The race to invest in AI has accelerated at a startling rate. The UK’s AI market, currently valued at £21 billion, is projected to soar to £1 trillion by 2035. With this level of investment, the demand for skilled talent has intensified, making AI-focused recruitment more crucial than ever.
The scope of AI-related roles is also expanding. According to McKinsey’s latest State of AI report, 13 per cent of respondents have hired AI compliance experts, while 6 per cent have recruited AI ethics specialists. This highlights the increasing demand for specialised expertise to help businesses navigate regulatory and ethical challenges, mitigate risks and maxmise their AI investments.
What to look for when hiring AI talent
While hiring AI specialists is critical, finding the right people is what truly determines success. Businesses must take a thoughtful approach in searching for candidates.
During the hiring process, it is important to distinguish between AI research and AI application – as these areas require very different skill sets. AI researchers focus on advancing the technology itself, while AI application specialists use AI to solve real-world business challenges. Clearly defining these needs helps place the right candidates in the right roles, leading to both high performance and strong retention.
Additionally, AI is ingrained across all industries, meaning technical expertise alone isn’t enough. Candidates should be able to demonstrate how they can integrate AI into existing technologies and business practices. The strongest hires will not only bring technical skills but also the ability to work seamlessly across departments, ensuring AI initiatives align with broader business goals.
A common challenge is that many candidates talk about AI, without being knowledgeable or experienced in its implementation. Hiring managers must assess practical expertise, looking for a track record of successfully deploying AI solutions in relevant environments. This will help them select the best fit for the role.
Hiring and implementation – key considerations
AI is already enabling us to do many incredible things – and its potential is even greater – but businesses must continue to be mindful about its use. Human oversight remains essential to maintain data integrity and organisations must establish governance frameworks that minimise risk without stifling innovation. Hiring the right AI talent is a fundamental component of this. Those with the right experience will not only be AI experts but also caretakers of the brand, helping to safeguard its reputation.
AI provides a powerful set of tools, but its value comes from how it can improve broader business results – and this usually falls into one of two categories:
- Improving existing operations: AI should improve current activities, such as generating personalised insights instantly, freeing up data analysts to focus on higher-value tasks rather than manually sifting through data.
- Creating new opportunities: Put simply, generative AI is creating possibilities that didn’t exist before. For instance, AI-powered chat interfaces enable non-technical users to analyse data independently, eliminating their reliance on business intelligence teams. AI-driven chatbots also provide 24/7 customer support, resolving common queries without long wait times or restricted service hours.
The process of implementing AI also brings with it the risk of bringing damage to a company’s reputation. One of the key issues with using AI is that it may exhibit bias. Those hired to work with these programmes must ensure that they do not break any of the numerous regulations that are now in place around the use of AI.
When hiring for AI related roles it is key that those potential employees are aware of a number of key considerations when utilising AI. They must make sure that AI is trained to not exhibit bias and to implement stringent privacy and data regulation to avoid putting customer data at risk. They need to be able to use AI observability tools to ensure compliance and demonstrate fairness in AI models. Lastly, they need to source data to be fed to the AI model from the correct areas and with the right permissions.
The importance of practical awareness
In my experience, hiring for AI isn’t just about technical skills, it requires an understanding of how different teams collaborate. Successful AI hires are leaders who can bridge the gap between research and application.
For example, AI researchers often work in iterative cycles, refining models over unpredictable timelines. In contrast, software engineers typically operate on structured schedules, needing completed research before they can build products efficiently. Without strong leadership to synchronise these workflows, the risk of misalignment, inefficiencies and delays increases.
Conclusion
AI’s disruption across almost every single industry is ongoing, evolving and difficult to measure. We need to hire the right people to manage this change. The skills of the candidates must align with the demands of their roles – not just for driving innovation, but also for maintaining data integrity and minimising risk.
Whenever a business adopts AI, it puts its reputation on the line – whether it knows it or not. That’s why the professionals brought in to oversee these initiatives must act as standard-bearers, protecting the brand from potential pitfalls while ensuring AI-driven processes run smoothly. By taking a well-planned and considered approach to AI recruitment, business leaders will secure the talent they need to thrive.
