Rethinking AI adoption: Why artificial intelligence breaks traditional IT rollout playbooks

Businesses everywhere are eager to deploy artificial intelligence (AI) across their operations. However, despite decades of experience delivering complex technology projects, many organisations are discovering that traditional rollout playbooks simply do not work for AI.

Bridging the gap between the promise of AI and its practical adoption requires a fundamental shift in how leadership teams approach technology strategy, user expectations, and vendor partnerships.

The core dilemma: A solution looking for a problem

Unlike traditional software rollouts, AI breaks the standard technology adoption cycle:

• Traditional IT projects: Built around a clear, one-to-one relationship between a specific operational problem and a technical solution (e.g., migrating to a new CRM or upgrading a firewall).
• AI deployments: Deliver broad, abstract benefits, such as "increasing efficiency" or "accelerating knowledge access", without solving a single, clearly defined workflow challenge out of the box.

AI adoption closely mirrors the historical challenges of workflow automation. To successfully implement automation or AI, leaders must look beyond high-level strategy and immerse themselves in the day-to-day workflows of individual employees. Because the capabilities are so vast and continuously evolving, businesses face a "chicken and egg" scenario: users often need access to the tool first to discover what specific operational problems it can actually solve.

Managing hype and avoiding adoption slumps

A major hurdle in rolling out AI across an organisation is managing expectations among end users.

AI technology is often hyped as a universal fix for workplace friction. When a firm deploys an AI tool without clear contextual guidance, end users quickly realise it does not magically solve all their tasks. This mismatch causes initial engagement to plummet.

While every business has "AI champions", enthusiastic staff members who experiment with tools in their personal lives, their enthusiasm is rarely universal. A successful adoption strategy cannot rely solely on early adopters; it requires structured guidance tailored to the wider workforce.

The disconnect between business intent and technical execution

A subtle structural disconnect frequently occurs when corporate management and technical teams sit down to discuss AI:

• The business strategy: Management states, "We want to adopt AI across the company."
• The technical response: IT or an IT-managed service provider (MSP) replies, "We can enable the technology and secure it safely."

Notice what is missing from this exchange: a discussion about specific business problems.

Because technical teams focus heavily on governance, licensing, and security controls, the conversation rarely digs into how the technology will reshape daily operational workflows. Enabling the tool is straightforward; identifying where it adds tangible commercial value is where most organisations stall.

The evolving role of the modern MSP: From technical validation to business analysis

Traditionally, businesses look to their IT managed service providers to validate whether a tool works technically, ensure cyber security, and monitor infrastructure. To guide clients through AI adoption, however, the modern MSP must step out of a purely technical role and act more like a business analyst.

The shift looks like this:

• The traditional MSP role: Focuses on technical setup, system maintenance, licensing, and cybersecurity validation.
• The modern AI partner: Focuses on deep workflow analysis, understanding business friction points, and applying industry-specific context.

To drive meaningful transformation, an MSP must:

• Understand internal workflows: Deeply examine operational bottlenecks within specific departments.
• Provide peer intelligence: Offer guidance on how similar firms within the same sector are successfully applying AI.
• Bridge the gap: Combine deep business analysis with technical expertise to evaluate which AI use cases are viable and which are impractical.

Because generalist MSPs serve a wide variety of industries, many lack the specialised domain knowledge required to consult at this non-technical level. For financial services and fund management firms, successfully navigating the AI journey requires partnering with providers that possess hyper-specific industry expertise, helping translate broad technological power into measurable commercial success.