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Marketers Seize the Future with Motivation AI


Across scores of top industries, in schools, coding, linguistics and translation, music, individuals’ personal lives and plenty elsewhere, artificial intelligence like OpenAI’s ChatGPT are revolution-starters. Insider recently wrote a piece titled “I outsourced my memory to AI for 3 weeks” and ChatGPT even has multinational technology giants on the fritz; Google just unveiled its long-awaited rival for ChatGPT, and Microsoft swung the other way and integrated ChatGPT to revitalize Bing. We’ve entered a weirdly wild era as AI stampedes ahead.

Needless to say, the topic of artificial intelligence and its use cases are all the rage, at the moment. Just yesterday, I published this AI article, covering Conversational and Generative AI and what’s next.

Now, let’s cover another form of AI in the fray, and where it could theoretically go:

Motivation AI.

Motivation AI focuses on driving better customer engagement. This class of AI, as with Conversational and Generative, uses advanced machine learning (ML) and natural language processing (NLP), along with deep learning models. These allow it to better understand customer intent and generate emotionally informed messages that, per a release from Business Wire, are quantifiably proven to engage individuals, activate certain behaviors, and motivate further action. Businesses and brands aplenty can utilize it to interact with their prospects, customers, and others.

Moreover, Motivation AI generates specialized language and personalized decisioning models to interact and, ideally, inspire. These are generated from how it’s trained on highly specific datasets of real interaction and transaction info from, you probably guessed it, existing U.S. customers. Motivation-focused language and emotionally sound replies are all in its purview.

According to Persado, “the average conversion lift of more than 40% reflects the true response that hyper-personalized communications receive from motivated recipients across programs, channels and audience sizes.” The legitimacy here feels like we’ve been dropped into the film Her, in terms of the calculable success of customer-AI interactivity. Well-known organizations including JPMorgan Chase & Co., Dropbox, Ally Bank, and Verizon have already been using Motivation AI for messaging optimization. Like with ChatGPT and sundry AI platforms, this is verifiable. It works.

So, where is this headed?

Boldly declaring where the next breakthrough in AI might be is a fool’s errand (given ChatGPT seemed to pop out of the ether overnight), but smart predictions can still be proffered. Alex Vratskides, CEO of the aforementioned Persado, believes different AI can serve as stepping stones, more or less, to more valuable AI advances.

“If you’re looking for a productivity jumpstart, ChatGPT is a great starting place,” Vratskides said. “But when more marketers start using Motivation AI to motivate people to engage and take action with their brand, they will truly be dazzled by the potency of this technology. I predict marketers will quickly embrace the combined value of automation, personalization and motivation to drive business success.”

In closing, something less tactical than Motivation AI; rather, a more lighthearted version of motivational-esque AI named InspiroBot.

As I wrapped up writing this article, I asked InspiroBot to generate an inspirational quote that would hopefully enrich this story.

The result?

Glorious is a student who knows every corner of their song.

Admittedly this, again, is not the kind of Motivation AI we’ve been discussing. It’s not the type that Fortune 500 companies are tapping into. Nevertheless, it presents a sort of upbeat opportunity for reflection: We are all students of technology, especially that of AI as it muscles through society and continues learning. We likely won’t know “every corner” of it; that’d be like trying to juggle melty gelatin.

That said, as we ourselves learn and keep carefully open minds, the vast potential of technology like Motivation AI may feel less mysterious, more quantifiable, and truly functional.

Edited by Alex Passett
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