To win the war for high-performing talent while reshaping recruitment and retention strategies, you need to outsmart, outmaneuver, and out-recruit the competition while keeping an eye on future changes in the talent market.
The Evolution of Online Recruitment: From Web 1.0 to Web 2.0
A significant change affecting recruitment and retention is unrelated to the talent market itself but is deeply connected to the technology we use to communicate within this market. This change involves the transition from Web 2.0 to Web 3.0.
Let me provide context about this specific aspect of Web 3.0 and how I believe it will impact recruitment and retention. Before that, I will briefly describe the Internet and its influence on past recruitment practices for context. Web 1.0 focused on making information available online and introducing web browsers to navigate this information. Users enjoyed novel features like e-mail and real-time news, which rendered newspapers obsolete. This led to a shift from newspaper job ads to new hiring methods, including creating online job boards, advertising on the World Wide Web, and introducing applicant tracking software.
Web 2.0 centered around sharing and the emergence of social media and third-party platforms. Giants like Google and Facebook offered their products for free to secure market dominance, with over 80% of internet traffic coming from Google and its properties, and Facebook and Instagram, along with their properties, as of the writing of this book. The rise of social media opened new avenues for businesses to recruit candidates.
For the first time, social media gave employees a platform to share their experiences at a company. This initiated a trend toward transparency in company compensation, benefits packages, and culture, transforming job seekers into informed candidates.
Web 2.0 revolutionized the recruiting landscape by liberating it from the constraints of being tethered to a desktop computer. The shift to mobile devices brought about a significant change in job search behaviors. Candidates could now search for jobs anytime and anywhere, simply by using their phones, and many job seekers began conducting their job searches while at work, impacting recruitment and retention practices.
This shift necessitated that all businesses with websites make them mobile-friendly to accommodate this new wave of traffic. Companies that could have adapted to this change fell behind. They were compelled to make the change, as failing meant that their websites would be invisible to over 80% of mobile-device candidates.
Web 3.0: The Next Frontier in Recruitment Technology
Web 3.0 represents a significant evolution in the digital landscape. While I know no standardized definition of Web 3.0, it is characterized by several key features, though this list is incomplete. A primary feature of Web 3.0 is decentralization, where information is stored based on its content using blockchain technology. Instead of being held in a few massive databases, data is distributed across multiple locations. This decentralization would diminish the control of internet giants like Google and Facebook (now Meta), giving users of these platforms greater control over their data.
This shift will profoundly impact any company that relies on Google, Facebook, Instagram, or other social media platforms for talent recruitment. The traditional options for targeting ads will no longer be as plentiful as before. This change will necessitate new skills to effectively target the right candidates on these platforms, potentially leading to fewer applicants and increased recruitment costs.
Demystifying AI and Machine Learning: Key Concepts and Their Evolving Role in Recruitment
Now, let's delve into a topic everyone discusses: AI (Artificial Intelligence) and machine learning. I understand that some may have misconceptions about utilizing AI and automation in recruitment and retention. What many need to realize is that we have been interacting with AI daily for years. It's important to clarify that AI and machine learning, although closely related, are not identical. AI is a more encompassing term that includes machine learning. There are three main concepts within AI: machine learning, deep learning, and neural networks.
Machine learning, a subset of AI, focuses on enabling machines to learn tasks without being explicitly programmed for each one. Deep learning involves computer scientists creating general-purpose algorithms enabling machines to learn various tasks. Neural networks represent the aspect of AI where machines learn from information available on the internet, using artificial neural networks spread across the web.
The next phase of machine learning is particularly intriguing. Initially, it required access to vast datasets and expertise to train AI, which was prohibitive for many companies due to the costs and data requirements. However, the landscape is shifting. AI is now leveraging these extensive neural networks to learn. Imagine the potential of accessing all the data available on the entire internet. This development in AI and machine learning is where things become truly exciting.
Chat GPT: A Milestone in AI Accessibility
On November 30th, 2022, a pivotal technological moment resonated globally. AI became accessible to the average person with the release of Chat GPT. This tool is unique because it doesn't require technical expertise or substantial financial resources. Open AI has made it freely available to anyone, greatly democratizing its use. This has opened countless possibilities for growing businesses and reshaping recruitment and retention strategies.
To understand the magnitude of this impact, consider the historical growth of major technologies. Twitter took two years to reach 1 million users, Facebook took ten months, and Instagram took just 2.5 months. Chat GPT, in stark contrast, reached 1 million users in only five days and had 100 million active users by January 2023. Forbes estimates it will contribute 15.7 trillion by 2030, only seven years from now, potentially generating more revenue than most European countries combined. Unsurprisingly, 80% of businesses have adopted AI technology, experiencing increased sales and revenue growth.
Regulatory Challenges and Ethical Considerations in AI-Driven Hiring Practices
Concerns about the use of AI in hiring processes are rising. With the introduction of NYC Local Law 144, employers are prohibited from using automated employment decision tools (AEDTs) to screen candidates unless they've conducted a bias audit. This isn't limited to the U.S.; similar laws are proposed in the E.U.
Through various AI and labor law trainings, I've gained an understanding of what AEDTs are. The best description I've come across defines them as computational processes from machine learning, statistical modeling, data analytics, or artificial intelligence that generate simplified outputs, like scores or classifications, to assist or replace decision-making in employment. This is not an official definition but a personal interpretation that might evolve.
The main concern with AI in hiring is potential bias in the algorithms. It's prudent for employers using AEDTs to conduct a bias audit, even if not legally required. Until there's definitive case law, seeking legal counsel before employing any AI decision tool in hiring is advisable.
This fear and concern have prompted officials to seek greater control over AI usage. The president issued an executive order on ethical AI practices, which includes using AEDTs, although the exact definition of ethical AI practices remains vague. These concerns highlight why it is imperative to use a combination of AI and human touch in the recruiting process. AI can help streamline operations, but it should never replace human judgment.
Maximizing Recruitment Efficiency: Leveraging Hybrid AI
I am aware of 1,100 different tasks that AI and automation can perform to aid in hiring without the use of AEDTs (automated employment decision tools). In deploying AI for your recruitment and retention strategies, there are numerous effective methods that don't rely on risky decision-making algorithms. AI can be utilized in many ways in recruitment and retention strategies without needing decision-making algorithms or posing a risk. AI can personalize outreach on a large scale, ensuring that your recruitment process remains personal. It can streamline the recruitment process, automate repetitive tasks, and create personalized messages at scale, making your recruiter 7 to 10 times more productive, all without the risks of bias and unfairness, data privacy issues, or job displacement. This is achievable with a platform that requires "no tech skills needed."
We understand the importance of responsibly applying AI in recruitment. We prioritize the human aspect in our evaluation process and steer clear of making automated decisions based solely on AI or machine learning algorithms. We firmly believe that data generated by AI should be considered just one component of the decision-making process, with human participation and interpretation playing a vital role.
Enhancing Recruitment Efficiency with AI: Lessons from Industry Leaders
Let me ask you this. Have you ever interacted with or done business with Facebook, Amazon, Apple, Google, Tesla, Airbnb, Microsoft, Shopify, Chase, Netflix, IBM, or JP Morgan? These companies have been using AI for years to enhance customer experiences, increase efficiency, improve customer service, boost sales, and much more.
This is how we employ AI in recruitment to craft a better candidate experience, by streamlining the recruitment process and making our recruitment team more efficient. By having AI and automation handle the low-skilled, repetitive tasks, your recruiters can bring more candidates to your job offers, increase the number of people hired, and achieve so much more.
As AI grows in power (intelligence) and becomes capable of answering more questions, those who know how to ask better questions and train AI to provide better answers will be the ones who succeed. I use AI in my recruitment and retention to enhance my systems, processes, and procedures, making more informed decisions across marketing, operations, and sales. Implementing AI in business settings not only reduces time spent on repetitive tasks but also improves employee productivity and enhances the candidate's experience. It can help avoid mistakes and detect potential crises.
By 2025, AI is predicted to eliminate repetitive and time-consuming tasks for 40% of workers in marketing, sales, recruitment, and customer service. What repetitive task have you automated?
Embracing Hybrid AI: Enhancing Human Workforce Efficiency in Recruitment
We believe in Hybrid AI: We do NOT use AI to replace people. We use AI so people can work at the top of their skill set by having AI do the lower-skilled or repetitive tasks. It is a form of augmented intelligence. Humanity is the new technology.