• February 1, 2023

Highest Paying Engineering Jobs Of 2023

It is pretty well known among Americans that engineers tend to make a good amount of money, especially compared to occupations in other fields. Though you can become an engineer after …

Leadership In The Trenches. After All The Off-Sites, Platitudes, Lunches & Tweets, What Do You Have?

Let’s once again look at “leaders” and “leadership.” Let’s laugh and cry together. Let’s look at failed leaders and how they’re so incompetent, rich and delusional. I’ve seen so many of …

New College: Abandonment Of Due Process

New College, the public Honors College of Florida, has a substantially new board of trustees which met for the first time today. The College has 13 board members six of whom …

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, and advance digital literacy foundations to support the evolution to intelligent enterprises.

See Blog One in this five part series here as this blog defines corporate purpose and its importance in creating stronger intelligent enterprises. See Blog Two in this five part series as this blog 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. See Blog Three as it identifies key questions that a Board Director can ask of its 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.

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This fourth blog focuses in on AI corporate purpose questions are although not exhaustive these questions can help guide board directors and CEOs to improve upon their digital technology operational practices and manage risk more effectively.

As I stated in my blast 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 that is everywhere. Without quality data foundations, AI relevance and predictive trusted reliability is a major risk to a company.

Artificial Intelligence

1.) Is your company’s Corporate purpose well defined does corporate purpose underlie all your Artificial Intelligence investments and risk reviews?

2.) Do you have an AI Ethics Trusted Framework, and 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 are 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 and stewards governed with enterprise wide data and AI operating guidelines and controls?

7.) How is enterprise data stored and labelled with strong data lineage practices and if AI is used in functions, are all these programs documented and models developed easily accessible?

8.) Which organization is your benchmark in 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 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 risk management questions in relationship to your auditors to further protect your organization against data management and AI Risks.

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