Four Steps for Using AI and Machine Learning for Succession Planning

Modern HCM tools can help organizations identify, develop, and retain future leaders.

By Thom Brockbank and Ed Turi

February 2018

Today, organizations across the globe are faced with an alarming reality: in the very near future, a significant number of senior leaders will hit average retirement age. According to the Pew Research Center, 10,000 baby boomers per day are turning 65 in the US alone. A vice president of human resources, realizing that 75% of the senior leaders in his large global manufacturing company are less than five years away from the average retirement age, described the situation as watching “a train wreck about to happen.”

It is not unusual for organizational leaders to find themselves with no particular idea as to who the future leaders will be, what the criteria is to select them, and how to prepare them for their future roles.

Part of the problem is the sheer number of leaders that must be groomed for leadership positions. For example, a company with 50,000 employees implementing a succession strategy down through the director level might need to have development plans for more than 3,000 leaders. How can the organization ensure that this new generation of leaders will be sensitive to its corporate culture and mission, possess the necessary subject-matter expertise, and foster appropriate contacts and networks?

Fortunately, modern human capital management (HCM) technology and the emergence of machine learning and artificial intelligence (AI) are now making it much easier for organizations to identify and provide ongoing development for large populations of employees, which can help prepare them for senior leadership roles. However, organizations must be thoughtful and deliberate in creating processes that take advantage of all of the tools and capabilities that modern HCM can bring.

With their machine-learning capabilities, modern learning platforms excel in delivering individual learning and development opportunities.”

Using a four-step process, organizations can effectively use AI and machine learning to solve succession problems and prepare future leaders effectively.

1. Develop the optimal success criteria with the help of modern content and social networking tools. Every position has essential criteria that candidates must possess in order to be successful in the role. These standards can be in areas such as knowledge, skills, competencies, experience, and education. Now, managers can determine success criteria using data and AI from sources such as syndicated content providers (for example, Bersin and Saratoga) and social networking sources (for example, LinkedIn and Slack), which can provide data and information on critical competencies of senior leaders. Once determined, the success criteria can then be uploaded into a performance management application, which is standard in most best-in-class HCM application suites, to become the comparator baseline with which to rank and score succession candidates.

2. Identify and assess specific candidates for successor-leaders. Companies can evaluate a candidate’s success criteria in relation to the comparator baseline described above. A talent management application can be used to find gaps in knowledge or experience a successor-leader candidate might have. Modern systems can graphically show and report on the gap between desired skills and current skills so that managers can better determine who is most ready to fill a role.

3. Create a plan to help candidates bridge gaps in skills and knowledge. In a consolidated architectural platform, the succession management module can identify the skills and knowledge a candidate lacks. It can then pass that information to the learning and development module, which can create a tailored curriculum that will guide a succession candidate in developing missing success criteria. It can also pass that information to the goal-setting module within the performance management application, which can assist both employees and their managers in setting and managing actionable development goals.

With their machine-learning capabilities, modern learning platforms excel in delivering individual learning and development opportunities. Future leaders can leverage best-practice tools to ensure that they are pursuing development opportunities in ways that are helping them actually learn, not just completing a “check the box” exercise. These tools include microlearning—in which small, very specific bursts of content are delivered—and spaced learning, in which the content is repeated three times. By utilizing modern learning platforms, organizations can better prepare leaders at an individual level, versus the more traditional learning programs in which everyone is given the same development opportunities, regardless of need.

Thom Brockbank

Thom Brockbank, Oracle Insight

Ed Turi

Ed Turi, Oracle Insight

4. Enforce a rigor of continual discussion among the leader and the successor. During the annual performance cycle, the leader reviews progress made against the development goals, reaffirms the successor candidate’s career goals, re-evaluates the relevance of the succession criteria, and makes course corrections, as appropriate. This can then feed into the learning module to adjust and build on content to ensure that the employee is receiving the appropriate development opportunities and moving towards being fully ready for the identified future role.

As an example, a promising young executive from a manufacturing company might have the technical experience required to make critical decisions in a senior role, but she might not know how to effectively lead change in an organization. Implementing a significant change without knowing how to create buy-in and acceptance can have disastrous consequences. A modern HCM application can help to build a succession plan that delivers educational content around change management, sets goals around leading a change effort, and provides the ability for peers, leaders, and employees to provide real-time feedback on these efforts.

Organizations have found it difficult to determine how exactly to provide learning and development opportunities to employees that can both benefit the individual and advance their organizational goals. However, as AI, big data, and machine learning continue to drive informed decision-making, companies can uncover and address learning needs in a more efficient way. This will also help the company develop, engage, and retain talent.

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