Marketing teams that feed AI tools data without brand-specific context will stall on performance gains, according to Sandeep Menon, CEO and co-founder of marketing orchestration platform Auxia. The company, which launched its Agent Studio product in August, is betting that AI trained on a company's own strategy, past performance, and team knowledge can move marketers from analysis to action.
"A lot of what we're trying to do is drive that transformation," Menon said, comparing the current AI adoption moment to factories that initially swapped steam engines for electric motors without redesigning their workflows.
Three problems Auxia set out to solve
Menon said marketing is "inherently tribal" - every company has its own internal approach that does not transfer easily across brands or industries. Life-cycle marketing at Google looks nothing like Uber's strategy. A second challenge is the sprawl of tools that "don't talk to each other," leaving no single employee with context across every platform and task. The third is that marketing is a "multiplayer" experience, so any AI tool must be accessible across teams.
Auxia's platform includes two products. Auxia Decisioning builds a unique profile for each customer to determine which ads they are likely to respond to across channels. Auxia Agent Studio, the newer offering, contains agents that measure campaign performance and execute changes. The agents connect to a client's existing content repository and external platforms like Figma and Salesforce, then suggest next steps - which assets might perform better for which audiences, or what creative needs refreshing.
Marketers are not satisfied with tools that stop at analysis, Menon said, because they are left asking, "What do I do next?"
How brand context shapes the training
Each company's agents are trained on its own first- and third-party data. No company's data informs another's decisioning. This addresses the tribalism concern and keeps brand context intact while upholding data privacy. When an employee switches teams or leaves, their knowledge is already built into the agent suite, so no gap appears in the workflow.
The agent studio also acts as a central "control plane," Menon said, determining which sub-agent is best suited for a given task. Most of Auxia's optimization runs on clients' owned and operated channels rather than large third-party ad platforms, giving Auxia direct access to real-time first-party data. For a publisher client, the system can target readers with ads related to articles they recently read, or track whether a reader arrived via another ad and use that signal to determine what content resonates.
An organizational problem, not a technology problem
Menon said making the most of AI is "not a technology problem. It's an organizational problem." Auxia partners with CMOs to help restructure teams so humans focus on strategic decisions and machines handle manual, repetitive work. The industry is early in this shift. Productivity will not improve until the process itself changes, rather than simply swapping old tech for new.
For marketing leaders navigating this transition, building AI literacy at the executive level is becoming a practical requirement. Resources like an AI Learning Path for CMOs can help senior marketers evaluate where AI fits into team structure and strategy. Broader training on AI for Marketing also supports teams learning to connect tools with brand-specific context.
Why this matters for marketers
The core message from Auxia's approach is that AI without brand context produces generic output. Marketers who want tools that actually optimize for future outcomes need to invest in connecting those tools to their own performance history, content libraries, and team knowledge. The technology exists. The harder part is reorganizing workflows so that people make strategic calls and machines execute the repetitive work - not the other way around.
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