The real shift: your “funnel” is now search + social + AI, all at once
Look at those headlines and a pattern jumps out: everyone is treating AI, social-first content, search, and CRM as separate topics. Operators don’t live that way. You’re staring at one messy reality:
- AI is rewriting how people discover and evaluate brands (answer engines, Gemini, ChatGPT, TikTok search).
- Social is quietly becoming the primary search interface for younger buyers.
- Search is fragmenting into classic SEO, “answer engine optimization,” Reddit/UGC SEO, and branded queries.
- Retention and “superfans” are where profit actually shows up.
The problem: most teams are still running a 2018 funnel in a 2026 environment. Paid search and paid social at the top, a few nurture flows in the middle, and a CRM that mostly hoards data instead of driving creative and bidding.
The opportunity: treat AI, social, and search as one connected decision system and rebuild your funnel around that. Not as a thought experiment, but as a media and budget architecture you can actually operate.
Step 1: Accept that “search” is now everywhere
Historically, “search” meant Google and maybe Bing. Now:
- Google is still dominant, but AI overviews, Gemini, and answer boxes are absorbing more clicks.
- Reddit, TikTok, Instagram, and even Bluesky are where people “search” for opinions, how-tos, and reviews.
- ChatGPT, Perplexity, and other AI engines are becoming the first stop for problem framing and vendor shortlists.
If your funnel assumes discovery happens on Meta and intent happens on Google Ads, you’re missing half the journey. The new journey looks more like:
TikTok / Reels / Shorts → Reddit / niche communities → AI answer engine → branded search → site → email / SMS / community.
That means your job is no longer “optimize channels.” It’s “optimize the hand-offs between these surfaces.”
Step 2: Redesign the funnel as a network, not a line
The linear TOFU-MOFU-BOFU diagram is now a lie that makes planning feel tidy and execution feel broken. Replace it with three operating layers:
1. Discovery layer: social-first and PR-as-distribution
This is where people first bump into you. It’s mostly:
- Short-form video (TikTok, Reels, Shorts).
- Creators and UGC.
- Digital PR that drives both coverage and search signals.
- Community surfaces (Reddit, Discord, niche forums).
The job here is not “awareness.” The job is:
- Plant a searchable idea (a phrase, promise, or problem framing people will later type into Google, TikTok, or ChatGPT).
- Generate branded queries and curiosity, not just impressions.
- Capture first-party signals you can reuse (video viewers, engagers, site visitors).
Your KPI here is not CPM. It’s cost per qualified search or visit triggered (you can approximate this with brand search lift, direct traffic lift, and engaged view-through traffic).
2. Evaluation layer: search + answer engines + your site
This is where people are trying to decide:
- “Is this real?”
- “Is it for people like me?”
- “Is it better than the alternatives?”
This layer now includes:
- Classic SEO (category pages, comparison pages, FAQs, reviews).
- Answer engine optimization (AEO) for AI overviews, Gemini, ChatGPT, Perplexity.
- Reddit and community SEO (threads, AMAs, deep dives).
- Your own site’s UX and conversion paths.
The job here is to:
- Be the most useful, quotable source for the key questions your category raises.
- Show social proof and comparison content that AI engines and humans can both digest.
- Make the path from “I’m curious” to “I’m testing this” as short and obvious as possible.
KPIs here: qualified visit-to-lead rate, lead-to-opportunity rate, and share of AI / SERP real estate on your top 50-100 queries.
3. Commitment layer: CRM, remarketing, and superfans
This is where the margin lives. It’s:
- Email and SMS flows that are actually personalized, not just “Dear <first_name>.”
- Remarketing lists built around behaviors, not just “site visitors in last 30 days.”
- Customer communities, referral loops, and “superfan” mechanics.
The job here is to:
- Shorten time-to-second-purchase or time-to-first-meaningful-action.
- Turn best customers into visible advocates (reviews, UGC, case studies, referrals).
- Feed the discovery and evaluation layers with fresh proof and stories.
KPIs: LTV/CAC by cohort, referral rate, payback period, and % of revenue from repeat or referred customers.
Step 3: Put AI where it actually moves money
Most “AI + marketing” content is about tools, not outcomes. You don’t need another list of AI hacks. You need a small set of AI jobs that change your unit economics.
Job 1: Turn raw data into creative and audience hypotheses
You already have:
- Search term reports from Google Ads.
- Site search logs.
- CRM notes and support tickets.
- Reddit and social threads about your category.
Feed this into AI to:
- Cluster real language into 5-10 problem narratives people actually use.
- Draft message frameworks and angles for each narrative, not final copy.
- Identify missing content types (e.g., “everyone is asking for side-by-side comparisons; we have none”).
Output: a prioritized list of creative angles and content gaps that your team then refines and tests.
Job 2: Build and maintain remarketing and CRM segments
Most remarketing is lazy. One or two broad lists, hammered with the same creative. You can do better:
- Use AI to cluster users by behavior (pages visited, time on site, SKUs viewed, content consumed).
- Map each cluster to a stage (exploring, comparing, ready-to-buy, at-risk, superfan).
- Auto-generate draft sequences and ad variants tailored to each cluster’s objections and goals.
You still need humans to:
- Set guardrails on tone and brand.
- Kill bad ideas and refine good ones.
- Decide what “good enough” looks like for each segment.
Job 3: Act as a “creative ops” assistant, not a replacement
AI is useful for:
- Versioning winning hooks into multiple formats (short scripts, captions, headlines, ad copy variants).
- Drafting briefs for creators and agencies based on what’s working.
- Generating structured test matrices (e.g., 3 hooks × 3 offers × 3 formats).
It is not good at:
- Finding the one sharp, uncomfortable insight your category avoids.
- Setting strategy or deciding which risks are worth taking.
- Owning your voice in a “SaaS recession” where trust is scarce.
Treat AI as your over-caffeinated intern: fast, prolific, occasionally brilliant, never in charge.
Step 4: Make social your ranking engine, not just your megaphone
“Social-first ranking strategies” and “discoverability in 2026” are all pointing to the same thing: social content is now a ranking signal, not just a reach channel.
Three practical moves:
1. Design content to seed search behavior
Every strong piece of social content should:
- Introduce a phrase or concept people can search later (“the 30-day reset stack,” “the quiet pipeline problem”).
- Explicitly invite search (“Google ‘<brand> ROI calculator’ when you’re at your desk”).
- Point to a specific, search-optimized asset (not just your homepage).
2. Treat Reddit and communities as SEO infrastructure
Reddit threads and niche communities rank. They also feed AI models. You don’t need to astroturf; you do need to:
- Host real AMAs and Q&As where your team shows up as humans, not brand avatars.
- Answer category questions thoroughly, even when the answer isn’t “buy us now.”
- Encourage power users to post their own walkthroughs, reviews, and benchmarks.
Think of it as “distributed documentation” that lives where people actually talk.
3. Use short-form video as your fastest testing lab
Short-form video is brutal but honest. If a hook works there, it probably works in search ads and landing pages. Use it to:
- Test 10-20 hooks cheaply before you commit budget to search and display.
- Identify which benefits and objections trigger the strongest engagement.
- Feed top-performing angles back into your PPC headlines, SEO titles, and email subject lines.
Step 5: Build for superfans on purpose, not by accident
“When customers create more customers” isn’t a feel-good slogan; it’s your hedge against rising CAC and expensive keywords. In a world of $50+ CPCs and AI-filtered inboxes, you need a deliberate superfan system.
Three components:
1. A clear “superfan path” inside your product and comms
Map out:
- What a superfan does (posts, reviews, referrals, content, events, feedback).
- What they need from you (access, recognition, early input, better support).
- What you give them (status, tools, data, small but meaningful perks).
Then build flows that invite customers down that path: “power user” emails, beta groups, VIP channels, and spotlight content.
2. Instrumentation that treats advocacy as a core metric
Stop burying referrals and UGC in a side dashboard. Track:
- % of customers who create at least one public artifact (review, post, video, testimonial).
- Revenue from referred and “influenced by UGC” customers.
- Time from first purchase to first advocacy action.
Set explicit targets. Give your lifecycle and community teams the same seriousness you give paid media.
3. Feed superfan output back into AI, search, and social
Superfan content is gold for:
- Training your AI assistants and chatbots with real language and real objections.
- Fueling digital PR and social proof in pitches and landing pages.
- Improving answer engine coverage (AI models love detailed, authentic explanations).
Step 6: Change how you budget and staff for this reality
None of this works if your org chart and budget still assume channels are silos. A few practical shifts:
Reframe budgets around journeys, not platforms
Instead of “search vs social vs CRM,” think:
- Discovery budget (social, PR, creators, some display).
- Evaluation budget (search, answer engines, content, CRO).
- Commitment budget (CRM, remarketing, community, referral programs).
Give each layer an owner and a P&L mindset. Their job is to improve their layer’s conversion to the next layer, not to hoard spend.
Staff for orchestration, not channel fiefdoms
You probably need:
- One senior operator who owns the entire funnel as a system (not just “growth” or “brand”).
- A small “AI ops” pod that sits between data, creative, and media, translating insights into tests.
- Fewer channel specialists who only know one platform, more “story + system” operators who think across surfaces.
Set fewer, sharper KPIs
If everything is a priority, nothing is. For most teams, the short list should be:
- Blended CAC and payback period by cohort.
- Share of search (including branded, category, and comparison terms).
- Visit-to-meaningful-action rate on key evaluation pages.
- % of revenue from repeat and referred customers.
Then let channel metrics (CTR, ROAS, open rate) serve these, not replace them.
The throughline in all those headlines is simple: the old funnel is structurally wrong for how people actually decide now. AI, social, and search aren’t separate trends; they are the same customer, on different surfaces, asking the same question: “Is this worth my time and money?” Your job is to design one coherent system that answers that question well, wherever it’s asked.