Across all industries, technology has become a key driver of growth and profitability for many organizations. Old industrial companies have been replaced by those with a high-tech orientation as the world's most valuable companies. Those with first-mover advantage into digital have prospered over the last two decades.
One of the next major inflection points for industries, companies, boards and executives alike is to determine how best to appropriately leverage artificial intelligence (AI) and machine learning (ML) within corporate strategy and human capital decisions.
As the role and scope of human resources and compensation committees (HRCC) continue to broaden, there are likely three areas where the issue of AI and/or ML is bound to surface more explicitly for future independent directors:
Broader workforce and executive succession planning
Full or partial automation is on the horizon for a growing number of industries. For example, the trucking industry is moving towards partial automation in the near future and potentially full automation in the decades to come. It is important that HRCCs have a good sense of an organization's cost of labor (both fixed and variable such as bonuses and stock), because technological advances will continue to reduce the upfront investment costs of technology. As a result, HRCCs will be faced with increased decisions around when and what jobs to automate.
Furthermore, competency-based talent models will likely be replaced with complex predictive succession planning tools. These tools are built on big data that will take into account a multitude of facts, circumstances and behaviors, from within the company and externally, to help predict which executive candidates are most likely to succeed in a particular role.
Pay design and Pay management
Today's flatter organizations, remote workforces, dynamic and global reporting structures, scarcity of the right talent at the right time have all created the need for organizational pay models and pay management to evolve.
Going forward, advances in AI and ML technology will likely allow organizations to use organizational, employee and financial data at a much more granular level. For example:
* Providing greater insight to HRCC members on "how" the results were achieved, not solely the results themselves. Leveraging organizational data to identify systemic behavior real time when assessing performance results annually.
* Ability to identify and then differentiate...