Do You Need an AI Engineer in Every Team - or Just Better AI-Literate Employees?

Faced with the pressure to “do something” about AI, a lot of organisations reach for the same solution: hire specialist AI talent and hope they sort it out. Job boards are suddenly full of AI engineers, AI leads and prompt engineers for every function.

Sometimes that makes sense. Often, it is an expensive way to avoid the real issue: most employees simply do not yet know how to use the tools they already have.

The ‘hire our way out of it’ trap

Specialist AI hires can be incredibly valuable - when you actually need them. But hiring an AI engineer into, say, marketing or finance without a clear plan can create problems:

• They become a bottleneck for every AI-related question.
• The rest of the team assume AI is “someone else’s job”.
• When that person leaves, much of the capability leaves with them.

Meanwhile, your existing people stay stuck in old ways of working, and your AI strategy becomes a series of disconnected experiments rather than a change in how the organisation operates.

When you really do need specialists

There are genuine cases where specialist AI roles are essential:

• Building proprietary models or agents that go beyond off-the-shelf tools.
• Integrating AI deeply into core systems, like risk engines, pricing or logistics platforms.
• Managing complex data architectures and MLOps pipelines.

In these scenarios, you need engineers, data scientists and platform specialists with deep technical expertise. But even then, they are only part of the story. Their work still needs to be shaped by people who understand customers, products and operations.

The overlooked option: AI-literate employees

For most business functions, the bigger win is not “one AI engineer per team”, but everyone in the team being able to use AI confidently and safely within their role.

AI-literate employees:

• Understand what the tools can do for their specific workflows.
• Can turn vague problems into good prompts and iterative conversations with AI.
• Know how to check outputs, protect sensitive data and avoid obvious pitfalls.
• Spot opportunities to streamline processes and improve customer experiences.

This is not about turning everyone into a coder. It is about making AI a normal part of how work gets done, just as email and spreadsheets once were.

What AI literacy looks like in practice

In a typical organisation, AI literacy shows up in lots of small but powerful ways:

• A project manager uses AI to draft stakeholder updates and risk logs, then edits for nuance.
• A customer service team builds a shared prompt library for tricky queries.
• A finance analyst prototypes scenarios with AI before building a full model.
• A learning designer uses AI to generate first drafts of resources and adapt them for different audiences.

None of these require a resident AI engineer. They do require people who have had practical, role-specific exposure to what AI can do and how to use it well.

How an AI academy can rebalance the equation

Instead of scattering a few specialists across the organisation and hoping for the best, an AI academy model builds a broad foundation of capability and then layers depth where it matters.

A well-designed academy will:

• Provide baseline literacy for everyone who works with information.
• Offer role-based pathways for different functions and levels.
• Create communities of practice where people share what works.
• Identify and develop AI champions who can support their colleagues.

You may still choose to hire AI engineers and data scientists - but they will join an organisation that understands their work and can make use of it.

A practical way to decide what you need

Before posting your next AI job advert, ask three questions:

1. Is this genuinely a technical build problem, or a capability and change problem?
2. Have we equipped our existing people to use the tools already available to them?
3. Do we have a clear idea of the business outcomes this role or training investment should deliver?

In many cases, the honest answer will be that a broad, structured investment in AI literacy will deliver more value, faster, than a handful of specialist hires.

You do not need an AI engineer in every team. You need teams that understand AI well enough to ask for the right support, use tools in the right way and keep learning as the technology evolves.