May 14, 2024

AI Recruiting – 7 Advanced Ways in Active Sourcing

Discover the fusion of active sourcing and AI in recruiting. How precise is the new talent acquisition tool?


Raising the curtain on "Active sourcing meets AI in recruiting" – is the future of recruitment already knocking at our door, or have we let it in unannounced? Well. This question captures the essence of where active recruiting is today thanks to artificial intelligence (AI): It feels a bit like the deck is being reshuffled.

We're not just talking about another tool in the recruitment kit. And we're not just talking about matching CVs with job descriptions. It's about a deep, nuanced understanding of both the skills of the candidates and the needs of the organisation. 

AI in combination with active sourcing sharpens the sword, or rather the scalpel of talent acquisition and turns it into a highly precise tool. An instrument that can cut talent out of the market with high precision ... or out of companies. A clean surgical procedure – but one that leaves a deep scar. One man's joy, another man's sorrow. That's business. Are you taking care of your corporate culture? This is the best way to ensure that you protect yourself as a company. On the other hand, it has never been more exciting to counter the shortage of skilled labour with new vigour and enhanced skills. Let's not forget the passive or hidden candidates at this point. Follow me into the exciting world of active sourcing in the AI age.

One last thing before we start: artificial intelligence should always be used in a complimentary way. Its output must also always be analysed with a watchful eye. It may be tempting to automate certain processes – but candidates can sense this. Or do you want to be hired by a machine or rather a human? The combination of humans and AI is what promises success in recruiting and active sourcing.

Automated Candidate Sourcing

Let's start with the obvious. Traditionally, manual sourcing consumes a lot of time. AI for the automated search for suitable candidates is synonymous with the use of a highly efficient digital scout. This scout is programmed to scour the vastness of the internet and find potential candidates that match your specific job criteria. It works with precision and speed – as long as the algorithm or tool is programmed accordingly. These are often just simple Boolean search strings.

Let's take a look at this in practice: You are looking for a project manager with experience in agile methods. Instead of manually filtering the profiles in selected networks or job boards, you use AI. This digital scout meticulously analyses the profiles and identifies candidates who not only mention agile methods, but whose career history and skills indicate that they excel in such an environment. It understands the context behind each candidate's experience, ensuring a match that goes beyond mere keyword matching.

Furthermore, this process is not static. The AI learns from each search and refines its understanding of what makes candidates a good fit for your organisation. It becomes increasingly adept at filtering out the noise, providing you with a curated list of potential candidates that better match your requirements with each search.

What recruiters need to consider

While AI algorithms quickly analyse vast amounts of data to identify suitable individuals – the accuracy and relevance of the results can be limited. Recruiters must always carefully review and validate the candidate recommendations generated to ensure they match the needs and preferences of the organisation.

Advanced Candidate Matching and Profiling

Some AI-driven tools use sophisticated algorithms to sift through information about candidates. This means not just keywords, but contextual analyses of the candidate's entire career. This includes projects, posts in professional forums and even interaction on social media platforms to assess soft skills and cultural fit.

Differentiated understanding of skills and experience: AI looks at candidate experience, taking into account the complexity of projects undertaken and the impact of their work. It's about understanding the 'how' and 'why' behind each bullet point on a CV, allowing us to better assess the skills of these individuals.

Suitability for the corporate culture: Cultural fit is just as important as skills. In other words, whether candidates will feel comfortable in a company's specific culture. AI analyses data points that could indicate a candidate's working style, values and ability to collaborate with potential future teams. This aspect of matching ensures that new hires are not only capable of doing the job – but are also a positive addition to the team dynamic.

Broadening the profile: While traditional recruiting relies heavily on the resume, AI-powered profiling utilises a broader range of data sources. This can include publications, code repositories for developers, design portfolios for creatives and also relevant activities in professional networks. 

With such advanced profile matching through AI, it's no longer just about finding someone who can fill a position. It's about finding the perfect match for the role and the company culture. This level of detail not only improves the efficiency of the recruitment process, but also significantly improves the quality of hires. While we have briefly touched on predictive analytics and bias reduction here, in the following sections we will dive deeper into these topics and explore how they further refine the hiring process.

What recruiters need to consider

Speaking of bias, especially because AI now takes into account factors such as skills, experience and cultural fit, there is a risk of bias in the algorithm. Recruiters need to be vigilant to ensure fair and equitable assessment processes for people.

Predictive analysis and success modelling

Next, we enter the fascinating world of predictive analytics and success modelling. This advanced application of AI dives deep into predicting not only the potential success of candidates in specific roles, but also their long-term impact and fit within an organisation. 

Forecasting Candidate Success: At its core, predictive analytics uses historical data, performance metrics and machine learning to predict a candidate's success in a particular role. It examines patterns in a candidate's career and education. This data-driven approach provides a predictive view of how well a candidate will perform, adapt and develop in a role.

Modelling Organisational Fit: Success modelling goes beyond individual performance and also considers how well the person will fit into the team and company culture. It analyses the characteristics of high-performing employees within the company and identifies similar patterns in potential candidates. 

Reducing Turnover through Predictive Fit: One of the most valuable aspects of predictive analytics is its ability to reduce employee turnover. By accurately modelling and predicting both the success of candidates and their operational fit, companies can make more informed hiring decisions. This not only improves employee retention, but also significantly reduces the costs associated with hiring and onboarding.

Predictive analytics represents a giant leap forward in recruiting and provides unprecedented insight into candidate selection. This approach transforms hiring from a reactive process to a strategic, predictive process. New employees not only bring suitability for the here and now, but are also a valuable asset for the future. 

What recruiters need to consider

Here, too, there is a risk of misjudgement and bias. Recruiters should always use predictive analytics to complement their judgement and experience, rather than relying solely on AI-generated predictions.

Bias Reduction for Diversity and Inclusion

Reducing prejudice, promoting diversity and inclusion and fairer recruitment practices: AI plays a key role in recruitment to develop just that. This technology offers a way to finally break down unconscious biases that have long influenced hiring decisions. Whether consciously or unconsciously. Reducing bias through AI in recruitment can be summarised in four key strategies to promote diversity and inclusion:

Objective assessment of candidates: AI systems are designed to evaluate candidates based on skills, experience and qualifications, minimising subjective judgements that can lead to biassed decisions. AI algorithms must be programmed to focus on job-relevant criteria. This approach helps to reduce biases based on age, gender, ethnicity or origin that have skewed hiring decisions in the past.

Structured and consistent interviews: In the interview phase, AI can provide structured interview frameworks that ensure that all candidates are assessed against the same criteria. This consistency further reduces the risk of bias and gives every candidate an equal opportunity to demonstrate their skills based on standardised benchmarks.

Expanding the talent pool: By searching beyond traditional networks on a wider range of platforms, AI diversifies the pool of potential candidates and invites a rich spectrum of backgrounds and perspectives.

Legal and ethical considerations: Compliance with ethical guidelines and legal requirements remains critical when using AI to ensure the fairness and transparency of the technology.

What recruiters need to consider

Even though AI excludes demographic factors from consideration, there is still the possibility of bias in the data used to train the AI algorithms. Recruiters must therefore regularly review the fairness and equity of AI-generated recommendations to ensure that they promote diversity and inclusion within the workforce.

Talent Pool Analysis

Think of AI as a modern-day Sherlock Holmes: This machine detective delves into the depths of your company's talent pool to find the candidates who are not only qualified, but also on the verge of seeking new adventures or ready to make a leap.
You can also think of it as a friend who remembers everyone's birthday and knows who's ready for a career move – before they do. Or predicting the next big talent trends. It's like having a super-smart friend who's always two steps ahead of you. Indeed, a little "creepy".

Analysing talent pools with AI is not just about scanning CVs. It's far more nuanced than that. This AI friend sifts through your existing database and recognises subtle clues that suggest someone is ready for a change or is perfect for the new role you're creating. It's the same when Netflix knows you're ready to watch a sci-fi series before you even know it. By analysing past developments, skills and even changes in activity levels, the AI can work out who is most likely to be your next star.

Here's a scenario to make the picture clearer: Let's say you want to start a new project for which you need a specific skill. Traditionally, you would advertise a position and hope for the best, right? But with AI, you have a personal assistant who already knows that there are three people in your current talent pool who have honed that exact skill. These individuals may not have labelled themselves as actively seeking, but AI knows they're ripe for an opportunity. Before you know it, you make contact with these individuals and they are intrigued because you are offering them exactly what they have been quietly searching for.

The magic and also the power of AI in terms of analysing talent pools lies in its ability to connect dots that we haven't even seen. Steve Jobs famously said: "You can't connect the dots looking forward; you can only connect them looking backwards."

But AI can do that. It transforms the huge, often underutilised databases of potential candidates into a dynamic, detailed map of opportunities. The result? A proactive, strategic approach to talent acquisition that is not only incredibly effective, but also remarkably intuitive. 

What recruiters need to consider

Again, AI should be seen as a tool that supports the recruitment process, not as a replacement for human interaction.

Sentiment Analysis in Communication

You could think of a mood analysis as a skilful barista:  It recognises from your tone of voice whether you need an extra shot of espresso and adjusts its service to sweeten your day. This is roughly how AI works in communication. It offers a nuanced insight into the emotions and engagement of candidates through their digital responses – much like our barista, who can correctly gauge the mood of his clientele.

Essentially, this means having a keen sense of the underlying emotions in communication with candidates. For example, the AI can distinguish between excitement, hesitation or confusion. For example, when candidates respond to an email about a new position, the AI recognises the excitement of one and the uncertainty of the other and gives you appropriate advice on how to proceed. This is about recognising the subtleties in communication and ensuring that people feel heard and valued. 

A sentiment analysis can therefore help to establish a deeper connection with the candidate. It's the digital equivalent of our intuitive barista, trying to make every interaction valuable and demonstrating the importance of understanding and responding to people's unspoken needs.

What recruiters need to consider

When using sentiment analysis, recruiters need to recognise and respect the limitations of technology. AI can provide valuable insights into the emotions of candidates, but it is not a substitute for human intuition and empathy. Recruiters must take a critical look at the data provided by AI and, when in doubt, always seek direct, personal dialogue to avoid misunderstandings and build authentic relationships.

Predictive Attrition Modelling

Predictive modelling of fluctuation is the alarm bell for companies. This AI tool helps organisations predict which employees might consider leaving. It's a bit like a weather forecast for employee satisfaction and retention, allowing organisations to take proactive action.

Let's say AI recognises signs of dissatisfaction in an employee, similar to when a storm is on the horizon. Before the situation escalates, the company can have a conversation with the employee, address their concerns and resolve any potential issues.

Predicting turnover highlights potential problems before they lead to the loss of valuable talent. It allows for personalised strategies to improve employee satisfaction and retention. This part is basically no longer part of active sourcing, but is already talent management. However, applicants can be made aware of this opportunity in advance – because this is a highly innovative benefit as part of the employee experience. It emphasises that the company is interested in long-term collaboration and values its employees.

What recruiters need to consider

Leveraging AI in this case requires a sensitive approach. And a deep understanding of the dynamics within the workforce. Recruiters need to be aware that while such predictions can provide valuable insights, they must always be interpreted in the context of human relationships and individual needs. Fundamentally, data-driven insights need to be complemented by face-to-face conversations and interventions. After all, this is also about a culture of trust and open communication. In addition, ethical considerations and data protection should be paramount in order to protect the privacy and rights of employees.

If you want to bring change to your company regarding AI processes, start with our 7 advanced ways in Active Sourcing.


AI Recruiting – 7 Advanced Ways in Active Sourcing
Discover the fusion of active sourcing and AI in recruiting. How precise is the new talent acquisition tool?
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