AI Visibility Depends on Operations—Not Marketing—Here’s Why COOs and CMOs Must Align

Why AI Visibility Starts with Operations – Not Marketing

From shipping delays to support hiccups, AI tools are increasingly sensitive to the signals operations send out. This is a clear call for COOs and CMOs to work closely together. Traditional SEO tactics no longer guarantee discoverability. AI-powered search platforms like ChatGPT, Gemini, Claude, and Google's AI Overviews analyze operational data—order accuracy, pricing gaps, and customer service experiences—to form brand perceptions.

These challenges aren't just marketing issues; they're organizational blind spots that hinder AI visibility. Many can’t be solved with content updates alone. They require real operational changes. Here’s why the initial hurdle for AI visibility lies with operations, not marketing.

Why Organizational Signals Shape AI Visibility

Every part of your company—operations, product design, fulfillment, customer service—sends signals that AI systems detect. These aren’t just internal metrics; they appear in online conversations that influence how Large Language Models (LLMs) assess your brand’s relevance.

Search engines focus on content matching. LLMs go deeper, evaluating the entire customer journey: shopping experience, product durability, lifetime costs, and after-sales support. Even past glitches or outdated technology can cause LLMs to skip or misrepresent your brand.

For example, one global client with a popular product was flagged by an AI audit. An LLM described their technology as “outdated” and concluded “the market has moved on.” This narrative is visible to everyone, including competitors—and no brand wants that.

LLMs Act Like a Buyer’s Advisor

Unlike traditional search engines, LLMs synthesize signals across the full operational lifecycle:

• Product design and innovation
• Quality of materials
• Cost of ownership and ROI
• Shipping accuracy
• Ease of returns
• Product durability
• Pricing
• Use cases and buyer personas
• Support experience

If operations sends even one negative signal that matters, your brand risks being excluded or negatively portrayed in AI-driven responses.

These are operational breakdowns, not marketing gaps. CMOs can’t fix them without the COO’s involvement. Resolving these issues can take months or even over a year. Roadblocks often hide in fulfillment logs, UX error reports, return rates, or outdated product specs.

LLMs don’t just process what you say; they learn from what the world says about your performance. This makes the COO a critical gatekeeper for AI brand visibility.

The CMO Needs Operations Metrics on Their Dashboard

Operational issues often signal early warnings for changes in AI visibility. These metrics don’t directly boost visibility but predict potential losses if ignored. Marketing teams should track bellwether metrics—indicators of broader impacts.

For instance, just like FedEx shipping volumes predict consumer spending trends, metrics like shipping delays, support wait times, and other operational issues hint at what LLMs will soon learn and reflect.

While LLMs may not have direct access to your internal data, public complaints and commentary shape AI perception. CMOs need these metrics to pivot marketing strategies before visibility declines.

The COO Needs to Monitor LLM Perceptions Over Time

COOs must track how LLMs interpret real-world operations, beyond internal performance numbers. AI platforms pull data from public forums, reviews, industry publications, and third-party comparisons.

Flawless execution alone isn’t enough. If LLMs detect outdated positioning, innovation lag, or recurring support issues, brand perception suffers. COOs should monitor AI interpretations and either fix operational problems or enable marketing to respond before negative narratives solidify.

What AI Perception Monitoring Looks Like in Operations

Operations teams don’t need to be AI experts but must track how AI reflects the brand. This responsibility can sit with marketing, operations, or both. Here’s how to put it into practice:

• Track forum and online chatter: Monitor mentions in forums, reviews, Reddit, and social media. These signals now influence AI visibility. COOs need to act quickly when negative patterns emerge. This pushes companies toward best-in-class operational standards.
• Monitor AI platform responses: Regularly check what LLMs like ChatGPT or Bing Copilot say about your brand. Look for outdated info, inaccuracies, or mentions of defects and support issues. This process requires manual review and a clear framework, as automated sentiment analysis alone can miss factual errors.
• Measure accuracy and consistency: Track how often AI responses get your facts, product specs, and use cases right versus wrong. Sometimes correct data exists but is inaccessible—locked in PDFs or behind lead-generation forms—limiting AI’s ability to surface it.
• Link operations events to AI narratives: Develop a dictionary of key operational signals and monitor them across internal data, public chatter, and AI outputs. For example, trace how a shipping delay appears first in ops data, then online complaints, and finally in AI-generated narratives. This builds a timeline to act before AI visibility is impacted.

Larger companies with high visibility face faster perception shifts because LLMs update their models more frequently with signals from these brands.

The Strategic Opportunity

AI visibility demands shared ownership between operations and marketing. When these teams align:

• Operational issues get resolved faster
• AI visibility improves
• AI tools reflect stronger, more accurate brand narratives

Brands that clear these operational hurdles position themselves well in AI-driven discovery. With solid operational signals, marketing can then amplify impact effectively.

For those looking to deepen their understanding of AI's impact on business operations and marketing, exploring specialized training can be valuable. Check out resources like Complete AI Training for courses tailored to AI’s role across functions.