As chatGPT debuts in another 11 countries, AI continues to permeate every sector by showing up in our daily lives in the following ways:
Offering automation of repetitive administrative tasks (such as data entry)
Content generation for all things written (including assisting with this)
Chatbots and virtual assistants replacing customer service and PAs
Self-driving delivery vehicles and drones replacing drivers
Robots and machines taking a spot on assembly lines
Providing the analysis of large volumes of data
Medical diagnostics for X-rays and MRIs
Financial analysis and algorithmic trading
Translation and language services
The art and science of recruitment is not immune to the wonders of modern technology either, as has been highlighted by three uber-names recently.
Microsoft, IBM and Google Cloud (in alliance with UKG, well-known in the HR space) announced separate automated tools that can perform a range of tasks which have traditionally been undertaken by people. These include creating job posts and listings, identifying and contacting potential candidates, managing employee requests, and generating learning programs for individual employees.
Furthermore, Elon and friends (1,000 tech leaders and researchers who live in this world) have raised the red flag by calling for a pause on AI development, citing ‘profound risks to society and humanity’.
With this backdrop of considerations, let’s take a peek at how AI is impacting on recruitment on both sides of the algorithm.
1. Efficiency: Yes AI systems can save time and effort by automating tasks in the recruitment process, such as resume screening and initial candidate assessments but may diminish the candidate experience by struggling to pick up on important nuances and/or provide personalized communication.
2. Enhanced candidate matching: AI algorithms can rifle through a lot of data to identify the best matches for specific job requirements, resulting in improved candidate selection and higher-quality hires but an over-reliance on technology may overlook exceptional candidates who possess qualities or experiences not easily captured by algorithms.
3. Reduced bias: While AI systems can minimize unconscious biases that may affect human decision-making and lead to fairer and more inclusive recruitment processes, there may be algorithm design bias that can perpetuate based on prior learning and lead to discriminatory outcomes or reinforce existing inequalities.
4. Cost savings: By giving more processing power and automating a number of recruitment tasks, AI can help reduce costs associated with hiring, such as advertising, manual screening, and administrative tasks but the thoughtful implementation and maintenance of such systems can be complex and require specialized expertise and intervention to achieve the right outcomes.
5. Improved candidate experience: in a world expecting immediacy, AI-powered chatbots or virtual assistants can respond often immediately to prompts along the candidate journey but the accuracy of the data may be affected by being incomplete or outdated and can generate candidate distrust and be brand damaging on the perception of the organization’s hiring practices.
6. Enhanced data analysis: AI’s super power is inhaling and analyzing large datasets to identify patterns, trends, and insights that can inform recruitment strategies, talent acquisition, and workforce planning but AI may have limited context understanding or subjective qualities that are crucial in certain roles or industries potentially leading to inaccurate assessments.
7. Scalability: Theoretically AI systems can handle large volumes of candidates and applications in the right way, enabling organizations to scale their recruitment efforts but we are on a steep learning path to mindfully managing the ever changing landscape of legal and regulatory compliance, such as data protection and employment laws, ensuring fairness, non discrimination, and compliance.
8. Streamlined workflows: AI can integrate with existing applicant tracking systems (ATS) and other HR tools, streamlining the recruitment workflow and providing a seamless experience for recruiters and hiring managers but inauthentic responses and errors can leave candidates at odds with their purpose-driven desire to work for a company that cares by putting its people first.
9. Predictive analytics: AI algorithms can predict the success of candidates based on historical data and performance indicators, assisting in making more informed hiring decisions but there are many ethical questions around consent, privacy and the responsible use of technology in making critical employment decisions and missing key information like social cues and non-verbal communication.
10. Continuous improvement: AI systems can learn from past recruitment processes and outcomes, continuously refining their algorithms to optimize candidate selection and process effectiveness but there can be a lack of transparency as some AI algorithms operate as black boxes, making it difficult to understand the exact decision-making process and criteria, which may raise concerns about fairness and accountability.
So what's the bottom line? Our answer to that is the one we always give regarding the business of finding people for your brand or business.
Partner. Partner with AI to unleash the efficiencies in cost, time, scaling, accuracy and prediction. But for a match made in heaven, keep a hand firmly on the wheel to deliver the human touch where it matters.
Help first. Hunt Second.
