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Taking the First Steps on your AI Talent Journey

So you’re thinking about using AI to level-up your talent acquisition. It’s a good idea: AI can help you work more efficiently and more intelligently, filling roles with better quality candidates, faster. By next year, almost half of organisations say they will use AI to optimise their HR functions, citing the significant improvements to decision making, employee experience, automation and cost savings that it offers.

But AI is a broad term and to non-technical people, often a vague one. For talent leaders new to using AI-based solutions, it can be difficult to understand what it does and how it works – let alone how to evaluate different providers. It’s critical that you get a grip on these things before you begin. AI might be becoming ever-more-ubiquitous, but there’s no point in buying it for its own sake, or to keep up with what competitors appear to be doing. To see results and avoid sunk costs, you need to be strategic. 

So what are the best first steps to take, and how do you ensure you’re getting your AI talent journey off on the right foot?

 

Understand what AI is

Let’s start by going back to basics. What do vendors mean when they talk about AI? It can vary significantly, and not everything AI platforms offer will be relevant to the particular business goals you’re trying to meet. Broadly, there are 3 subsets of AI. They all feed into each other, but work in different ways and produce fundamentally different outcomes. Our in-depth guide to AI for talent acquisition explains them all in more detail, but in brief:

Automation: Allows recruiters to run rule-based systems ("if this, then that") without human intervention. It means they can do the same things faster, and at scale.

Machine Learning: Machine learning  is more intelligent than automation, which only follows orders. Machine Learning is the ability for an algorithm to learn from a dataset how to perform a task, and then keep learning to do it better over time, sometimes based on feedback that it gets from users.

Deep Learning: Deep learning goes even further. It is able to look at a data set, and figure out what is most useful and interesting about it to solve a task – without human intervention. It can also analyze unstructured data in multiple formats in a way that other types of machine learning cannot.

Set clear goals

Clearly, AI can behave and be used in very different ways. Now that you have a basic understanding of what’s possible, you can start to think about what’s most useful and relevant to your business.

Ultimately, any AI tools you buy should be assisting your recruiters – not governing them. Think clearly about what your goals are and then look for tools that help you achieve them faster and more efficiently, perhaps by suggesting tasks, enriching profiles and recommending potential candidates. You don’t need AI for everything, so don’t get distracted by shiny technology that doesn’t clearly drive towards your goals. AI isn’t a silver bullet, so think carefully about whether your business has the maturity, financial and human resources to get value from whatever you’re investing in.

 

Collect quality data

AI is only possible with data – and lots of it. It’s well-established that in the world of computer science, garbage in equals garbage out: in short, that the models you’re able to build are only as good as the data you use to build them.

Producing good-quality, consistent and structured datasets is a big job that requires a lot of resource and expertise. Data needs to be collected in high volumes, cleaned, and normalised, and algorithms constantly audited to ensure they remain accurate, fair and free of bias. AI tools might boast cutting-edge features and sophisticated models, but they’re useless if the data you feed into them isn’t rich, accurate and properly integrated between systems. Make sure you have the means to keep data clean, structured and up to date before you begin.

 

Consolidate systems

With good data hygiene established, you can start to put AI to use. Driving value from AI means going beyond collecting data to understanding and examining the relationships between data points, and putting those insights into action.

This is where AI platforms like Beamery can help. What you need is a system of intelligence, not just a system of record: a single source of truth for data that allows you to gather insights, and easily build solutions around them. Make sure that the vendors you work with have fully integrated tech stacks that can surface and consolidate data from the other systems in use across your business. It prevents complexity and manual work trying to plug the gaps between tools, and minimises the risk of data falling through the cracks.

 

Action insights responsibly

Data is a sensitive subject. It’s important to remember that whatever tools you invest in, you are accountable for the way that data is handled and the results that they produce.

A lot of strict regulation surrounds the collection, handling and use of data, and pleading ignorance won’t cut it if something goes wrong. Not being able to explain what your software does puts you at compliance risk, so make sure you understand exactly what you’re buying and how it works before you sign. Getting comfortable with the technical details also makes good practical sense – you  need to understand what goes in, what comes out and what influences decisions in order to predict issues and mitigate risk. It’s very difficult to spot when things aren’t working as they should, or know how to correct them otherwise. 


AI is an incredibly valuable tool offering huge potential for talent teams to improve processes, add efficiency and create superior experiences. But it’s critical to ensure the right foundations are in place before investing in software – otherwise it could wind up adding cognitive load and detracting from ROI. Clear goal-setting, data maturity and operational alignment are critical first steps on the road to using AI for talent acquisition. Get those right, and the rest of the journey will be easier, faster and more impactful.