Spend two minutes scrolling your LinkedIn feed right now, and you’re guaranteed to see someone showing off a brand-new AI build for competitive intelligence.
We’ve all seen how such posts end:
“Normally this would cost $XXX, but comment ‘CI’ below and I’ll DM you the prompts for free.”
As product marketers, we’re juggling so much: product launches, messaging updates, and enablement. The fact that we can build these AI-powered automations so easily today feels like a lifeline.
I saw one from a PMM recently who had built an automated battlecard agent in Claude. They hooked up integrations to scrape different sources, format the markdown into a Google Doc, and drop the battlecard directly into their #competitive-intel Slack channel:
“Readable. Usable. Visible.”
But working “flawlessly”? The workflow is impressive. The problem is what’s underneath it. If the data powering your AI builds isn’t trusted, accurate, and fresh, it doesn’t matter how clean the output looks or how slick the automation is.
You’re delivering bad intelligence faster.
So, I talked with customers, tested tools myself, and sat down with Klue’s product team to understand exactly why general-purpose LLMs struggle to provide an accurate data layer for competitive intelligence.
Here are six ways that it falls short, and the risk that presents for your AI builds.
6 Areas where Generic LLMs fail for competitive and buyer intelligence
1. Source Weighting
General purpose LLMs don’t have a mechanism to distinguish a trusted source from an unreliable one. A verified buyer quote, a competitor’s own marketing page, and a broken link all carry the same weight. The output arrives with equal confidence regardless of what’s underneath it.
I tested this firsthand. I asked Claude why security teams choose Wiz over CrowdStrike, then clicked every citation in the output. Some were what you’d expect: G2 reviews, a comparison post, Wiz’s own marketing page.
Then there was “Oreate AI,” cited with the same authority as everything else, anchoring one of the six win reasons.
I clicked it. It returned a 404. A broken link, powering a claim your rep might use in a live deal, and no way to know it was there.
When using ChatGPT or other open LLMs, I didn’t know where information came from or if we can trust it. We still needed a system tied into what we were already doing and how we position ourselves, which open LLMs will never have because you don’t completely control where they learn.
Dustin Ray
Head of Competitive and Market Intelligence
2. Data Decay
General LLMs have no internal clock. They do a poor job discerning between when a source was written, whether a newer version exists, or how much the market has moved since. Instead, it pulls whatever matches the query best.
Our team saw this firsthand using Gong’s AI builder to analyze why we win against a competitor. The output looked thorough: structured sections, specific quotes, clear themes. Then we looked at the sources it was anchoring on.
The calls it weighted most heavily were from 2023.
Our positioning has shifted since then. The competitive landscape has shifted. The buyers we’re talking to today are asking different questions than the buyers from three years ago.
Sales call recordings are one of the highest-signal sources of competitive intelligence you have. But at any meaningful scale — hundreds of calls across months or years — most systems have no logic to prioritize recent signals over old. It treats a call from two years ago the same as one from last week.
3. Context Stripping
When generic AI processes your call transcripts or documents, the system breaks them into chunks for the LLM to retrieve. What it doesn’t do is attach the context that makes those chunks meaningful.
Let’s take competitor detection as an example.
A mention of a company in a discovery call could mean three different things: an active threat in a live evaluation, a platform the prospect already uses and loves, or a passing reference to something a colleague mentioned. Strip the context and every mention looks identical. The system can’t tell the difference and so it either misclassifies the signal or misses it entirely.
When Klue analyzed competitive detection across customers, it found more than double the competitors present in active deals than what those customers had recorded. They weren’t blind to half their competitive pipeline because they weren’t paying attention. They were blind because the systems processing their calls weren’t extracting real buyer insights on competitors.
Looking for more examples of CI workflows you can use with trusted data? We walk through four you can use today here.
4. Retrieval Lottery
Another impact of context stripping data is that the retrieval system has no way to consistently surface the right signal.
The generic LLM has to search through a pile of everything and return whatever is most semantically similar to that specific query.
This means that there is no consistency in what it will pull from to provide answers.
I tested this directly by asking the same question, with slightly different wording as different account executives are likely to do:
- “What are the main reasons security teams choose Wiz over CrowdStrike?”
- “Why do companies pick Wiz instead of CrowdStrike?”
The final three points for each response are completely different, and pulled from different sources.
Due to how Claude and ChatGPT processes the data, there is no ability to trust a consistent answer grounded in the same truth; whether you’re conducting deep competitive research, building a battlecard, or pushing sellers to the system for answers.
When you build an AI automation on top of this system, you’re getting one draw from that lottery every time it runs instead of a reliable answer your whole team can align on.
5. Silent Contradiction
Competitive intelligence is inherently messy. Vendors make conflicting claims about each other. G2 reviews from two years ago say different things than ones from last month. A win-loss call says one thing about a competitor’s capability and a recent analyst report says another.
And general-purpose AI irons it out silently and hands you a clean answer.
For example, I ran a follow-up prompt after getting the Wiz vs. CrowdStrike output asking directly: “Do any of these sources contradict one another, and how did you resolve this in your answers?”:
The system admitted it had papered over a direct contradiction between CrowdStrike and Wiz on runtime protection capability; a material claim that would change how a rep handles a specific objection in a live deal. It had sided with Wiz’s framing without flagging that CrowdStrike disputes the claim entirely. It also admitted it had ignored a source claiming Wiz is free, without noting it had done so.
Most importantly, it flagged something it should have flagged in the original answer: four of the ten sources were vendor-authored pages: CrowdStrike and Wiz writing about each other. Every claim from those sources carries a conflict of interest that the original output never mentioned.
This is the silent contradiction problem in action. The conflicts, the source bias, the editorial choices the system made are not visible unless you know to ask. This puts the burden on PMMs and CI leaders to interrogate every output AI offers, eating away at all the time savings we supposedly gain from our automations and agent builds.
I examined competitive comparisons generated by ChatGPT. They were so generic it made me cringe. It was like asking an intern who knows nothing about our company and has no opinion on where the market is going to write our strategy. It lacked any real perspective on who we are and why we matter.
Andrew Bartholomew
Market Insights Analyst
6. No Expert Context
The thing that makes PMMs and CI leaders indispensable to their business is their unique expertise.
They know which sources to trust and which to discount. They’ve watched certain competitive claims land with buyers and others fall flat. They adjust how a competitor is framed based on what actually resonates in a specific segment.
That expertise is the difference between a competitive program that feels ‘meh’ and one that actually influences deals.
Generic LLMs has no mechanism to receive this expert context.
You can’t tell it which G2 reviews reflect your real ICP and which ones skew the picture. You can’t adjust the framing on a capability comparison based on what your best AEs have learned through hard experience. There’s no feedback loop, to set direction, or to make corrections that carry forward.
That last part is the biggest problem with generic LLMs. Corrections don’t propagate. The system doesn’t get smarter, and doesn’t apply what your team has learned.
Those feature comparisons, that kind of slide needs thorough human review and the tact to position it in a way that’s the right amount of aggressive, but also true. That art — I have not found AI to be able to do alone yet.
Garrett Gomez
Product Marketing Manager
What a trusted AI foundation for competitive intelligence actually looks like
Every failure in this piece traces back to the same root cause; raw data piped straight into an LLM with nothing in between.
A competitive intelligence system worth trusting runs on four layers:
Data sources are the raw material: calls, emails, CRM updates, win-loss interviews, web and news. This is the foundation that every system starts with.
The intelligence layer is where raw data gets processed into something an LLM can actually work with: extracted, scored, and reconciled into a single perspective of truth. Contradictions are surfaced, noise is discarded, outdated signals are deprecated.
The context layer is where your team’s expertise gets captured and carried forward. You set direction once; which sources to trust, how to frame a competitor, what answers need nuance. Corrections propagate automatically, and the system learns from the expert’s context.
Agents and workflows built on top of this foundation are the payoff: accurate, consistent, and relevant agents and workflows.
The PMMs sharing their AI builds and workflows on LinkedIn aren’t wrong to be excited. The automation is real, and so are the time savings. The question is whether the intelligence underneath it is real too.
Get the foundation right and the work changes. PMMs stop auditing outputs and start using them to shape positioning, surface product opportunities, and drive competitive enablement before the deal turns. That’s the version of competitive intelligence worth building toward.
SHARE THIS POST