The Margin You Lose Before the First Call
The Margin You Lose Before the First Call
Gravton Labs CRO Raoul Hingle on three ways AI invisibility moves the P&L: shortlist exclusion, cost-of-sale inflation, and renewal risk. Anchored in Seer Interactive's 2026 AIO study and Gravton audit data across insurance, fintech, SaaS, and hospitality.
Haritha Kadapa
A conversation with Gravton Labs' CRO and Co-founder
on why AI discovery shows up in cost of sale, deal velocity, and renewal risk, and why the CFO should be in this conversation.
The budget is in marketing. The damage is in the P&L.
AI visibility usually lives inside the marketing function. It sits next to brand and content. It gets measured in mentions and share of voice. When it does not perform, it gets treated as a messaging problem.
Raoul Hingle thinks that framing is costing companies money they cannot see.
As CRO and co-founder of Gravton Labs, Raoul works at the intersection of revenue architecture and go-to-market. In his view, the financial consequences of AI invisibility are not abstract. They show up in cost of sale, in deal velocity, in win rate, and in the margin, you burn re-educating buyers who already formed a view of your category before your team got the meeting.
We sat with him to make the case in detail.
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Q: You describe AI visibility as a P&L problem. That is a strong claim. What do you mean by it?
Literally that. This is not a metaphor for importance.
When a buyer researches your category in an AI tool, and you are absent or misrepresented, the financial consequences ripple through several lines of the business. The cost of sales rises because your reps spend more time establishing credibility that should already have been established. Deal velocity slows because buyers arrive with assumptions that need to be unwound before the conversation can begin. Win rate drops because competitors named during the AI research phase enter the deal with a head start that your team cannot see.
None of that appears as a line item called "AI invisibility loss." It shows up as sales inefficiency, longer cycles, and lower conversion. The root cause is upstream of every CRM event you measure.
The CFO should be in that conversation. So should the CRO.
*P&L: Profit and Loss
Q: Why do you think most companies still treat this as a marketing issue?
Because the inputs look like marketing inputs.
Content, positioning, third-party coverage, category narrative. All of those lives in marketing's domain. So, when someone says, "we need to fix how AI talks about us," the natural response is to brief the content team or adjust the messaging framework.
That confuses the input with the outcome. The outcome you actually care about is whether buyers include you on the shortlist, whether they arrive at the sales conversation with a fair view of your value, and whether your team starts on level ground or spends the first thirty minutes climbing out of a hole someone else dug.
Measured that way, this is a revenue-efficiency question. And revenue efficiency is a P&L conversation.
Q: Can you put some shape on the financial impact? How does AI invisibility actually move numbers?
Three mechanisms, in roughly increasing order of how cleanly we can measure them.
First, shortlist exclusion. If AI tools consistently recommend three or four vendors and you are not among them, a portion of qualified demand in your market never reaches your pipeline at all. It goes to competitors. You never see it, which is what makes it dangerous. You cannot recover a deal that never started.
The second mechanism is the one I would put in front of a CFO first, because the data is now there. Seer Interactive's 2026 AIO study, led by Tracy McDonald, analysed 5.47 million queries across 53 brands. On informational queries where an AI Overview appeared, brands cited inside the Overview received roughly 20,743 organic clicks per million impressions. Brands not cited on the same queries received 9,445. That is a 2.2x gap on the same impression base. Seer's headline number shows that cited brands receive approximately 120% more clicks per impression than uncited brands.
BrightEdge data shows AIOs account for about 48% of tracked queries as of early 2026, up from 31% a year earlier. So, on roughly half of all informational queries in your category, being cited is worth twice the qualified traffic as being uncited. The half where you are uncited got materially more expensive to serve.
Third, price pressure. Buyers who arrive better informed about credible alternatives walk in with more bargaining power. If AI research surfaced three serious options before the first sales conversation, your negotiating position is not the one you trained your reps for.
Across a portfolio of deals, those mechanisms compound. The margin impact is real. It is not being attributed correctly yet.
Q: How should finance and revenue leadership be thinking about measuring this?
Start by accepting that the current attribution model has a blind spot.
Most revenue attribution starts at the first known touch. A form fill, a content download, an event registration. That is where the data picks up. The buyer's journey now starts earlier, inside AI tools, before any trackable event.
You are measuring from the second chapter of the buyer's story. The first chapter, where they defined the problem, formed a category view, and built a mental shortlist, is invisible to your CRM.
Finance needs to understand that the pipeline-quality metrics they review (conversion rate, velocity, average deal value) are downstream of what happened in that first chapter. If those metrics are softening and your top-of-funnel inputs look stable, the place to look is the pre-funnel research phase.
That requires a different measurement layer: what AI tools say about your category, whom they cite, and how consistently your positioning appears across the prompts buyers are actually using. That layer is what connects AI discovery to pipeline quality.
Q: Where does this show up most acutely? Are there specific deal scenarios where the P&L impact is clearest?
I will give you three patterns we see across audits.
The first is competitive displacement. A B2B SaaS buyer comes in and says a competitor was already on their list before they started talking to vendors. That is almost always an AI-influenced shortlist. The competitor did not outwork the sales team in that deal. They got there before the sales team arrived.
The second pattern is one we found in an insurance audit last quarter. A regional insurer, strong site, healthy organic. We pulled their citation pattern across ChatGPT, Perplexity, Gemini, Claude, AI Overviews, and Meta AI. > 90% of the citations that referenced them came from media they owned. Less than 10% came from third parties.
On a presence dashboard, that looks like dominance. It is not. When a buyer asks an AI tool, "Is this insurer reliable?" the model has very little material that is not the brand talking about itself. The first credible third-party source that surfaces (a review aggregator, a comparison blog, a community thread) reshapes the entire answer. The position is fragile in a way that the dashboard does not flag.
The third is renewal risk. Buyers do not stop using AI tools after they sign a contract. In a luxury hospitality engagement, we found a competing property consistently surfacing as a credible alternative in answers to questions our client's own customers were likely asking before their next booking. That is retention exposure. The renewal conversation has already started in a room where no one on the account team is sitting.
Q: Some segments you mentioned, insurance and hospitality, run heavily on aggregator economics: Broker platforms and OTA marketplaces. Does AI visibility play any role or have any impact here?
This is a hotly debated question - this helps turn the P&L conversation from a cost-of-sale story into a distribution vs margin-mix story.
Aggregators help in increasing distribution. Rather than a brand doing its own selling, they use aggregators as a channel with existing demand, and this helps in a quick start as well as a wider distribution. There is, of course, a cost to it in the form of commissions that aggregators charge.
With AI search, aggregators do dominate AI citations - their content is built exactly the way a large language model wants its source material: comparison tables, structured filters, third-party voice, breadth of options. When a buyer asks, "compare term insurance for a 35-year-old" or "best luxury camps in the Masai Mara," the model has thousands of well-structured comparison pages from brokers and aggregators, as well as Booking.com and Tripadvisor, to draw from. It does not have the same density of structured comparison content from any single insurer or property.
The default behaviour of AI tools is to funnel buyers into the aggregator channel even when the buyer was open to going direct.
The P&L consequence is concrete. In hospitality, OTA commissions range from 15% to 30% of the booking value, depending on the property and platform, with luxury inventory often at the higher end. In insurance, aggregator commissions on a first-year policy land in the 15% to 25% range, sometimes with trail on renewals. Every booking or policy routed through the aggregator costs you that margin, permanently. AI invisibility for your direct brand has a second-order cost that few revenue teams have priced in. The buyer who eventually finds you does so via a channel that takes a cut you cannot recover.
Routing back to direct is possible, and it has a specific shape.
The aggregator wins comparison queries, but the brand can win the queries that are more product-specific and can come after the buyer has narrowed their shortlist. "Is [property] worth the price?" "What is the claims process like with [insurer]?" "How does [a camp] handle conservation fees?" Those are the queries where the buyer is choosing between booking direct or via an aggregator, and where the AI tool needs trust signals the aggregator cannot supply: founder voice, claim outcomes, property videos, owner Q&As, named customer experiences.
If those queries get cited from your owned domain alongside third-party validation that names you (a journalist's review, a community thread, a specialist comparison blog), the buyer arrives at your direct channel with a reason to skip the aggregator.
We saw this most clearly in a hospitality audit last quarter. The client was being cited inside aggregator-format answers as one of three options, which felt like a healthy presence on a dashboard. They were almost never cited inside trust-format answers, the "is it worth it" questions where the booking decision actually gets made. The work was to build out that second category of content and earn third-party validation for it. The economics of a direct booking versus an OTA booking made the case for itself.
For a CFO, the simplest frame is this. AI visibility on comparison queries protects the topline. AI visibility on trust queries protects margin. Most brands chase the first and ignore the second. The margin-mix savings live in the second.
Q: What would you say to a CFO or CEO who hears this and says, "Prove it before I invest"?
I would say the proof exercise is cheap and fast.
Pick your five most common buyer prompts. The questions a real prospect in your market would type into ChatGPT or Perplexity in the thirty days before they talk to sales. Run them across the major platforms today. Look at what comes back.
If your competitors appear and you do not, that is not a data gap. That is a revenue gap with a research trail. If the category is described in a way that misrepresents your positioning, every buyer who did that research arrived with a distorted view.
You can see that in one afternoon. The question is not whether the gap exists. Across the audits we have run in fintech, B2B SaaS, insurance, and hospitality, the default condition is the gap, not the exception. The question is how much revenue is flowing past you while you wait for the attribution model to catch up.
That framing tends to land with a CFO. They understand opportunity cost. They understand that the absence of proof is not proof of absence. By the time you have clean attribution data on a slow revenue leak, the cost of closing it has usually increased.
Q: As CRO, how has this changed how you build revenue strategy at Gravton?
It changed how I think about the inputs to the pipeline, not just the pipeline itself.
Demand generation usually starts from the assumption that demand is something you create. AI discovery forces a different assumption: a portion of demand in your category is being pre-allocated in tools you do not own, before you ever touch it. Buyers are arriving at shortlists you had no role in shaping.
So, when I size an opportunity now, I treat AI visibility as an input variable on par with brand, content, and sales capacity. It is part of the revenue plan, not a parallel workstream owned by someone else.
Q: What is the conversation that needs to happen in leadership teams that is not happening yet?
The one where someone asks what it is actually costing the company.
Most leadership teams have asked whether they are doing something about AI visibility. That conversation has happened. Someone got a mandate. Someone briefed an agency. A board slide exists.
The conversation that has not happened is the one where someone pulls the closed-lost data, maps the competitor names that keep appearing in the loss notes, runs the five buyer prompts across the AI platforms, looks at the answers, and asks: given what we are seeing, what is the realistic revenue impact of this gap over the next four quarters?
That conversation changes the nature of the investment. AI visibility moves from a content initiative to a revenue risk with a number attached. Urgency and resource allocation both changes.
That is the conversation worth having. Not because the answer is always alarming, but because you cannot manage a risk you have not sized.
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