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5 Mistakes You Are (Almost Certainly) Making When It Comes to Artificial Intelligence

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Many marketers make mistakes when it comes to AI. That's natural: We've done it, too. It takes plenty of time to get comfortable with AI.

But some mistakes are more costly than others. And these ones are the mistakes that take your organization down the wrong track when it comes to AI.

Nobody wants that. So consider this some tough love. These are the mistakes marketers need to avoid now, so they can profit from AI later.

1. Thinking AI implementation is easy.

There are a lot of marketing AI tools out there. Some are quite easy to use and you can get started fast. But turning your organization into an AI-centric company is another task entirely. Adopting AI organization-wide takes time. It takes money. And it takes trial and error.

Too many marketers think if they have the right information, implementation is simple. In reality, the right information and strategy is essential. Implementation is a different beast.

You need to get the right tools in place using the right data and arming your team with the right training. These things don't happen overnight.

AI might seem like magic once it works. But getting it working is just like any other serious software implementation. You need to commit for the long haul.

2. Obsessing over where AI is going.

We get it. We love speculating about where AI is headed. But too much speculation on the far future of AI is distracting.

It diverts people from the very real use cases for AI that are possible right now.

Intelligent automation. Personalization at scale. Vast cost and time efficiencies.

These are all possible today with marketing AI tools. (We know: They're not sexy. But they're important.)

There are many wonders ahead as we enter the age of AI. But spend too much time dreaming of what AI could be, and you won’t understand what it is today—and what it can do for you.

Give yourself a little AI daydreaming time, sure. But then find a couple real world use cases you can start applying AI to starting now.

3. Discounting artificial intelligence entirely.

The other side of the coin is marketers who believe AI is all hype. There is a ton of hype out there and a lot of really bold (and inaccurate) claims. Naturally, you might believe AI is just another buzzword.

Nothing could be further from the truth. In the last few years, significant advances in AI and machine learning have occurred. This is a very real, very impactful set of technologies that will affect your work and career. The time to start understanding the technology is now—not a couple years from now. You’ll be ahead of many marketers if you do.

4. Thinking AI can’t do what marketers do.

Even with a healthy appreciation for AI’s potential, it’s easy to scoff at it. How could it replace you or your colleagues? We can't predict how powerful AI will be, so we're not saying it'll replace anyone. But it will change the nature of your work.

AI can do plenty that marketers do today—faster, cheaper and at scale. Within this fact lies either promise or peril, depending on how you view it.

It's full of promise if you start assessing how the power of AI can augment your current work. Or, if you explore how it makes possible the work you want to do.

But it's peril if you turn a blind eye to the possibilities AI presents and fail to adapt.

Marketers need to carefully consider how they create value for organizations. Emphasize the high-value creative work, and leave the drudgery to the machines.

5. Thinking getting started with AI is too hard or too technical.

It definitely takes some time to get comfortable with concepts in artificial intelligence. And deeply understanding the tech might not be easy for the non-engineers among us. But you don’t need to know everything—or even that much—to get started applying AI.

This is not a discipline only for the technicians. As a marketer, you have an enormous opportunity to tie the technical to the practical and find real use cases for AI. Don’t sell yourself short. You can begin testing tools and learning what’s possible even with just a basic understanding of the technology.

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