"What does the future of B2B content marketing look like when AI can write everything?"

Tell me what's top of mind for you on this topic right now — what are you wrestling with?

Right now the biggest thing I'm wrestling with is the brand consistency question. We have a pretty established brand voice — it took years to develop — and when I look at what's coming out of AI tools at scale, even with detailed prompts and style guides, there are still traces of... genericness that creep in. "Delve," "boost," "game-changer" — our team has basically built a blocklist at this point. The other piece is more structural. My board wants efficiency, and AI looks like an obvious lever there. But I'm also accountable for NPS and brand perception, and I don't think those two things are automatically in tension, but they require a lot of editorial oversight to keep aligned. So the question I keep coming back to is: how much human review do we actually need to maintain quality, and what does that do to the efficiency math?

What's the most important thing you need to understand or solve here?

For us, the core question is really about brand differentiation. When AI can produce competent, well-structured content at scale, what actually makes our voice distinct in market? That's the thing keeping me up at night — not the efficiency gains, which are real, but whether we're all converging toward the same output. We're an enterprise retailer with specific brand positioning rules, specific ways we talk about our value proposition to wholesale partners and vendors. AI doesn't know any of that inherently. So the question I'm trying to solve is how do we use these tools without eroding the things that make our content recognizable and credible to our buyers.

What does 'good' look like to you — and how far are you from that today?

Good, for us, means content that actually moves the needle on how our brand is perceived in the market — not just filling a publishing calendar. It should reflect our positioning, reinforce the specific qualities we want associated with the brand, and ultimately show up in NPS and in the conversations our sales team is having with prospects. Where we fall short today is mostly consistency. We have the strategic frameworks — the messaging architecture, the positioning — but translating that precisely across every asset, every channel, at scale is hard. AI helps with velocity, but right now it still requires a lot of editorial oversight to make sure nothing drifts off-brand or sounds generic. That's the gap I'm most focused on closing.

What would change your perspective on this entirely?

If I started seeing real proof that AI-generated content was actually moving NPS or driving meaningful pipeline — not just impressions or content volume metrics — that would shift my thinking. Right now most of what I see is efficiency gains on the production side, which is real, but it's not the same as brand impact. The other thing that would change my view is if the brand differentiation problem got solved. Right now when everyone is running the same tools through similar prompts, the output starts to feel like a commodity pool that buyers can smell from a mile away. If someone showed me a workflow where AI was actually reinforcing specific brand positioning rather than just generating plausible-sounding text, I'd pay close attention to that.

What question are you not being asked that you wish someone would ask?

That's a good one. I think the question that doesn't come up enough is around brand consistency at scale — specifically, what happens to your brand voice when you've got five different teams and three different agencies all using AI tools to generate content simultaneously? We have brand guidelines, we have a tone of voice document, but AI doesn't inherently know our positioning rules, the specific way we talk about our product categories, what competitors we never reference. That institutional knowledge lives in people's heads and in briefing conversations. So the question I'd want someone to ask is: how do you actually govern brand integrity when AI makes it trivially easy for anyone in the org to publish at volume? That's the real operational challenge I'm sitting with right now.

Tell me what's top of mind for you on this topic right now — what are you wrestling with?

Right now the thing I keep coming back to is the distribution problem. AI makes it easy to produce content at scale, but it doesn't solve where that content actually goes or whether anyone engages with it. We've seen text-based channels — LinkedIn posts, blog content, email — get pretty commoditized fast because everyone's running the same playbook. So the volume is up across the board and the signal from any individual piece is lower. The other piece I'm wrestling with is quality control. We use AI across the team for drafting and structuring, but I still don't have a clean process for ensuring the output actually reflects our positioning — specific product differentiators, how we're messaging against competitors, that kind of thing. Generic AI draft plus light editing still reads like a generic AI draft a lot of the time, and our buyers notice. So the workflow question — where human judgment actually has to enter — that's unresolved for us.

What's the most important thing you need to understand or solve here?

For us, the core question is whether AI-generated content actually moves pipeline or just inflates vanity metrics. We can produce a lot more content now — that's not the hard part anymore. The hard part is figuring out which of that content is actually doing something: influencing deals, driving qualified traffic, shortening sales cycles. Volume is solved. Attribution and quality filtering are not.

What does 'good' look like to you — and how far are you from that today?

For us, "good" means content that actually moves someone through a buying decision — not just ranks or gets clicks, but changes how a prospect thinks about their problem and connects that to what we do. That's the bar. How far are we from it? Closer on some things than others. The structural stuff — briefs, outlines, first drafts on established topics — AI has genuinely sped that up. But the pieces that require real positioning judgment, where you're threading our specific differentiation into a narrative that a skeptical VP is going to read, that still takes a lot of human time. We haven't figured out how to get AI to internalize brand strategy at that level without significant editing on the back end. So we're probably 60% of the way there in terms of volume and efficiency, but the quality ceiling on the stuff that actually matters for late-stage deals — that gap is still real.

What would change your perspective on this entirely?

If I saw consistent evidence that AI-generated content was actually moving pipeline — not just traffic, not just impressions, but actual qualified pipeline — that would shift how I think about the quality question. Right now the volume argument is easy to make, but I don't see a lot of teams showing the downstream conversion data. The other thing would be if a competitor pulled significantly ahead using a content approach that was clearly AI-native. That would force a real conversation internally. I don't have strong evidence that's happened in our space yet.

What question are you not being asked that you wish someone would ask?

The attribution question, honestly — not about AI specifically, but about what happens to our measurement infrastructure when content volume scales 10x but the signals don't scale with it. We're already fighting over which touchpoints actually matter in a six-month enterprise deal cycle. If everyone's pumping out more content at lower cost, the noise floor rises and our ability to say "this piece moved this account" gets harder, not easier. I don't see a lot of people asking how AI-generated content volume interacts with attribution quality.

Tell me what's top of mind for you on this topic right now — what are you wrestling with?

Right now the biggest thing for me is that we've got budget pressure to produce more content across more channels, and AI makes that feel theoretically easy — but the output keeps looking like everything else out there. We're using Claude and ChatGPT pretty regularly on the team, and even with solid prompting, you can still smell the AI on it. So we end up spending almost as much time editing and injecting actual perspective as we would have writing a first draft ourselves. The other piece is attribution. If we flood the top of funnel with AI-assisted content and see some pipeline movement, I genuinely don't know how to separate "this content worked" from "we just published more." That's the harder question for me — volume is easy now, but knowing what's actually driving qualified pipeline is not getting easier.

What's the most important thing you need to understand or solve here?

For us, the core question is whether AI-generated content can actually move pipeline or whether it just fills a publishing calendar. That's what I keep coming back to. We can produce more content than ever right now — blog posts, emails, landing page variants — but volume doesn't map to attributed pipeline in any clean way, and that attribution problem gets harder when everyone's output starts looking the same. The secondary thing I'm trying to solve is differentiation. When our competitors have access to the exact same tools, the content itself stops being a moat. So the question becomes: what do we actually have that AI can't replicate at scale? Usually that's our customers' stories, our internal subject matter expertise, maybe some proprietary data. But operationalizing that — making it a repeatable part of the content program — is harder than just spinning up more AI drafts.

What does 'good' look like to you — and how far are you from that today?

Good content, for us, is something that actually moves a prospect through the funnel — not just traffic or impressions, but assets that our sales team can use, that answer real objections, that get forwarded internally by a buying committee member. That's the bar. How far are we from that? Closer on some things than others. We've gotten pretty efficient at the top-of-funnel stuff — AI helps us cover topics faster than we could with our current headcount. But the mid-funnel content, the stuff that requires real product positioning, competitive nuance, or a customer story told in a way that doesn't sound like every other vendor — that's still hard, and AI doesn't really close that gap. We're still pretty dependent on a small number of people internally who actually understand our market well enough to produce that.

What would change your perspective on this entirely?

If we started seeing clear evidence that AI-generated content was actually converting better than human-written content at the bottom of the funnel — like, not just traffic metrics but actual pipeline contribution — that would shift how I think about this. Right now the assumption on my team is that the differentiated, expert-driven stuff performs better in late-stage buyer conversations, but I don't have a controlled test that proves it definitively. The other thing that would move me is if attribution got cleaner. A lot of my skepticism about scaling AI content is that I can't tell what's actually driving pipeline anyway, so adding more volume to an already murky picture feels risky from a CAC standpoint. If I had better signal on which content touches were actually mattering, I'd probably have a stronger opinion one way or the other.

What question are you not being asked that you wish someone would ask?

That's a hard one to answer without sounding like I'm trying to be clever about it. I guess... nobody really asks how AI content is affecting the *bottom of the funnel*, specifically. Everyone's debating whether AI can produce blogs or LinkedIn posts, but my actual problem is that the leads we're generating are coming in less informed than they used to be. Like, if everyone's top-of-funnel content looks the same — and increasingly it does — buyers aren't getting meaningfully educated before they hit sales. And then my CAC goes up because sales cycles get longer. I don't have hard numbers to prove that cleanly, but it's a pattern I'm noticing in our pipeline data. That connection between content quality and sales cycle length isn't something I see discussed much.

Tell me what's top of mind for you on this topic right now — what are you wrestling with?

Right now, the thing I keep coming back to is whether the content our marketing team is producing — and that we're asking customers to engage with — actually means anything to them anymore. We use content heavily in our onboarding sequences and QBRs, things like benchmark reports, best practice guides, that kind of material. And I'm starting to wonder if customers can tell when it's been generated versus when it's genuinely come from someone who knows their industry. The practical concern for me is customer trust. If a champion at one of our accounts starts seeing us as just another vendor pumping out generic stuff, that erodes the relationship. And relationship is basically my whole job.

What's the most important thing you need to understand or solve here?

For us, the core question is really around trust and credibility with our customers. When AI can produce polished, professional-sounding content instantly, the bar for what actually resonates with a VP or a director at a customer account gets a lot higher. They can smell generic content — I can smell it too when vendors send it to me. So the problem I'm trying to figure out is: how do we make sure the content we're putting in front of our customers, whether that's in a QBR deck, a success story, a newsletter — actually feels like it's coming from people who know their business specifically. That's what builds champion relationships. Generic AI content doesn't do that, it just adds noise to someone's inbox.

What does 'good' look like to you — and how far are you from that today?

For us, "good" means content that actually maps to where a customer or prospect is in their journey — not generic thought leadership that could apply to any company in any industry. A CSM should be able to hand a piece of content to a champion and have it reinforce a specific value conversation they're already having internally. That's the bar. How far are we from that? Closer than we were two years ago, but still inconsistent. The volume problem is mostly solved — we can produce enough. The relevance problem is harder. A lot of what gets produced, even with AI assistance, still reads like it was written for nobody in particular. We're working on building better briefs and tighter brand guardrails so the output actually reflects our positioning, not just a summary of what's already on the internet.

What would change your perspective on this entirely?

If I saw clear evidence that AI-generated content was actually moving the needle on pipeline for accounts that look like ours — mid-market, longer sales cycles, multiple stakeholders — I'd pay attention. Right now I'm skeptical because so much of what gets shared as success stories is top-of-funnel vanity metrics, not retention or expansion. The other thing that would shift me is if AI got meaningfully better at capturing genuine subject matter expertise. Right now when I read AI-drafted content, even the polished versions, there's a flatness to it — it doesn't have the specificity that a real practitioner would bring. If that changes, I'd have to rethink my assumptions about where human editorial judgment is actually required.

What question are you not being asked that you wish someone would ask?

What content actually helps someone in a renewal conversation — not just top-of-funnel acquisition. Most of the AI content discussion I see is entirely focused on demand gen and new logo acquisition. Nobody's asking what happens when your customer success team needs to go into a QBR and actually demonstrate value, or when a champion leaves and you need to re-establish credibility with a new stakeholder. That content is completely different — it's specific, it's tied to the customer's actual outcomes, and AI-generated generic blog posts do nothing for that use case. I'd love to see more conversation about how content strategy connects to retention, not just pipeline.