Expert Insights on Passive Recruiting and AI in Recruitment from StrategyBrain

It is challenging to find high-quality talent within an adequate time frame, and an average job vacancy lasts 42 days, with companies losing $98 in the process each day. Furthermore, 70% of job seekers are passive candidates, not actively looking for new opportunities but are interested; there is a need to focus on passive recruitment as a valuable recruitment strategy. This article discusses the expert insights on passive recruiting from StrategyBrain, the insights on the implementation of the same in recruitment AI, and the relevant AI-driven recommendation for practice.

Mastering Passive Recruiting

What is Passive Recruiting?

To master passive recruiting, one first, need to understand the concept. Passive recruiting is a recruitment strategy aimed at attracting and engaging prospective employees who are not actively looking for new job opportunities. Passive candidates are not on the job boards but are currently employed or seem “to be living under a rock” and not interested in new employment opportunities.

Active Vs. Passive Recruiting

Active recruiting involves job boards, using ads to reach prospective employees interested in applying for new job opportunities. Passive recruitment is targeting the best hires, not willing to shoot their shot in their career, seemingly happy on their current employment. A better strategy to unveil the better pool of passive recruitment is through credible job abstraction.

The Benefits of Passive Recruiting

Access to a Broad Pool Prospective Employees:

The passive recruiting strategy gives HR access to a potential broad pool of prospective employees. It is a critical recruitment strategy access for prospective employees not traceable through the innovations of job tracking. Passive candidates have suitable skills, employment experience, and suitable for different roles. They will make a better option if interested since they have a clean background, and companies will not be prepared for any compensation post-recruitment.

• Higher Quality Candidates: Passive candidates are often more skilled and more successful in their job, factors that offer a considerable degree of stability. The statistics seem to confirm this view, as one recent study shows that passive candidates are 120% more likely to make a lateral move that will strongly impact your company.
• Reduced Cost-Per-Hire: Although a passive candidate recruitment strategy might require considerably more resources and time, it may be considered to be more affordable in the long term. The need for expensive job postings and recruitment agencies is reduced, while the quality of candidates received is superior.
• Improved Employer Branding: In addition to attracting high-quality candidates, a proactive recruitment strategy will also serve to enhance a company’s reputation. By making efforts to approach candidates, the employer improves its position and benefits from a powerful strategy to attract highly-skilled individuals.

The Role of AI in Recruitment

AI’s Use in Passive Recruiting

The use of AI in recruitment is increasing in popularity, as it helps search data and automate several routine tasks. Several ways in which AI can help in passive recruiting are:

• Intelligent Candidate Sourcing: AI-powered tools such as the considerably powerful ChatGPT can browse through vast sets of data to find potential passive candidates that meet a company’s requirements. Interestingly, even platforms such as the LinkedIn Recruiter use AI to recommend potential candidates that may be passively looking for new opportunities.
• Enhanced Communication: The use of AI allows the recruiter to be more efficient in contacting passive candidates. They can use personalized messages and automatic follow-up emails that considerably lower the number of recruiting attempts needed. Moreover, a tool such as Outwrite helps enhance the effectiveness of communication, ensuring that the correct message is transmitted.
• Predictive Analytics: By building algorithms that calculate by how much a likelihood a candidate might be seeking for a job, recruiters could ensure that they target the most likely candidates. The task has to be performed fast and efficiently, as other companies also seek the same employees.

AI can help reduce bias in recruitment, focusing purely on the skills and experience of the applicants. Algorithms can be programmed to ignore factors, such as gender, age, or ethnicity, therefore promoting a more diverse and all-encompassing workplace. In addition, job matching performed by AI is more advanced. The given technology allows systems to analyze the profiles of the candidates and match them with job descriptions, detecting which professionals are the best fit. The process is much faster, improving the onboarding experience and increasing the success rate of employees in the workplace. Another benefit is related to the continuous improvement: AI can learn from each recruitment cycle and improve its performance, design a more accurate hiring strategy, and make more correct predictions. Similarly, the usage of AI chatbots allows the company to continue passive recruiting efforts 24/7, reply and send information to the candidates during different time zones. AI is not about replacing but about improving the job of HR managers. Passive recruiting with AI is about enabling employees to be smarter, more data-driven, and more innovative in their approaches to hiring.

Direct Strategies for Using AI in Passive Recruiting

To ensure that the AI system efficiently supports data-driven passive recruiting, HR managers must adopt particular actionable strategies that allow them to implement the given approach with minimal effort. Some of these strategies include:

Creating a comprehensive candidate database. Passive recruiting cannot be successful without a comprehensive candidate database that allows HR managers to have an accurate and realistic understanding of the available workforce. The database, however, must be updated, allowing the staff to know which specialists are available at any time and have a valid email address. Further, the information must be detailed.

Automated Data Collection

Below are some recommendations on how your organization can automate data collection to boost your passive recruitment efforts:

1. Data enrichment

Another key area where AI can be successfully employed is data enrichment. You can leverage AI to supplement and update candidate profiles with additional data:

• Career progression. AI can help you track the candidates’ career development by following their career changes, promotions, responsibilities, specialization, and improvement in qualification.
• Skills acquisition. Similarly, AI can help you track the development of the skills a candidate lists in their profile. The technology can help you assess their training, qualification certifications, and other data points.
• Professional achievements. Finally, AI can help you track the achievements of a candidate at work – e.g., patents, innovations, awards, and other performance-related data.

2. AI-driven talent mapping

Talent mapping is a strategic activity aimed at predicting the company’s future hiring needs and identifying potential candidates to fill positions in the future. Here is how you can use AI in this process:

• Market analysis. Use AI to keep track of the market situation in your industry and predict hiring needs companies like yours may face in the future.
• Candidate fit analysis. AI can be used to understand how specific candidates might be valuable for your company and how to respond to their interest in your company proactively.

3. Personalized engagement

Engaging with passive candidates is only effective if the engagement is personalized and resonates with a person on an individual level. Here are some AI-driven strategies to achieve this goal:

• AI-powered personalization. Use AI to tailor your message to the candidate’s interests, aspirations, and their current job. For example, an AI tool can analyze a candidate’s LinkedIn profile to understand his/her current job, recent achievements and bring them up in the message.
• Automated follow-ups. Finally, leveraging AI in sending follow-up letters can help you ensure timely responses and absence of human factor in these communications.

4. AI for competitor insights

As a passive recruiting tool, it is critical to understand who your top talent is where they work. AI can help you assess this information so you have an idea which roles you should be most interested in and which competitors you should target.

• Competitive analysis. AI tools can be used to track the hiring and firing activities of competitive companies. Monitor the changes in competitive companies and their staff to understand their weak points and focus on the candidates they are losing.
• Strategic outreach. Based on the analyzed data, develop a communication strategy to reach out to these candidates and present them with offers and opportunities.

5. Optimize job postings with AI

Finally, while passive recruiting does not rely on job postings, you can optimize job descriptions to make them more attractive to your future passive candidates.

• SEO optimization. You can make use of AI tools that tailor job postings to how potential candidates search for them, selecting the most suitable keywords and phrases.
• Dynamic content. Finally, AI tools allow real-time editing of job description content based on the applicants’ reactions to make it better match the potential candidate.

6. Measure and Refine Strategies

It is important to continually evaluate recruiting strategies through AI-derived data in order to develop and optimize them on a consistent basis.Passive Recruiting Performance Analytics: It is recommended to regularly analyze the AI-derived analytics which refers to the performance of passive recruiting strategies. In doing so, one is able to identify the most and the least efficient passive recruiting tools and practices and optimize them. A/B Testing: A/B tests help to determine which approaches, be it an outreach method or a part of a message, work better with a desired target group.

Quick and easy-to-implement, these systematic passive recruiting strategies allow professional HR managers develop more effective and efficient strategies. Implementing the use of AI in a passive recruiting process increases the recruitment activities’ scope and their impact on the candidate pool. It also ensures that the engagement with each potential candidate is of high quality, creating the necessary preconditions for successful inclusive hiring practices.

Examples of Successful AI Implementation

Unilever’s AI Recruitment Strategy

Unilever, a popular consumer goods producing company that operates across the globe, implemented AI technologies in its recruitment practices. Unilever uses AI-derived performance and personality profiles of its employees to develop similar profiles for potential candidates and to assess the internal candidates’ achievement-related and speed-of-learning traits. It also makes use of AI-driven platforms such as Pymetrics and HireVue to assess the applicants’ tense precision, tone, and friendliness through playing a set of online mobile games and by conducting video interviews. The results are impressive: the time spent on finding the right candidate has reduced by 75%, and the company managed to hire 50% more diverse candidates.

Hilton’s Chatbot for Candidate Screening

Hilton Worldwide, a popular hospitality organization which is regarded as one of the biggest hospitality employers in the world, implemented an AI-based recruiter called “Connie”. Connie is capable of pre-screening the prospective employees by asking them the same pre-set questions, getting an instant performance measurement, and offering hints and tips when necessary. The accessibility and speed at which the application is processed is ensured by the opportunity to contact the chatbot 24/7.

IBM’s Predictive Analytics for Talent Retention

IBM uses artificial intelligence and predictive analytics to predict employees who will leave the company and thus managing the turnover through selection and retention programs. Input data for the AI algorithm are multiple sets of data usually collected on employees. They include information from the talent and performance review system, including history of promotions, the results of engagement surveys, attrition and bonus information, as well as data related to employee sentiment from career sharing platforms such as Twitter, Facebook, and LinkedIn. By analyzing that information, the AI system estimates the probabilities of employees’ quitting the company that yields results with an impressive 95% accuracy. The company’s HR can predict at the current time what percentage of people from a certain group – women, eastern workers, fourth-level software engineers – will be still working for the company in six months and apply retention strategies for the best-performing employees.

Vodafone’s Automated Interview Scheduling

Vodafone is a British multinational telecommunications company that has redesigned the process of interview scheduling using AI-reliant systems to do the administrative work that human recruiters were doing before. The tools are automated systems that analyze all available options for the interview date and time, staff availability, and interviews’ duration. Simultaneously, the machine monitors accepted interviews and assigns them to the employee closest to the examination place. As a result, recruiting staff optimized its workflow, has got higher results in their work and process more interviews because automated systems replace significant disadvantage – the willingness of a human being to negotiate optimal interview dates. The peripheral effect of such an innovation is optimization and overall improved satisfaction of the candidate during the interview process.

Amazon’s AI-Driven Diversity Hiring

Amazon, in turn, applies AI technologies in hiring systems to increase the number of women in its technical teams due to expanding choice of words in job announcements. Besides, AI helps recruiters find resumes that were overlooked due to recruiters’ bias. The company acknowledges the leakage of CVs of women in resumes but dess not help the interaction with the recruiter. To some extent, AI technologies may help avoid such leaks due to the ability of the brain to process more information and make more consistent decisions.

Conclusion

AI has transformed passive recruiting strategies and redefined the way companies can identify and attract top talent. Combining vast data management and retrieval capabilities with an ability to locate qualified talents among vast quantities of information, AI-supported tools allow companies to create more personal recruitment approaches. Examples from Hilton, IBM, Vodafone, and Amazon demonstrate how AI technolog ies can be used to drive effeciency and equality throughout the workforce. In addition, it is critical for all companies to understand the implications of using AI. Any company that considers using AI-supported technologies in passive recruiting needs to consider pervasive issues of data bias, privacy, transparency, and too much reliance on the technology. Without these considerations, AI tools will be used improperly and unethically. With timely and continued human intervention, organizations can only benefit from AI-supported solutions. They are critical for improving passive recruiting results, and companies that use AI will ultimately have more opportunities and a better chance to reach their recruitment goals.