Experienced marketing experts share what AI needs to deliver maximum ROI this year
The last year in AI has been a dizzying whirl of feature releases, model updates, and imagination-bending use cases. It seems, at times, that this generative technology can do anything. But in practice, having limitless possibilities is nowhere near as impactful as having a collection of concrete solutions. If we do not know the problems we need AI to address, the technology will never be more than a trinket.
Understanding our audience’s problems is core to what we do as marketers. It is cannon by now that we communicate solutions over features and speak to our audience’s pain, not our product’s specs. But amid all the fascination with AI and rush to deploy it, at times last year, we may have lost that instinct. We may have prescribed AI without properly diagnosing the problems it’s meant to address. So, looking ahead to the fresh year ahead of us, I reached out to some of my peers to ask the question:
It is not a glamorous problem, but it is one of the oldest in the modern marketing tome. We have too many disparate systems and not enough connectivity across them. Over the last year, I’ve heard this problem from startups and I’ve also heard it from global giants: Save us from our own tech stacks, please. Marketers, for the most part, aren’t frivolous with their software. Each tool does something essential. It’s just that marketing is so varied and we need a central nervous system that unites them all. Data in, data out; connected, aligned, and humming like a symphony.
In action, this looks like being able to tap into AI anywhere you create online and knowing that when you do, all of your approved company data, positions, messaging, and more would be securely accessible too. This should be the case no matter what tool the data originated in and no matter where you are creating. In this scenario, AI becomes the stream that flows through all marketing systems.
Marketing teams are high-speed, detail oriented, resource-limited, and time-rushed operations. They can parallel the intensity of a restaurant’s back kitchen with twice the context-switching. Marketing leaders are constantly assessing whether a project’s scope can be achieved in the available time. Entire teams are built up around project management and campaign orchestration.
The initial allure of AI was that it would speed one key part of marketing processes up: content production. And it’s safe to say by now that that box has been successfully checked. But project orchestration is still a major pain point. What if AI could make project orchestration, team collaboration, and review cycles faster?
There’s no joy in project scoping. There’s no creativity in intake forms. Campaign and creative review cycles are where dreams go to die. There is real impact in developing and adopting AI systems that address these potholes, which slow down our processes and hinder our ideas from getting to market.
It is great that AI has led to greater efficiency. But looking ahead, marketers don’t just want to create faster, they want their marketing to perform better. They want AI-assistance to move from driving efficiency to driving results. And this starts with better analytics and clearer insights.
Years ago, I was running product marketing and web design for a global company when word came in that we needed to change the name of one of our core products. For the product organization, it was a quick fix: change the internal navigation and they were done.
Marketing was a different case. The old product name was everywhere, across hundreds of website pages, nurturing flows, ad copy, video soundbites, blog posts, sales collateral, review sites, and help documentation. And we had to find every instance. That seemingly small change meant weeks of work to find and update everything. So this one project, which came out of nowhere, set our creative team back significantly. It required heavy orchestration across teams and was error prone. I remember exclaiming to my colleague on the product team that I wished I had a self-cleaning website.
Change like that is constant in growing companies with new pricing, packaging, products, executives, and positioning to announce and update. Deciding on the new language around these updates is no problem. Replacing old language in an unknown number of existing assets to reflect the updates is a nightmare. AI has a lot of opportunities to reshape this tedium.
In addition to change management on your own properties, CMO of SoftwareReviews Christina Kearney extends this idea to being able to follow and adapt to changes on your competitors’ properties:
Speed is not just about production. It can be about adaptation. How can we leverage AI to move through changes better and without so much accompanying friction? The possibilities are truly exciting, like adapting content to different languages for example:
Any business or organization that has been around for longer than a year is drowning in information. Our internal wikis are awash with it and scores of pages are outdated but still living on. We are also inconsistent in where to find information. Was that document emailed or sent in Slack? Is this the right version of the brief?
This clutter and noise is hard enough for seasoned employees who have the muscle memory of internal communication patterns, but it can be near impossible for new hires or outside vendors to navigate. Michael Applebaum, VP of product marketing for Indigo, pinpoints this challenge well and extends his AI wishlist to wanting a central knowledge base that can not only be queried but also personalized for different roles and needs:
Imagine the return on investment when a newly hired sales rep can get the kind of just-in-time training that will cut their ramp time in half. Imagine a single source where you could ask any question and it would return the latest company data, stance, or materials. That world could bolster companies in a meaningful way.
In 2023, the world was awed by the speed and efficiency gains of generative AI, particularly how easily it knocked down writer’s block and how well it repackaged work into new formats. So many of us jumped into the pool and started experimenting with AI, finding its strengths, its weaknesses, and testing it out on different platforms.
But if 2023 was about efficiency gains, 2024 needs to be about business gains. With the addition of analytics and a growing number of integrations, I think we’ll get closer to moving from faster outputs to better outcomes. We’ll be able to leverage AI not just to help us move faster through our work, but to help us surface our best work and further optimize it. This is a necessary evolution in the technology and the time has come for it. I hope that AI in 2024 will follow-through on that potential. I know Jasper plans to.
“There’s so much promise, so I’m not down on AI at all, I’m just impatient. I mean excited,” wrote Rashel Stephenson, founder of Ohello.Video when I posed the question.
Our team here at Jasper, and me personally, are impatient — I mean excited — too Rashel.
To all of my fellow marketing leaders, thank you for the ideas you contributed to this article. As we head into 2024, we don’t need to just accept the out-of-the-box use cases of AI. We can define our own uses based on real problems that will result in better outcomes for all.
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