How does one approach proposing AI to management? This is something I have struggled with for a long time. I want to implement AI toward a specific problem in my place of work. My supervisors are generally willing to listen; but they want to know how the algorithm(s) is going to work. They are not programmers. My tendency is to write out the math and step through it. However, most of them don't want to do that because they have a limited amount of time to sit there and listen. On top of that, some of these algorithms can get somewhat complex.

Lets take a simple neural network for example; how would you explain the way it works without diving into the math?

  • $\begingroup$ Have never had to explain a neural network to management, but I'd probably approach it from the angle of "the computer program tries changing itself randomly until it finds one that's improvement, and it does that over and over again a hundred million times, and then we ship it" $\endgroup$ Commented Aug 6, 2021 at 11:15
  • $\begingroup$ (the direction is set by gradient descent, but you don't need to explain gradient descent - explaining it as random should be good enough) $\endgroup$ Commented Aug 6, 2021 at 11:16
  • $\begingroup$ Explain an example of your algorithm solving their problem using Flow Chart. if they don't even have time for that. explain by simple example. write down your technical explenation here so we can help. $\endgroup$
    – cgraider
    Commented Aug 6, 2021 at 18:19
  • $\begingroup$ Please, narrow the scope of this question. If you're looking for ways to explain in simple terms what a neural network does, then, please, be specific. Moreover, please, put your specific question in the title, as it's currently misleading for people that are familiar with "explainable AI", which is a specific subfield of AI, which is not strictly related to what you're asking, although it could be used to answer your question. $\endgroup$
    – nbro
    Commented Aug 6, 2021 at 20:32
  • $\begingroup$ I would not recommend explaining HOW it works unless pressed. Explain what it does: what input it needs and what it can do. Explain why it will help to achieve business goals and deliver sustainable competitive advantage. $\endgroup$
    – Howlium
    Commented Aug 7, 2021 at 22:14

2 Answers 2


There are a lot of ways to describe "Artificial Intelligence".

This form of automation/computing/AI goes back to neolithic times.

Early AI was purely heuristic. (Also known as "good old fashioned AI" aka "Symbolic Intelligence" aka classical expert systems.)

The current generation of strong (narrow) AI is statistical, which encompasses both neural networks and evolutionary/genetic algorithms.

Artificial intelligence is a machine that makes a decision. Modern statistical methods allow these machines to learn and improve their decisions.

Current best AI is "narrowly superintelligent" in that it can exceed humans at most definable tasks, but machines still lack the intuitivity of biological brains, and this strong intelligence is narrow—restricted to single problems or classes of problems.

  • $\begingroup$ I think you misunderstood what I think is the question. I think the question was: "How would you explain e.g. neural networks in simple terms to people that don't know the mathematical details?" So, I guess the OP was not looking for a definition of AI, but they wanted more to know "how to explain technical stuff in an easy way to lay people" $\endgroup$
    – nbro
    Commented Aug 13, 2021 at 23:45

In this context, I would focus on the what and not the how.

  • What part of the business problem will it solve?
  • How does that fit into the bigger solution (AI model is probably making a prediction - is that it? Is there an application or report built around it?)
  • How do you expect it to perform compared to alternative solutions?
  • What do you need in terms of resources: data, computation, time?

As far as how it works - I would just describe it as a “probabilistic model” and leave it at that. If they want to go deeper, they’ll ask. You may not even know the exact model/algorithm/approach yet, as often experimentation and iteration are necessary.


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