How to Run AI-Forward B2B Campaigns That Build Real Pipeline

B2B buyers form opinions about vendors long before they open a search bar. Pauline Jakober, CEO of Group Twenty Seven, makes the case bluntly: brands that limit their Google Ads strategy to brand and non-brand keywords are actively capping their own growth. This tutorial breaks down the AI-forward campaign architecture — using Google’s Performance Max, Demand Gen, and the 4S+Ask behavioral framework — that earns buyer familiarity at every stage of the research journey, then converts that trust into measurable pipeline.

What This Is

AI-forward campaigns are a multi-channel, AI-optimized advertising approach designed to build brand presence across every surface a B2B buyer touches before they conduct a branded search. The term “AI-forward” doesn’t just mean using machine learning for bid optimization — it means structuring your entire campaign architecture around how AI-driven platforms like Google, YouTube, and conversational tools like ChatGPT now mediate buyer discovery.

According to Search Engine Land, the core insight driving this approach is straightforward: B2B buyers research solutions on Reddit, ask ChatGPT for vendor comparisons, scroll LinkedIn, and watch YouTube demos long before they ever type a company name into a search box. If your advertising only appears at the search stage, you’re showing up at the end of a decision process that was largely settled earlier.

The two primary campaign vehicles for this approach inside Google Ads are:

Performance Max (PMax): A fully AI-driven campaign type that runs across Google’s entire inventory — Search, Display, YouTube, Gmail, Maps, and Discover — from a single campaign. PMax uses your asset groups (headlines, descriptions, images, video) and audience signals to autonomously find the highest-converting combinations. For B2B advertisers, it surfaces product demos and testimonials on YouTube, retargets warm prospects across Display, and ultimately drives branded search conversions further down the funnel.

Demand Gen: Google’s successor to Discovery campaigns, Demand Gen is specifically engineered for awareness and consideration. It places immersive visual ads on YouTube (including Shorts), Gmail, and Discover with a focus on driving action — video views, clicks, and engagement — from audiences who are actively researching but haven’t yet expressed direct purchase intent. Demand Gen supports lookalike audiences based on your CRM data, making it particularly powerful for B2B teams that can feed it lists of closed-won customers.

Together, these two campaign types execute across what Jakober calls the 4S+Ask framework: the five behavioral modes through which modern B2B buyers discover, evaluate, and decide on solutions.

The research report synthesized from multiple B2B AI sources shows that AI-driven campaigns launch 75% faster and deliver 47% better click-through rates compared to traditionally structured campaigns. This speed advantage is significant: as Jonathan Costello, Senior Content Strategist at Demandbase, notes, competitors using AI to test more ideas and personalize messaging faster will learn faster than teams still manually managing every ad variable.

The critical caveat — the “if you’re patient” qualifier in Jakober’s original headline — is that AI-forward campaigns require a learning period that can stretch weeks to months in B2B contexts. One life sciences client Jakober worked with took nearly a year before Performance Max could demonstrate measurable value, because individual deals took months to close. The platform’s machine learning needs enough conversion signals to optimize, and in B2B, those signals accumulate slowly.

Why It Matters

B2B marketing has a structural problem that keyword-only advertising cannot solve: the buying committee. Enterprise purchases involve an average of six to ten stakeholders, each researching independently, each building opinions through different channels. A VP of Sales watches YouTube demos. A procurement manager searches Reddit for vendor horror stories. A technical evaluator asks ChatGPT which platforms integrate with their existing stack.

If your paid media only appears when someone types a search query, you’re invisible to every one of those touchpoints that didn’t end in a search. Jakober’s framework addresses this directly: “If your strategy only covers one or two of those behaviors, you’re missing how growth actually happens.”

The business case is backed by research. According to the AI-Driven Revenue Execution research report, companies integrating predictive AI into marketing see conversion rates rise by 20–30%, and AI-driven pricing optimization increases profit margins by an average of 12%. Meanwhile, 86% of sales teams using AI report positive ROI within the first year.

For marketers, the practical implication is a budget reallocation decision: move 5–10% of existing search spend into Demand Gen and Performance Max to test upper-funnel AI-forward campaigns, measure the effect on branded search volume and pipeline velocity over time, and scale based on proof. This isn’t a “burn your keyword strategy” argument — it’s an additive layer that builds the awareness that makes your existing search campaigns more efficient.

Who benefits most from this approach:

• Mid-market B2B companies with 3–12 month sales cycles where awareness campaigns have time to mature before deals close
• Enterprise SaaS vendors where multiple stakeholders independently evaluate solutions
• Professional services firms where trust and credibility precede any RFP stage
• Life sciences, fintech, and legal tech vendors where buyers ask peers and AI tools before they search

The stakes of staying keyword-only are rising as Google’s AI Mode and AI Overviews increasingly intercept navigational and informational queries. Advertisers who depend entirely on traditional search will find their impression share compressed by AI-generated answers that don’t trigger paid placements.

The Data

AI-Forward Campaign Impact vs. Keyword-Only Approach

The 4S+Ask Framework: Channel-to-Behavior Mapping

Source: Pauline Jakober / Search Engine Land; Research Report

Step-by-Step Tutorial: Building Your AI-Forward B2B Campaign Stack

Prerequisites

Before you start, you’ll need:
– An active Google Ads account with conversion tracking configured (not just MQL form fills — ideally pipeline stage and CRM data synced)
– A minimum budget of $5,000–$10,000/month to give AI campaigns enough spend to learn
– At least three creative assets per format: 1 demo video (30–90 seconds), 3–5 static display images, and 5+ ad headlines
– A CRM export of closed-won customers for lookalike audience seeding
– Google Ads linked to Google Analytics 4 with enhanced conversions enabled

Phase 1: Audit Your Current Strategy (Days 1–3)

Start by mapping where you currently appear against the 4S+Ask framework. Open Google Ads and run a campaign-level performance report segmented by network. If 90%+ of your impressions are on Search alone, you have coverage gaps.

Step 1: Run the Network Coverage Audit
In Google Ads > Reports > Predefined reports, pull “Campaign performance” and add the “Network” segment. Export to a spreadsheet. For each of the five behavioral modes (Search, Scroll, Stream, Shop, Ask), note whether you have any active coverage. Most B2B accounts will show Search coverage and nothing else.

Step 2: Review Conversion Tracking Depth
Go to Tools & Settings > Conversions. If you’re only tracking form fills tagged as a single “Lead” conversion, your AI campaigns won’t have enough signal quality to optimize properly. You need conversion events that map to actual pipeline stages: Demo Requested, Proposal Sent, Opportunity Created.

Work with your CRM admin to import offline conversions via the Google Ads API or by using HubSpot/Salesforce native integrations. This is non-negotiable for Performance Max to work in B2B contexts — the platform needs to understand what a qualified conversion looks like, not just any form submission.

Step 3: Set a Patience Benchmark
Before launching, document your average sales cycle length. If your typical deal closes in 90 days, your AI campaign needs at least 90 days of data before you can draw meaningful conclusions about pipeline contribution. Write this number down and share it with every stakeholder who will ask about campaign performance. This is your “minimum evaluation window.”

Phase 2: Launch a Demand Gen Campaign for Upper-Funnel Awareness (Days 4–14)

Demand Gen is your entry point into AI-forward advertising. It’s lower risk than Performance Max for B2B because you maintain more control over placement and audience targeting.

Step 4: Create Your First Demand Gen Campaign

Navigate to Campaigns > New Campaign > Awareness and Consideration > Demand Gen.

Configure targeting with these B2B-specific settings:
– Audience signals: Upload your closed-won customer list as a seed list for Google’s lookalike expansion. Add custom intent audiences based on competitor brand searches and category keywords (not for targeting — as signals only).
– Geography: Limit to your actual serviceable market. Demand Gen can spend budget internationally if you leave geo targeting too broad.
– Bidding: Start with “Maximize clicks” for the first 30 days to accumulate data. Switch to “Maximize conversions” once you have 30+ conversions recorded.

Step 5: Build Asset Groups

Create a minimum of two asset groups per campaign. Each asset group should map to a distinct buyer persona or pain point — not a product feature.

Example asset group structure for a project management SaaS:
– Asset Group 1 (VP Operations persona): Headline: “Stop managing projects in spreadsheets” / Video: 60-second demo showing workflow automation / Image: Dashboard screenshot
– Asset Group 2 (IT Director persona): Headline: “Enterprise security without the implementation pain” / Video: Security and compliance overview / Image: Admin interface screenshot

Upload a minimum of one video asset per asset group. Demand Gen heavily favors video on YouTube, and campaigns without video will default to image ads that run primarily on Gmail and Discover.

Step 6: Set Up Brand Lift Measurement

If your monthly Demand Gen budget exceeds $5,000, you may qualify for Google’s Brand Lift measurement study, which surveys exposed vs. non-exposed users to measure awareness and consideration lift. Request this through your Google Ads rep or the Brand Lift section under Measurement > Brand lift.

This is your primary evidence layer for justifying upper-funnel spend to stakeholders who want direct conversion attribution. Brand Lift data will show whether the campaign is moving the awareness needle even before pipeline shows up.

Phase 3: Layer in Performance Max for Full-Funnel Coverage (Days 15–30)

Once Demand Gen is running and collecting data, add a Performance Max campaign to close the loop between awareness and conversion.

Step 7: Configure Performance Max Campaign Settings

Create a new PMax campaign under Goals > Leads.

Critical settings for B2B:
– Final URL expansion: Turn this OFF initially. PMax will otherwise send traffic to pages you haven’t reviewed, including blog posts or careers pages that won’t convert.
– Audience signals: Add your CRM customer list, your website remarketing list (all visitors last 180 days), and custom intent audiences based on category keywords.
– Ad schedule: If your sales team only works Monday–Friday, restrict ad delivery accordingly. There’s no reason to spend budget when no one can follow up.
– Brand exclusions: Add your own brand terms as negative keywords at the account level so PMax doesn’t cannibalize your branded search campaigns.

Step 8: Feed Richer Conversion Signals

Go to Tools & Settings > Data Manager and connect your CRM via the native integration (HubSpot, Salesforce, or Zoho are all supported). Configure the integration to pass pipeline stage changes back to Google Ads as offline conversions with assigned conversion values.

For example:
– MQL created = $0 conversion value (or exclude entirely)
– Demo Scheduled = $500 value
– Opportunity Created = $2,500 value
– Proposal Sent = $5,000 value

This conversion value structure tells Google’s AI which signals to optimize for. Without it, PMax will optimize for whichever action generates the most volume — almost always the least qualified form fill.

Step 9: Define Your Learning Phase Protocol

Document an internal rule: no campaign pauses, asset deletions, or targeting changes during the first 6 weeks of a PMax campaign unless spend is catastrophically misallocated (e.g., 90%+ going to a single irrelevant keyword). Every change you make resets the learning phase and delays optimization. This is the patience factor Jakober references — the platform needs continuous signal accumulation to learn what your ideal customer looks like.

Phase 4: Monitor, Measure, and Scale (Ongoing)

Step 10: Build Your Attribution Dashboard

Connect Google Ads to your CRM using UTM parameters on every ad. Create a dashboard in your CRM that tracks:
– First-touch channel for every deal in pipeline
– Influenced revenue by campaign (deals where Demand Gen or PMax was in the touchpoint history)
– Branded search volume trend over time (this is your leading indicator that upper-funnel awareness is working)

Pull the branded search volume from Google Search Console weekly. If Demand Gen and PMax are working, you should see branded search queries increase over weeks 8–16 as buyers who were previously exposed start actively searching for you by name.

Step 11: Scale Budget Based on Evidence

Once you’ve reached your minimum evaluation window and have data showing pipeline influence, increase budget in 20–30% increments rather than doubling overnight. Each budget increase triggers a new learning phase for AI campaigns. Gradual scaling gives the algorithm time to adjust without disrupting optimization.

Expected Outcomes by Timeline:
– Weeks 1–4: Impression volume and CTR data only; no pipeline conclusions yet
– Weeks 4–8: Early branded search lift visible in Search Console; Brand Lift data if study is running
– Weeks 8–16: First influenced deals appearing in CRM; Demand Gen driving warm audience pools for PMax remarketing
– Months 4–12: For deals with 90-day+ cycles, pipeline contribution becomes measurable and attributable

Real-World Use Cases

Use Case 1: Life Sciences SaaS Vendor Targeting Hospital Procurement Teams

Scenario: A clinical workflow software vendor sells to hospital systems with 9–12 month procurement cycles involving 8–12 stakeholders including clinical staff, IT, and finance.

Implementation: The team runs Demand Gen targeting healthcare administrators (via LinkedIn audience import to Google) with a YouTube video series showing real-world workflow improvements. PMax runs simultaneously with offline conversion data synced from Salesforce, passing Opportunity Stage as the conversion event. No PMax pauses are allowed for the first 90 days per a documented protocol.

Expected Outcome: Branded search volume increases 35–50% over 6 months. When procurement managers finally begin their formal vendor evaluation, they’ve already watched multiple demos and the brand feels familiar. This familiarity shortens the late-stage sales cycle because trust was built earlier. As Jakober documented with her own life sciences client, measurable Performance Max value took nearly a year to surface — but it did surface.

Use Case 2: Enterprise Cybersecurity Firm Running Thought Leadership Campaigns

Scenario: A zero-trust security vendor needs to reach CISOs who research solutions primarily through industry publications, peer communities, and YouTube conference recordings.

Implementation: Demand Gen is used to serve 90-second “threat briefing” video ads on YouTube targeting viewers of security conference channels and competitor demo videos (using custom intent audiences). The ads don’t pitch a product — they provide a genuine threat analysis that establishes credibility. PMax handles the conversion layer, retargeting viewers who watched 50%+ of the video.

Expected Outcome: Video view rates above 40% (benchmark for B2B video is 20–25%) because the content is genuinely useful. CISOs who view the briefing are 3–4x more likely to request a demo when the PMax remarketing ad appears later. The “ask” behavior is addressed by ensuring the thought leadership content is also published on the company blog so AI tools like ChatGPT and Perplexity can surface it in vendor recommendation queries.

Use Case 3: Professional Services Firm Building Pipeline Through Demand Gen

Scenario: A management consulting firm targeting CFOs at mid-market manufacturers wants to build brand presence before RFP season.

Implementation: Rather than running search ads (CFOs rarely search for consulting firms by category — they ask peers or get referrals), the firm invests 100% of their paid budget into Demand Gen. LinkedIn audience lists of manufacturing CFOs are uploaded to Google for lookalike seeding. Creative focuses on ROI case studies and cost-reduction frameworks delivered as short video clips.

Expected Outcome: No direct conversions from Demand Gen — that’s not the goal. The KPI is branded search volume increase and direct website traffic from organic sources over a 6-month period. When RFP inquiries arrive, post-sale surveys reveal that 60–70% of new clients had seen the firm’s content on YouTube or Display before the formal evaluation began.

Use Case 4: HRTech Platform Targeting PE-Backed Portfolio Companies

Scenario: An HR software vendor is specifically targeting portfolio companies of private equity firms, a narrow audience not easily reached through keyword targeting.

Implementation: The team builds a custom audience from a manually curated list of PE-backed companies (sourced from Pitchbook exports) and uploads it to Google Ads as a customer match list for PMax targeting signals. Demand Gen runs creative focused on rapid implementation timelines (“deployed in 60 days”) to address the urgency that PE-backed companies face post-acquisition.

Expected Outcome: Higher audience precision than traditional keyword targeting, with impression share concentrated among the actual target accounts rather than broad category searchers. Pipeline quality improves as the AI learns from conversion signals tied to accounts that match the ideal customer profile.

Common Pitfalls

1. Pausing Campaigns During the Learning Phase

The most common and most damaging mistake. Every pause — even a weekend pause — resets the AI’s optimization model and forces it to relearn from scratch. This is especially destructive in B2B, where the learning phase already takes longer due to lower conversion volumes. Establish a documented “no pause” protocol and enforce it with stakeholders before campaigns launch, not after performance questions arise.

2. Using Only MQL Form Fills as Conversions

If PMax optimizes for “Contact Us” form submissions without any downstream qualification signal, it will find the audiences most likely to fill out any form — which is often not your buyer. According to the research report, 72% of AI investments are destroying value because they lack proper instrumentation. Feeding garbage conversion signals to PMax is the paid media version of this problem. Connect CRM pipeline data before you launch.

3. Evaluating Performance Before the Minimum Evaluation Window

A campaign running for two weeks in a 9-month sales cycle tells you nothing about pipeline contribution. Stakeholders will ask for ROI data before it’s possible to have any. The fix is establishing the evaluation window in writing before launch and tying reporting to milestones (weeks 8, 16, 24) rather than arbitrary check-ins.

4. Ignoring the “Ask” Behavior

Jakober’s updated framework includes a fifth “S” — Ask — representing buyers who query ChatGPT, Gemini, or Perplexity for vendor recommendations. Paid campaigns alone cannot influence this channel. You need organic content that AI tools can cite: authoritative blog posts, case studies, and structured data that makes your brand easy for AI to summarize and recommend. Treating paid and organic as separate strategies leaves the “Ask” behavior unaddressed.

5. Scaling Budget Too Aggressively After Early Success

Doubling a PMax budget overnight triggers a full learning phase reset. Campaign performance will drop temporarily while the algorithm recalibrates to the new spend level. Scale in 20–30% increments with at least two weeks between increases.

Expert Tips

1. Use Conversion Value Rules to Prioritize Ideal Customers
In PMax, you can apply conversion value multipliers based on audience membership. If a converting user is on your “Enterprise Accounts” remarketing list, assign a 1.5x value multiplier so the AI knows to prioritize those conversions over smaller accounts. This is available under Campaign Settings > Conversion goals > Conversion value rules.

2. Build a Separate Branded Search Campaign to Protect Attribution
Run a dedicated branded keyword campaign and exclude brand terms from PMax using account-level negative keywords. This prevents PMax from cannibalizing branded search conversions that would have happened organically and inflating its own attribution numbers.

3. Sync CRM Closed-Won Data Back as Conversion Signals Quarterly
Re-upload your latest closed-won customer list to Google Ads every 90 days and use it to refresh your PMax audience signals. As your customer base evolves, so does the profile the AI is optimizing for. Stale audience signals lead to audience drift over time.

4. Run Brand Lift Studies Before Every Major Budget Increase
Before each significant budget increase, request a new Brand Lift study to benchmark awareness and consideration. This gives you a before/after measurement that quantifies the impact of the additional spend, making future budget requests easier to justify.

5. Optimize the “Ask” Channel with Structured Brand Content
Per the research report, brands need to structure content with clear hierarchies and authoritative data so AI tools can easily summarize and surface them. Publish detailed comparison pages, clear “who we serve” pages, and customer outcome case studies with specific results. These are the pages ChatGPT and Perplexity are most likely to cite when buyers ask for vendor recommendations in your category.

FAQ

Q1: How much budget do I need to run AI-forward campaigns in B2B?

The practical minimum for Performance Max to exit the learning phase is roughly $5,000–$10,000/month. Below that threshold, conversion volume accumulates too slowly for the algorithm to optimize. For Demand Gen, you can start testing at $3,000–$5,000/month. Jakober recommends reallocating 5–10% of your existing search budget to test these campaign types before scaling — so a company spending $50,000/month on search could run meaningful tests for $2,500–$5,000/month.

Q2: How do I prove these campaigns are working when attribution is indirect?

Use three measurement layers: (1) Brand Lift studies from Google to measure awareness shifts, (2) Branded search volume trends in Search Console as a leading indicator that upper-funnel exposure is driving later search behavior, and (3) CRM pipeline influence reports that track whether deals had PMax or Demand Gen in their touchpoint history. Direct last-click attribution will never fully capture upper-funnel contribution — you need to instrument all three layers before launch.

Q3: Should I run Performance Max and Demand Gen simultaneously or sequentially?

Start with Demand Gen first (2–4 weeks) to build warm audience pools from video viewers and engagers. Then layer in Performance Max with those warm audiences as signals. Running them simultaneously from day one works, but PMax will be less efficient in the early weeks without warm audience data to build from.

Q4: What creative performs best for B2B Demand Gen?

According to practitioner experience and the research report, AI-driven campaigns deliver 47% better click-through rates — but that lift depends on asset quality. For B2B specifically: product demos outperform lifestyle imagery, customer outcome testimonials (with specific numbers) outperform generic brand statements, and 30–60 second videos outperform 90+ second videos on YouTube. Avoid talking-head videos without captions — most YouTube ads are watched without sound.

Q5: How do I handle the “Ask” channel if my company doesn’t appear in ChatGPT recommendations?

This is an organic content challenge, not a paid one. AI tools pull from publicly indexed content that they deem authoritative. Publish detailed, well-structured pages covering: (1) what your product does and who it serves, (2) comparison pages versus named competitors, (3) case studies with specific outcome metrics. Ensure your website has proper structured data markup. Monitor your brand in AI tools monthly by manually querying “[your category] vendors” and “[your category] software” in ChatGPT, Perplexity, and Google’s AI Mode. Use the results to identify content gaps.

Bottom Line

AI-forward B2B campaigns — built on Performance Max, Demand Gen, and the 4S+Ask behavioral framework — are not a replacement for search advertising. They are a demand generation layer that earns brand familiarity before buyers ever begin their active search, which makes every downstream conversion more efficient. The evidence is clear: companies using AI-forward marketing approaches see 20–30% conversion rate improvements and campaign performance lifts of up to 47% in CTR. The constraint is patience: B2B sales cycles mean you need a minimum evaluation window of at least one full average deal cycle before drawing conclusions. Teams that build the right measurement infrastructure — CRM-synced conversions, Brand Lift studies, branded search monitoring — will have the data to scale confidently. Those that demand week-two ROI from a strategy designed for six-month sales cycles will pull the plug before the results arrive.