Why CMOs’ unrealistic AI expectations will cause projects to be abandoned

Chief marketing officers may be at odds with reality as their expectations for immediate returns from AI projects clash with the technology’s more gradual impact. Eight out of 10 CMOs said they expected investments in AI, including in generative AI, to positively impact their marketing this year, according to a CMO report recently published by research firm Gartner. However, separate research published earlier this summer by Gartner showed that generative AI requires time and continued investment to bring significant returns—a reality that will cause at least 30% of projects that hinge on generative AI to be abandoned by executives by the end of 2025. These conflicting outlooks suggest that head marketers may need to adjust their perceptions about AI if they hope to see their efforts through. This shift entails increasing one’s tolerance in the short term for indirect results, such as wins in change management, over financial returns on investments, said Nicole Greene, a VP and analyst for Gartner and the author of the CMO report. Change management refers to altering one’s organization to prepare for new technology or other transitions. “Generative AI can do a lot of things, but it’s going to require more resources and more budget to be successful,” Greene told Ad Age. Check out the latest Ad Age events and award programs Misguided expectations are one of a number of growing pains that marketers face as their AI strategies mature. CMOs have struggled to advance the technology’s use cases beyond low-hanging fruit, such as using it for social media content creation. Also, anti-AI sentiment—which has gained steam in the industry’s creative circles—continues to be a thorn in the side of CMOs. High costs associated with AI are making matters more difficult, especially because many CMOs appear to incorrectly assume that returns will quickly offset their investments. Consider a popular AI use case among marketers—using generative AI to create personalized content. Implementing the software behind this capability could cost $750,000 to $1 million, according to Gartner. And keeping the system updated with data, human training and other resources leads to an insidious investment, requiring between $790 and $1,200 per user, per year, according to Gartner. The same Gartner research shows that other generative AI deployment approaches are even more costly, such as creating a virtual assistant, which can call for between $5 million and $6.5 million in upfront costs and $8,000 to $11,000 per user per year. Virtual assistants are typically chatbots that can perform a variety of support roles, including for customer service and internal use cases. Brands such as Volvo and Walmart have experimented with virtual assistants, although the specific costs of these activations are unclear. Also read: 5 AI marketing activations to know about right now Further, developing one’s own custom AI model can cost between $8 million and $20 million upfront and $11,000 to $21,000 per user per year. These are AI platforms that are built entirely in-house, as opposed to relying on foundations developed by OpenAI, Google or other providers. Several agencies are designing custom models because they can play roles that are ultra-specific to their businesses, as well as be trained on mostly proprietary data. Edelman, for example, has built a custom large language model (LLM) that is meant to help marketers cultivate brand trust. Greene admonishes marketers to remember that AI is not a one-and-done implementation—it requires a continuous flow of updating to not only stay operational but also to scale to a company’s ambitions. More from Ad Age: 9 AI tools agencies are embracing right now Aside from having unrealistic investment expectations, CMOs often demonstrate misunderstandings about how AI actually works, said Adam Birkenhead, chief operating officer at Digitas North America. For example, CMOs have a desire to craft a marketing message that is virtually the same every time, but that’s not how AI works, Birkenhead said. Variations are noticeable between almost any two outputs generated by AI, even when drawn from the same input within the same system during the same AI session. For example, asking a chatbot to write ad copy, using identical parameters, will likely return nonidentical results. The work required to smooth out an AI system’s outputs and find more enriching use cases requires many resources, one of the most important of which may be time, Birkenhead said. Programming AI to write alt-text, for instance, may seem undaunting for a single image, but applying that same task to scores of images—in a way that delivers a consistent output—requires building additional infrastructure that changes the process altogether. Results through change management While financial returns will take time, Greene sees three opportunities in change management through which organizations can find returns in the near term: data hygiene; technology and talent; and trust. Data hygiene refers to the quality, governance and management of proprietary data. Proprietary data—data that is unique to the organization and its customers—is the lifeblood of generative AI platforms. It is the information that is used to train the systems in the first place. However, only 4% of CMOs said their data is AI-ready, according to Gartner. Greene’s advice around technology and trust refers to preparing one’s internal culture for the implementation of AI. This has been another bottleneck for marketing organizations: 41% of CMOs view employee resistance to AI—a function of their fears of being replaced—as a challenge to their generative AI efforts, per Gartner research. And trust can be understood as preparing one’s external culture for AI, this being a company’s audience of stakeholders and customers who show their own skepticism toward this technology. While these three areas can provide CMOs a short-term barometer for future AI success, they are mutually dependent, and they require their own investments of time, energy and other resources, Greene said. “It’s like a three-legged stool—remove one of the legs and it’s going to tip over,” she said. As CMOs address change management, level-setting expectations may also require them to reassess the fundamental purpose for why they are adopting AI, said Craig Elimeliah, chief creative officer at Code and Theory. The thinking is that organizations aren’t adopting AI just to adopt AI, but rather they are seeking specific gains in some area of their business. Some companies are seeking gains in engagement, while others are focused on efficiency, productivity, security or scale. Re-evaluating these first principles could help them stay the course when the return on investment remains elusive. “CMOs have to really step back and realize what flavor of AI they’re licking,” Elimeliah said. Sign up for Ad Age newsletters From influencer marketing to agencies, get the latest news and analysis delivered to your inbox Click here

Why CMOs’ unrealistic AI expectations will cause projects to be abandoned

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