AI’s B2B Data Problem
There is no doubt that Artificial Intelligence (AI) is an incredibly powerful tool for B2C marketers. With very large target audiences and endless supplies of consumer data, Artificial Intelligence is very useful for marketers who must effectively harness this volume of information to make effective decisions for their brand.
Where’s the Value for B2B?
There is a lot of interest in AI for B2B—it seems like every day there is a new vendor presenting their solutions as “AI-driven [fill in the blank].” But with much narrower pools of data available, does AI truly offer value for B2B? AI at its core has the potential to automate tasks and analyses across the spectrum of internal marketing and sales processes, provided that organizations have good alignment and are working from a “single source of truth.” Additionally, it can be useful in helping B2B marketers with lower-level campaign tactics and areas like A/B testing of email subject lines and messaging, etc.
Garbage in, Garbage out
But just like any other technology that has come and gone before it in B2B, such as predictive analytics, the value of AI really lies in the quality of data. No matter how good your AI models are, if the data going into the model is poor, then you have no actionable insight. Applied to your own first-party data, AI has a lot of promise, but no appreciable scale. And when expanded to larger pools of third-party data, you must ask yourself if vendors are merely applying AI to reduce a large pool of poor data down to a smaller pool of poor data for you to feed into your systems. Often, you are better off starting with more targeted pools of insight and applying your internal marketing and sales resources more strategically to drive success.
Cashing in on “Conceptual Fuzziness”
In addition to the “garbage in, garbage out” phenomenon, the truth is, most B2B marketers just don’t know that much about AI yet. And due to it’s conceptual fuzziness, vendors cashing in on “AI-driven” solutions have three weapons that could potentially mislead buyers.
- The label “AI” makes a solution sound intrinsically hi-tech, even if it’s just statistical analysis software.
- Because few understand how AI truly works, vendors take the “trust us, we know what we’re doing” approach.
- Vendors trade on the human tendency to look for easy fixes to difficult problems.
So before you consider the next “AI-driven solution, ask yourself: What is the source of their data? What do vendors mean by AI? Do their solutions really have it and is it even appropriate to the type of problem they claim to solve? To help answer these questions, I’ve provided some additional resources.
Additional Resources to Help B2B Marketers
1) ‘Artificial Intelligence’ Has Become Meaningless—Ian Bogost—The Atlantic
According to Ian Bogost, it turns out that most AI—despite the hype—is nowhere near as advanced as the celebrated machines of science fiction.
2) If Your Data Is Bad, Your Machine Learning Tools Are Useless—Thomas C. Redman—Harvard Business Review
Poor data is the problem, according to this HBR article. The vast majority of data fails to meet the basic standards needed to properly train a predictive model. It’s a case of “garbage in, garbage out.”
3) Help Your People Help Your Customers—TechTarget CMO John Steinert—MarTech Today
TechTarget CMO John Steinert explains that there’s a human tendency to look for easy fixes to complex problems—in both marketing and sales. Before you throw AI at your problems, you should use better insights for real sales enablement.
4) What is AI washing?—TechTarget
Learn more about how to recognize marketing efforts designed to imply that a company’s brands and products involve artificial intelligence technologies, even though the connection may be tenuous or non-existent.
5) 6 Limitations of AI in Marketing That You Need to Know Before You Buy—Mike Kaput—Marketing AI Institute
In this useful article, six marketing AI experts share their perspectives on the limitations of AI. One of the limitations is the human expectation of what it can do.