Why AI visibility starts with ops...

SEO is no longer enough.

Discoverability in AI-powered search now depends on operations – a shift many marketers haven’t accounted for.

AI platforms like ChatGPT, Gemini, Claude, and Google’s AI Overviews aren’t fooled by clever messaging. 

They synthesize operational signals – from order issues to pricing gaps – to form brand perceptions.

These aren’t marketing problems. They’re organizational blind spots that block AI visibility.

I see them constantly in my audits – and most can’t be fixed with content alone. They require operational change.

This is a strategic wake-up call and a blueprint for CMOs and COOs who need to align.

Here’s why the first visibility hurdle in AI is no longer marketing-owned.

Why organizational signals shape AI visibility

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

These aren’t just internal data points. They surface in online chatter that shapes how LLMs assess your brand’s relevance to customer queries.

• 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That means even outdated technology or past operational glitches can lead an LLM to omit your brand or misrepresent it. 

The chart below shows how negative signals from operations are picked up and learned by LLMs.

Sometimes, product design is the visibility blocker.

One of my clients – a global industry leader with a well-made, widely used product generating millions in sales – was flagged in an AI visibility audit. 

An LLM described the product’s technology as “outdated” and concluded “the market has moved on.”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 act like a buyer’s 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 the LLM deems important, your brand may be omitted from discovery or negatively portrayed in AI responses.

Below are a few examples from my audits:

Dig deeper: 7 ways to grow brand mentions, a key metric for AI Overviews visibility

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 needs operations metrics on their dashboard

Operational issues are early-warning signals for changes in AI visibility. 

These metrics don’t directly drive visibility – but if left unaddressed, they often foreshadow visibility loss.

That’s why I recommend marketing teams track operational bellwether metrics – indicators of broader downstream impact. 

In finance, FedEx shipping volume predicts consumer spending.

In AI visibility, metrics like shipping delays, support hold times, and other operational issues can forecast what LLMs will soon learn and reflect. 

LLMs may not access your internal data, but issues may surface in complaints and commentary that shape AI perception.

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, because they were his greatest indicator of what would happen in the future for his business.

The COO needs to monitor 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 – and either course-correct or enable marketing to respond before those perceptions solidify.

What AI perception monitoring looks like in operations

Operations teams don’t need to become AI experts – but 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. Track forum and online chatter

Track what’s being said about your brand in forums, reviews, Reddit threads, and social posts. 

These external signals now influence AI visibility. 

In the AI era, this can’t be left to marketing alone – COOs need to act when patterns emerge.

I predict AI visibility will pressure companies to operate at best-in-class levels, driving continuous improvement like never before. 

In-house process analysts and change management consultants will become critical. 

They will be tasked with responding quickly as patterns emerge in online chatter before LLMs solidify inaccurate or negative perceptions.

Dig deeper: Reddit: Your new online reputation challenge

2. Monitor AI platform responses

Regularly review what LLMs (ChatGPT, Bing Copilot, etc.) say about your company. 

Watch for red flags like outdated descriptions, inaccuracies, or mentions of defects or support issues.

This requires training or a clear framework. 

While tools can assist, much of the early work will be manual – reviewing AI responses directly to spot concerns. 

Sentiment analysis can flag tone, but even positive narratives may be factually inaccurate.

3. Measure accuracy and consistency

Track how often AI responses get facts, brand statements, product specs, use cases, and messaging right versus wrong.

Inaccuracies often reflect how your information is surfaced. 

The correct data may exist. But if it’s locked in sales-only PDFs, buried behind lead-gen forms, or embedded in interactive web components (like JavaScript tabs), LLMs may miss it entirely. 

Visibility isn’t just about accuracy – it’s about accessibility.

4. Link ops events to AI narratives

Create a dictionary of key operational signals, then monitor them across internal data, public forums, reviews, and LLM outputs. 

For example, track when a shipping delay first appears in ops metrics, then in online chatter, and finally in AI responses. 

This connects specific faults to shifts in AI perception.

Over time, you’ll start to see how long it takes for LLMs to absorb brand signals and adjust their narratives. 

With a consistent methodology, you’ll build an evidence-backed timeline for how long you have to address issues before they impact AI visibility.

My hunch is that larger companies in high-profile sectors will experience faster perception shifts because LLMs process their signals more frequently than those from niche players.

Dig deeper: Your brand in the age of generative search: How to show up and be cited

The strategic opportunity

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. 

Once operational signals are strong, marketing can amplify impact – if they adapt to how AI now drives discovery.