To create a digestible framework for the role of AI in business, organizations need to figure out  how to optimize three key factors: competitiveness, compliance, and conscious capitalism. What  do we mean by that?  

1. Competitiveness:
   Are we performing well compared to our peers and delivering on financial  results for our stakeholders? What role can AI play in making us more competitive and driving  stronger results from a top and bottom-line perspective? What use cases are most ripe for AI in  driving faster decision-making and better outcomes?
    
2. Compliance:
   Are we in good standing with the relevant regulatory bodies that we are  accountable to, the stakeholders we engage, and the regions where we operate? Are there  ways that AI could help us achieve global compliance more efficiently and effectively, or  perhaps, compromise our regulatory-related risks in one way or the other?
    
3. Conscious Capitalism:
   Are we considered to be good corporate citizens of the world from an  ESG and/or DEI perspective? What role can AI play in helping us further the objectives we need  to achieve? Do we have AI-based systems in place that might compromise these objectives  for us as an organization or society more broadly? What can we do proactively to ensure  our systems are “doing the right thing” as part of optimizing outcomes for our workforce,  customers, and other stakeholders?  

This big, audacious goal for AI could be inherently conflicting if not done thoughtfully. So what  is the best way to get started with an approach that is impactful yet entirely consumable and  actionable by any organization? How do we make sense of our role in doing “good” for all of our  stakeholders, particularly as it relates to the C3 imperative for corporate excellence?  

The first step is figuring out how to answer the fundamental question, “Am I good?” By “good”,  we mean several things—good from an efficacy or productivity standpoint to ensure you’re as  competitive as you can be in a hyper-dynamic global economy; good from a compliance standpoint  so you can avoid having your top- and bottom-line achievements foiled by regulatory violations or  damage caused by algorithmic indiscretions; and good from a stakeholder standpoint where you  are part of the solution for ensuring equal opportunity, not a root of the problem.  

All of this can sound quite esoteric and insurmountable, but the genius lies in the simplicity of  starting with visibility into how you compare through a series of exploratory questions, such as: 

• How does your applicant pool look from a demographic diversity standpoint versus the general  population?
    
• How does your workforce portfolio stack up compared to the people who are available to you  with the relevant skill set?
   
• What does the portfolio of human attributes look like for your peers across various roles in the  organization compared to what you have achieved?
    
• How do the outputs of your consumer risk scoring system compare to the available cohort of  consumers in your relevant community?
    
• Do your digital marketing systems target the right mix of stakeholders from a demographic  standpoint versus what is available in the marketplace?
   
• Are your pricing optimization solutions being applied in ways that are fair from a demographic  standpoint or are you yielding outcomes that are correlated too strongly to a demographic  attribute?  

The challenge and opportunity around the C3 imperative are that it’s very similar to personal fitness  as there are myriad versions of what “good” looks like because we are all different—male/female/ non-binary, short/tall, slim/stocky, high/low metabolism, fast twitch/slow twitch, etc. And yet the  thing we all share is that finding our “fit” is a unique set of activities and outcomes that make sense  for each of us, and then once we find the sweet spot for ourselves maintaining that distinct version  of our fit selves is a relentless effort; day after day in a world that is changing and we are evolving  with it as we age, face adversity and triumphs, have more or less freedom and constraints. 

Such is the life of an organization endeavoring to maximize its best, unique version of “good”  continuously across time and constant change. This is not a binary concept that can be applied  bluntly across all geographies, industries, and companies of all sizes, but instead more of a  subjective moving target. That said, an organization’s position on all of these demographic  attributes compared to peers and the general population is completely measurable and can provide  invaluable visibility and insight into strengths and vulnerabilities. Furthermore, this level of detailed  benchmarking provides very specific, prescriptive context needed to drive continuous improvement.  More specifically, human portfolio benchmarking can help organizations know when they need  to tune algorithmic models, bolster training data sets, and/or incorporate external factors that  materially impact outcomes (a la Airbnb’s price optimization system noted earlier). 

Organizations can benefit from starting with the question, “Am I good?”, as it can serve as the  jumping off point for self-discovery among executives, boards, and the broader organization.  This exploration can define a sense of purpose and a bird’s eye view from point A to point B that everyone can engage in as a team. By looking at the intersection of competitiveness,  compliance, and conscious capitalism as it relates to your unique organization and a portfolio  of concrete metrics that inform where you stand with respect to each of them, an opportunity  exists to see your organization more clearly, define a future state you want to achieve, and  then pursue the initiatives that can drive you to that point. 

For the purposes of this exercise, let’s focus mainly on the Social component of the ESG  equation. Historically, this category has been relatively ill-defined in terms of guideline metrics  and targets, but where there has been something prescriptive, it has typically focused on  things like DEI as it relates to the employee base and board of directors. More specifically, do  the organization’s hiring practices enable a reasonable level of diversity within the workforce  and can it demonstrate parity between men and women on compensation in all forms (e.g.,  base, bonus, stock, and other benefits) for a given role given comparable experience,  responsibility and contribution? Beyond DEI in the workforce and board level, the only other  evaluative criteria seem to be mostly focused on community engagement through volunteer  projects and other types of contributions for non-profit initiatives such as affordable housing,  education, healthcare, or other types of services for disadvantaged people and their families. 

Given everything we now know about the dynamics presented by the new world order, let’s  endeavor to define more clearly what should be tracked by organizations so they can mitigate  risks and simultaneously capitalize on opportunities presented to them. This approach would  help leadership achieve a simple, straight-forward approach that can be broadly applied,  regardless of the specific use case, and consumable by anyone in the organization or other  relevant stakeholders who want to understand the impact of various systems. By starting with  a look at demographic portfolios for the organization itself and then how that compares to  relevant peer competitors and the general population more broadly, we can quickly and easily  ascertain where we stand on the “Are we good?” question. More specifically, we might glean  that our gender diversity is not what it could be, or that our ethnic/racial mix could be much  stronger based on what is available from the general population we engage and serve. 

That demographic context gives us a point-in-time look at where we stand, but the challenge  is that all of these variables are dynamic—i.e., our systems and their outputs are ever-evolving;  the data going into and out of them is a moving target; and the benchmarking cohorts  (competitors and general population) are constantly changing. As a result, knowing where  you stand on the benchmarking front is a departure point for understanding who you are and  who you want to be, where you are now, and where you could go, but it is just the start of  your journey. Once you have decided on the results you want to achieve and the initiatives  that will help move you from “x” to “y,” it is important to dynamically monitor how things are  going, recognizing the fluid nature of the variables that underpin your current and future state.  Ongoing monitoring can surface achievements toward and deviations from where you want to  go, creating opportunity for continuous improvement and powering an organization to be more relentlessly responsive in a way that will help ultimately maximize the C3 imperative to achieve the  optimal intersection of competitiveness, compliance, and conscious capitalism. 

Let’s bring to life how this might work with a portfolio of use cases.  

1. Talent Management—Healthcare US:
   A healthcare organization based in the Midwestern  region of the United States might want to understand how things are going with their DEI  imperatives. This can best be understood by looking at their talent portfolio demographics  by role and then comparing how they look in terms of gender and racial percent contribution  vs. their peer competitors and also what the general population looks like who qualify for that  particular role and could be recruited and considered for it. They might also want to analyze the  population they serve in more detail from a public health standpoint, understanding how their  team looks by specialty area (e.g., primary care, cardiology, oncology) and role versus the major  disease categories impacting their community (e.g., diabetes, heart disease, cancer).
    
2. Consumer Risk Scoring—Banking Europe:
   Consider a pan-European banking operation  with a large consumer credit card business. Leaders would want to begin by looking at their  consumer portfolio demographics, both in terms of the full applicant pool as well as those who  were accepted and denied to determine if they are anomalously high or low by race or gender.  They would also want to evaluate the interest rate pricing, or appraisals might vary materially  based on race, gender or another relevant demographic trait. By evaluating all stages of the  funnel, they could determine how their business aligns to the general population or market  they’re targeting from a demographic standpoint. They would also benefit from evaluating how  their employees compare to their customers and the broader community from a demographic  standpoint to determine if those representing the bank and engaging with clients are reflective  of who they serve.
     
3. AI System QA—Enterprise Technology APAC:
   Let’s take a fast growth tech organization based  in Asia producing technology platforms that capitalize on AI. Regulations related to responsible  AI require testing models to determine if they are yielding efficacious results before applying  them to hundreds or even thousands of businesses around the globe, taking multiple sample  customers and having them validate and monitor their output versus the peer/competitor data  and broader population early and often.
    
4. Fan Engagement—Professional Sports US:
   Increasingly, professional sports teams are  realizing the fan base they are engaging in is not reflective of the rich diversity in their  communities, in terms of gender, race/ethnicity, education/income, and other relevant  demographic attributes. Seeing how their actual demographics look versus the larger  population and also how they compare to peer teams in their league can help them start to  evolve with programs that will enrich their community while driving to better results from a  financial performance standpoint.

5. Digital Marketing Segmentation & Customer Management—Consumer Products:
   All types of  organizations are capitalizing on AI-based technology to segment their target prospects globally,  drive automated engagement based on scoring models, and ultimately manage their customers  through different types of activities once they are in the mix. While digital marketing can be a  powerful tool for businesses to drive top-line growth, bottom-line productivity gains, and a better  experience for customers, the models that underpin them can impose a risk of bias if not validated  carefully for disproportionate impact in terms of race, gender, and other relevant demographic  attributes. 

For all of these examples and many others where AI is being applied to various organizational use  cases that might have an adverse impact on a stakeholder population in some form, this simple  starting point of gaining visibility to the “Are we good?” question can be quite powerful. Moreover,  gaining clarity and transparency can motivate action and focus initiatives, fundamentally changing the  mindset of an organization and how it operates in a way that is straightforward yet impactful. 

Beyond achieving that initial view, the next opportunity lies in more dynamic monitoring of the same  factors explored in the initial view, but through a more integrated approach that provides a continuous  read-out of what is aligned, what might be veering into a risky state, what the target should be,  and what the organization should change or pursue to achieve it. Achieving this next step requires  a bit more investment in integration and monitoring, but can yield a strong, positive impact on the  organization from a compliance and risk management standpoint as well as a continuous improvement  perspective. Most significantly, tighter integration places the approach at the core of other operational  systems and applies more seamlessly across the entire organization, providing dynamic views into  performance and tradeoffs that should be made.  

Next, the organization may want to embark on some level of exploration and experimentation with  their model outcomes versus alternative approaches, running both in parallel with controlled groups  and comparing the demographic portfolios of the two over time vs. the peer/competitor group and  the general population at-large. That comparison might provide meaningful insight and a paradigm  shift in the way that the organization looks at its stakeholder groups, achieving more of a portfolio  optimization approach to managing talent, customers, markets, and suppliers. While many investors  are continuously seeking their own unique “Efficient Frontier” of risk-reward tradeoffs related to their  portfolio allocations, a similar concept could be applied to evaluating the efficacy of stakeholder  portfolios based on the most important impact metrics. Moreover, this kind of dynamic analysis could  help organizations achieve the best possible version of their C3 imperative in a way that becomes  central to the way they operate.  

Let’s consider how this “Efficient Frontier” modeling might play out for two different industry  sectors—healthcare and banking. With healthcare, this approach could prove to be compelling as  organizations struggle to balance several complex outcomes simultaneously, particularly related to  overcoming staffing shortages while minimizing rising costs and maximizing diversity, care quality metrics, and patient satisfaction scores. In the consumer banking world, the model might be used  by organizations to better understand the communities or markets they address and then aligning  people, processes and technology to optimize for three key variables—maximizing diversity of the  human portfolio demographics while minimizing default rates and growing interest rate-related revenue. Moreover, by widening the aperture of how organizations view themselves in the context  of the world in which they operate, particularly by combining demographic insights with traditional  financial performance metrics, an opportunity exists to mitigate and manage risks while unlocking  new levels of sustainable growth.  

The three initiatives described thus far—stakeholder portfolio analysis and benchmarking; dynamic  system monitoring for stakeholder impact and alignment; and experimentation via parallel  modeling and efficient frontier optimization across target metrics—individually or together can  also provide a strong foundation for stakeholder reporting, either for ESG scoring models, various  regulatory agencies such as the SEC, boards, or other relevant stakeholder groups. Having a  programmatic way to capture this kind of information demonstrates a strong commitment to  getting these important imperatives right—i.e., DEI, ESG, and the broader “getting to good”  imperative. Many organizations are endeavoring to figure this out now, but most are struggling  with manual processes that are onerous, disconnected, inconsistent, and incomplete while also  imposing unnecessary cost, complexity, and risk on internal teams. Meanwhile, firms are also facing  credibility challenges and skepticism from the outside that is further exacerbating the pressures  cited earlier. All of these factors can put organizations on the defensive, distracted from competing  effectively in a fast changing environment and precipitating an unhealthy organizational dynamic  that can be tough to overcome. 

Last but not least, the scarce skills challenge around all things AI creates an opportunity for a more  effective publish and subscribe model—i.e., a centralized, cross-functional team purpose-built  to help organizations access impossible-to-find talent. This interdisciplinary team would include  technical, business, social, ethical and legal domain that could help organizations on their journey  to maximizing the C3 imperative. Through meeting with cross-functional stakeholders internally  to understand their objectives and challenges, the experts would collaborate on thinking through  the most important system dynamics impacting its stakeholder groups. They could also provide  support around achieving robust testing data sets that can be excessively expensive and onerous  to achieve while also advising on algorithmic modeling or a/b testing that can yield dramatic  improvements in impact and performance metrics while mitigating risks.