Marketing teams are embracing generative AI to transform campaign design, personalization, and delivery. Two years into adoption, these efforts have started to deliver significant results for some major brands.
Retailers, for example, use AI to refine customer segments, accelerate content creation, and personalize recommendations. Those leveraging AI-driven targeted campaigns are achieving 10 percent to 25 percent higher returns on ad spend. For instance, Booking.com, a travel company, has expanded AI-powered features to simplify trip planning, refine searches, analyze guest reviews, and book with confidence.
And consumer goods giant P&G, conscious of the gap between reported and actual consumer behavior, applies AI to analyze real-time usage data from smart products such as the Oral-B iO toothbrush to tailor offerings. Even companies in regulated industries such as financial services, healthcare, and telecommunications are realizing benefits consistent with legal and privacy concerns.
Still, scaling up is not easy, as the complexity of digital ecosystems and rising demand for personalized customer experiences challenge marketing teams. CFOs and CEOs, meanwhile, are pressing their CMOs to deliver more with fewer resources and to innovate faster. These factors make it urgent for senior marketing leaders to move beyond pilots and infuse their data, technology, and processes with generative AI.
Read: Global AI market to reach $4.8 trillion by 2033: 40 percent of jobs at risk
Where generative AI is paying off
Our work with leading marketers reveals that early adopters of generative AI are already reaping benefits, such as:
• Speed: Campaign time to market has been reduced by up to 50 percent.
• Efficiency: Content creation time has dropped by 30 percent to 50 percent.
• Return on investment: Hyper-personalized campaigns have boosted click-through rates by up to 40 percent.
The next phase of AI adoption will focus on four high-impact areas, driven by business goals more than by the promise of the technology:
• Workflow simplification: Processes such as creative concept drafting, image production, content translation, brand compliance checks, and asset tagging are becoming more streamlined.
• Content creation and personalization: Generative AI automates copywriting, image production, ad versioning, and other creative tasks.
• Customer insights and intelligence: Real-time analytics and AI-driven segmentation enhance targeting by predicting customer behavior.
• Measurement and optimization: The technology can automate campaign performance analysis and integrate unstructured data. Generative AI is transforming how brands engage customers, but scaling requires strategic investment in high-impact areas rather than scattered pilots. To spur widespread adoption, marketing teams must enhance AI literacy, modernize workflows, and streamline operations.
Five ways to gain an edge
Our analysis of leading marketers finds that five practical steps will allow organizations to accelerate the next phase of generative AI maturity.
1. Commit to bold ambitions — and results
Too often, marketing organizations focus on individual use cases rather than broad, transformative goals. CMOs should set measurable, ambitious targets (whether operational, customer-centered, or financial) and hold their teams accountable.
For example, a global financial services firm aimed to cut campaign time to market by 50 percent, through AI-driven content innovations and tech stack upgrades. Similarly, a media company leveraged generative AI for personalized marketing, boosting click-through rates by 5-7 times. By setting clear goals and integrating AI-supported solutions, these companies achieved lasting impact and efficiency.
2. Focus on big wins rather than letting a thousand flowers bloom
Building early proof points promotes credibility and momentum, with fewer solutions in production demonstrating scalability. Rather than prioritizing broad AI experimentation, marketers should start with manageable, high-impact use cases — such as automating direct marketing copy, social media posts, or personalized content — to boost engagement.
A consumer bank followed this approach by implementing a generative AI-powered creative assistant for personalized search and social media campaigns. The initiative cut production time by 75 percent and revealed a 20-25 percent opportunity to increase new account volumes by scaling up experiments.
3. Design for users’ needs
Adoption falters when solutions do not align with how teams actually work, or when those solutions are pushed by internal IT teams rather than marketing leaders. Broad adoption stems from marketers defining their workflows, identifying opportunities for improvement, and co-creating AI solutions with the data and technology teams.
A financial services firm enlisted internal “super users” to guide development of a custom AI content-creation tool, ensuring relevance and buy-in. Those employees then helped train their peers and proselytize the technology. At the same time, leaders should encourage their teams to reimagine entire processes and current roles rather than making incremental improvements to individual tasks.
4. Never stop learning and raising the bar
While many marketers have a basic familiarity with what generative AI is, few companies have invested to equip their frontline marketing staff in using it effectively and at large scale. Training needs to be tailored to employees’ day-to-day work, showing where generative AI can complement and enhance specific roles, from creatives to data analysts.
One media company integrated AI training into weekly meetings, teaching prompt writing and encouraging experimentation, with teams refining techniques together. To pull off real change, CMOs must push teams to rethink workflows — simply issuing Gemini or ChatGPT Enterprise licenses without a clear, ambitious strategy won’t shift long-standing work habits.
5. Expand the partner ecosystem
Managing the array of marketing technology has become even more difficult as vendors jockey for position. Vendors can assume some work that used to be completed by creative and media agencies, but the vendor landscape is still messy.
The best approach is to pilot marketing-focused vendors such as Adobe, Jasper, Synthesia, or Typeface, so as to identify the best fit for scaling up efforts. Unlike selecting a core marketing tech vendor, testing generative AI solutions should be quick. Agencies are also innovating, and marketing teams must stay updated, as most solutions won’t be built in-house.
Generative AI has shifted from novelty to necessity. Leading teams are already rethinking partnerships, preparing for the decline of link-based search, and exploring marketing to AI-powered bots. For those still in pilot mode, it’s time to scale up and attain real gains in productivity, personalization, and return on investment.
Brahim Laaidi is a partner at Bain & Company in Dubai, while Jeff Katzin is a partner at Bain & Company in New York.
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