This week we ran a live workshop on how to build AI-powered competitive intelligence workflows. Actual builds — seller emails, competitive briefs, comparison page audits — on camera, in real time.
Before we got into the workflows, I wanted to address something that doesn’t get talked about enough when everyone’s sharing their shiny new automations: if the data powering those workflows isn’t accurate, you’re just moving faster in the wrong direction.
Here’s what we built, what we showed, and what our customers had to say about where this is all going.
Why accurate data is the foundation of every AI-powered competitive intelligence workflow
Everyone’s building right now. Scroll LinkedIn for 30 seconds and you’ll see it. New workflows, new agents, new automations. It’s genuinely exciting.
But here’s the question I keep coming back to: what are you building on top of?
First problem: out-of-the-box LLMs. Ask ChatGPT, Claude, or Gemini a competitive question with no additional context and it looks pretty good at first glance. Scratch the surface and you’ll find broken links, 404 errors, Reddit threads from two years ago, and your competitor’s own marketing copy fueling the answer. Most people understand this limitation by now.
But here’s the second problem, and it’s less talked about: what happens when you do hook up your own data. You dump in your call transcripts, G2 reviews, decks, and docs, and now the answer looks way more credible. The issue is what’s happening underneath.
This is what we call the retrieval lottery. A standard RAG system stores your raw data, then goes hunting through millions of chunks every time someone asks a question. Sometimes it finds the right thing. Often it doesn’t. Two reps asking similar questions can get completely contradicting answers, because the system pulled from different chunks each time.
Jacques Begin, CI leader at 8×8, put it plainly during our Q&A:
“You kind of feel that temptation to let the AI just do its thing because it does it so quick and it sounds like it’s correct. And from a sales perspective, it’s magnified — there’s a faster need for it. I need this information right now because I’m on a deal.”
What Klue’s StaKs Engine does differently: it uses streaming agents to generate insights proactively, before questions are even asked, and stores those insights. So when a rep asks “what’s the latest on Competitor X pricing?” — they’re not rolling the dice. They’re pulling from pre-sorted, pre-generated intel that was already there waiting.
That’s the foundation. Everything we built on top of it only works because the data underneath it is trusted.
4 AI-powered competitive intelligence workflows you can use
1. Seller outreach: competitive prospecting and deal follow-up emails
Zach showed two seller workflows using Claude Cowork connected to the Klue MCP server.
The first: prospecting against a named competitor. One prompt asking Claude to draft an outreach email. Claude pulled from Klue’s battlecard data — not the public web — matched it to Zach’s personal writing profile (built by our AI Ops team across 300 data points), and produced a competitive email grounded in actual win reasons.
The second: deal follow-up using Klue’s Deal Tips.
Instead of a generic “here’s why we win” email, Claude pulled the deal tip Klue had already sent to Zach’s inbox — which included the prospect’s specific requirements mapped against competitor weaknesses — and built a follow-up around what that prospect actually said they cared about.
What used to take 15-30 minutes of jumping between tools: now one prompt.
2. Automated CI-powered follow-up emails at scale across your GTM org
Isaac showed the system he built so every Klue AE gets a competitive-aware follow-up email after every call — automatically.
The workflow pulls the Gong transcript, loads the rep’s writing profile, reads three months of email history between the rep and that prospect to pick up the conversational dynamic, and then calls the Klue MCP to pull relevant win-loss context and competitive data before drafting the email.
The contrast between without Klue and with Klue was the clearest demo of the session. Without: a fine email that sounds like the rep. With: an email that knew they were likely to lose if they didn’t anchor on a specific outcome, knew that feature-to-feature felt commoditized so it shifted to this outcome, and chose a case study that matched the prospect’s use case specifically.
3. Using AI to audit and refresh competitive comparison pages
This one was a little self-roasting.
Our competitor comparison page is out of date. Like, 2022-era out of date. So I built a competitive analysis workflow that audits our page against a competitor’s page on us, then queries Klue’s MCP to cross-reference against our actual win stories, loss stories, talk tracks, and buyer quotes from Gong calls.
The output: a full structural critique and prioritized list of differentiators we should actually be hammering — with evidence. Not just “here’s a claim,” but “here are four win stories where this specific angle was the deciding factor.”
It also caught a key differentiator missing from the page entirely. Unified CI and win-loss in one platform. Buyers are picking us because of it. It wasn’t on the page. Klue told me. Not a great look, but a fast fix.
From there, it generated a mockup comparison page I can hand to brand to work from, right differentiators, right evidence, actual starting point instead of a blank doc.
4. Building an AI-generated competitive brief for leadership in minutes
Jenna got asked to get up to speed on a competitor fast and put together something for leadership. One prompt, Klue MCP connected, and Claude distilled a full competitive brief: win reasons with supporting buyer quotes, loss patterns, where the competitor has an edge, and five go-to-market actions.
Every quote was linked back to the source card in Klue. Clickable. Verifiable. The kind of thing you can show a VP without sweating.
She also ran it without Klue as a test. Without the MCP data, it started making things up: fabricating win stories and buyer quotes that sounded real. The moment Klue data wasn’t in the loop, the output had a mind of its own.
What CI leaders are building next with AI workflows
We asked two CI leaders the same question: what’s the next workflow you want to build?
Jacques Begin, 8×8: moving CI from reactive to proactive
Jacques has already automated about 80% of his executive competitive intel reporting. The next push is getting ahead of deals before they go sideways — surfacing signals early enough to actually do something about them.
“Competitive intelligence historically has always been very reactive. We’re finally getting to a point where we might be able to outsource that drudgery to AI and move forward to looking at: what is coming up?”
Prathith Venkatakrishnan, Sisense: using win-loss data to shape the product roadmap
As a PMM at Sisense, Prathith is using Klue’s automatically generated win-loss stories to bring real deal data into product roadmap conversations, segmented by product vertical, ACV, and where deals are actually being won and lost.
“We’re not doing guesswork on which functionalities will yield actual revenue. We’re using solid information, backing that up with examples so that the product team can prioritize their roadmap.”
The workflow: pull win/loss stories from Klue, segment by vertical and opportunity size, identify where the company is losing and why, then bring that directly into roadmap planning with the product team.
What makes it work is the specificity of the data. Klue’s win/loss stories don’t just tell you a deal was lost — they tell you which product gaps came up, how often, and what ACV was attached to those conversations. That’s the difference between a PMM saying “we need to improve X” and a PMM saying “here’s the revenue attached to X — and here’s the evidence.”
What makes a great CI practitioner in an AI-driven world
We ended by asking both Jacques and Prathith the same question. The answers were worth saving.
Jacques: the fundamentals still matter; good data, research-focused thinking, understanding what sales is doing. But now you can offload the drudgery and actually get ahead of deals, markets, and renewals instead of just reacting to them.
Prathith: business strategy. Five years in his org means he understands the analytics landscape deeply. That context — pricing, packaging, what buyers actually need — is what AI can’t replicate. The critical thinking and decision-making skills everyone said didn’t matter? They matter more now, not less.
The one thing every AI-powered CI workflow depends on
Every workflow we showed — seller emails, automated follow-ups, comparison page audits, leadership briefs — was only as good as what was powering it.
When Klue data was in the loop, the outputs were specific, grounded, and usable. When it wasn’t, things got made up fast.
The limit of what you can build on top of accurate CI data doesn’t really exist right now. We’re going to keep building and sharing what we find.
If you want to dig into any of the workflows — especially Isaac’s N8n setup — we’ll be sharing out the recipes soon.
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