Could generative AI be a ‘skill leveller’ in marketing?

ChatGPT makes “the average really visible”, says Kate Cox, CMO at BrightBid, “so you know what the baseline is.”

“If you know that that is ‘average’, you can then spend your time making it better, and just invest that human skill into that 20%-30% lift,” she adds.

Cox was speaking at Econsultancy Live this month about the use of generative AI to set standards for content and communication within marketing teams. She joined a panel of experts from the Financial Times, Barilla and esure Group to discuss the possibilities and practical reality of this recently hyped tech.

The marketing world has talked of little else since the release of ChatGPT just under a year ago – and even before ChatGPT, generative AI was a hot topic thanks to image creators like Dall-E and the text generator GPT-3. However, ChatGPT unquestionably changed the game.

AI as the great equaliser in marketing?

Cox pointed out that generative AI tools can not only promote efficiency amongst marketing teams (which is generally sought-after), but they can also improve the quality of work produced by team members. She cited a recent study carried out at Boston Consulting Group, in which half of a group of 758 consultants used ChatGPT to complete a fictional consulting project, and half did not. Overall, the less skilled consultants saw a 43% performance increase from using ChatGPT, while the higher-skilled consultants also saw a performance increase, but only of 17%.

“It also worked really well in getting everyone up to the same level – so actually, it’s a skill leveller. And I think there are some implications around how we manage some of that that we will undoubtedly figure out over the next year or two,” she said.

Elisabeth Ling, non-executive director and product advisor at eSure Group, and Arsalan Baig, global data science and AI manager at Barilla, both pointed to the potential for AI to take on more rote tasks, such as producing content at scale across multiple campaigns and multiple languages, leaving humans free to spend time on creative and strategic thinking.

“I think we can all say that what you get out of these tools is usually not on-brand, if you haven’t trained the algorithm,” said Ling. “And it’s usually not very unique. So, how do you use your brain to [differentiate]?

“You can spend your time on what you always wanted to do – thinking of your strategy, thinking of how you’re going to be unique,” she added.

“The tool can generate creativity,” said Baig, “but you need to then QA it. So, you can really start focusing on, ‘What do I want it to say? How can I improve it?’ … Maybe you’ll start spending 80% of your time on that strategy and creative aspect.”

“The scaremongering [about AI replacing jobs] needs to stop”

Katrina Broster, marketing performance and technology director at the Financial Times, sounded a determinedly positive note for the future of human work. “The subject lines that I’m sure everybody is getting in their inbox every day – ‘Is your job at risk?’ – frankly, I think the scaremongering needs to stop. Let’s put that energy into figuring out better uses of AI.”

“That’s why I’m focused on efficiency – because efficiency has humans at the centre,” she said.

Barilla’s Arsalan Baig highlighted the importance of ‘democratising’ generative AI by teaching people the skills they need to get the most out of the tool – such as how to create an effective prompt – and then letting them use it in the most appropriate way for their role.

“[Marketers] know what their pain points are – so, educating teams in order to help them understand how to use the tool is really where the productivity will come.”

Start with the problem, not the technology

“The first area to start [with generative AI] is identifying your use cases,” said Baig. “Getting down on paper what you want to do and what you want to achieve is really where you need to start.”

“Where we saw the best use case was in productivity,” he added. “Enabling the company to become better at communicating is where we see a lot of [increased] productivity” thanks to generative AI.

Elisabeth Ling echoed this advice when considering AI in general: “I very much agree that you need to start with the problem, not with the technology,” she said.

“Secondly, you need to do your homework … Use [AI] in your daily life, and then when you know what type of algorithm does what – immediately you will make the connection. ‘I have this type of problem, I can use a recommender… [or] I can use generative AI’. Starting with understanding what these tools are is really important.”