advanced rapidly. Its capabilities are no longer theoretical — they are practical, accessible, and improving at an accelerating pace.

Yet adoption, particularly across small and mid-sized businesses, remains limited.

This is not due to a lack of awareness. It is due to a lack of capability.

Most businesses do not have the internal expertise required to evaluate, implement, and maintain AI systems. They are constrained not by ambition, but by execution. The gap between what is possible and what is implemented remains wide.

The prevailing narrative suggests that innovation in AI will be driven by new models, new tools, and new platforms.

We take a different view.

The primary constraint on AI adoption is not technology. It is trust, distribution, and execution.

For the majority of businesses, technology decisions are not made internally. They are outsourced to partners who are already embedded in their operations — partners who understand their systems, manage their infrastructure, and are trusted to implement change.

In this context, the question is not “what can AI do?” It is “who will deliver it?”

Managed Service Providers sit at the centre of this dynamic.

They manage the technology environments of their clients. They control the software stack. They are responsible for uptime, security, and day-to-day functionality.

Most importantly, they are trusted.

This trust has been earned over years of consistent service delivery. It gives MSPs something that new entrants cannot easily replicate: the right to act.

They already have direct access to customers, deep integration into systems and workflows, and an ongoing service relationship.

This positions them uniquely to introduce, implement, and support AI solutions at scale.

Despite their strategic position, most MSPs remain under-optimised.

They are typically labour-intensive, reactive in nature, constrained by margins, and reliant on manual processes.

A significant portion of their cost base is tied to repetitive, low-value work — particularly Level 1 support.

At the same time, demand from customers is evolving. Businesses are increasingly seeking ways to automate workflows, improve productivity, and integrate AI into their operations.

This creates a structural mismatch: demand for transformation is rising, but the providers of that transformation have not yet evolved.

When AI is applied effectively within MSPs, two things happen simultaneously.

First, the internal cost base improves. Repetitive support tasks can be automated. Workflows become more efficient. Teams can shift away from reactive support toward higher-value activities.

Second, the revenue model expands. MSPs are no longer limited to managing infrastructure. They can become active partners in transforming how their customers operate.

This includes implementing AI tools, automating business workflows, and providing ongoing support and optimisation.

We are not building new models. We are not betting on speculative technology.

We are focused on application.

The tools required to drive meaningful change already exist. The challenge lies in selecting the right tools, integrating them effectively, and ensuring they deliver measurable outcomes.

This requires operational understanding, disciplined implementation, and continuous iteration.

Value is created not through invention, but through execution.

MSPs are our starting point. But the underlying thesis extends further.

Many essential service industries share similar characteristics: fragmented markets, labour-heavy operations, recurring customer relationships, and low levels of automation.

Industries such as accounting, strata and property management, and other service-based sectors are structurally similar. Each presents an opportunity to apply the same playbook.

Acquire strong businesses. Improve them through technology. Expand their capabilities. Compound their performance over time.

Traditional holding companies have focused on stability and preservation.

They acquire cash-flowing businesses, maintain operations, and rely on long-term compounding.

We believe the next generation of HoldCos will be more active.

They will improve businesses through technology, increase margins through operational leverage, unlock new revenue streams, and scale through disciplined acquisition.

They will not simply hold assets. They will enhance them.

We acquire high-quality service businesses and partner with strong operators to run them.

We apply a repeatable playbook: automate where possible, improve efficiency, introduce new capabilities, and support growth.

We combine disciplined capital allocation, operator-led execution, and practical application of AI.

And a HoldCo that compounds not only through acquisition, but through transformation.