The 4 Biggest Challenges to AI Adoption – And How to Tackle Them

Everyone is talking about the transformative power of AI, but research suggests that many Scottish SMEs are struggling to onboard the next generation of intelligent systems. In fact, a recent paper published by Sharp Europe found that 55% of small business leaders feel that their business is not utilising AI as much, or as effectively, as it should be.

We recently published a practical guide to AI adoption, which aims to close this adoption gap by equipping you with a detailed blueprint for successful enablement. We’ll also be talking about the best ways to map out and experiment with AI in future blog posts too, but we wanted to start by looking at some of the hurdles that prevent or complicate AI adoption for Scotland’s SMEs .

Comprehensive research published by the UK Government’s Department for Science, Innovation and Technology points to four core barriers that are present across most industries and sectors These are:

• Ethical concerns
• Concerns about cost
• A lack of skills and training
• Confusion around products and/or platforms

If these sound familiar, read on. AI adoption can be time-consuming, but the returns are considerable and learning to overcome common pitfalls is key to unlocking the full potential of AI tools. Below, we explore each challenge in detail, and map you towards effective solutions. 

1. The ethical dilemma

Ethical concerns around AI adoption are complex, but McKinsey & Company’s excellent piece on putting AI ethics into action does an excellent job of answering common complaints.

To paraphrase, questions around the morality of AI adoption and/or the impact it will have on people’s livelihoods are both existential and deeply personal in nature but carefully considered projects, implemented with the proper guardrails and managed by a well-informed team, shouldn’t raise any significant ethical concerns.

In our experience, AI tools empower staff instead of replacing them, and teams who make considered use of AI quickly come to realise that it significantly enhances productivity.

Similarly, the best antidote to fears about data security is, ultimately, an initiative that chooses platforms with great care. The overwhelming majority of problems involving the disclosure of sensitive or privileged problems occur when staff onboard and use AI tools without proper oversight, which is why a robust adoption strategy is vital.

2. Balancing financial concerns

Many SMEs are concerned about the cost of AI adoption. Improperly governed projects, or projects that aren’t scoped out correctly, can snowball in record time.

According to research published by SmartDev, the median total cost of an AI adoption project is around $55,000 (£40,743) for SMEs, but real-world costs often rise to $100,000+ (£75,228.17) when business owners don’t plan for or adopt AI with due care and attention.

The best remedy? Well-planned pilot studies, careful tool selection and a phased adoption plan that allows you to pace training and licensing costs. Taking the time to put proper governance in place can also cut down on (or completely eliminate) the unforeseen legal costs that can hit mismanaged adoption projects, which is why this document is very careful to provide a robust AI governance framework.

3. Skill gaps

In our experience, AI adoption projects are as much about people as they are about cutting-edge technology. Investing in staff training; communicating the scope and potential of your initiative and bringing your team in on the vision is key to realising the true potential of artificial intelligence.

This is a lofty and roundabout way of saying that every AI adoption project has a training and upskilling element. Whether it’s teaching staff how to use database retrieval tools, or coaching team members on the right way to vet and use the output of generative pretrained transformers, you need to invest in building human capability alongside the technology itself. That means carving out time for structured learning, creating space for experimentation and mistakes, and establishing shared norms around when to trust AI output and when to push back on it. 

Staff will also need to understand the vulnerabilities created with AI, and the way your chosen AI platforms try to guard against serious security breaches. While proper planning and implementation minimises security risks, you’ll still want staff to understand the limitations of your AI toolkit, and the potential for harm.

4. Option paralysis

The last and most addressable reason for stalled AI adoption is the size and scale of today’s marketplace. The intersect between AI technology and business growth is crowded with vendors clamouring for attention and most Scottish business owners are inundated with a steady stream of ads for ‘must have’ AI products.

Unsurprisingly, most of these products are positioned as urgent and transformative, but this is rarely the case. It’s important that you and your senior leadership learn to separate signals from noise: drilling down on products that match the specifics of your business and ignoring offers for products that will not mesh with the way you work.

As an example, businesses with significant supply chain or forecasting issues shouldn’t be jumping at agentic HR tools, or marketing automation software, but we’ll talk more about tool selection in upcoming articles. For now, it’s enough to know that tool selection is complicated, and the sheer volume of options can complicate enablement initiatives.