How to leverage AI in your recruiting processes on a global scale

Recruiting has always been an inherently human-led process — and we don’t see that changing any time soon. Humans are still better than computers at making decisions, reasoning, seeing nuance, and understanding emotions.

But the recruiting process as a whole is imperfect. Sourcing the very best candidates from a global talent pool is a needle-in-a-haystack job that could take a human months. Time- and resource-stretched talent teams have little time to architect the perfect candidate experience when moving at speed on key hires is a priority. And the manual process of reviewing and shortlisting every resume will overwhelm even the biggest of teams.

These are bread-and-butter tasks for recruiters — but they’re also often time-consuming, highly repetitive, and high-volume in nature. And because these tasks don’t rely entirely on human input, specialized knowledge, or decision-making, they’re all tasks that can be taken on by AI.

AI’s power for recruiters lies in its ability to enhance and augment day-to-day processes by taking over the parts that take up a lot of time and resources, without sidelining the critical role humans play in hiring. 

Research is already showing some promising results, particularly in terms of productivity and task efficiency. One 2023 paper found that workers’ generative AI resulted in productivity gains of 14%. Meanwhile, a 2023 meta-review by Microsoft found that employees with access to large language models (like ChatGPT) completed tasks 26% to 73% faster than those who did not.

In the recruiting and talent acquisition space, these benefits can add up to some huge wins for teams:

• Speed and efficiency: Implementing AI for non-technical parts of the hiring process, such as sourcing candidates and scheduling interviews, removes huge bottlenecks and drives efficiency, freeing up recruiters to focus on strategic work.

• Global scalability: Less time spent on admin processes and high-volume repetitive tasks means recruiting teams can scale their talent operations on a global scale without needing to scale up on resources. 

• Reduced operating costs: A 2024 McKinsey report found that organizations were most likely to see the biggest cost decreases from in their HR function. Greater operational efficiency and time saved translate into a better bottom line.

• Nimble decision-making: With AI, recruiting teams can analyze talent data at scale and get to decisions more quickly — meaning they’re not only only able to secure key talent faster, but also build a responsive strategy. 

Improved candidate experience: Poor communication and long hiring cycles are two of the biggest hallmarks of a bad candidate experience. Implementing automation and leveraging generative AI to craft customized responses will improve efficiency and the candidate experience — freeing recruiters up to focus on engaging with candidates.

AI is a technology that is continually evolving — and we’ve really only seen the tip of the iceberg on what it can do. But it’s precisely because there are so many potential ways to use this technology that implementing it effectively — and ethically — may feel a little overwhelming. 

This is why to maximize AI’s potential and drive improvements to recruiting outcomes, you need to start with evaluating your recruiting process itself, rather than focusing on the tech — and find out your biggest heavy-hitter use cases for adoption.

Start by taking stock of your end-to-end recruiting process, mapping out all of the steps from sourcing to hire to identify where your biggest challenges, priority areas, and opportunities are right now. 

Use hiring KPIs that give you the bigger picture of your recruiting process success and efficiency, such as time-to-hire, time-to-fill, offer acceptance rate, quality of hire, diversity hires, or other key metrics that best reflect your team’s current priorities. Segment your data by factors including hiring stage, hiring market, role type, and seniority to gain deeper insights as to where AI can have the biggest impact on your processes.

For example, you might identify that time-to-hire for engineering candidates is higher than marketing, indicating potential bottlenecks in how candidates are assessed that are impacting your ability to win over key talent in a scarce market. Addressing this issue with an AI-enabled tool that automates the assessment process and delivers insights to your recruiters can help optimize the process for speed.

AI tooling is popping up on the market at lightspeed. And often, the pressure of FOMO means talent teams are getting caught in a trap of adopting the tooling first without a clear understanding of how it fits into their future strategy.

Once you have an idea of your key challenges and priority areas, it’s time to map those to AI use cases. With each challenge or priority, view your recruiting process as a product in service of the organization, using the following questions as a guide:

• What are the most important problems we need to solve?
• What are the most important things we need to fix?
• How does solving this problem benefit the organization or candidates?
• How will we test our approach and track its success?
• What are the key risks or challenges of implementing AI to solve this problem?

Consider the feasibility of each opportunity, and highlight any potential tooling or technology needs to achieve your goal. For example, do you need to build or buy tooling? How will your approach integrate with your existing tech stack?

When it comes to hiring, AI truly feels like the final frontier — it’s an unexplored territory and the consequences for getting things wrong can be severe. Candidates are understandably worried about the potential for an overlord recruiter-computer deciding their fate, while talent teams and other internal stakeholders may harbor some reservations in how AI will be used, how you’ll mitigate the potential for AI bias, and other concerns relating to ethics and fairness.

Addressing these concerns upfront and transparently is critical to driving trust in your strategy. Talent teams must focus on keeping the hiring process as a whole human-led, and identifying opportunities where they can augment humans using technology. This is likely to mean setting out some golden rules as to where AI won’t apply — such as final decision-making.

Recruiting teams must also put safeguards in place to audit any tooling and measure its success on hiring outcomes, as well as build a communications plan to explain their approach to candidates in their hiring funnel.

According to our 2024 State of Talent Acquisition report, 67% of talent teams globally have already integrated some form of AI-enabled tooling into their regular processes. But to drive long-term success

Recruiting, by its nature, is taken up with a lot of repetitive tasks, such as emailing candidates, data entry, screening resumes, and coordinating with hiring managers. But thoughtful implementation of AI here can create more streamlined, efficient workflows, relieving some of the heavy burden on recruiters.

Defining which tasks to outsource to AI depends on identifying the high-volume tasks in your workflow that don’t require human input or decision-making. Examples of effective applications of AI could include:

• Summarizing candidate interviews to roll up insights for recruiters
• Implementing a candidate chatbot to handle simple job application queries
• Automated interview scheduling and coordination with candidates and hiring managers, particularly across global borders
• Using generative AI (such as ChatGPT) to write job descriptions and candidate emails
• Automated candidate reference checks
• Automated workflows for candidate onboarding

Sourcing the best talent has always come at a huge time cost to recruiters, with hours spent combing every corner of the internet for the perfect candidate-role match. Multiply that by every open role, and you’re looking at a lot of hours dedicated to clicking through candidate profiles looking for the right keywords.

But AI’s ability to parse thousands of profiles and match candidates to roles based on their skills in mere minutes means this labor-intensive task could be a thing of the past. AI tools like Talentverse can intelligently assess not only a candidate’s explicit qualifications but also their underlying skills and experiences, using natural language processing to identify the candidates who have the best alignment for your organization and role. 

By focusing on deeper skill analysis, AI can help recruiters build a strong pipeline of candidates from the outset, ultimately reducing time-to-hire.

Every organization wants to hire the best of the best — and with organizations putting a premium on quality of hire and talent density right now, predicting long-term success beyond the interview process is a critical part of talent strategy. 

But because the interview process is a situational challenge, even when a candidate’s skills and values alignment seem on point, it’s still no guarantee that they will go on to be high-performers long-term. 

Implementing predictive analytics here can help recruiting teams analyze patterns from historic hiring data, identifying the traits and characteristics that make a candidate more likely to perform and stay at their company. This not only adds extra confidence to hiring decisions, but also could lead to increased retention, engagement, and performance long-term.

Retaining top performers long-term can only work when paired with a strong talent development strategy. And often, organizations overlook internal mobility as their untapped superpower for optimizing on quality of hire and talent density long-term. This is often due to a lack of visibility and awareness between the talent and people teams around shifting skills needs, current skills gaps, evolving team skills, and opportunities to promote from within. 

AI can help address this challenge by identifying internal candidates for new roles or promotions, and helping talent teams better match current employees to open opportunities. Getting this right can lead to some significant bottom line wins, slashing hiring costs while increasing retention and performance through talent development.

Macroeconomic instability has meant that workforce planning has felt like a black box for many teams over the past year. But as organizations start to regain their footing after years of uncertainty, forward-thinking talent teams will need a more agile approach to predicting future talent needs — and this is where AI can step in. 

By leveraging predictive analytics to analyze talent data, and integrating it with insights from global labor market trends, competitor hiring activities, and economic conditions, talent teams take a more data-driven, tactical approach to what’s coming next. They’ll be able to proactively identify upcoming skills and capability gaps, build pipelines for critical roles, identify new potential global hiring hubs for remote workers, and focus recruitment efforts with more precision.

We’re at the point of no return on AI. For talent acquisition and recruiting teams, AI’s ability to streamline essential recruiting processes is where it will shine for near-term efficiency gains. 

But embedding it more deeply into analytics and insight-driven processes will be the strategic enabler that will help maintain their competitive advantage and drive measurable hiring outcomes long-term. This is where the rubber will meet the road for recruiting teams, meaning they’re able to be more nimble and responsive to current and future challenges.

Successful implementation of AI hinges on balancing technology with human input, and mapping recruiting bottlenecks and priority areas to potential use cases, rather than retroactively refitting processes to match tooling.