My 10 Rules for Corporate A.I.

What is Artificial Intelligence?

There are many definitions of Artificial Intelligence. This is my favorite:

Q. What is artificial intelligence?
Machines acting in ways that seem intelligent.
It’s a computational system powered by information that is cataloged and interpreted by algorithms that, if designed correctly, should aim to augment human capabilities.
This is my interpretation and definition.
However there are many other definitions of what is Artificial Intelligence. If you ask ten AI experts what AI is, you’ll get ten different answers and they’re all correct. One of the most accepted is from Professor John McCarthy, called the father of AI.


Q. What is artificial intelligence? (Professor McCarthy’s definition)

A. It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.

My 10 Basic Mandates for Corporate A.I.

  1. Your A.I. project should be developed and guided by a concern for its impact on humans.
  2. Your A.I. project should focus on augmenting the current human skills you have available. Not replacing it. The exception would be if you can save lives.
  3. Your A.I. project should incorporate the versatility, the depths and different approaches of a multi-disciplinary, multi-background and diversity teams.
  4. Agility or fast don’t apply for Corporate A.I. at early stages. Ban from the room whoever says “It’s quick and fast”. It’s not the time to cut corners. There’s a lot in risk, ethically, legally, financially.
  5. A decent first go for an A.I. project has at least 6 to 12 months cycle. Of course, there will be early outputs. but the real outcome should be excepted for 12 months cycles.
  6. Your A.I. project should have a list of “No’s”. Things you won’t do for ethical or financial reasons. This will help, along the project, to focus on the core of your problem of opportunity.
  7. Your Data:
    • What segments of data will you work with?
    • What segment of your customers/clients you will work with?
    • What’s your ethical position?
    • What’s your legal position?
  8. Define your one objective or one opportunity.
  9. Test exhaustively in a super controlled environment. Don’t rush into launching something just because you can. Abandon the “fail fast” and “Break things” mentality. Not here.
  10. The first step for your A.I. enterprise project is diagnosis. What:
    • Data do you have? Is it clean, biased, legally to use?
    • Tech do you have? Are you using proprietary or 3rd party algorithms?
    • People do you have? Can you assemble a work-force or task-force with different representations – people who will be excited with the opportunity?

Lucio Ribeiro ( hello (at) )


You have responsibilities. This is the first time that the A.I. technology is leaving labs/academia and hitting the world. Things eventually can go wrong.

AI Transformation Playbook by Andrew Ng (Landing AI)

Dr. Andrew Ng is the founding lead of the Google Brain team,  former Chief Scientist at Baidu, professor at Stanford, co-founder of coursera and

These are the steps recommend by him for transforming an enterprise with AI:

  1. Execute pilot projects to gain momentum
  2. Build an in-house AI team
  3. Provide broad AI training
  4. Develop an AI strategy
  5. Develop internal and external communications

All the steps are explained in his playbook “AI Transformation Playbook – How to lead your company into the AI era”.

A.I. Risks and Controls + Unwanted Consequences.

Artificial intelligence (AI) is proving to be a double-edged sword. While this can be said of most new technologies, both sides of the AI blade are far sharper, and neither is well understood.

While AI and advanced analytics offer many positive benefits,
they can lead to significant unintended (or maliciously intended) consequences for individuals, organizations, and society.

The graphics are from Mckinsey.

McKinsey Unwanted consequences of AI
McKinsey Unwanted Risks and Controls