Use AI- Based Talent Practices to Move from Reactive to Predictive Hiring
Part Six of our "Talent and the Future of Work" Series
Preparing a future workforce for pending changes in business wasn’t always such an ambiguous challenge for talent management strategists. In the past, new major skill requirements might develop over decades. There was time to assess, promote and coach. Today’s pace and scale of disruption demands we adapt now, and adapt continually. Talent-based AI-enabled hiring practices can help you keep up where traditional competency models no longer can.
Only 53% of Chief Human Resource Officers (CHROs) surveyed by the World Economic Forum are reasonably or highly confident regarding the adequacy of their organization’s future workforce strategy. Why is it so challenging?
- Decision-making is inhibited by lack of understanding of disruption ahead
- Resource constraints and short-term profitability pressures
- Lack of strategic workforce planning (SWP), which aligns workforce and organizational goals.
Most importantly, competency models used to identify, coach, promote and retrain talent can’t effectively anticipate the needs of new, emerging roles. With predictive, consistent AI-based talent management quantifying the types of attributes that matter more than educational and job background, you can more effectively strategize for the future, employing data to quantify applicant and existing talent attributes.
Only 30 percent of HR professionals are satisfied with their organization's ability to meet its internal talent mobility goals.
— IBM Smarter Workforce Institute
Planning for the Future
McKinsey has identified three horizons of growth. Horizon One is focused on improving performance to maximize the value of “core businesses most readily identified with the company name and those that provide the greatest profits and cash flow.” Horizon Two embraces emerging opportunities and ventures requiring “considerable investment” but “likely to general substantial profits in the future.” Horizon Three “contains ideas for profitable growth down the road.”
It’s no longer enough to focus only on the first horizon. For future success, your company needs to pay attention to all three horizons concurrently. This is particularly challenging for human resources teams and talent professionals trying to predict what will be required of employees in the future.
Augmented AI, which moves past automation to incorporate human expertise and knowledge based in I/O Psychology, tries to help the organization move beyond the focus on skills and experience alone. Drawing on decades of deep research into talents and attributes that indicate an individual’s adaptability, creativity, problem solving, critical thinking, or ability to communicate — characteristics that will be needed no matter the job title — this approach helps developing talent pipelines that are dynamic and reactive.
On average, by 2020, more than a third of the desired core skill sets of most occupations will be comprised of skills that are not yet considered crucial to the job today.
Predictively Filling the Talent Pipeline
Adapting to the future of work requires talent acquisition to go beyond “one and done” to instead take an active, ongoing approach to recruiting, hiring, retaining, and advancing the great candidates already in line of sight. This is where you use what you learned in getting to know the job candidate to drive strategic workforce planning. You protect your investment in people by using available data and the power of AI and machine learning, to not only identify great candidates for the short-term but also put together excellent teams for the long-term.
There is a proliferation of historical, descriptive, and predictive data available today — yet many organizations don’t actually apply the information they collect in the recruitment phase in relation to advancement and development decisions. Many talent acquisition processes are relatively static. Company A has a job opening, and posts it. Candidates 1 - X show interest and apply. Candidates are screened for the one job opening using resumes. This limits the pool to say 8-10 candidates. Additional skills-based screening narrows the pool further. A few final candidates are interviewed, and a job is offered to one person. Then, the HR team moves on to the next job opening.
Why, though, are talent professionals not actively working to prospect their own candidates? Those other final candidates in this scenario have shown interest in the brand and a desire to be part of the business. Perhaps they are qualified for another role. In fact, any of those early 1 - X candidates could have something valuable to share in another position. But they are typically not being considered in this broader perspective.
Between 2016 and 2030, demand for social and emotional skills will grow across all industries by 26 percent in the United States and 22 percent in Europe.
Hiring for the Future of Work
Too few organizations are working to actively quantify applicant and existing talent. Instead of focusing on education and previous job experience, a predictive talent pipeline will identify attributes such as empathy, initiative, advanced communication, creativity, critical thinking, decision making, and complex information processing.
Using these insights about its people, an enterprise can better ensure that it has the right team in place to reach its goals (efficiently and effectively) in the short-term. Simultaneously, it can proactively plan to make sure it is investing in the right development areas to help its people grow and advance. Working with a company focusing on equipping its employees for the future can improve employee engagement and increase their motivation to innovate and adapt.
Yet the future of work-ready attributes needed are difficult for HR or talent professionals to identify in others (we’re lucky to be able to honestly assess these in ourselves!). That’s where expert-augmented AI offers value. A solution (such as Plum’s) can test, sort, and screen individuals around these characteristics to make recruiting more productive and more predictive too.
"Fast" talent reallocators were 2.2 times more likely to outperform their competitors on total returns to shareholders.
Taking a more adaptive, forward-thinking approach, an organization might also use its augmented AI tech to generate a candidate database identifying people well qualified for jobs other than the ones they applied for. This database could also encompass existing employees to enable sharing of candidates across teams, departments, geography. Plus, while the HR and talent acquisition professionals focus on other aspects of their jobs, the AI could run the data to continuously and automatically identify new potential fits.
Augmented AI integrating the knowledge gleaned from I/O Psychology enables talent professionals to:
- Better identify candidates — not for just one job, but future opportunities too
- Facilitate objective assessment — let AI identify what ways of managing people’s work are most effective and which characteristics translate best to certain roles, retention success, or real, measurable contributions
- Embrace talent diversity — with objective, consistent, and reliable candidate identification done by AI platforms, businesses can advance workforce parity
- Encourage employee growth — identify those who are most likely to show flexibility in the process of lifelong learning and can best collaborate and innovate
- Advance SWP based on deep data insights into talents and behavior that can allow an organization to better plan for growth, innovation, and changes in the marketplace
Further, using quantifiable measures and analytics can help the HR team find the most critical roles creating the most value. Crunching the data and looking for trends helps determine where to allocate talent and reallocate high performers to achieve strategic priorities.
With digital transformation systemizing your talent acquisition practices, you can manage current and potential employees, establish standards, and use data to develop long-term plans for workforce needs, reduce attrition, and boost engagement and innovation. All while better preparing for the future of work.