What is AI?

The term “AI” denotes any intelligence demonstrated by machines. While there are theoretically three different types of machine intelligence — narrow, general and super — only narrow, task specific AI has been developed thus far. Computers don’t ideate. They solve the specific problems that they are programmed to do: How to get from point A to B as fast as possible, or, how to suggest items that customers are statistically likely to buy.

What sets good AI apart from other labor-saving machines is that machines can learn over time. Programmers do not need to write all the rules for a program to follow. Instead, they can simply expose algorithms to data and let them learn. This is called “machine learning,” or ML. This does not mean that programs become smart in a global way, of course. They simply become better at specific tasks.

Consider an email spam filter. We rely on this form of AI every day, but we rarely question how it works or how much time it saves us. How do email servers save us from reading tons of junk mail? How are they intelligent enough to know what mail we want and don’t want?

Email filtering programs use ML to learn over time. They analyze tons of data and eventually become capable of flagging spam. Programmers don’t have to write “if an email contains ‘Easy Cash,’ or ‘EZ Cash,’ or ‘Easy $,’ and so on, then it’s spam.” Instead, users simply flag spam emails and allow programs to find common patterns in unwanted messages.

Machines are good at this because they can process mountains of data quickly and in ways that people can’t. And data are everywhere. Not only is each word data, but the sender’s address, time stamp, font used and message format are all also data.

ML programs comb through all the data to find patterns and perform assigned tasks. Sometimes patterns are intuitive: maybe all spam contains the “$” sign. Sometimes they are unexpected: spam might often use a certain font. People struggle to find these patterns because they can’t keep track of hundreds of emails at once. Luckily, computers can.

After all the processing and pattern identification, we end up with a clean email box: a mailbox devoid of generic spam and full of only personally relevant messages.

If that seems unimportant, consider how many cumulative hours might be wasted on deleting spam if AI filters didn’t exist. Or how long it would take an employee to manually check for and delete spam in everyone’s inboxes. It would be absurd. AI removes unnecessary labor, so that humans can work more effectively.

If your business already relies on AI to boost efficiency — which it probably does…unless you love reading spam mail — then why not apply that same technology to your actual focus, Distribution?

The same AI process and tactics (data accumulation, processing and pattern recognition) can accomplish many tasks that benefit distributors, be that product recommendation or inventory management. All that is required is good programming and plenty of data. If distributors already have the data — customer purchases, items viewed online, call center response rates, etc. — all they need to add is the programming.

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