CIO-CMO Alignment Drives Centralized Oversight For Scalable AI-Led Brand Discovery

AI agents are starting to convert customers more effectively than many traditional marketing channels, reshaping how brands are discovered and chosen. As large language models take on a frontline role in influencing purchase decisions, generative engine optimization is moving into the C-suite, with CIOs expected to treat autonomous agents as governed infrastructure, complete with clear ownership, controls, and risk oversight.

Helping organizations manage the shift is Pranav Kumar, Sr. Director of Digital, Data, & AI at global consulting and technology services company Capgemini. With a career spanning senior roles at firms like Adobe and PwC, Kumar is skilled at leveraging generative AI to craft cutting-edge conversational experiences. In his view, enterprises benefit from reframing GEO.

"Generative engine optimization isn’t a marketing upgrade. It’s a leadership test of whether your enterprise can orchestrate AI agents safely, strategically, and at scale." He said it's no longer about mastering a single algorithm, but about enforcing a consistent narrative across a fragmented environment of interacting agents.

• The new brand guardians: In this context, the CIO's role is changing to that of co-custodian of brand authority alongside the CMO. "As a CIO, you are responsible for the brand as well," Kumar said. "So how do you go beyond the standard Google to drive those zero-click searches while promoting better governance across the brand when you interact with the end customer?"

• Governance built in: As agents begin communicating through emerging protocols, Kumar noted, the risk of unmonitored outputs grows. Effective oversight requires embedding brand compliance, bias filtering, and safety directly into the enterprise tech stack, from content management systems and Git integrations to the recommendation engines themselves. "It's essential to govern GEO as an integrated workflow, using centralized platforms to track metrics and connect them to enterprise-wide observability and guardrails."

Without a centralized strategy, he cautioned, organizations can inadvertently create agent sprawl, resulting in a governance vacuum where disparate teams deploy AI independently. The result is conflicting outputs, duplicated costs, and serious security risks. To counter this, Kumar's approach focuses on three operational levers that establish a trust layer across the entire AI lifecycle, from data preparation to ongoing governance.

• Distilling the data: The first lever Kumar described is model efficiency. "We need to leverage techniques like pruning and distillation to reduce computational overhead. This allows us to improve efficiency and avoid model drift by managing performance within the established guardrails."

• Setting standards: Next, he said, comes protecting the brand. "This means focusing on ethical and safe generation, making sure all AI outputs meet the required standards for quality, coherence, and accuracy." Bias removal falls under this umbrella, with Kumar pointing out that it can originate anywhere.

• Proof of performance: The third lever is performance monitoring. "This means using metrics, like technical scores such as BLEU and ROUGE, or direct human assessments, to continually evaluate the performance of your AI-generated outputs." Together, he said, these levers give a CIO a firm grasp of AI's impact.

According to Kumar, many in the industry are finding success by deploying agentic library constructs where every agent, internal or external, coexists within a governed ecosystem. "The agentic libraries have the different agents with autonomy, but common enterprise guardrails that govern them." This framework, together with human-in-the-loop controls, provides a defense against agents going rogue and offers a way to intervene if things go wrong. In Kumar's view, the effectiveness of this architecture hinges on tying every agent to a measurable business outcome before development begins, which prevents fragmentation and ensures each AI initiative drives value for the organization.

• The value cascade: Kumar advocated for distributed governance, a model in which KPIs are cascaded across different business functions to measure outcomes appropriately. "For a customer experience use case, the KPI might be CSAT. For finance, it could be ROI. For marketing, it's the return on marketing investment," he explained.

Ultimately, he asserted that one of the modern CIO's core challenges is building a system that continuously checks for algorithmic conformance and prevents models from introducing new financial or reputational risks. "You have to run stress tests and simulations based on the different kinds of prompts and queries fired by end customers. Use those simulations to understand potential agent risks and resolve them in the pre-launch phase." Kumar emphasized that completing this step before you go to production not only leads to more positive business outcomes later on, but promotes robust security and governance frameworks. "Otherwise," he said, "you're inviting governance failures."

The views and opinions expressed are those of Pranav Kumar and do not represent the official policy or position of any organization