What is Artificial Intelligence, and Where Is It Going?

What is artificial intelligence? It could simply be defined as the ability of computers to learn, and to analyze information.

Predicted for many years, artificial intelligence has finally arrived and is with us today. We see it with Apple’s Siri and Alexa from Amazon. We witness it with the navigation systems we have in our cars. We see it in air traffic control, and in the big jetliners themselves. Because it has arrived, there are many speculations about things that AI cannot yet do, and may never be able to do. These ideas have come from movies and TV shows, not from actual science.

Artificial intelligence has been portrayed as a live being encased in a machine body–Lt. Commander Data from Star Trek: The Next Generation, the alluring Rachael from the original Blade Runner film, and of course the notorious HAL 9000 from 2001: A Space Odyssey. But the actual development of AI has come nowhere near this level of cognitive ability.

Support, Not Replacement

Where is AI today? It is actually in a place of supporting humans, not in any way replacing them. Above we mentioned that AI is being utilized in jetliners–but you’ll note that when a complex or difficult situation comes about, a human must take charge. As a significant example, artificial intelligence could never have landed a jet in the middle of the Hudson river and saved all the lives that were saved that day. That took a human.

In our own field–sales–one prediction that’s been bandied about in the last few years is one that says millions of sales jobs will be replaced by artificial intelligence. I make the exact opposite prediction–that instead salespeople will be incredibly assisted by artificial intelligence, and will be able to sell like never in history. AI will be supporting different roles in a company such as SDRs, sales reps, customer success manager, or even the sales manager.

Big Data and Simplicity

A primary support that AI brings to sales is the analysis of an unbelievable amount of data. How much data is out there today? Every single day, more knowledge is uploaded to the Web than was contributed to humankind in all of the last 2,000 years.

How can all this data possibly be evaluated? It certainly cannot be efficiently accomplished by humans scrolling through it all. That is where artificial intelligence comes in.

The business aspects of big data are basically answers to three very important questions: What has happened? Why has this happened? What could happen?

In answer to the question “What has happened?” we have data mining, which has been discussed and innovated for over 20 years, and is now at the forefront of big data. It is the exploration of huge data sets for the extraction of data relevant to a particular project, business, or industry.

Data mining is utilized in descriptive analytics, or lagging indicators–those metrics that reflect past performance. What happened with your leads, your opportunities? This is where you look.

Then we want to know “Why has this happened?” For that we are utilizing diagnostic analytics, statistical relationships in data. For instance, why was that lead hanging in that one stage of the sales process for so long before it was converted? When it became an opportunity, there was another competitor already there. If you have a sales force that is 35,000 strong, you cannot possibly take the time to ask one of them why they were too slow converting a lead.

Lastly we want to know “What could happen?” For that we use prescriptive analytics. This is the modeling of data, and the utilizing of leading indicators which show us possible futures, the exploration of possibilities.

For example, in What has happened? we found out we have not contacted our last 8 customers. What should we do? Contact our best customers once per month.

A technology being applied to this data is cybernetics—what W. Ross Ashby called “The Science of Simplicity.” With our product Pipeliner CRM, we apply cybernetic principles in order to reduce the great complexities of data to simplicities for understanding.

Not only must data be simplified for understanding, but it must be done in a timely manner. Otherwise data becomes stale and unusable. This is especially true in sales, where response to data (for example, leads) should be as instantaneous as possible.

Cybernetic principles–the science of simplification of big data—is a primary application of artificial intelligence. Interestingly, just as we cannot understand big data without artificial intelligence, we also would not have artificial intelligence without big data to support it.