AI and data strategy in the recruitment sector

22 January 2024

Artificial Intelligence (AI) can bring new opportunities and efficiencies for your recruitment business, but to leverage it as part of your operational strategy, it’s essential to have a data/digital strategy.

This article summarises themes from a roundtable held by RSM’s recruitment sector team in October 2023, attended by a range of recruitment sector businesses. As part of the event, we ran an online survey, which revealed some interesting sentiments on the opportunity to harness AI within the recruitment sector. 

The rise of AI

The prevalence of ChatGPT, a large language model chatbot created by Open AI which hit the market late 2022, has raised general awareness and debate around generative artificial intelligence. Significant investment by Microsoft has led to the product being cascaded through the Microsoft product suite and competition amongst Big Tech to bring out equivalent products. 

In RSM’s survey of mid-market business leaders, 45% are actively using generative artificial intelligence in their business, with a further 37% experimenting. Over three quarters of respondents were positive about generative AI, wanting to adopt it to access productivity gains.

61% of respondents expect an increase in employee headcount which is counter to the general narrative that automation leads to a smaller workforce. This could be due to two factors: generative AI triggers growth; or due to the risks and challenges of using AI, new people need to be recruited with different skill sets, such as data engineers and AI specialists.

However, risk is on the agenda with AI, with over half of our survey respondents indicating they thought there was some form of threat to the business. Crucially, almost 60% of businesses have some form of artificial intelligence policy in place, which could be as simple as just banning it, and another 32% were in the process of creating an AI policy, 80% of people who are using generative AI are actively monitoring and making sure they understand what data their staff are putting into the software products.

What are the opportunities for AI within the recruitment sector?

Our online survey conducted during our recruitment roundtable of almost 40 recruitment sector guests revealed some interesting themes on the use of AI and data analytics. These are summarised below.

  • Excited but need to know more
    The general appetite for AI was fairly evenly distributed with just over half of respondents (55%) saying they were excited about AI and 45% needing to know more about it.
  • A broad range of use cases for AI within recruitment businesses
    In terms of the main opportunities from implementing AI, it was interesting to see the range of ways our recruitment sector respondents felt AI could improve their business. Over three quarters of respondents cited automation of repetitive tasks, closely followed by enhancing the candidate experience (69%). Other use cases for AI included analysing trends (52%), better data-driven assessment of key performance indicators (48%), and optimisation of communications (41%).
  • Lack of confidence in the data
    Only 10% of respondents were confident in their data needed to deliver analytics and AI use cases, with a close to even split between those feeling there were gaps in the data or data quality issues.
  • Perceived risks of AI
    In terms of the perceived biggest risk of implementing AI, over half of our respondents felt they didn’t have the sufficient technical expertise necessary to implement and maintain AI within their business. A further quarter of the respondents flagged concerns over potential human bias from the data feed, and just 18% raised ethical and legal concerns over data privacy and security. 

How to leverage AI through a data strategy 

The deployment of successful AI requires a clearly defined strategy underpinned by your vision for AI, concrete use cases and reliable data. Sarah Belsham, data analytics and insights partner, highlights the key components of an AI strategy below.
  • Vision: Whilst AI is a great tool, it isn’t a silver bullet, and it doesn't work in isolation. Therefore, in a business context, it’s important to think about AI strategy as part of your overall business vision and strategy.
  • Use cases: You can then consider potential use cases in your business, ie what problems you want to solve with AI. This requires input from all parts of your business, especially those involved with day-to-day tasks that could be improved. It’s worth noting that, depending on what you are trying to solve, AI might not be the best tool.
  • Data: As good quality data underpins AI, analytics, and automation, AI should be considered within your broader data strategy. It’s important to understand the data you have, where it sits and how much you trust it, and therefore what use cases a data set could help you solve.
  • Technology: With so many new technologies emerging, there is a lot of choice. However, publicly available generative AI tools aren’t likely to be the backbone of your strategy as they're based on a huge amount of public data from the internet, often of unreliable quality or unknown source.
  • People: The people aspect to your AI strategy is key to success, not just to identify use cases but also to help your people on their journey to introduce and adopt automation into their day-to-day work.
  • Governance: It is essential to think about how to control the use of AI and public tools available. For example, what business data can be put into the tools and what can be done with the output.

Finally, AI needs the right messaging, commitment, education and awareness to get people in your business involved and collaborating. This will help them to understand when AI is a good tool to use, and what the potential pitfalls could be.