May 1, 2019 Executive Compensation ESG & Human Capital Management Articles

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Comp Committees Should Broaden Tech-Ethics Scope

Across all industries, technology has become a key driver of growth and profitability for many organizations. Old industrial companies have been replaced as the world’s most valuable companies by those with a high-tech orientation. 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 is likely three areas where the issue of AI and/or ML is bound to surface more explicitly for future independent directors:

There are three topics that are likely to have a more explicit connection with AI and ML.

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 predicative 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.

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 individuals that create real/sustainable value for the company by allowing direct sharing in the monetary gains they create over time. For example, allowing profits interest sharing of R&D professionals at pharmaceutical companies depending on what stage they become involved in clinical trials.
  • Use of deep analytics to identify and reinforce what elements of the employee value proposition will yield the highest return on investment for varying employee populations and demographics.
  • Increasing executive accountability by providing a means for remediation when AI/ML solutions fail through incentive program outcomes. For example, EH&S measures commonly found within Oil and Gas companies could see parallels such as data privacy, self-driving car accident rates and related metrics find their way into technology-oriented incentive plans.

Company Values and Risk Management

Increased automation and the broader application of AI/ ML in corporate decision making will bring increased complexity. As a result, we believe committees will spend greater time in two specific areas:

  • AI- and ML-related issues will be brought more explicitly into annual risk reviews. In today’s best practice framework, specific risks related to AI/ML should be identified as part of the annual enterprise risk identification process.
  • Heightened by the recent focus on diversity and inclusion, there are issues related to inherent biases, safety, transparency and broad accountability for AI and ML. It is important that companies establish a set of ethical standards and principles aligned with the organization’s core values. Design of performance measurement, management and succession planning tools using AI and ML capabilities will require ethics professionals to be an integrated member of the design team.

It’s unlikely that any one of these trends will broadly change current responsibilities and decisions of directors and HRCC as they make pay and succession planning decisions near-term. However, in aggregate and over time, there’s an expectation that these technological advancements with help to drive a continued evolution across multiple human capital decisions facing directors and committees.

View the full article as it was originally published.

Stephen Charlebois

This article was originally published in Directors & Boards.

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