Is Integrating AI into Your Business Worthwhile?

August 31, 2023 | BusinessFocus-Business Focus

We hope your summer is going well and business is good thus far in 2023! We continue to see volatility in many areas of our economy this year. One of these areas has been technology, and more specifically, artificial intelligence. This month’s article may help you determine if there is some aspect of A.I. that could be implemented in your business, and a methodical way of determining whether it is a consideration.

 

 

Should You Use AI in Your Business?

By:Joe Procopio, Founder, Teachingstartup.com

 

First, Ask the Right Question

The part of A.I. that makes all the money isnot so much about getting the right answeras it is about asking the right question. So,let's make sure we do that here too.

This new flavor of A.I. isn't all that new. In 2010 and 2011, Ico-invented the first commercially available naturallanguage generation (NLG) engine and platform at AutomatedInsights, which is a fancy way to say that we taughtcomputers how to write articles based on data.

While we used both A.I. and machine learning(ML) to enhance the engine and theplatform, our product was neither pure A.I.nor pure ML. Since those early days, NLG hasbeen combined with natural languageprocessing (NLP), a science that startedgoing mainstream with Alexa and Siri, andhas now evolved to become generative A.I.-- what we think of as OpenAI and ChatGPTand the like.

But back in 2010, the term NLG hadn't beencoined yet, or at least it wasn't mainstream enoughto get on into our consciousness.So the real question you should be asking is, "Howmuch automation should I use in my business?"And to get to that answer, we have to understandthe difference.

Thinking Versus Acting

Machine learning is "thinking" and automation is"acting." As technology continues to blur the linesbetween machine learning (thinking) and automation(acting), we roll it all into one smart technologycalled A.I.

Think of a self-driving vehicle. The ML tells it whereto go, the automation executes those decisions.Those technologies are unrelated and usuallyself-contained, but they have to work in complete harmony.

This is what ChatGPT is. It has an engine that takes ina prompt and uses ML to turn that into awell-formed question and answer -- the logic thatdecides what and how to write, draw, or code. Thenit uses automation to generate the words or pixels orsyntax and make the results available somewhere forsomeone to see.

This is where today's A.I. seems magic enough to behuman because we do these tasks every day. Ourbrains take in the question and provide the answer,and then our mouths or hands act to deliver that answer. Thinking and acting.

Develop Use Cases for A.I.

There are a ton of derivative use cases for machinelearning. But in a business context, they all prettymuch boil down to one thing: Predictive decision-making. That's it. That's the use case.

Predictive decision-making is about using themachine to line up all the data needed to make adecision, and then spitting out that decision. Verysimple. When this is done really fast, like in microseconds,it can power automation to act on those decisions as well.

The predictive nature of those decisions is based onboth prior data (knowledge) and perpetual datacollection (learning).

In one of my actual work examples, if we have years ofdata telling us that, every Thursday in a particulargeographic location, we end up needing six trucks onaverage, we can determine that next Thursday, weshould schedule six trucks.

Then we can also take into account data aboutgrowth, seasonality, weather, holidays, traffic, etc., andwe can improve the accuracy of that decision. Theresult is we roll only five trucks on the Thursdays weneed only five trucks, thereby saving money, and weroll seven trucks on the Thursdays we need seven,thereby capturing more revenue.

Boom. Benefit. If that benefit is worth more thancreating and implementing that tech (and providedwe have the data and the means to use it), we adoptA.I. for that use case. If our calculations are accurate,we can do all the scheduling automatically as well.

That's just scratching the surface. So this is where youbring in ...

The 80/20 rule

This means that outside of the simplest tasks, automationshould always be 80 percent machine and 20 percent human.

Those percentages should shift on a scale of "whathappens when you're wrong" being more expensivethan the benefit derived from the automation. Inother words, the more expensive the mistake, interms of both money and time, the more humaninput you need.

Once the machine learning is experienced enoughand robust enough to make the decision to act anddevise the instructions for how to act -- at close to 100 percent accuracy -- the task becomes repetitiveenough to be able to 100 percent automate.

So, the reason why the question "Should I use A.I. inmy business?" is so difficult to answer is because thereis no single broad answer. That means it's the wrongquestion. You must consider all the decisions andtasks that make your business work, then ask thequestion "Should this be automated?" to get a 100percent accurate answer.