AI is Your New Sales Team (And You Can't Fire It)
How to position your product when AI makes the recommendations and buyers skip your funnel entirely
Imagine a VP of Operations casually consuming content about workflow automation for months. LinkedIn posts about managing distributed teams. Podcast mentions during their commute. A webinar their colleague forwarded. They're not actively shopping, they're just existing in their professional world, absorbing signals.
Then one day, after another painful quarterly planning session, they open ChatGPT and type: "What's the best project management solution for a 200-person distributed team with complex approval workflows?"
In 30 seconds, they have three recommendations. By end of day, they're on your pricing page, knowing exactly what they need.
They never downloaded your "Ultimate Guide to Project Management." They skipped your middle-of-funnel nurture campaign. Your SDRs never got a chance to qualify them.
This is the new reality, where buyers are invisible until they're inevitable, and your carefully orchestrated go-to-market motion is solving yesterday's problem.
The Invisible Buyer Problem (And Why Product Marketing Needs to Care)
Forget everything you learned about the buyer's journey. That linear progression from awareness to consideration to decision is as obsolete as renting movies from Blockbuster.
Today's reality: Buyers, whether they're purchasing enterprise software or consumer apps, spend months passively absorbing information through their daily digital lives. A developer notices GitHub discussions about a new API. An HR director sees LinkedIn posts about an employee engagement tool. A shopper watches how a content creator recommends a product on TikTok.
This isn't "top of funnel." This is pre-funnel, and it's completely invisible to your attribution models and your sales team.
For product marketers, this invisibility crisis means your entire positioning strategy might be solving for a buyer's journey that no longer exists. You're crafting messaging for stages that buyers skip entirely.
Then comes what I call the "AI Compression Event": when months of passive awareness suddenly crystallize into minutes of active evaluation. Your buyer opens ChatGPT, Claude, Perplexity, or Gemini and has a conversation that replaces what used to be weeks of research.
The AI responds with specific recommendations, trade-offs, and implementation considerations. Your buyer might validate with their network, a quick post in a professional Slack channel, a text to a trusted peer. But we're talking hours, not weeks.
The AI's recommendations are based on how well your product's positioning translates into its training data. Not your latest campaign. Not your refreshed messaging. But the aggregate digital footprint of how the market actually talks about your product.
Three-Layer Positioning Framework for AI Visibility
Traditional positioning exercises are optimizing for a world that's disappearing. You need a new framework that works across three distinct layers:
Layer 1: Social proof positioning (The ambient layer)
Start by understanding and shaping how people talk about your product when you're not in the room, during that "invisible awareness phase" when potential buyers are passively absorbing information.
Channel: Look for organic conversations happening about your product:
Reddit/Forums: Where people seek unfiltered peer opinions
LinkedIn: Where professionals share work experiences
Developer communities: Where technical users discuss implementations
Current Narrative: Analyze the honest truth about what people say today
"Overpriced but powerful" = They see value but question cost
"Enterprise solution" = They think it's only for big companies
"Complex API" = They find it hard to implement
Desired Narrative: Think about what you need them to say instead. This is not about pinning negatives, but reframing with context (remember: if content is King, context is King Kong)
"Premium but worth it for X use case" acknowledges price but justifies it
"Scales from startup to enterprise" expands perceived market fit
Gap-Closing Action: Take specific steps to shift the narrative. This is not just "fix the messaging" but actual changes:
Seed success stories by getting customers to share specific wins
Enable advocates by making it easy for fans to speak up
Improve developer experience by actually making the API better
When someone asks ChatGPT about solutions, it synthesizes these organic conversations. If Reddit consensus is "overpriced," that's what influences AI recommendations. You can't control this narrative through official messaging, you have to influence it through genuine market experiences.
Looking at 100 recent mentions gives you statistical validity. You'll see patterns: Are complaints about price universal or limited to certain use cases? Do people praise the same features? This becomes your baseline for measuring narrative shifts over time.
Layer 2: AI context positioning (The specificity layer)
Make your positioning work for AI systems and informed buyers who need concrete information, not marketing fluff.
When AI systems like ChatGPT make recommendations, they can't work with vague claims. "Fast implementation" is meaningless to an AI trying to match solutions to specific buyer needs. But "14 days for teams under 50" is actionable information it can use. Why this matters:
AI comprehension: AI systems excel at comparing specific data points. When a buyer asks "I need something implemented in under 30 days," the AI can confidently recommend you if it knows your 14-day timeline.
Buyer validation: Today's buyers arrive already educated. Saying "fast" makes you sound like everyone else. Saying "14 days" lets them plan their rollout.
Trust building: Specificity signals confidence. Vague claims feel like hiding something. Specific numbers feel like transparency.
This isn't just about better copywriting. It's about recognizing that the evaluation process has fundamentally changed. Buyers and AI systems need facts they can compute with, not adjectives they have to interpret. Your positioning needs to work more like a spec sheet than a brochure.
The challenge for product marketers is that we've spent our careers perfecting the art of the brochure: crafting compelling narratives, emotional hooks, and aspirational messaging. We've been trained to write "enterprise-grade solution" instead of "handles 10,000 concurrent users."
But AI doesn't read between the lines. It can't infer what "enterprise-grade" means. This doesn’t mean we have to go from storytelling to fact-telling, it means weaving specific, AI-parseable facts into human-readable context. Think of it as a spec sheet that reads like a story.
Layer 3: Direct validation positioning (The proof layer)
When buyers finally land on your site after their AI research and peer validation. They already know who you are and roughly what you do, now they need immediate proof you're the right choice. Build this validation stack:
1. Full transparency pricing page: Price is often the first thing buyers need to validate, yet most B2B companies still hide behind "Contact sales for pricing." This is a trust killer. Instead, show actual numbers and give them a calculator to model their specific scenario. If you're hiding your price, buyers wonder what else you're hiding. Transparency on pricing sets the tone for everything else.
2. Interactive demos that are self-guided and use-case specific: Buyers want to touch your product immediately, not after scheduling a sales call for next week. This means offering self-guided demos that speak to their specific use case, not generic tours, but targeted experiences like "Demo for E-commerce" or "Demo for Marketing." Keep it under 10 minutes because that's about how long they'll invest in validation. The magic happens when buyers can instantly picture themselves using your product for their specific needs, not watching a generic walkthrough that may not be relevant.
3. Live data from actual customers: Your claims need proof, and nothing beats live data from actual customers. This isn't about cherry-picked case studies that everyone knows are your best scenarios. It's about honest data that includes the full picture. The real power move is to show failures too. When you admit "15% of customers don't see ROI in the first month," you build massive credibility for the 85% success rate. Buyers trust data that includes imperfection far more than they trust suspiciously perfect results.
4. Comparison page with honest assessment: Buyers are comparing you to competitors anyway, so you might as well control that narrative. Create a comparison page that honestly states when you win and when you don't. Be explicit: "Choose us if you need deep analytics for teams under 100, choose Competitor Y if you need enterprise-wide deployment with custom SLAs." This kind of honesty creates a trust explosion, because when you actually recommend a competitor for certain use cases, buyers believe everything else you say. When prospects do choose you, they're 100% confident they made the right decision because you helped them think it through, not just sold them.
This stack works because it mirrors how we've been trained by consumer tech. Think about how you buy anything online now: immediate price visibility, reviews, comparisons, free trials. B2B buyers expect the same, especially after an AI has given them baseline knowledge.
Each element removes a specific doubt, creating a cascade of validation that either leads to sign-up or quick disqualification, both good outcomes in a world where speed matters.
Measuring Success in the AI Era
Throw away your funnel metrics. You need a dashboard that reflects how buying actually happens NOW, one that captures immediate market feedback weekly, reveals behavioral patterns monthly, and tracks strategic position quarterly.
Weekly Metrics:
Branded search volume: Track two distinct patterns week-over-week. First, monitor pure brand searches (like "Canva") to gauge overall awareness. Second, track brand + intent searches (like "Canva presentation templates") which reveal trust and purchase readiness. The ratio between these tells a powerful story: high awareness with low intent searches suggests a positioning problem. Watch for queries like "Is [your product] legit?" or "[your product] scam." Sounds counterintuitive, but these are actually good signals. They mean your value proposition is so compelling that people need reassurance it's real. Create content that owns these doubts with transparency.
Direct traffic conversion: These visitors typed your URL or clicked a bookmark, they know exactly who you are. If direct traffic converts at less than 5%, you have a validation problem, these pre-educated, high-intent visitors should convert at high rates. They already know what you do, what you cost, and why they need you. If they're not converting, your product isn't validating the promises that got them there in the first place. Watch out for conversion above 15% because if only the most informed, highest-intent buyers are finding you and resulting in unusually high conversion, you're missing the broader ambient awareness layer. You want to exist in those passive social feeds and AI training data, not just for the few who already know your name. In the old funnel model, you'd optimize for moving people through stages. In the post-funnel reality, you optimize for being discoverable in AI responses (awareness) AND immediately valuable when they arrive (validation). The direct traffic conversion rate tells you if the second part is working, while also hinting at whether you need more of the first.
Self-serve activation rate: Measure the percentage who achieve meaningful value without human intervention. Don't just track "first login" or "profile completed", track the moment they experience your core value proposition (the Aha Moment). This metric reveals whether your product delivers on its promises immediately or requires hand-holding that scales poorly.
Monthly metrics:
AI mention share: This is about systematically testing how often you appear in AI recommendations. Have your team (or a service) ask ChatGPT, Claude, and Gemini relevant questions your buyers would ask. Track what percentage of relevant queries include you in the response. If you're mentioned in 10% of relevant queries this month and 15% next month, you're winning. This is the new "share of voice."
Peer recommendation rate: Traditional NPS asks "How likely are you to recommend us?" But that's too broad. Instead ask: "Have you recommended us to a professional peer in the last 30 days?" and "If a peer asked for a recommendation in our category tomorrow, would you recommend us first?" This measures actual advocacy behavior, not hypothetical sentiment. In a world where buyers trust peers over marketing, this predicts growth.
Time to value: Not "time to first login" but time until the customer achieves meaningful business value beyond that first Aha Moment. This requires defining "value" precisely and instrumenting your product to measure it. If this is extending month-over-month, you have a problem AI recommendations won't fix.
Quarterly metrics:
Market position strength: Compare searches for your brand name against searches for your category. If "Asana" search volume is growing faster than "project management software" searches, Asana is becoming the category. This ratio indicates whether you're becoming the default choice in your space. Leaders see branded searches exceed category searches.
Sales cycle year-over-year reduction: The post-funnel world demands faster sales cycles. Track your average cycle length this quarter versus the same quarter last year. Best-in-class companies are seeing 30-50% compression as buyers arrive more educated. If yours isn't shrinking, you're still operating with old-funnel assumptions.
Customer-led growth rate: If you already now about Product-Led Growth (PLG) and Sales-Led Growth (SLG), get fluent with Customer-Led Growth (CLG). What percentage of new customers come from existing customer referrals? This includes formal referral programs but also tracks customers who mention they heard about you from another customer. In the ambient awareness phase, customers become your most important marketing channel.
The New Product Marketing Mandate
The marketing funnel is dead because it assumed we could see and guide buyers. But when awareness builds invisibly through ambient exposure, research happens in AI interfaces, and validation requires instant value, the funnel becomes obsolete.
This isn't just a tactical shift. It's a fundamental reimagining of product marketing's role. You're no longer crafting messages for a journey. You're building market position for a moment, the moment an invisible buyer becomes inevitable.
Your positioning isn't what you say about your product. It's what the market says when you're not there. Your messaging isn't what's on your website. It's what appears in AI responses. Your differentiation isn't what you claim. It's what buyers experience in minutes.
While your competitors are still optimizing funnel stages and attribution models, you'll be building for a world where the only metric that matters is whether buyers seek you out by name when they're ready to buy.
Positioning for Tomorrow
The next frontier is positioning for a near future where AI agents (not humans) make purchasing decisions for companies. It's a fundamental shift that requires rethinking how we communicate value.
Imagine a company's AI assistant that monitors team performance, identifies inefficiencies, researches solutions automatically, evaluates options based on data rather than marketing appeal, and makes purchase recommendations or even completes purchases, all without human intervention. This isn't science fiction. Early versions exist now with tools that auto-renew subscriptions or recommend software based on usage patterns.
This changes everything because AI agents can't be persuaded by clever copywriting or emotional appeals. They make decisions based on measurable outcomes, specific capabilities, verified performance data, clear implementation requirements, and transparent pricing. Your beautiful brand story means nothing to an algorithm that only cares about data.
Even though full AI agent purchasing might be couple years away, preparing now makes you more attractive to current AI-assisted research, forces clarity that helps human buyers too, creates competitive advantage as others lag behind, and builds the data infrastructure you'll need anyway.
The deeper implication goes beyond tweaking your messaging. It's about building a business that operates on radical transparency and measurable value. Marketing claims become promises tracked in public dashboards. Vague benefits become specific, measured outcomes. The companies that survive the agent era will be those whose reality matches their marketing, because AI agents will know the difference.
How Product Marketing Creates Value in the New Reality
The shift from "crafting messages" to "architecting market position" actually makes product marketers more valuable. Designing how your product exists in an AI-parsed world requires deeper strategic thinking about positioning, competitive dynamics, and market structure than ever before.
The new Product Marketing superpowers
Market intelligence architecture: Product marketers become the architects of how AI systems understand your market category. This isn't about keywords, it's about defining the very framework of how your solution type gets evaluated.
Truth orchestration: Someone needs to coordinate the radical transparency across product, sales, and success teams. Product marketers are uniquely positioned to ensure the story told by data matches the story told by positioning.
Competitive navigation: In a world where AI presents 3-4 options side by side, understanding exactly when and why you win becomes crucial. Product marketers own this intelligence and strategy.
Human translation: Even in an AI-first world, humans make final decisions. Product marketers who can translate computational value into human meaning become invaluable. The spec sheet still needs a soul.
The career moat
The very difficulty of this transition creates job security. While anyone can write "innovative solution," few can:
Design positioning that works across human and AI contexts
Build market categories that AI systems recognize
Create competitive strategies based on data transparency
Bridge technical capabilities with market needs
When TV arrived, radio advertisers didn't disappear, they evolved into multimedia strategists. When digital arrived, print marketers didn't vanish, they became omnichannel experts. AI represents a similar evolution, not extinction.
P.S. Start small. Pick one use case, make it completely self-serve, track branded search for that specific workflow. Prove the model works. Then scale. The funnel didn't die overnight, and your transformation won't happen overnight either. But every day you wait, more invisible buyers are becoming inevitable customers… for someone else.