AI has entered the zeitgeist, and it seems folks either embrace it with zeal, reject it as the zombie apocalypse or just don’t know what to do with it. My approach to AI? Learn deeply. Test widely. Incorporate quickly. And no, this wasn’t written by AI.
After attending the recent American Marketing Association conference on AI in Marketing, one thing became clear: AI supercharges our efforts, but it’s no silver bullet. It won’t solve all your marketing woes as a panacea, but it will dramatically amplify your strengths if used correctly and strategically. The real questions are: what can AI do well and where does it fall short?
Let’s start with its strengths.
Generative AI can crank out content at blazing speeds. It helps build surveys, beef up SEO and annihilate “blank page” syndrome, our paralysis when facing the literal and figurative blinking cursor. It can run automated campaigns, generate hyper-personalized content in real-time and remove friction from your creative process. In short: AI can amplify your brilliance, but it can’t replace it.
Because the bottom line is AI without strategy is motion without a destination, a vortex of energy with no place to go. Anyone can run Chat GPT searches, but only experts can adeptly situate that data into a holistic approach.
AI still needs a human editor to ask:
- Is this on brand?
- Is this audience-centered?
- Is this what the client actually asked for?
If it’s not aligned with your brand voice, your business goals and your audience’s needs, it’s just another tool, more data, more noise.
And if you think sticking your head in the sand until the storm passes is best practice, we are approaching a capability gap between those who use AI and those who don’t. McKinsey reports that 85% of enterprises are already leveraging AI in marketing and sales. If you’re not in the game, you’re already behind.
So, what skills do marketers need to make the most of AI? What does it practically take to use AI well in marketing? How do we operationalize it?
First, you need data literacy. That means knowing how to assess the quality of your data, whether it’s relevant, and, most importantly, how to turn it into action.
Next up is prompt engineering. Think of it as talking to AI in its native language. The clearer and more specific you are with roles, scenarios and context, the better the output.
Then there’s critical evaluation. AI can spit out content all day, but it’s on you to ask: Is this accurate? Is it relevant? Does it sound like a human? Where is the veracity check?
And finally, ethical fluency. Be transparent. Respect your audience’s privacy. Use data in a way that’s fair, inclusive and aligned with your brand values.
AI is just the latest in a long line of tools we’ve had to learn, like social, like SEO, like CRMs. The marketers who win will be the ones with an experimental mindset, a data-driven approach and a feedback loop that includes a real human touch.
There’s no better time than the present. So, jump in. The water’s fine. Just don’t forget that in the end, expertise still reigns supreme.
