A system built from actually handing content to a sales team and watching what they open, and more tellingly, what they never touch. For marketers who write battlecards, one-pagers, and objection handlers, and want reps to believe them instead of quietly filing them away.
Sixty to seventy percent of content produced by B2B marketing organizations goes unused by sales, a Forrester statistic from 2013 that Forrester itself says some clients report is even worse, over 80% (Forrester). Ninety-six percent of go-to-market leaders report breakdowns tied to content use, deal velocity, or shifting priorities, and 39% specifically say their content isn’t being used effectively by sellers (Highspot, GTM Performance Gap Report). AI won’t fix that by itself. Where it genuinely helps: drafting a first-pass battlecard from competitor research in an afternoon instead of a week, updating a one-pager’s pricing section without touching the rest of the document, and turning win/loss call transcripts into objection handling built on what reps actually hear, not what marketing assumes they hear. One data point worth sitting with: Mindtickle’s 2026 State of Agentic Revenue Enablement Report found that content engagement jumped from 4% to 55% in a single year at organizations that cut their content library by 76%. Less content, used more, is the goal I’d chase, not a bigger content library produced faster.
I once sat in on a deal review where a rep pulled up our competitor battlecard, glanced at it for maybe four seconds, and closed the tab. The pricing section was six months out of date. The “objection handling” was three bullet points a product marketer had written before the competitor’s last release. He didn’t crack that document open to figure out what to say next; he needed an answer in the next ninety seconds, and that battlecard wasn’t going to give him one.
That moment stuck with me because it wasn’t some fluke. It’s the norm, in my experience, across more deal reviews than I’d like to admit. Forrester found that 60% to 70% of content produced by B2B marketing organizations goes unused by sales, sitting on portals and shared drives nobody opens. Forrester has also noted that some of its own clients report the real number is worse: over 80% of their content never gets viewed or downloaded at all. More recently, Highspot’s GTM Performance Gap Report found that 96% of go-to-market leaders report breakdowns tied to content use, deal velocity, or shifting priorities, and 39% say sales content specifically isn’t being used as effectively as it could be by sellers.
None of those stats say this outright, but ten years of handing content to sales teams taught me something: laziness isn’t the reason reps skip a battlecard. The content itself is usually stale, generic, or built from a guess about what the sales conversation sounds like instead of what it actually sounds like on the call, and reps can tell the difference in about four seconds, sometimes less. A one-pager that hasn’t been touched since a competitor’s last pricing change does real damage, too. It makes the rep look unprepared in front of a buyer who did their own research an hour before the call.
Before you touch a tool, be honest about your own content library first. Can’t say when your top three battlecards were last updated? That’s the real starting point, not some faster way to produce more of the same stuff nobody opens.
This is where AI earns its keep, and I’ll say it plainly: writing the first draft of a battlecard used to eat a full day, sometimes two, once you count pulling competitor pricing pages, combing through review sites, and reading old win/loss notes. AI compresses that into an afternoon, as long as you feed it real material instead of asking it to invent competitive positioning from nothing.
The workflow that actually works: gather your source material first (competitor pricing pages, recent G2 or Capterra reviews, analyst notes if you have them, and any win/loss interview summaries you already have), then ask the AI to organize it, not originate it. A prompt like this holds up well: “Using only the competitor information I’m pasting below, draft a battlecard with these sections: how we win, how we lose, top three differentiators, pricing comparison, and the three objections reps hear most often about this competitor. Do not add any claims about the competitor that aren’t in the source material.”
That last instruction matters more than it looks. Generic-purpose models will happily fill gaps with plausible-sounding claims about a competitor’s product if you don’t explicitly forbid it, and honestly, a battlecard with even one fabricated claim is worse than no battlecard at all, because a rep who repeats it to a buyer and gets corrected loses credibility for the rest of the call.
Treat the AI draft as the skeleton, never the finished card. I still send every first draft to a rep who’s actually run deals against that competitor recently and ask, plainly: does this match what you’re hearing? If it doesn’t, the draft gets rewritten before it goes near a sales floor.
This next part almost nobody talks about, and honestly, it’s arguably the more useful of the two AI use cases here. A competitor drops their pricing by 15%, or launches a feature that closes a gap you’d been using as a talking point, and now your battlecard is wrong. The instinct is to treat this as a full rewrite. It isn’t, and treating it that way is exactly why content goes stale: rewrites take time, so they get deprioritized behind whatever’s due this week.
AI is genuinely good at surgical updates if you frame the task that way. Paste the existing battlecard and the specific change, then ask: “Update only the pricing comparison section and any objection that references the old pricing. Leave every other section exactly as written. Flag anything else in the document that might now be inconsistent with this change.” That last instruction catches the thing a manual update often misses: a pricing change in section two that quietly makes an unrelated claim in section four wrong too.
This is also where dedicated sales enablement platforms have built real functionality worth knowing about. Highspot’s AutoDocs pulls data from your CRM and other connected sources to regenerate personalized documents on demand rather than requiring someone to manually edit a static file, which is the same principle at a platform level: update the data source, not the document by hand. If you’re not on a dedicated platform, a shared prompt template that always references “the current version” of a battlecard as its source of truth does something similar with general AI tools.
Accuracy isn’t the only thing that matters here. Recency is too, maybe more: a battlecard that’s 80% right and updated last week will serve a rep better than one that’s technically 100% right but six months stale, because there’s no way for that rep to know which parts of the old one have quietly rotted underneath them.
Most objection-handling documents I’ve seen get written the same way: someone in marketing or product marketing sits down and imagines what a buyer might object to, then writes a clever response. It reads well. It’s also frequently wrong, because it’s built on assumption instead of evidence, and reps can tell within one sentence when a script doesn’t match what they’re actually hearing in the room.
The better source material is sitting in your CRM and your call recordings right now, mostly unused. Win/loss interview notes, Gong or Chorus call transcripts, and CRM close-lost reason fields contain the actual objections buyers raise, in their actual words. AI is well suited to the specific job of finding patterns across dozens or hundreds of these that no single person has time to read end to end.
A workable prompt: “Here are transcripts and notes from 15 recent sales calls. Identify the five most frequently raised objections, quote the exact language buyers used for each (don’t paraphrase), and note which objections appeared most often in deals we lost versus deals we won.” Asking for exact quotes rather than paraphrases is the difference between an objection handler that sounds like your buyers and one that sounds like a marketing team’s idea of your buyers.
Gong’s conversation intelligence already surfaces competitor mentions, objections, and sentiment shifts automatically across recorded calls, which is a useful starting point if you have it. Whether you’re pulling from a platform like that or working from raw transcripts pasted into ChatGPT or Claude, the principle is the same: the objection handler is only as good as the realism of the objections it’s built to answer.
Before any objection handler goes anywhere near the sales team, read the ten most recent transcripts yourself, I still do this every time, and check whether the objections in the document match what’s actually being said. Buyer objections shift as competitors change tactics and the market moves. A handler built from six-month-old calls is answering a conversation that isn’t happening anymore.
Once you’ve done the research work for a battlecard, a competitor comparison one-pager is mostly a formatting exercise, not a new research project, and this is where AI saves real time without any real risk if you’re feeding it verified source material. The mistake I see most often is treating the one-pager as a separate deliverable that needs its own research pass, which just doubles the work for a document reps will spend fifteen seconds looking at.
Ask the AI to repurpose the battlecard content into a different format: “Turn this battlecard into a one-page comparison table a rep can screen-share on a call. Keep it to five rows maximum: pricing, the top differentiator, one thing we do better, one honest limitation of our product versus theirs, and a one-line summary of when we’re the better fit.”
That “honest limitation” row is deliberate, and it’s worth defending if anyone pushes back on including it. Reps who’ve been burned by a one-pager that oversells your product against a specific competitor stop trusting every document after that, and I’ve heard this straight from reps: the one row admitting a weakness is often what makes them trust the rest of the page. A one-pager that admits where you’re genuinely weaker, once, builds more credibility than five that pretend you win every category.
If you’re also producing customer-facing proof points alongside your competitive content, How to Write a Case Study With AI covers that adjacent workflow in more depth. It’s a different document with a different job (proving outcomes rather than positioning against a specific competitor), so I won’t rehash it here beyond pointing you there.
Keep one-pagers to a single page, actually. I’ve watched “one-pagers” balloon to three pages because nobody wanted to cut anything, and at that point it’s a different document doing a worse job of the original one’s task.
This is the uncomfortable part of the whole conversation, and honestly, it’s why I’d argue speed was never the real problem sales enablement needed AI to solve. If your team produces content faster with AI but reps still aren’t opening it, you haven’t fixed anything. You’ve just automated the production of unused content, which if anything makes the 60% to 70% unused figure Forrester found look worse in absolute terms, because there’s simply more of it sitting unread.
Mindtickle’s 2026 State of Agentic Revenue Enablement Report has a data point worth sitting with here: organizations that cut their content library by 76%, keeping fewer, sharper assets instead of more of everything, saw content engagement jump from 4% to 55% in a single year. That number isn’t mainly about AI making content faster. Read it as a discipline story instead: teams got ruthless about what doesn’t get published at all, and that’s a harder, far less flashy thing to pull off.
If you can’t tell me which battlecard your top rep used on their last competitive deal, production isn’t your problem. Adoption is, and no amount of AI-assisted drafting speed closes that gap on its own.
You don’t need a dedicated sales enablement platform to start. A general AI assistant paired with real source material (competitor pricing pages, CRM notes, call transcripts) covers most of what a small or mid-size marketing team needs for drafting and updating battlecards, one-pagers, and objection handlers.
ChatGPT and Claude both handle the drafting and editing workflows described above well, and Gemini is a reasonable option if your team already lives in Google Workspace, since it can work directly with docs and sheets you already have open. None of the three has a meaningful edge over the others for this specific task; the difference comes down to which one your team already has licenses for and is comfortable using.
Once your content volume and update frequency grow past what a small team can track manually, platforms like Highspot and Seismic add real value: content analytics that show exactly which pieces reps open (and which they never touch), automated content assembly tied to CRM data through tools like Highspot’s AutoDocs, and conversation intelligence tools like Gong that surface objections and competitor mentions directly from call recordings rather than requiring someone to manually review transcripts. If you’re building AI workflows across other marketing functions too, The AI Retention Playbook covers a similar build-versus-buy decision for a different part of the funnel.
Don’t buy a dedicated platform to solve an adoption problem. If reps aren’t using what you have now, a more sophisticated way to produce more content will not fix that. Fix what gets used first, then decide whether volume and update frequency justify the platform cost.
No, and you shouldn’t ask it to. General AI models will fill gaps in competitive knowledge with plausible-sounding claims if you let them, and a battlecard with even one fabricated claim about a competitor can cost a rep credibility mid-deal. Always feed the AI real source material (competitor pricing pages, reviews, win/loss notes) and explicitly instruct it not to add claims that aren’t in what you provided.
Tie updates to real triggers, not a calendar. A competitor pricing change, a new feature launch, or a shift you’re hearing repeatedly in win/loss calls should trigger an update immediately, since content built around outdated pricing or an outdated gap in the competitor’s product actively misleads reps rather than just being unhelpful.
Only if you’ve checked what your company’s data policy allows and stripped anything identifying the buyer by name or account where required. If you’re using an enterprise or team plan with data controls, that’s generally safer than a free consumer account. Platform-native AI features inside tools like Gong or Highspot that already have your call and CRM data under existing access controls are usually the lower-risk default for sensitive material.
Stale or generic, more often than simply missing. A battlecard that hasn’t been updated since a competitor’s last pricing change, or an objection handler that reads like it was written from a guess rather than a real call, teaches reps not to trust the next document either. Forrester’s research puts the unused-content figure at 60% to 70% of what B2B marketing produces, and staleness and genericness are the two reasons that show up most often when you actually ask reps why.
No. Content volume is close to a meaningless metric on its own, and can actively mislead you into thinking you’re succeeding while reps ignore most of what you’re shipping. Mindtickle’s 2026 research found content engagement jumped from 4% to 55% in a year at organizations that cut their content library by 76%. Track usage and adoption (what reps actually open and use in live deals), not how many pieces you published this quarter.
I checked the sales content utilization statistics directly against Forrester’s own published blog post and Highspot’s GTM Performance Gap Report executive summary, and verified the Mindtickle content engagement figures against Mindtickle’s 2026 State of Agentic Revenue Enablement Report press release rather than a secondary summary. Highspot’s AutoDocs and Gong’s conversation intelligence claims are drawn from each vendor’s own current product pages. Every statistic and tool claim below is sourced and linked.
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