The traditional marketing funnel feels completely broken with AI search - how is everyone adapting their customer journey strategy?

You’re not alone. This is THE conversation happening in every marketing leadership meeting right now.

The fundamental shift:

The traditional funnel assumed sequential touchpoints where you could influence buyers at each stage. AI search compresses this into what I call “simultaneous intent resolution.”

When a buyer asks Perplexity a complex question, they’re expressing:

• Awareness needs (“What solutions exist?”)
• Consideration needs (“How do they compare?”)
• Decision needs (“Which one fits my specific situation?”)

…all at once. The AI synthesizes everything and delivers a recommendation. Your funnel is now a single interaction you don’t control.

The data is stark:

• 90% of B2B buyers now use generative AI during purchase journeys
• 83% of the buyer’s journey happens before talking to sales
• Traditional attribution models miss most of this

The new mental model:

Stop thinking “funnel stages” and start thinking “AI recommendation eligibility.”

Your goal isn’t to move buyers through stages - it’s to be the brand AI recommends when buyers collapse those stages into a single query.

We restructured our entire go-to-market around this reality 6 months ago.

What we call the “AI-Era Funnel”:

Instead of TOFU/MOFU/BOFU, we now think in terms of:

1. AI Visibility Layer

• Are we mentioned when buyers ask AI about our category?
• What’s our share of AI voice vs. competitors?
• How are we positioned in AI recommendations?

2. Brand Reinforcement Layer

• When AI mentions us, does the buyer remember us?
• Is our brand strong enough to survive AI summarization?
• Do we appear across multiple AI touchpoints?

3. Conversion Layer

• When buyers arrive (pre-educated by AI), do we close them?
• Is our website optimized for AI-informed visitors?
• Does sales know how to handle AI-educated prospects?

The metrics we track:

• AI citation frequency (weekly via Am I Cited)
• Share of AI voice by category
• Branded search volume trends
• AI-to-branded-search correlation
• “Pre-educated” prospect conversion rates

We can’t track the middle, so we focus on being visible at the input (AI recommendations) and optimizing the output (conversions).

Attribution specialist here. Let me validate your concerns with data.

The “attribution dark matter” problem is real:

We analyzed our last 500 closed deals:

• 34% showed “direct” as first touch
• Of those, 78% mentioned AI research when asked how they found us
• Traditional attribution assigned ZERO credit to AI awareness

The math problem:

If a prospect asks ChatGPT about our category, gets recommended, then types our URL directly into their browser - that’s “direct traffic” in GA4. But it’s really AI-driven demand.

How we’re adapting:

1. Post-purchase surveys - Simply asking “How did you first learn about us?” reveals AI’s role

2. Branded search correlation - When our AI visibility increases, branded search follows 2-3 weeks later

3. Marketing Mix Modeling (MMM) - Statistical models that infer impact without tracking individual paths

4. AI citation tracking - Using Am I Cited to measure what we can’t track through traditional analytics

The uncomfortable truth:

Traditional funnel metrics (MQLs, SQLs, touched attribution) increasingly measure activity, not impact. The real influence is happening in conversations we can’t see.

Here’s how we’ve restructured content strategy for the AI funnel:

Old approach (funnel-stage content):

• Awareness: “What is [category]?” blog posts
• Consideration: Comparison guides, feature lists
• Decision: Product pages, case studies

New approach (AI-citable content):

Comprehensive Intent Content

• Single pages that answer the complete buyer question
• Cover what it is, how solutions compare, and who should use what
• Structured for AI extraction (clear headers, direct answers, supporting data)

Authority Content

• Original research AI can cite
• Expert perspectives AI can quote
• Industry-specific use cases

Validation Content

• Customer proof points across third-party sites
• Review site presence optimization
• Industry publication features

The key shift:

We stopped thinking “which funnel stage does this content serve?” and started thinking “what complete question does this content answer?”

Because AI doesn’t care about your funnel stages. It cares about comprehensively answering user questions.

Sales perspective on this transformation:

What’s changed in prospect conversations:

Buyers used to arrive with questions. Now they arrive with AI-informed opinions.

They’ve already:

• Learned about the category
• Compared vendors
• Formed preferences
• Identified concerns

Sometimes their AI-driven research is accurate. Sometimes it’s not. But they’re confident either way.

How we’re adapting:

1. “What did AI tell you?” discovery - We now ask early in conversations what AI research they’ve done and what they learned. This reveals misconceptions we need to address.

2. AI-informed objection handling - Common AI-driven objections get documented and addressed proactively.

3. Faster sales cycles - Buyers arrive further along, so we’re optimizing for shorter cycles with AI-educated prospects.

4. Win/loss analysis includes AI - We now track whether AI mentioned us (or competitors) in lost deals.

The silver lining:

When AI recommends us favorably, prospects arrive as warm leads with implicit trust. Those deals close faster and at higher values.

The challenge is ensuring AI recommends us accurately and favorably in the first place.

Startup perspective - this is actually GOOD for smaller companies.

Traditional funnel advantages:

• Big brands with massive content libraries
• SEO authority built over years
• Brand recognition at every touchpoint

AI funnel advantages:

• Relevance matters more than size
• Best answer wins, not biggest budget
• Newcomers can get recommended alongside incumbents

What we’re doing:

1. Niche down aggressively - AI recommends specialists over generalists for specific questions

2. Out-answer, not out-rank - We can’t compete for traditional rankings, but we can create the best answer to specific questions

3. Third-party validation focus - Getting mentioned in reviews, comparisons, and discussions that AI trusts

4. Monitor AI recommendations obsessively - We use Am I Cited to track every mention and adjust strategy weekly

Our results:

We’re getting mentioned alongside competitors 10x our size because AI doesn’t care about company size - it cares about relevance to the query.

The playing field is more level than it’s ever been.

I consult on this transition for enterprises. Here’s the framework I use:

The “Collapsed Funnel” Strategy:

Layer 1: Be Findable

• Optimize for AI discovery (structured content, comprehensive answers)
• Build presence on platforms AI cites (Wikipedia, Reddit, G2, industry pubs)
• Ensure consistent, accurate information everywhere

Layer 2: Be Recommendable

• Position clearly for specific use cases
• Accumulate third-party validation
• Address comparison queries directly
• Maintain strong review presence

Layer 3: Be Convertible

• Optimize site for AI-educated visitors
• Enable fast self-service evaluation
• Train sales for shorter, more advanced conversations

Layer 4: Be Measurable

• Track AI visibility as primary metric
• Use MMM for influence attribution
• Correlate AI mentions with downstream metrics

The implementation reality:

Most companies can’t transform overnight. Start with measurement - track AI visibility. Then work backward through the layers.

If you can’t see your AI visibility, you can’t improve it.

Counterpoint here - I don’t think the funnel is dead, just transformed.

Buyers still move through stages:

• They become aware of problems
• They consider solutions
• They make decisions

What’s changed is WHERE these stages happen and HOW FAST they compress.

The new funnel isn’t “no funnel” - it’s “accelerated funnel in AI environments”:

• Awareness happens in AI conversations
• Consideration happens in AI comparisons
• Decision happens in AI recommendations

Practical implication:

You still need content for each stage - but it needs to exist WHERE AI can find it and be structured HOW AI can use it.

The funnel psychology is the same. The implementation is completely different.

This discussion has fundamentally shifted how I’m thinking about our strategy.

Key reframes I’m taking away:

1. From funnel stages to AI recommendation eligibility - The goal is being the brand AI recommends, not moving people through stages we control

2. From content-for-stages to comprehensive answers - Single pieces that answer complete buyer questions beat stage-specific content

3. From attribution tracking to influence measurement - Accept that traditional attribution is broken, use proxies like AI visibility and branded search correlation

4. From traffic metrics to AI share of voice - Being mentioned matters even without clicks

5. From SEO optimization to reputation building - Authority across the web matters more than individual page rankings

What I’m changing:

1. Setting up AI visibility monitoring with Am I Cited
2. Auditing all content for AI comprehensiveness vs. funnel-stage thinking
3. Building AI citation tracking into our dashboard alongside traditional metrics
4. Adding “how did AI describe us?” to win/loss analysis
5. Proposing MMM investment to leadership for better influence measurement

The uncomfortable acceptance:

The funnel I spent years optimizing was a mental model for a different era. Time to build new mental models for the AI era.