Most conversation intelligence works like a search filter. You decide in advance what matters, you build a tracker for it, and the tool flags every time those words come up. It is useful, right up until a question arrives that you did not think to prepare for.
In a real go-to-market team, that is most questions.
Chad Boersma spent a decade in revenue operations trying to get answers out of tools that could only look for what you told them to find. He is now GTM Agentic Architect Lead at Airspeed, and he puts the limitation plainly.
“The benefit of being an AI native company is that we’re not just using keywords or a list of specific concepts that we want to track. You can ask Airspeed any ad hoc question that you have about your customers, and it can even generate charts for you.”
Chad Boersma, GTM Agentic Architect Lead, Airspeed
The problem with predicting your own questions
Keyword trackers assume you already know what you are looking for. Sometimes you do, and that is worth building for. You can set up a standard prompt for the objections and topics you monitor every week, and Chad is clear that structured tracking still earns its place. “We can certainly help you build those standard prompts,” he says.
But a business does not only run on standing questions. It runs on the ones that appear on a Tuesday afternoon and need an answer by Wednesday morning.
A competitor launches something and your CEO wants to know how prospects are reacting. A security review flags a concern and you need every account that has raised the same worry. A board member asks a question in a meeting that nobody has a dashboard for.
“Sometimes questions come up throughout the day or throughout the week that you need an answer to that you can’t, don’t have time to build a tracker for, or rework your CRM to capture that data, or you don’t have time to listen to a bunch of call recordings or ask sales reps their opinion on things.”
Chad Boersma, GTM Agentic Architect Lead, Airspeed
With a tracker-based tool, each of those is a small project. Define the terms, build the filter, wait for enough calls to match, review the output, sanity check it. By the time you have an answer, the moment that prompted the question has passed and the decision got made on instinct anyway.
The three old workarounds, and why each one fails
Before AI-native tooling, teams had three ways to answer an unplanned question. All three were poor, and most teams used all three in the same week.
Listen back to the calls. Accurate, and it does not scale past a handful. A day disappears, and you have covered maybe six conversations out of two hundred.
Ask the reps. Fast, and it gives you opinion and recollection rather than what customers actually said. Every answer is filtered through what one person remembers and already believes, which is exactly the bias you were trying to design out.
Rework the CRM to capture a new field. Thorough, and it tells you nothing about the conversations you have already had. You get data starting next quarter, for a question you have this quarter.
Each path trades away either time, accuracy, or history. Usually more than one. It is a specific case of the gap between conversation data and revenue action that shows up across the category.
Ask anything, get an answer
An AI-native platform inverts the model. Instead of predicting your questions, you ask them.
“Here are the top reasons why we’re winning and losing deals. Here are a list of all of the customers who have mentioned this one security concern. You can literally just ask Airspeed and it will come up with an appropriate answer based on the actual customer conversations that you’ve had specifically.”
Chad Boersma, GTM Agentic Architect Lead, Airspeed
This is the same shift described in voice of the customer analysis, seen from the query side rather than the insight side. The distinction he keeps returning to is that you are having a conversation with your data, not querying a fixed set of fields. The answer is grounded in your real calls, so it reflects your customers and your market rather than a general model of how deals tend to go.
“That’s the benefit of being AI native,” he says. “You can ask these ad hoc questions, do an analysis, have a conversation with your library of call data, not just look for specific trackers or update a CRM field to populate the data that you’re looking for.”
Two things make the difference between an answer you can use and one you have to caveat. The analysis has to read for concepts rather than phrases, so a compliance worry registers even when nobody uses your preferred term for it. And you have to be able to open the calls behind the answer, because the first question anyone asks in a leadership meeting is where that came from.
What to ask a vendor about this
The category language has converged, so nearly every platform now describes itself as AI powered. Four questions separate the claim from the capability.
Can I ask a question nobody configured? Ask it live, in the demo, about a topic you did not send ahead. The answer tells you most of what you need.
Does it read concepts or phrases? Ask how the platform handles a concern raised in words your team would never use, because your buyers do not know your internal vocabulary.
Can I see the calls behind the answer? An answer you cannot trace is an answer you cannot defend in a leadership meeting.
Can it do something with the answer? Analysis that ends at a chart still leaves the work to a person. The same understanding should be able to write the CRM record or assemble a post-sales handoff without anyone retyping it.
How long until it can answer about my business? Some platforms need months of tracker configuration before the output is useful. The point of AI-native analysis is that your existing call library is already the input.
Standard prompts and ad hoc questions, together
None of this is an argument against structure. The strongest teams run both.
They set up standard prompts for what they track continuously, the objections, the competitive mentions, the feature requests, and they keep the freedom to ask anything the moment a new question appears. The standing prompts give you a steady pulse. The ad hoc questions let you chase whatever the week throws at you.
That combination is what separates an AI-native platform from a keyword tool with a newer badge. One gives you a fixed report and asks you to predict what you will need. The other gives you a source you can interrogate, in your own words, whenever you need to. If you are weighing options, our conversation intelligence buyer’s guide works through how the major platforms compare on exactly this.
The same reading of a conversation is what lets an agent act on the answer rather than hand you another report.
If your current tool makes you build a tracker every time leadership asks a new question, you are managing the tool instead of learning from your customers. It should be the other way around.
See what it feels like to ask any question of your customer conversations and get a real answer. Book a demo.
Frequently asked questions
What is AI-native conversation intelligence?
It is conversation intelligence built so the analysis is generated at the time you ask, rather than matched against trackers configured in advance. The platform reads the full conversation for concepts, understands your business and market, and answers questions in your own words, including questions nobody set up beforehand.
How is it different from keyword trackers?
A tracker finds the words you told it to look for, so every new question is a small project: define the terms, build the filter, wait for calls to match, then sanity check the output. An AI-native platform answers the question directly against the conversations you have already had, with no setup and no waiting for new data to accumulate.
Do standard prompts and trackers still have a place?
Yes. The strongest teams run both. Standing prompts give a steady pulse on the things you track continuously, such as objections, competitive mentions, and feature requests. Ad hoc questions cover whatever the week throws at you. The difference is that structure is now a choice rather than the only option.
What kinds of ad hoc questions can you ask a call library?
The ones that appear on a Tuesday and need an answer by Wednesday. Why are we winning and losing right now, which accounts have raised a specific security concern, how are prospects reacting to a competitor's pricing change, which objection is slowing one segment. The answer is grounded in your own calls, and can come back as a chart.
How do you know the answer is trustworthy?
Because it is generated from your actual customer conversations rather than a general model of how deals tend to go, and because you can trace it back to the calls it came from. Evidence you can open is the difference between an answer you can take into a board meeting and one you have to caveat.
