By Assaf Baciu, COO and Co-founder
Sometime in the not-too-distant future, we’ll wonder how we ever wrote marketing copy without the help of AI. Many forward-thinking leaders are already there. They see the need to tackle language-market fit in the same way they started thinking about product-market fit a decade ago.
These leaders understand that molding their product to fit the needs of the market will only get them so far if they aren’t sharing the innovation with messaging that meets the market needs, at both an audience and individual level.
For marketers looking to speak to individuals at scale, the combination of human-generated ideas and algorithmic choice of words can create superhuman impact. But only if it’s applied correctly within an organization, which requires three important steps.
The leaders and companies that are already embracing AI language generation are seeing an increase in conversion rates of up to 40% across engagement channels—and grabbing an early slice of the more than $800 billion in incremental revenue projected to be generated worldwide by AI-driven personalization in the next few years.
Forward-thinking leaders believe, and are seeing firsthand, that AI-generated content is key to their overall personalization strategy. They need to be able to put the right words in the right order for the right customer—at scale.
But the level of coordination needed for a successful partnership between AI and human creativity must be championed at the executive level. It has to be an imperative that all internal and external teams that contribute to the AI language generation processes come together to figure out how to make the most out of the technology, from copywriters and marketing managers to data practitioners and even agencies. This level of cross-functional cooperation can typically only come about with a clear directive from the top.
Individual marketers need to get more comfortable with data itself. After all, the algorithm is learning to speak our human language. The least we can do in return is put greater effort into understanding how the machines and algorithms work, so we can be better decision-making partners. This will require training existing staff, whether that means leveraging the knowledge of internal experts, leaning on consultants available through our tech partners, or bringing in other thought leaders in the AI/machine learning space.
Forward-thinking leaders believe, and are seeing firsthand, that AI-generated content is key to their overall personalization strategy.
When we hire the next wave of marketers, we may want to look for candidates with stronger technical backgrounds, maybe even those with some background in data science, who understand at a high level how to interpret data. Creative leaders may also need to reiterate that marketers are not hired to come up with ideas simply for the beauty of the ideas.
If you’re working for a brand, your highest purpose is to drive connections and relationships that connect to the growth of the company. And working in a complementary fashion with AI will make us far more effective at that goal than we could ever be on our own.
For generations, marketers have learned to trust in their gut. And intuition still matters, but not when it comes to questions that can be clearly answered by data. Today, it’s essential to trust in AI. We may be tempted to say, “Well, that wording doesn’t sound very compelling to me. It’s not phrased the way I would say it.”
But if the data shows that it’s the most powerful way to communicate that idea to that group of customers, who are we to get in the way? We’re still entitled to our opinions, but the machine isn’t working from opinion; it’s working from data. It asked a million people how compelling the message was, and they answered by either interacting with it or not.
The upside of this new way of working is that marketers are freed from subjective arguments about wording. They can let the algorithm handle that. Instead, the focus must shift to generating the next creative and innovative idea.
Some members of your team will see these and other advantages of the human/AI partnership right away. Others may take time to build trust, as they interact with the AI and gain familiarity with its capabilities and how it fits into their workflows. Upscaling your team’s data fluency will also help accelerate this process.
Today, with the extreme pressure to be relevant in an environment where we’re talking to millions of customers and never meeting them, human creativity can only take us so far. We’re simply not capable of achieving language-market fit at scale on our own.
The ideal partnership involves humans specializing in one kind of creativity (innovation/coming up with completely new ideas), while leaning on AI capabilities to assist with the other kind of creativity (adaptation/expanding on those ideas and targeting them to individuals). As the AI revolution marches on, humans will continue to be the innovators. The adaptors will be the machines.
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