Spotlight.ai in Good Morning US: Fewer Entries in the Competitor Field
A new contributed article in Good Morning US argues that a shorter competitor field is a sign of better data rather than worse, and explains why checking one name at a time against the evidence changes what win-loss analysis is worth.
BOSTON. Lolita Trachtengerts, VP GTM Ops & Growth at Spotlight.ai, has published a contributed article in Good Morning US titled "Why Our Competitors Field Has Fewer Entries Than Yours." The piece looks at how most AI tools fill the competitor field on a deal, and why a field that holds fewer names is usually the more honest one.
The article starts with a familiar failure. A model reads a sales transcript, is asked who the competitors are, and records every name it hears. A vendor someone mentioned in passing lands in the same field as a real competitive threat, with no note of which was which and no way to see how the model decided. That field then feeds win-loss analysis and the forecast, and quietly corrupts both.
One name at a time, against the evidence
Trachtengerts describes how Spotlight.ai reads the same conversations differently. Rather than ask an open question about who the competitors are, Spotlight's Calibrated-Decision AI-Model (CDAM) checks each known competitor name individually against each deal's conversations, returns a probability, and points to the specific sentence behind it. A name is written to the competitor field only when the probability is high and the evidence is specific.
Names the list does not know about are not discarded. They are captured separately and watched over time, so a new vendor that keeps recurring can be promoted onto the tracked list, and a name that stops appearing can be retired from it. The list stays short because nothing earns a place on it without evidence.
Why fewer entries is the point
The article is direct about the trade-off. When you first switch to evidence-backed entries, the competitor field looks less complete than it used to, because the model is no longer filling it on every mention. Teams read that as a regression at first. The point the piece makes is that a shorter field built only on evidence tells you more than a long field built on everything the model overheard.
What structured competitor data unlocks
Once competitor presence is evidence-backed rather than guessed, the same rigor runs on the other things buried in a conversation: the pains the buyer raised, the objections that came up, and who was actually in the room. That is what makes real pattern recognition possible, so a team can see that when a given competitor is present alongside a particular pain and a particular objection, these deals tend to go a certain way. The analysis is only as trustworthy as the fields underneath it, which is the whole argument for filling them on evidence.
How this feeds winning and losing patterns in the product: How the Spotlight Score works (Spotlight.ai Support)
The cost question
The piece also answers the question a technical reader asks next. Because the work happens inside the model with a closed set of possible answers rather than open-ended prose, the decision layer writes nothing back, so there are zero output tokens to bill. A team can ask the pipeline as many questions as it wants without the cost growing alongside its curiosity.
"When you are looking at any tool that claims to read your deals, ask it to show you the sentence," Trachtengerts writes. "Not the accuracy rate. The sentence."
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Spotlight.ai qualifies deals from evidence across every call, email and meeting, checks each competitor against the conversation one name at a time, and keeps what it heard separate from what a rep assumed. Tulip ran this across a 65-person sales team and moved conversion from 6.3% to 15% over twelve months, a 2.4x improvement.
About Spotlight.ai
Spotlight.ai is the autonomous deal execution company. Headquartered in Boston, it replaces manual qualification, inspection and forecasting with an AI agent squad that listens to every buyer interaction, qualifies deals against the customer's own playbook, generates customer-facing assets and produces evidence-based, bottom-up forecasts. The platform has autonomously qualified and executed more than $8B of pipeline and is SOC 2 Type 2 certified.
About Lolita Trachtengerts
Lolita Trachtengerts is VP GTM Ops & Growth at Spotlight.ai, where she leads go-to-market operations and growth. She co-leads the Boston chapter of RevGenius and writes regularly on RevOps, qualification and how AI is changing the work of revenue teams.
FAQs About Evidence-Based Competitor Tracking
Why would a competitor field with fewer entries be better?
Because the entries that remain are backed by specific evidence. A model asked an open question will record every vendor it hears, including passing mentions, which makes the field look complete while quietly corrupting win-loss analysis. A shorter field built only on high-probability, evidence-backed entries is a more reliable input to the forecast.
How does Spotlight.ai decide whether a competitor is really on a deal?
Rather than asking who the competitors are, Spotlight's Calibrated-Decision AI-Model checks each known competitor name individually against the deal's conversations, returns a probability, and points to the specific sentence behind it. A name is written to the field only when probability is high and the evidence is specific.
What happens to vendors that aren't on the tracked list?
They are captured separately and watched over time. A new name that keeps recurring can be promoted onto the tracked list, and a name that stops appearing can be retired, so the list reflects what is actually showing up in deals.
Does running this analysis increase our AI costs?
No. The work happens inside the model against a closed set of possible answers, and the decision layer writes nothing back, so there are zero output tokens to bill. Asking the pipeline more questions does not grow the cost.
Where was the article published?
The article was published in Good Morning US as a contributed piece by Lolita Trachtengerts, VP GTM Ops & Growth at Spotlight.ai.
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