CMO optimism and increased investment in generative AI
The mindset around artificial intelligence, specifically generative AI, is shifting fast. A year ago, most CMOs saw it as something worth testing on the edges. Today, they’re starting to position it at the center of their strategy. It’s not theoretical anymore. According to Boston Consulting Group’s latest global report, 83% of CMOs are optimistic about what generative AI brings to the table. That’s up from 74% in 2023. More importantly, 71% of these marketing leaders now plan to invest at least $10 million in the technology over the next three years. That figure was just 57% a year ago. The pace is increasing.
This kind of shift doesn’t happen by accident. Organizations are starting to realize that generative AI reduces friction across marketing systems, writing copy, generating visual assets, personalizing messages, and it does it without sleep. It allows teams to move faster, optimize budgets more effectively, and focus on things with higher strategic impact. If you run a business, it comes down to speed and output. Generative AI delivers both.
The category is moving out of pilot mode. High-trust indicators, like rising investment commitments, tell us we’re well past the early adopter phase. If marketing leaders are not in the game now, they’re going to be playing catch-up later.
David Edelman, Senior Advisor to BCG, underlined the shift when he said AI is no longer something to “experiment and play around with”—it’s becoming a core and embedded part of modern marketing operations. That tracks. When an emerging tech starts showing up in budget allocations of this size and regularity, it’s mainstream.
Expanding AI applications beyond static content creation
Generative AI does more than generate text or images. CMOs are now applying it to increasingly sophisticated tasks, especially in areas that demand scale and high personalization. According to the same BCG survey, 68% of CMOs are already using or planning to use AI to generate live-action-style video, without human actors. Another 68% are using or planning to use AI for video enhancements like editing and layering effects. And 91% intend to use it for multilingual text translation to support international reach. That range of functions isn’t just about automation, it’s about reach, speed, and adaptability.
This matters because content strategies are no longer bound by traditional limitations, people, time, or production costs. AI is effectively unblocking the system. Videos, which used to take weeks to produce, can now be created or enhanced in hours. Brands no longer need massive film crews or editors to launch high-quality digital campaigns across markets. They can scale this content globally while tailoring it at the local level. That’s impact at speed.
Text translation might seem like a supporting function, but it’s critical. For companies operating in more than one market, which is most enterprises, it means delivering content that actually connects. AI translation helps ensure consistency of voice and tone without losing precision. In multilingual markets, this is a core enabler. It ensures message alignment across regions, lowers localization costs, and speeds up go-to-market timelines.
The scalability of generative AI is no longer an abstract concept. It’s showing up in marketing execution right now, and CMOs are moving quickly to embed it where it works. The next wave of strategic advantage will come from those who can build this into their systems immediately, without waiting for perfect conditions.
The risk of overproduction in AI-generated content
Generative AI can produce content at scale, faster, cheaper, and with fewer barriers than ever before. That’s not a theory. It’s already happening. But rapid production comes with a trade-off that few in the C-suite are openly addressing: too much content risks turning customers away. It’s easy to push out thousands of messages, but harder to make sure they’re the right ones. And when every brand floods channels with AI-generated materials, attention becomes limited and fatigue increases. That’s where the problem starts.
David Edelman, Senior Advisor to BCG, pointed this out clearly. He warned that marketers might end up “bombarding consumers” simply because they can. With the cost of content near zero and production timelines compressed, the volume could rise to a point where consumer engagement actually drops. More content does not mean more value. Without restraint and strategy, it undermines brand trust and connection.
The issue isn’t with the technology. The issue is with how it’s applied. Smart executives will focus on relevancy, not just volume. AI should be used to target high-value touchpoints, not fill every possible space with noise. This means aligning content production with consumer behavior data, context, and tactical priorities. It also means building rules and review mechanisms into content workflows to prevent over-saturation.
Decision-makers should look beyond automation metrics. If the only success indicator is content volume, that’s the wrong target. Attention spans are fixed, and customer experience erodes quickly under content fatigue. There’s more value in precision than in output speed alone.
Leveraging AI for personalized customer engagement
AI isn’t just a tool to develop content faster, it’s being used to shape entire customer experiences in real time. This is where real ROI begins to show. Personalization is moving from optional to required. Market pressures and tighter budgets are forcing brands to do more with less, and that means smarter targeting, better timing, and higher relevance. Generative AI supports that pivot.
The BCG data shows that 50% of CMOs are already using AI for product recommendations. Another 37% are planning to deploy it. This is critical: personalization leads to higher engagement, stronger retention, and better conversions. On timing, 43% use AI for custom outreach, and another 29% are planning on it. These numbers tell the story, AI is becoming core in managing the who, when, and how of customer messaging.
Audience segmentation and performance forecasting are also transitioning from manual processes to AI-enabled workflows. Right now, 36% of CMOs are using AI to segment and optimize audiences, and 44% plan to follow. For content forecasting, 39% are using it, and 40% are piloting. In fast-moving markets, insights produced after the fact aren’t useful enough. Executives need tools that can predict outcomes early and adjust before mistakes cost time and money.
For executives running large customer-facing operations, this is the direction to prioritize. Use generative AI not just to make things faster, but to make things better targeted and dynamically adaptive. This is what makes marketing systems perform at a higher level, with fewer resources and tighter alignment to customer expectations. Profitable growth comes not from volume, but from outcomes. And outcomes improve when every message is relevant, timed well, and delivered to the right person.
Necessity for cross-functional collaboration in AI integration
Generative AI is no longer just a marketing asset, it’s a company-wide capability. CMOs may be leading the charge, but to capture real returns, AI must be embedded across business units, not isolated in standalone campaigns. Marketing, product, customer service, sales, these functions all touch the customer journey. If AI is deployed only in one area without coordination from the others, the impact will be fragmented and inefficient.
This matters to C-suite leaders because AI’s value increases when systems work together. Product teams can feed data into marketing platforms to improve personalization. Service operations can help train models based on customer feedback loops. Sales can align with AI-driven segmentation to ensure messaging lands at the right time. Without this synchronized effort, AI capabilities reach a ceiling.
David Edelman, Senior Advisor to BCG, made this clear when he said, “Marketers are stepping up to take more of a lead in the C-suite on how AI can help drive the business… But a lot of that can’t all be done by marketing.” He’s right. The technology itself is neutral. The outcomes depend on structure, processes, and collaboration. Cross-functional integration isn’t a bonus feature, it’s a performance requirement.
Companies that get this right will move faster and adapt more effectively. They’ll test and implement AI solutions where it produces direct operational gains. That means reworking workflows, aligning tech stacks, and creating internal feedback loops between teams. It’s less about having the best tools, more about using them in the right places at the right time.
For executives, the takeaway is clear. Generative AI isn’t a siloed innovation, it’s a shared responsibility. The companies that push AI beyond the walls of the marketing department, not just in theory but in execution, will unlock deeper productivity, better customer experiences, and real business-wide scale.
Key takeaways for decision-makers
- Growing CMO confidence drives major AI investment: CMOs are no longer experimenting, 83% now express confidence in generative AI, with 71% planning to invest $10M+ over the next three years. Leaders should ensure AI implementation moves beyond trials and into core business functions.
- AI use is broadening well beyond static content: CMOs are expanding AI into video generation, editing, and text translation at scale. Executives should fund multi-format AI capabilities that support global content workflows and reduce production bottlenecks.
- Overproduction of AI content poses consumer risk: Increased content velocity can backfire if it overwhelms audiences, reducing engagement. Leaders should implement guardrails around content volume, emphasizing relevance and strategic distribution.
- AI is powering rapid personalization and performance gains: CMOs are actively using AI for product recommendations, outreach timing, and segmentation to optimize the customer journey under tighter budgets. Decision-makers should prioritize AI tools that improve precision and ROI across customer touchpoints.
- Cross-functional alignment is critical for scaling AI: CMOs are taking the lead, but real value comes from coordination across product, service, and sales teams. Leaders should align departments early to unlock enterprise-wide benefits from AI adoption.
