The most innovative marketers routinely pick up new things, try them out, and succeed or fail.
- When email became popular in the late 1990s, innovative marketers latched onto it.
- When websites and SEO surged in popularity in the early 2000s, marketers were there.
- When social media picked up in the mid-2000s, marketers cranked out the MySpace pages with reckless abandon.
- When consumers pivoted to mobile devices in the early part of this decade, marketers squeezed messages into tiny screens and apps.
So, given that marketers have a solid track record of adapting to new realities, why are marketers struggling so much to adapt to artificial intelligence and machine learning?
Two Challenges of Artificial Intelligence and Machine Learning
Marketers face two challenges with AI that they didn’t face with prior new technologies.
First, AI isn’t a consumer technology. The end consumer isn’t an artificial intelligence; the end consumer remains largely the same. What has changed is that the conduit to the consumer is now powered by machine learning and AI, from social media algorithms to screenless assistants. As a result, marketers face a new intermediary, rather than a new direct channel to the consumer. The closest analogy to this situation is the advent of SEO in the early 2000s, when marketers needed to understand how SEO worked – and SEO for search engines was radically different than the way consumers searched for things. Back in the early 2000s, SEO for machines was very different than SEO for people. Today, thanks in part to AI, they are largely the same thing.
Second, AI and its underpinnings are deeply entrenched in mathematics and statistics – two fields which are not strong points for most marketers. For example, let’s consider one of the most powerful and common ensemble machine learning technologies available today: the random forest. To understand the random forest and its value, we must first understand the decision tree – and everything that typically goes wrong with decision trees. To understand the decision tree, we must understand statistics and probability.
The Difference Between Math and People
When we compare statistics to communications, we see the stark difference between AI and other new technologies. When consumers use social media, email, or mobile devices, at the end of the process we are still interacting with another human being. We can transfer our domain knowledge of how people interact from one medium to the next.
When we try to tackle AI, we now switch from people as the end interaction point to machines and mathematics, areas which marketers typically have less experience.
What this means for us is that we shouldn’t feel bad if we’re struggling to incorporate AI and its various component technologies into our marketing. If you’ve been feeling left behind or out of the loop in all the AI hype, understand that it’s not because you’ve failed as a marketer. You’re not a mathematician, statistician, data scientist, or programmer. Know that to adapt to the new AI landscape, you’ll simply need to hire these people to complement your marketing and domain expertise in humanity.
Next: Basics of Machine Learning Technologies
In the next post in this series, we’ll look at the basics of machine learning technologies, define artificial intelligence more clearly, and help build your knowledge of how AI and Machine Learning fit into marketing.
The AI Primer for Marketers Series
- Part 1: Why AI and Machine Learning are harder for marketers
- Part 2: Algorithms and Machine Learning basics
- Part 3: Types of Machine Learning
- Part 4: The AI landscape
- Part 5: AI-Powered Content Marketing
- Part 6: AI-Powered Conversation
- Part 7: AI-Powered Analytics
- Part 8: AI-Powered Distribution
- Part 9: Preparing Your Career for an AI Future
Be sure to catch my keynote talk about this topic as well.
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