Jeet is a Product Manager working on Beamery's Talent Intelligence products. Before joining Beamery, he was a strategy consultant specialising in value proposition design and digital experience delivery. Jeet has a background in media, music and marketing.
Talent automation and AI are often lumped together in the Talent Acquisition space, sometimes interchangeably, sometimes as different concepts.
Talent teams are aware of these techniques, and they accept that both are valuable, but they often stop there and leave the nuances to whoever is in charge of buying the technology or implementing it.
However, recruiters today need to know when, why and how talent automation and AI are used in their workflows, and how they affect the evolution of their job. Talent automation and AI will not replace recruiters, but recruiters who understand how to use them will replace those who don’t.
Talent automation and AI defined
Automation is the technique of running a process without human intervention. These are rule-based systems, such as "if this, then that". Automation follows orders. It allows recruiters to do more of the same things, faster.
It's like having more of the same team members, who are never on vacation or sick, and don't mind applying the same rule to a vast number of inputs. You can scale your recruitment process, reduce human error, and save a vast amount of time. Recruiters can focus on building meaningful relationships with candidates, and leave the repetitive tasks to the machine.
Artificial intelligence is any technique that allows machines to mimic human behaviour. There are many ways to do this, like machine learning, or "ML", for example, which we will discuss later. AI allows recruiters to do more of the same things, but also to do things better. What most people refer to when they say "AI" is that "better" part.
There is a nuance here, a difference between repeating the task a human is already doing, and enabling a human to do something better. It's like having a team member that can give you all the information you need, at the right time, to be the most effective and make the best possible decisions.
Unlike automation, AI doesn’t always follow orders. It uses pattern recognition to derive the most appropriate output. That means that the use cases for artificial intelligence in recruiting span a huge range, including things like recommended workflows, personalized experiences and predictive analytics. The outcome remains the same—allowing recruiters to build meaningful relationships with candidates.
Distinguishing AI from automation in talent acquisition
The two concepts can sometimes look similar from the outside because the automation of tasks is achievable through other AI techniques, like machine learning. By definition, AI includes automation, but the reverse is not true.
Let's take chatbots as an example. A chatbot can be a rule-based system that gives the candidate the right FAQ or schedule based on specific keywords. Or, it can use natural language processing, or "NLP" to have a conversation, understand intentions and offer advice to the candidate on what to do next. In both cases, there is no human intervention, and the system is mimicking human behaviour. So technically, it is both automation and AI. Only in the last case, however, is the chatbot doing something we would call "intelligent".
The limitations of talent automation
Automation has some severe limitations, which is why techniques such as ML are so valuable. ML 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. This is useful when there are many variables or new data points, which is difficult to account for in automation.
There are two primary use cases for ML that automation cannot do too well when there is variance in data:
The first is grouping similar things together based on common data points. Think job-candidate matching, ranking candidates based on an ideal persona, suggesting messages, performing a semantic search.
The second is making predictions based on previous performance. This can be recommending tasks, flagging risky requisitions, checking message quality, forecasting skills gaps.
You could achieve some of this—particularly grouping—with automation. But it would miss cases that don't meet the rules precisely as you laid them out, and you would need to keep the rules up to date to account for new cases. Not only that, you also have to assume that you actually know all the rules at all times.
What happens if the skills found in a certain job title change over time? Or if a new type of profile emerges as the best fit for a specific role? The talent market changes quickly, and recruiters cannot assume that they will be aware of these changes the minute they happen.
Automation engines are rarely flexible enough to account for nuances in data, even ones that are easy to pick up for a human brain, such as spelling mistakes and different languages.
Where automation fails is when we need to understand the meaning behind the data. For example, that a 'Design Lead' and a 'Senior User Experience Designer' are likely to share the same skill set. Or that a 'Product Manager' in a tech company is not like one in banking. Or that a 'VP of Sales' at a five-person startup is very different from one at a five-thousand person enterprise.
Finding the right recruiting AI solution
It’s easier to think about the differences between talent automation and recruiting AI through a lens of inputs and outcomes. In other words, what are you trying to achieve, and what resources do you have available to help you? The answer is more likely to be a combination of AI techniques rather than a clear-cut answer, but it will help guide the conversation.
Take machine learning, for example. ML can make you more productive and impactful, but it is not a silver bullet. It's important to recognise when cheaper methods can help you achieve your outcomes.
Machine learning requires a lot of data to train accurate models, and the model is only as good as the data going in. A considerable amount of effort goes into making the data usable by ML algorithms: you would first need to collect enough data, clean and normalize it, then check that it is free of bias. Then more resources would have to go into ensuring the models are accurate and fair, and that they remain that way. These are important considerations when thinking about buying ML-powered technology.
The best examples of AI-powered technology don’t use AI for AI's sake. It is a means to an end, and there to augment human judgement and support execution, in talent acquisition or in other fields. At Beamery, for instance, we use AI to suggest tasks, enrich profiles and recommend candidates. We also use it in our automation engine, which customers use to scale their talent operations, communications and compliance practices.
The Data Science and AI team at Beamery is looking at new ways to leverage Machine learning, and developing deep learning technologies and graph databases. These techniques merit a few posts in their own right, but they are all related to the use of talent automation and AI Assist. For now, we're just excited about how this technology opens up a whole new world of possibilities for recruiters and talent teams everywhere.