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The 3 Biggest (& Most Common) Mistakes Made With AI

by Doug Davidoff | Mar 1, 2019 12:02:00 PM

AI-1FOMO: The Fear of Missing Out

It was Bill Gates who said, “We always overestimate the change that will occur in the next two years and underestimate the change that will occur in the next ten.” Nowhere is the outlook more on display that when it comes to the topic of artificial intelligence (AI).

Make no mistake, technology in general and AI specifically are having a major impact on the work growth-focused organizations are taking. AI is absolutely something you should be aware of, and, to some degree, keeping track of. It is not, however, something that should be at the top of any small or mid-market growth company executives attention or worry list.

What Is Artificial Intelligence

Part of the difficulty with addressing AI is that it often means a bunch of different things to different people. What’s more, the term AI is often used to infer things that are not necessarily in place. AI is very confusing to many, so I turned to my friends at HubSpot, who published a nice piece on important definitions surrounding AI. Here are some of the key terms:

Artificial Intelligence: In the most general of terms, artificial intelligence refers to an area of computer science that makes machines do things that would require intelligence if done by a human.

Machine Learning: In short, machine learning is the ability of a program to absorb huge amounts of data and create predictive algorithms.

If you’ve ever heard that AI allows computers to learn over time, you were likely learning about machine learning. Programs with machine learning discover patterns in data sets that help them achieve a goal. As they analyze more data, they adjust their behavior to reach their goal more efficiently.

Deep Learning: On the far end of the AI spectrum, deep learning is a highly advanced subset of machine learning. Deep learning can find super-complex patterns in data sets by using multiple layers of correlations. In the simplest of terms, it does this by mimicking the way neurons are layered in your own brain. That’s why computer scientists refer to this type of machine learning as a “neural network.”

Natural Language Processing: Natural language processing (NLP) can make bots a bit more sophisticated by enabling them to understand text or voice commands. On a basic level, spell check in a Word document or translation services on Google are both examples of NLS. More advanced applications of NLS can learn to pick up on humor or emotion.

The 3 Biggest Mistakes Being Made

There’s an old joke told amongst veteran salespeople that goes, “What’s the difference between a used car salesman and a high-tech salesman? Easy, at least the used car salesman knows he’s lying.”

Mistake 1: Believing the MarTech Hyperbole Machine

Unfortunately, the truth about AI is that there often isn’t much truth. The number of SaaS reps and marketers that are promoting their “proprietary AI algorithms” has reached caricature proportions. You can’t really blame the reps, because most of the time they don’t even know that they’re not telling the truth.

The reality is that AI is hot, and the promise is compelling. But my mom’s advice still holds true - if it sounds too good to be true, it probably is.

Remember, all of the definitions I shared earlier are, technically, AI. Realize that spell check and grammar check is a form of AI. Simply identifying that you’ve used a word more often than others is another form of AI.

So, when someone is telling you about how their product or service is powered by artificial intelligence, be certain to ask them how AI is applied, how the algorithms are determined and what is different as a result of that AI is. If they can’t answer that question clearly, or even worse, if they say something like, “Well, we can’t really share that information as that’s central to our IP,” be sure to run away from that conversation as fast as you can.

Good, legitimate AI can be (keyword “can be”) a powerful accelerator. But realize, that’s what it is - an accelerator. It won’t fix a bad strategy or bad process. I’d add that if you’re considering a new technology that’s powered by AI, use the “inverse principle” when determining its legitimacy - the more the vendor talks about AI, the less likely it's valuable.

Mistake 2: Viewing AI Through the Prism of Quick Wins or Easy Solutions

Maybe one day in the future, I’ll be able to plug in an AI algorithm and it will do all the hard thinking for me, make crystal clear recommendations, and even take the actions associated with those recommendations.

Maybe, one day. But, not today.

As I shared earlier, AI can be a powerful accelerator, but it is not the “easy button.” Good, legitimate, AI requires setup, training, monitoring, and maintenance. Let me give you two examples of how AI creates a great promise - but not only fails to deliver on the promise, it actually works against you.

Content Scoring: Several months ago I tested a tool that promised to score each piece of content. If the content scored higher than a “magic number,” it meant that content would likely outperform content below that score. It sounded great. Unfortunately, it scored every piece of content exactly the same - regardless of the company, the type of content, its role/purpose and more. It was easy to implement, but it led to a decrease in impact, not an increase.

Predictive Lead Scoring: There are several companies offering to predict the likelihood that someone will buy from you. What’s more, it promises to do so right out of the box. No muss, no fuss. Again, it would be SOOOO awesome if it worked. But how can it? Lack of sufficient data and programming of causal factors means that the “predictive” algorithm is just shooting for the lowest common denominator.

Here’s an important lesson I’ve learned about any AI solution that provides impact - it requires work to set up and keep it working. If there’s no work involved, then it’s unlikely to have much impact. (Remember my mom’s advice.)

Mistake 3: Looking to AI to Fix Things That Aren’t Working (Rather Than Optimizing & Accelerating Things That Are)

AI is a good to great solution. So if your processes and strategies aren’t at least good than AI solutions will likely create more pain than gain. The old adage garbage in, garbage out applies.

Remember AI is an accelerator, so if you’ve got a crappy process and you apply good AI, you do more crap, just faster. AI is simply an advanced form of automation, and as with any automation, if you don’t understand the inputs and can’t manage effectively manually, then automating will just wreak more havoc.

Our Recommended Approach to AI

Let me emphasize that I’m a BIG fan of AI. In July I’ll be sharing how sales organizations are using AI successfully at the first ever Marketing AI conference - don’t miss MAICON.

We approach AI the same way we approach every other technology decisions:

  • We make sure we’re clear on what it is we’re solving for.

  • We define the job to be done, and what the critical success factors are.

  • We review the alternatives to determine the best way to do the job.

  • We decide on the best technology (or AI)

Follow these four steps and you’ll be certain to capitalize on the advantages of AI rather than falling prey to the hype.

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