Prioritisation ‘paradox’ emerging around adoption of gen AI, research shows
While most marketers accept the importance of generative AI, few have it at the top of their to-do list.
There is a growing divide between the urgency marketing leaders ascribe to adopting generative AI and how important this tech is to the day-to-day running of the marketing function.
The State of Marketing Europe report from McKinsey released today (20 November) shows implementing generative AI and agents is seen as a top-three priority for the 500 surveyed marketing leaders across Europe – alongside branding, budgeting and ROI. However, when these same marketers were asked to rank the top 20 marketing topics by order of importance to the business, AI came in a lowly 17th.
The data reveals a growing “paradox” in that marketers are aware of the strategic and investment opportunities that can come from bringing AI into the marketing function, but are struggling to push it to the top of their priority list.
This attitude could see them give up significant competitive advantage, the research suggests.
AI is seen as a top five priority for what McKinsey dubs “AI leaders”, whereas it ranks at the bottom of the list for “AI laggards”. This distinction is playing out in efficiency savings.
One in four companies have already achieved marketing efficiency gains of more than 20% in the past two years using gen AI. According to the data, companies with high gen AI maturity have unlocked average marketing efficiency gains of 22% and are projecting 28% gains in the next two years.
You can’t dismiss AI ads as slop when they’re winning in testingCompare this to companies struggling to implement AI, 89% of whom have not or have barely seen any efficiency gains. In fact, the data shows 94% of organisations are still reporting low gen AI marketing capabilities two years into gen AI being a mainstay.
Much of this is thanks to pilots and tests that went nowhere and were written off too soon. The research reveals the biggest obstacle for AI laggards is technological infrastructure (81%), followed by adoption and scaling (65%). By contrast, infrastructure is only an issue for one-fifth of AI leaders, who are now more concerned with how AI can impact strategy (47%).
It’s telling where the possibilities offered by AI rank for those further along the adoption process.
So-called AI leaders see personalised content at scale (80%), media optimisation (60%) and product discovery (57%) as having the highest potential to impact on their business.
On the other hand, AI laggards point to automation of non-creative tasks (62%), media optimisation (54%) and market research (43%) as the areas where AI will be most useful.
This data indicates that early adopters see gen AI as more of a strategic tool, whereas those trailing behind still view it as a way to cut drudge work and little more.
