(Editor’s Note: The following is an excerpt from “AI and the World of Work: Embracing the Promises and Realities,” a free white paper available on the Allegis Group website that tackles the rise of AI and its influence on workforce strategy and innovation.)
At its core, the job of HR begins with knowing. That means knowing the organization’s industry and competitors, knowing the core strategy of its business, and knowing the details of its workforce plan. It involves getting to know hiring managers and candidates, job requirements, and real talent gaps. It means knowing the right hire — and knowing the workings of the individual employee after hire. Backed by access to vast amounts of data, knowing should come naturally, right? Think again.
Every path to knowledge involves processing information that is unstructured, subjective, and infinitely variable. When it comes to workforce planning and talent strategy, HR success requires an ability to turn that vast supply of information into relevant and actionable knowledge. Artificial intelligence (AI) is positioned to do just that. At a strategic level, look for AI to join big data and analytics to enable and support a powerful workforce planning function. At a tactical level, AI can provide benefits in many areas, such as helping to define individual job requirements.
Determining workforce needs is one of the most complex challenges in business. New demands for skills constantly arise. A growing mix of flexible labor is joining traditional employees in the workforce, and automation is forcing companies to question whether the “best person for the job” may be a machine. An effective workforce planning function must accommodate these variables while also predicting attrition, identifying skills gaps, and discovering new opportunities to optimize the workforce. Thanks to powerful analytics tools, planners have the opportunity to develop a mature workforce planning function that delivers data-driven results, supports predictive planning, and even tests scenarios and outcomes. At present, the job of deciding inputs and data points in the planning process, as well as making judgments and evaluating outcomes, is still in the hands of humans. These functions are areas where AI has the potential to learn from analytics and help people build stronger workforce plans.
Outside HR, the overlap of AI and analytics is already happening. As an example, consider Google Analytics’ addition of automated insights to its mobile app. According to Google, “this addition to Google Analytics lets you see in 5 minutes what might have taken hours to discover previously. Even better: it gets smarter over time as it learns about your business and your needs.”
Google’s AI innovation focuses on marketing intelligence, but it clearly demonstrates how analytics and AI can deliver intuitive, strategic insight. The same kind of innovation in talent technology will likely influence how companies approach workforce planning in the future.
Job Definition and Qualification
While companies wrestle with complex “big picture” workforce planning issues, the day-to-day operations of the recruiter and hiring manager are also affected by the planning functions of HR. Most visibly, the success of the hiring manager/recruiter relationship hinges on the accuracy and practicality of job definitions and requirements.
In many cases, hiring managers will adopt a “this is how we did it before” strategy for both defining a role and determining how it is filled. Smart companies are building a culture that is shifting toward a more collaborative recruiter/hiring manager relationship. In an ideal situation, that relationship would look beyond past practices to explore critical questions that should shape any job requirement — questions such as:
Historically, the challenge to asking these questions has been twofold. First, there is human nature. Even with large amounts of data to inform decisions, the answers to the questions would likely be biased toward “what worked in the past.” Second, the sheer volume of available data has made research in answering the questions a daunting task.
AI solutions will evolve to address these obstacles. First, AI can focus on comparing current needs to historical data, such as past job requirements, hires, and subsequent performance. With this analysis, an AI program can learn from its calculations to provide meaningful insight to determine what works, what doesn’t, and how to make adjustments.
For example, a company may decide that it is looking for a high-performing engineer who worked at Google. If a hiring manager can apply an AI function that drills down and sees the résumés that draw their attention, the filters they apply, and the people they interview and hire, the profiles may look different than their stated predilections. This type of objective analysis removes the human bias, and it helps to answer the questions with clear and objective input.
Second, AI simply makes analysis easier by examining and digesting mass amounts of information in a fraction of the time required by humans. The result? Hiring managers and recruiters approach a requisition based on what they “know” from the machine analysis instead of what they “think” based on their experience. Together, the benefits of objective analysis and quick generation of relevant insights will give AI a significant advantage in better defining requirements and improving hiring speed and quality.
Whether an organization is involved in predicting talent needs for the next five years or simply optimizing requirements for a current opening, AI is poised to make a positive impact on planning. New applications may be made available through standalone solutions or through added AI features in existing platforms. In either case, the planning capabilities of AI will prove essential in a talent marketplace that is growing increasingly complex every day.
To learn more about the rise of AI and its projected influence on workforce strategy, jobs, and innovation, download our full white paper today.
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