In this piece, I am going to explore how companies can use AI to analyze and optimize their board composition. I was inspired to write this piece by the events at GE, which has recently announced that it will slash its dividend by 50%. GE also announced that it was getting out of several of its business lines and is revamping its current board; including downsizing the board from 18 to 12. Of the 18-current members, four of the current directors have been identified as remaining and one is scheduled to leave. GE’s CEO, John Flannery, has indicated that there will be three new board members. If I am keeping track correctly of the future 12-person board, that means there are 13 current board members vying for five of those board positions.
GE says it will go through its normal governance process, including retaining a headhunter to find the three new directors, focusing on candidates with expertise in four areas: aviation, power, healthcare, and digital manufacturing. These four areas are where GE is going to focus its efforts in the future; several current key areas will be divested.
So, how to choose who stays and which new board members to bring aboard? One of the significant values of using AI is to analyze board member composition based on background or a myriad of other different factors. So, let’s get started…
Analyzing board members
The process of using AI to map the board members to a company’s key strategic focus areas is fairly straight forward. Each board member is identified and a profile of each is built with public information. Next, we have to figure out what are the key strategic focus areas of GE, which we’ll call concept areas. This information is from GE’s 10K. We use AI’s semantic analysis capabilities to identify and group the key strategic areas into these concept areas. The final step is to use AI’s mapping engine to find the relationships, or correlations, between board member’s background and each of the concept areas. The result looking at the current board is the following:
The chart shows where each board member’s experience correlates to the concept areas based on using the semantic relevancy in our AI tool. Of course, the analysis is reliant on the information we utilized. Add more data, such as resumes or other work history that is not contained in the public documents, and the correlations could change.
The new board composition
In looking at the previous chart, one of the most interesting take-aways is that no board members strongly correlate to either the power or aviation concepts, two of the ongoing critical areas for GE! So, let’s rerun the analysis to look at GE’s critical ongoing areas and M&A Restructuring. The M&A concept includes the experience area of divestitures which will be an important asset for a board member, given the number and size of businesses GE intends to exit. We’ll run it with all the board members except Andrea Jung who will be leaving the board, and we’ll show the four directors who are already identified as continuing on in a darker shade of correlation line.
With the GE Capital concept area removed, a less cluttered chart highlights the experience challenges of the GE board. This chart is the real starting point for GE in ensuring its directors have adequate business experience as part of their oversite responsibilities. And, it should raise a number of questions to explore starting with, do the directors really lack experience in the aviation and power sectors or is GE not reflecting that experience in its public documents that we used for this analysis? And, if this information is true, should the focus solely be on bringing in new directors with the requisite experience (meaning with power and aviation experience) and is 3 new directors enough? Of course, those are questions for GE and for its shareholders to wrestle with when they vote on the new slate of directors. But, the chart shows what AI can rapidly do to help assess board experience and relevancy to corporate mission.
Using the power of AI
Using AI to analyze a board can offer some good insights into how aligned your board’s experience is with your own strategies. In a recent article from The Business Journals, ten criteria for selecting board members were identified. Most of these criteria can be readily incorporated into AI as concept areas. Not all the criteria may be relevant to a company and each criterion may need to be weighted differently in terms of importance, but AI tools can manage this process very readily.
While analyzing existing boards is certainly useful, especially for identifying experience gaps, the real power of AI for the board selection process is in developing the candidate pool. Using similar processes, AI can use its semantic analysis capabilities to search through large volumes of profiles against key concepts to identify and prioritize candidates so that companies can rapidly move to the candidate interview process.
This is just a start
Whether a company is adding one board member or several at one time, AI is a great tool for analyzing board composition against strategic direction and developing tools for screening large volumes of profiles to identify the most relevant candidate pools. Not only is this important to all boards, it is especially important when going through large transformations such as GE is currently doing.
Founded in 2014 and based in Bellevue, Washington, Vettd.ai is setting the standard for AI-guided decision management. Vettd.ai uses customized deep learning AI models at the core of their advanced decision management solutions for organizations across a number of industries. These solutions help organizations make more informed decisions by quickly and comprehensively interpreting natural language data including resumes, personal essays, and more. Vettd.ai was founded to help people and organizations thrive together through better alignment with one another. For more information on how Vettd.ai is enhancing the capabilities of decision-makers, visit. http://www.vettd.ai.