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5 Ways to Use AI to Drive Talent Results

Building a talent pipeline can be a slow process. Against the backdrop of a spiralling skills shortage, finding even one candidate with the right profile is tough. Repeating the process to build a bank of qualified candidates for every open and upcoming role is even tougher – especially for scaling businesses with very specific and constantly-evolving requirements.

Having the right tools in place helps. AI makes it easier to quickly and consistently source highly-skilled talent. It can learn to look for relevant talent in less obvious places, widening your potential talent pool. It can also automatically match skills to roles, taking pressure off frustrated talent teams and freeing them up for more valuable work. It helps you plan better, work smarter and hire faster. In short, AI turns your trickle of talent into a tidal wave.

Why use AI for talent acquisition?

AI is everywhere – but till now, it’s been comparatively slow to embed into tools for talent teams. That’s in part down to lack of data. All AI – whether it’s rule-based or built on machine learning – relies on huge volumes of high-quality data, which it uses  to recognise patterns and learn to perform tasks like a human would (we’ve written more about that here). 

But for that process to work properly, the data the AI learns from needs to be accurate, uniformly structured and up-to-date. In the talent world, that kind of dataset is hard to create. The market is incredibly fast-moving: new roles are emerging all the time as industries transform. Within established roles the skillsets required can change as new technologies come to the fore. And even if the role stays the same, there can be inconsistencies in job titles. There’s a world of difference between a VP of Sales at a startup and one at an enterprise business, but quite a lot of overlap between a Design Lead and a Senior User Experience Designer. Historically, AI has struggled to pick up on those nuances.

Now, that’s starting to change. With more data flowing into businesses than ever before, and more sophisticated means of capturing, modelling and using it, AI for talent is gaining ground. The benefits are many, and they’re significant: around 80% of organisations are already using AI to optimise everything from workforce management to learning and development, skills management, performance management and more. That can be through using AI for automation, or for machine and deep learning.

But just because talent teams can use AI doesn’t mean they should – at least, not without thinking critically about it. As AI becomes increasingly prevalent in the HR tech stack, it’s important to know what it can really do so talent teams can implement the right solutions, put them to best use, and drive maximum results.

5 ways to use AI to drive your talent goals

So, how can talent teams use AI intelligently to build their talent pipelines – and what outcomes does it deliver?

1. Create more diverse talent pools

When there’s already a skills shortage, the last thing talent teams need is to further limit their candidate pools. That can happen when there’s bias in the hiring process. Conscious or unconscious, it can slip in at any stage. Recruiters can fall foul of their own presuppositions about anything from race and gender to communication style or zipcode. That’s not only bad for building a talent pipeline, but for business prospects overall: diverse workforces are proven to perform better.

AI’s not foolproof either. It can introduce bias into hiring processes when the datasets it learns from are limited or skewed. But with well-trained, sophisticated algorithms, AI can remove the potentially negative influence of individual biases, reset the playing field and open up talent pools. Among other things, AI can look for candidates similar to those successfully hired in the past – but without looking at characteristics that might cause humans to be biased, such as name, address, or age. Instead, it helps talent teams focus on the most important and relevant qualities for the role.

2. Reduce manual workload

Recruitment at scale is a repetitive process. It necessitates a lot of arduous manual labour to source candidates, schedule interviews and communicate feedback. That’s not the best use of talent team-members’ time, or their valuable interpersonal skills. It can leave them feeling frustrated, and even risk churn.

Fortunately, this is exactly the kind of work that lends itself well to automation. AI  can look deep into job roles to identify the specific skills and competencies required for them. Then, it can search new talent pools and existing databases to identify relevant candidates. Crucially, it can do so across industries, finding matches in places talent teams might not think to look. And it makes the process faster, too, matching candidates to roles automatically.

3. Reduce time-to-hire

Top talent is in high demand. The speed of the hiring process can be a critical factor in securing candidates: over a third of candidates will consider withdrawing their application if they don’t receive timely feedback. But that speed can be hard to achieve when talent teams are speaking to tens or even hundreds of candidates for multiple roles, all of whom are at different stages in the process. Inevitably, some fall through the cracks.

Using AI to automate repetitive manual tasks and free up talent team time can have the knock-on effect of reducing time to hire. Rule-based automation can perform administrative tasks in hiring workflows more efficiently, batch adding relevant candidates to open roles ready for recruiters to vet. It can also automatically enrich profiles with publicly available data,  so they’re ready to hand off to interviewers right away.

4. Deliver a better candidate experience

Impersonal candidate experiences can cause people to drop out of the application process. When candidates are looking for new roles, they want to feel wanted – not that they’re just another CV on the heap. Personalisation is worth doing well: Beamery customers using it see average open rates of 72% and click through rates of 18%. But personalising experiences at scale can be a challenge.

Using AI to populate talent pools with candidates who are more likely to share the same preferences enables more targeted communication. And machine learning-based tools can continue to learn more about them over time, helping refine outreach and improve engagement further. It can analyse candidates’ behaviour across careers portals to understand more about what they’re looking for, and deliver individual recommendations for particular roles or content.

5. Optimise workforce planning

Workforce planning can be painful.  In order to forecast the skills and numbers of people needed to execute on business goals, finance and talent teams have to work together – but it’s often a disjointed and difficult process. Finance looks at past performance and future plans to tell talent how much headcount they’re allowed. Talent has to find a way to make those numbers work, struggling to fit resources and budgets around a fluctuating jobs market. They use different data points spread across different systems, making it hard to derive holistic insights. 

An AI-based talent data platform can help plug the gaps, unifying information across solutions to surface meaningful, reliable insights. AI can analyse business goals and automatically translate them into forecasts and job openings. It can also provide insights into the costs and sticking points in the hiring process, helping find areas for improvement. It streamlines the planning process, and gives talent teams the means to take more ownership of it. We’ve written more about how AI can help with workforce planning here.