At a glance
- Small businesses need to strategically embrace AI to continue thriving.
- Barriers to AI adoption range from cost considerations to process integration.
- Accountants can support clients with decision-making to get the best out of implementation.
AI is already a key strategic focus for SMEs, with an American Express survey revealing that half (49%) of businesses are integrating or planning to expand their use of generative AI and/or machine learning. It can offer immediate benefits, from cost savings to improved efficiency.
But in these still relatively early days of AI, small businesses can find it difficult to know where to start. A new report from Employment Hero claims that AI is the top challenge for small business leaders, with four in five putting it ahead of current talent shortages and even general economic pressures. (Employment Hero says its online survey covered 1000 business leaders, including senior executives, as well as 1200 employees.)
We spoke to experts about the barriers to adoption, and accountants’ role in demystifying AI for small business clients starting their journey.
Solve the blank page problem
AI Bridge Solutions is a consultancy that aims to connect other businesses with the right AI. CEO Kevin Quirk says the businesses that he talks with often find their biggest AI challenge is knowing where to start without being overwhelmed by the scale of change that AI can bring.
Quirk says that the most effective way for SMEs to begin is by focusing on specific, well-defined use cases where AI can relieve operational pressure.
“Start small, measure impact, and build confidence before expanding, he says. “Free or low-cost tools such as Microsoft’s Copilot, Google’s AI features in [Google] Workspace, or research assistants like Perplexity.ai can be great entry points without requiring major financial commitment.”
Tools such as these can help businesses and their accountants to uncover their larger AI opportunities. These tools can let businesses evaluate ROI, scrutinise vendor proposals for hidden costs or compliance risks, and ensure AI investments align with financial and data governance strategies.
“The key is treating AI as a business transformation tool, not just a technology upgrade. Success comes from marrying financial discipline with strategic thinking about where AI can genuinely add value.”
Tom Lorimer, Founder and CEO, Passion Lab
Quirk adds that asking vendors the right questions is vital if clients are to choose the right tools for them. “How is data handled and stored? Are there clear safeguards for compliance and security? Can the tool be easily integrated into existing systems without disruptive changes? And importantly, how is transparency maintained in how the AI works?”
Lead from the top
Small businesses often have tighter budgets than larger counterparts, and must sometimes make changes using the tools they already have. But eventually the advance of technology sometimes requires selecting entirely new technology.
“Becoming AI-ready starts with leadership,” says Aidana Zhakupbekova, CFO at expense management firm Rydoo. “Senior teams need to clearly communicate why AI is being adopted and ensure employees are properly trained to use it.”
Accountants have a key role in helping SMEs find tools that fit into their current processes and platforms, supporting smarter, more strategic decision-making.
Says Zhakupbekova: “The SMEs seeing real results from AI are those treating it as a strategic partner, an extended part of the team that requires collaboration, in-depth understanding, and deliberate execution.”
Businesses should choose technology that integrates seamlessly with existing systems, or try a phased rollout to minimise disruption and ensure a smooth transition. For Zhakupbekova, this isn’t just about efficiency; it’s also about making sure investments deliver value and drive long-term ROI.
Don’t overhaul completely
As managing director of small mortgage advice business Boon Brokers, Gerard Boon says he has seen both the promise and the practical hurdles of attempting to integrate AI into a small and developing business.
Boon Brokers has actively explored AI to improve client communication, streamline internal processes, and enhance content creation. “That journey has taught us a few things,” says Boon. He says the biggest barrier was the lack of in-house expertise.
But Boon adds that getting started didn’t require a full overhaul. “We began by identifying everyday bottlenecks – like time-consuming admin or repetitive queries – and testing low-cost tools to relieve them.”
Boon recommends platforms with AI features like Tidio or QuickBooks, which can offer real utility. He adds: “One of the most important lessons that we learnt was to tailor AI to your specific requirements. This roughly translates into the ability to tailor your AI tool of choice to better understand you, your requirements, and by proxy, your targeted clientele.”
“For regulated sectors like ours, compliance and data protection are paramount,” Boon says. Now his firm has a platform that suits its requirements, saves time across the board and shows a tangible ROI.
Boon doesn’t believe that AI needs to be revolutionary from day one. Instead, he says, firms can gradually expand their commitment, “taking measured steps that will free up time without compromising on quality.”
Establish clear success metrics
Finally, accountants should help clients establish clear success metrics before implementing AI, including developing frameworks for measuring productivity gains and time savings.
Most importantly, says Tom Lorimer, founder and CEO of Passion Lab, setting success metrics can help clients avoid “shiny object syndrome” by focusing on tools that genuinely improve bottom-line results. “The key is treating AI as a business transformation tool, not just a technology upgrade. Success comes from marrying financial discipline with strategic thinking about where AI can genuinely add value.”
“Focus on truly understanding one or two large language models (LLMs) – ChatGPT and either Perplexity or Claude, for instance.”
“After that, turn your attention to your data: understand where it lives, and spend time cleaning and organising it. That investment will pay dividends, especially as AI agents and model context protocols (MCPs) become more reliant on well-structured external data sources.” Model context protocols are standard connectors that link AI systems to people’s applications and data – the sort of connections we’re likely to see much more in the years ahead.
