This blog series has been exploring the meaning of corporate purpose and looking at the importance of AI Ethical frameworks, and evolving audit practices internally and externally to improve risk management practices. This blog series has also stressed the leadership imperative to establish strong digital literacy foundations to support the evolution to intelligent enterprises. Not doing so will decrease the value of your company over the long term as asset valuations will continue to shift to data and AI intelligence economics.
Blog One in this five part series is here , defines corporate purpose and its importance in creating stronger intelligent enterprises. Blog Two identifies leading AI principles, and frameworks or standards to guide board directors and CEOs to increase their knowledge in these areas, as well as their C-Suite. Blog Three identifies key questions that a Board Director can ask of his/her CEOs, and in turn, the CEO should be well prepared to answer data management strategic questions to manage data risk and ensure data value realization.
This fourth blog identifies AI corporate purpose questions, although not exhaustive, these questions can guide board directors and CEOs to improve their digital technology operational practices and manage risk more effectively.
As I stated in my last blog, every CEO in today’s world must be digitally literate, have training and expertise in technology, data management and AI fundamentals to lead in our data tsunami world - everywhere. Without quality data foundations, AI relevance reach and predictive trusted reliability is a major risk to a company.
An AI Centric World is Fast Emerging: Are you ready?
Artificial Intelligence
1.) Is your company’s Corporate Purpose well defined and does corporate purpose underlie all your Artificial Intelligence/ML investments and risk reviews?
2.) Do you have an AI Ethics Trusted Framework, and a set of operating principles to guide your AI initiatives?
3.) Is there an AI Strategy and AI Value Matrix (Value and Risk Levels) well defined with clear accountabilities?
4.) Is there an AI governance council with cross functional AI leaders advancing your AI strategy?
5.) Is there an AI operating review process and appropriate risk management controls established and monitored?
6.) Are business process owners easily identified across the enterprise with certified data and AI skilled stewards in each functional unit and are these process owners / stewards governed with enterprise wide data and AI operating practices?
7.) How is enterprise data stored and is it labelled with best in class data lineage practices and if AI is used in functions, are all the AI programs documented and models easily accessible for corporate wide knowledge?
8.) Which organization is your benchmark for corporate purpose aligned with AI ethics and using robust data management practices?
9.) Where is your organization in its AI and ML maturity practices against your competitive landscape? Are you a benchmark for your industry?
10.) Do you have AI and ML experts in your company and are they centrally managed from a governance and career development perspective?
9.) Do you have a digital literacy program for all employees with a foundation on data management and AI?
11.) Do you conduct third party audits on your AI practices?
12.) Does your procurement function guard against using black box AI methods and audit AI risks to your organization?
13.) Do you know how many AI Models are operating in your enterprise and how are you managing against AI model drift risks?
My next blog in this series will identify questions relevant to AI board director or CEO risk management questions in relationship to your auditors to further protect your organization against data management and AI Risks.
