Over the past two years, companies have consistently and safely adopted generative artificial intelligence to transform their marketing strategies. According to a Bain survey of over 180 large North American companies, 27% of respondents said generative AI exceeded their expectations in the sector.
In retail, for example, the technology has enabled more precise customer segmentation, agile content creation and testing, and highly personalized recommendations. AI-driven campaigns already show a 10% to 25% higher return on investment.
Despite initial success, scaling these initiatives is not simple. The corporate digital ecosystem is complex, and the demand for personalization requires new processes. Additionally, CMOs are under pressure to innovate with fewer resources, making it essential to accelerate the implementation of generative AI at scale.
The survey shows that 25% of organizations are still in the early stages of implementing AI in marketing, with proof-of-concepts and few technology-driven business model changes. Most (65%) are part of a second wave, creating value in product feasibility and expanding IT and data teams. The remaining 10% already have autonomous tasks, redesigning processes, adding operational value, and profit through expanding use cases and innovation.
The key benefits cited by companies adopting AI are agility, with up to a 50% reduction in campaign launch time; efficiency, as content creation time has decreased by 30% to 50%; and return on investment, with up to a 40% increase in click-through rates for hyper-personalized campaigns.
Defining priorities to leverage the impact of generative AI in marketing depends heavily on each company’s business objectives. However, Bain identified four key areas for this purpose: workflow simplification; content creation and customization; customer intelligence and insights; and campaign measurement and optimization.
Additionally, five actions are critical to accelerate technological maturity:
- Set ambitious and measurable goals, establishing clear objectives for operational efficiency, personalization, and financial impact;
- Prioritize major gains over multiple tests—organizations that focus efforts on few high-impact use cases scale faster.
- Develop end-user-focused solutions, integrated into daily team workflows, involving their own professionals in tool development;
- Continuously train employees, providing practical training and real-world challenges essential to broaden technology adoption;
- Expand the partner ecosystem—the vendor landscape in this area is still evolving, but companies should quickly test specialized solutions and track innovations from their partners.
Generative AI has moved beyond novelty and become a competitive necessity in marketing. More advanced companies are already rethinking their partner strategy, large-scale personalization, and even the future of the sector with AI assistants. For those still stuck with occasional tests, it’s time to accelerate adoption, scale initiatives, and capture the real benefits of this technological revolution.
