For addressing recruitment challenges, AI recruitment firms advise small and medium-sized enterprises (SMEs), particularly tech SMEs, to consider partial process outsourcing to benefit from “AI + Recruitment” technologies.

By collaborating with partners like LinkedIn, Pymetrics, and HireVue, it has been observed that Unilever is able to collect candidate information and conduct initial assessments and interviews. This “collaborative” model leverages third-party mature technologies and products, helping companies quickly implement “AI + Recruitment” strategies. However, executives also face key issues such as effectively integrating their HR systems with third-party data and managing the rising costs associated with scaling recruitment.

On the other hand, a “self-built” model involves upgrading a customized ATS system to an AI version, which deeply integrates the company’s advanced operational concepts and practical experience accumulated over the years, showcasing the company’s value propositions within the recruitment system. However, this model requires strong technical capabilities and comprehensive underlying data support, with a long construction cycle and significant investment. For example, Ping An Group’s self-developed integrated HR platform HR-X provides twelve full-scenario applications for six major recruitment user categories. HR-X also features a comprehensive objective and subjective labeling system based on 180,000 employees, creating a complete employee profile and significantly enhancing the functionality of various HR modules.

In the process of fully embracing “AI + Recruitment,” large enterprises need to pay special attention to the following two aspects:

1. Although AI can help track and locate senior talent globally, caution should be exercised in the actual contact and evaluation stages. It is recommended to use point-to-point contact or direct high-level dialogue. Senior talent often represents a focal point of industry competition and has a decisive impact on the company’s future. The standardized processes of “AI + Recruitment” and the “digital distance” may result in senior talent feeling undervalued or disrespected by the company.
2. While leveraging “AI + Recruitment” to gain a competitive advantage, large enterprises should also assume greater ethical responsibility, particularly in adhering to data privacy protection clauses and establishing rules for data use and circulation. Only in this way can “AI + Recruitment” be further developed and promoted.

For SMEs, fully implementing “AI + Recruitment” across all processes may not be feasible due to cost, technical, and infrastructure constraints. It is advisable for SMEs, especially tech SMEs, to adopt partial process outsourcing to enjoy the benefits of “AI + Recruitment” technologies.

SMEs typically have fewer positions to fill and higher customization needs. During recruitment, SMEs are more concerned with subtle differences in candidates’ project experience and individual achievements. Therefore, AI applications are often used more like search engines to help locate talent matching specific granular profiles. When choosing outsourcing solutions, SMEs are better suited to find search engines or job matching models that align with their industry segments to maximize the value of industry talent pools. In the later stages of assessment and interviews, SMEs are better served by in-depth, point-to-point communication to understand candidates’ backgrounds and experiences, as standardized AI interviews and assessments may not meet their needs.

How should executives or HR professionals respond to the challenges of “AI + Recruitment”?

1. Uphold company values and not become “slaves” to AI. In recruitment, executives or HR managers should use AI to find candidates that align with the company’s values rather than abandoning the values and cultural orientation upheld in manual recruitment due to the adoption of AI. Clear values are also an important source of employer brand attractiveness.
2. Shift the approach to building HR platforms and upgrade to smart systems comprehensively. AI recruitment is just one application of AI in many HR functions. Large enterprises should use it as a starting point to comprehensively build a smart HR system. On one hand, a fully digital HR system can provide data feedback on the effects of early “AI + Recruitment” use, allowing technicians to adjust parameters and algorithms to optimize job matching models and create a positive cycle. On the other hand, AI recruitment can be extended to internal promotions, performance evaluations, compensation and benefits, and other HR modules, implementing AI-based decision-making models. The upgraded AI HR system also provides a solid foundation for HR to truly become a strategic partner, empowering managers at all levels.
3. HR personnel need to learn to collaborate with AI. AI technology can complete a large amount of time-consuming administrative work faster, better, and cheaper. For HR professionals, it is better to view AI as a partner rather than a competitor. AI is a technological tool that supports HR decision-making rather than a replacement. HR should focus on adding real value in areas where human involvement is high, such as creativity and communication, and build interpersonal relationships and emotional connections in recruitment. As stated in the Harvard Business Review, “Artificial intelligence will ultimately become a cheaper, more efficient, and even fairer technology. But managers should not be worried; it just means that their focus will shift to areas that only humans can handle.”

Despite some risks and drawbacks of “AI + Recruitment,” it is important for companies to embrace the changes brought by AI with a positive attitude and drive the digital and intelligent evolution of recruitment.

As Phillips said, “Automation can make cars, but it can’t tell you where to go. You need to know where you want to go.” AI provides standardized decision-making tools, but recruitment cannot be entirely governed by AI. Executives or HR managers must guide the strategic direction of AI integration and application, avoiding the cold digital technology distance and algorithmic erosion.

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