From shipping delays to support problems, AI tools are noticing where operations go wrong. This is a reminder for COOs and CMOs to work together.

SEO is not enough anymore.

Finding information in AI-driven searches now relies on operations a change many marketers haven’t noticed.

AI platforms like ChatGPT, Gemini, Claude, and Google’s AI Overviews see past clever messaging.

They analyze operational issues like order problems and pricing mistakes to shape how people view a brand.

These aren’t just marketing issues. They are gaps in the organization that prevent AI from seeing clearly.

I notice them all the time in my reviews and most can’t be fixed with just content. They need changes in operations.

This is a crucial reminder and a guide for CMOs and COOs to align.

Here’s why the first challenge in AI visibility is not just a marketing responsibility anymore.

How Organizational Signals Ignite AI Visibility!

Every facet of your organization – operations, product design, fulfillment, and customer service sends signals that influence AI systems.

These are not just internal data. They appear in online discussions. These discussions influence how LLMs determine your brand’s relevance to customer questions.

• Search engines rely on content match. 
• LLMs evaluate the entire customer journey, from shopping experience to product longevity, lifetime cost of ownership, and after-sales support.

That means even old technology or previous problems can cause a language model to ignore your brand or misrepresent it.

The chart below shows how negative issues from operations are noticed and learned by language models.

Sometimes, product design is the visibility blocker.

I have seen in the past, a global industry leader be flagged by the AI.audit. They have a well-made, widely used product generating millions in sales.

An LLM described the product’s technology as “outdated” and concluded “the market has moved on.”

No company wants a customer to see that narrative, yet it is visible to everyone, including competitors.

LLMs: Your Ultimate Buying Advisor!

Unlike search engines, LLMs aren’t just crawling content. They’re synthesizing signals across the operational lifecycle, including:

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

If operations sends even one negative signal that the LLM deems important, there could be consequences. Your brand may be omitted from discovery. It may also be negatively portrayed in AI responses.

Below are a few examples from past audits:

AI Visibility Roadblock	Roadblock Owner
Company #1 isn’t being recommended because customers complain about receiving the wrong products, difficult returns, and poor online ordering experiences.	Fulfillment (COO)
Company #2 has an LLM perceiving their technology as outdated. The market has moved on to a different technology, making them less appealing to recommend.	Product Design (COO)
One LLM perceives Company #3 as serving only one niche of its market. However, the company actually serves the entire category. The reason came down to the type of products sold. This one niche makes up a large percentage of their whole catalogue.	Merchandising (COO)
Company #4 a SaaS provider with software to aid in product pricing. An LLM flags the pricing platform as causing overpricing for their customers. These customers report needing to purchase a second system to compare pricing recommendations.	Product Development (COO)

These aren’t marketing gaps. They’re operational breakdowns. 

CMOs can’t resolve them without COO involvement. Fixing them will take months, and in some cases, a year or more.

AI visibility roadblocks are buried in:

• Fulfillment logs.
• UX error rates.
• Returns.
• Even outdated technical specs or product design. 

LLMs don’t just see what you say. They learn from what the world says about your performance.

That makes the COO a critical gatekeeper for brand visibility in AI.

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The CMO Craves Vital Operations Metrics for Their Dashboard!

Operational issues can be early signs of changes in how visible AI is.

These problems don’t directly cause visibility issues. However, if we ignore them, they can lead to a drop in visibility later.

That’s why I suggest marketing teams keep an eye on key operational metrics that hint at bigger impacts to come.

For example, in finance, FedEx shipping volume can predict how much consumers will spend.

Issues like shipping delays and long support wait times are important for AI visibility. Other operational problems can also help predict future trends. They indicate what Large Language Models (LLMs) might learn and show later.

Even if LLMs don’t see your internal data, problems can appear in customer complaints. Feedback can shape how AI is viewed.

Example Metric	Warning Trigger*	Signal Source	AI Visibility Risk	Review Cadence**
Order Error Rate	Orders with mistakes >2%	Fulfillment logs reviews comments	Customers comment online that they received the wrong items. LLMs surpress brands if fulfillment appears unreliable.	Weekly
Return/Recall Rate	Return rate >10%	Returns system customer comments	Customers comment online indicate they returned the products. Signals poor product/experience AI may skip recommending your brand	Monthly
First-Contact Resolution (%)	FCR drops below 70%	Support tickets support chat logs	Customers comment online that talking to customer service was unpleasant, unuseful, didn”t solve the problem, etc. LLMs support negative support	Monthly
On-Time Delivery (%)	On-time falls below 90%	Shipping system tracking data	AI infers poor logistics supresses recommendation triggers	Monthly
Customer Satisfaction (NPS)	NPS<30	Survey Tool	Low NPS = low trust. AI may interpret brand sentiment as negative	Monthly

_{*Warning trigger metrics subject to change based on current metrics vs. issues identified in AI visibility audits}

_{** Review cadence subject to change based on issues identified in AI Visibility Audits}

CMOs need bellwether metrics to recognize when to pivot marketing tactics and avoid downstream visibility losses.

I had a mentor who called these crystal ball metrics. They were his greatest indicator of future business outcomes.

The COO Must Vigilantly Track Evolving LLM Perceptions Over Time

The COO needs visibility into how LLMs interpret real-world operations – not just internal performance metrics.

These systems pull from:

• Public forums.
• Reviews.
• Industry publications.
• Third-party comparisons.

Even flawless execution isn’t enough if LLMs detect innovation lag, outdated positioning, or recurring support issues.

That’s why COOs must monitor how AI platforms interpret their operations. They should either course-correct or enable marketing to respond before those perceptions solidify.

The Transformative Power of AI Perception Monitoring in Operations

Operations teams don’t need to become AI experts. However, they do need to track how AI platforms reflect your brand. 

This work can live in marketing, ops, or both. Here’s what that looks like in practice.

1. Monitor Forum and Online Conversations

Track what people are saying about your brand in forums, reviews, Reddit threads, and social media.

These outside opinions now affect how visible your brand is with AI.

In this AI-driven world, marketing can’t handle this alone; COOs need to act when they notice trends.

I believe that AI visibility will push companies to work at their best, leading to constant improvements.

In-house process analysts and change management consultants will be essential.

Their job will be to respond quickly when trends appear in online discussions. They aim to prevent AI from spreading wrong or negative views.

2. Assertively Monitor AI Platform Responses

Regularly check what LLMs (like ChatGPT and Bing Copilot) say about your company.

Look out for problems like outdated info, mistakes, or mentions of issues with products or support.

You will need some training or a clear plan for this.

While tools can help, much of the first work will be manual reviewing AI responses to find issues.

Sentiment analysis can show tone, but even positive comments may not be true.

3. Achieve Unmatched Accuracy and Consistency

Track how often AI answers get facts, brand statements, product details, use cases, and messages right or wrong.

Mistakes often show how your information is presented.

The right data might be there. It could be hidden in PDFs meant only for sales. It might be behind lead-gen forms. It could also be tucked away in interactive web components, like JavaScript tabs. In these cases, AI may completely overlook it.

Visibility is not just about being correct; it’s also about being easy to access.

4. Connect Ops Events to AI Narratives with Confidence

Create a list of important signals to watch for in your business. Then, keep an eye on these signals in your own data, social media, customer reviews, and AI responses.

For example, check for a shipping delay in your operations. Then see if people are talking about it online. Finally, notice how AI responds to it.

This helps you link specific problems to changes in how people perceive your brand.

Over time, you’ll learn how long it takes for AI to notice signals about your brand and change its stories.

By using the same approach each time, you’ll create a timeline. This timeline shows how long you have to fix problems. Address them before they hurt your brand’s image in AI.

I think bigger companies in well-known industries will see these changes happen faster. This is because AI looks at their signals more often. It does so compared to smaller, niche companies.

Seize the Strategic Opportunity Now!

AI visibility is a cross-functional challenge that demands shared ownership. 

When operations and marketing align:

• Issues get resolved faster.
• Visibility improves.
• AI tools reflect stronger brand narratives.

The organizations winning in the AI era are those that have cleared the brand signals hurdle. This means they have successfully established a strong and recognizable brand identity that resonates with their target audience. Brand signals that are clear and compelling provide power. They become a foundation for these organizations to build their marketing strategies.

Operational signals need to be strong. This means the internal workings and messages of the organization align effectively with their brand identity. When this alignment is achieved, marketing can amplify impact significantly. This is especially true in the context of AI, which has transformed how consumers discover products and services. Marketing strategies must be adapted to harness the capabilities of AI. Data-driven insights are utilized to reach audiences more effectively. These insights also help personalize experiences.

The integration of AI in marketing allows organizations to engage with potential customers at various touchpoints in their journey. By leveraging AI tools, brands can analyze consumer behavior, preferences, and interactions, enabling them to tailor their messaging and offerings. Marketers must embrace these advancements. Their strategies should not only recognize but also anticipate shifts in consumer discovery processes influenced by AI.

Organizations that successfully navigate these changes will find themselves at the forefront of their industries. They need to align their marketing efforts with the new landscape driven by AI. They will enhance their visibility. They will also deepen their connection with consumers. This lays a robust groundwork for sustained growth in an increasingly competitive environment.