Why AI Infrastructure Investment Matters, Even for SMEs

Small and medium-sized enterprises (SMEs) account for 99.85% of all private sector businesses in the UK. Yet when discussions turn to Artificial Intelligence (AI), many business owners still assume that the real investment and opportunity sit with large corporates that have vast budgets, dedicated IT teams and sophisticated technology platforms.

The reality is very different.

Today, the AI tools available to an SME are often the same tools available to a multinational organisation. Whether it’s Microsoft Copilot, ChatGPT or another AI platform, access to the technology itself is no longer the barrier it once was.

The real differentiator is not the AI tool. It’s the infrastructure behind it.

Many businesses believe they have already invested in AI because staff use it to draft emails, summarise meetings or create social media content.

Whilst these uses can provide valuable productivity gains, they only scratch the surface of what AI can offer.

Buying an AI licence is not the same as building AI capability.

The organisations seeing the greatest return from AI are investing in the foundations that allow it to work effectively. Those foundations include organised information, documented processes, accessible knowledge, strong governance, automation-ready workflows and people who understand how to use the technology effectively.

One of the most common misconceptions is that AI only benefits “knowledge workers”.

In reality, every business relies on knowledge.

A manufacturing company may manufacture products. A construction company may build houses. A farm may grow crops. A retailer may sell goods.

Yet every one of those businesses still has finance, HR, marketing, compliance, reporting, customer communications and operational planning to manage.

These areas are rich with information and processes, making them ideal candidates for AI-assisted productivity improvements.

The question is not whether your business uses knowledge. It is whether that knowledge is organised in a way that AI can use effectively.

In our own experience, we have found that a useful test of any process is whether someone with little or no prior knowledge can follow it successfully. That is one reason why we have involved a recently recruited school-leaver Audit Apprentice in reviewing and refining parts of our audit process documentation.

If a new starter can understand and follow a process, it is usually a strong indication that the process has been documented clearly. The same principle often applies when preparing a business for AI.

AI is remarkably effective at interpreting and analysing data, but only when that data is structured appropriately and readily available.

Many businesses have spreadsheets and reports that have evolved over years and still resemble digital versions of paper-based analysis sheets. Information may be spread across multiple tabs, manually formatted, heavily reliant on colour-coding or arranged for visual presentation rather than analysis.  Data is also commonly stored on local servers that cannot be readily accessed by AI.

When AI struggles to acess and interpret poorly structured information, the technology is often blamed.

In reality, the issue is frequently the data, not the AI.

Just as poor-quality ingredients produce poor-quality results in cooking, poorly structured information limits the value AI can provide. Investing in data quality and structure is often one of the most valuable AI infrastructure investments a business can make.

Technology continues to evolve at an extraordinary pace.

New AI capabilities are being introduced regularly across familiar business applications including Outlook, Excel, Word, PowerPoint and Teams. Organisations that want to benefit from these innovations need to invest time in learning what is available and understanding where it can deliver value.

In my own role, I spend time almost every day researching developments within Microsoft’s AI ecosystem, watching demonstrations, reviewing updates and exploring how new capabilities could be applied in practice. As a regulated professional services firm, we place significant emphasis on keeping our data within our Microsoft 365 environment, making Microsoft’s continued investment in AI particularly relevant to our business.

This ongoing learning is part of AI infrastructure too.

Without investment in people, training and knowledge sharing, even the best technology will fail to deliver its full value.

“If I had asked people what they wanted, they would have said faster horses.”

If you don’t understand what AI can truly do, you’ll only ever use it to make small improvements to old ways of working – a faster horse instead of a fundamentally new way of moving forward.

Real value comes from learning its capabilities properly, because only then can you reimagine what’s possible rather than just optimise what already exists.

For SMEs, AI infrastructure is not about building data centres or spending millions on technology.

The software may be the visible part of the investment.