Best of LinkedIn: AI in B2B Marketing CW 35/ 36

GEO moved from theory to execution, with guidance on key questions marketers must solve to improve visibility in AI search

AI rankings differ from classic SEO, pushing teams to prioritize brand authority and foundational SEO over keyword tricks

Perplexity’s trajectory was framed as early marketing upside for those shaping presence in AI-native search experiences

Brand consistency, Reddit presence, and meaningful PR emerged as practical levers to influence AI-driven discovery

Trust-building through diverse content sources was highlighted as central to AI visibility strategies

Marketing agents were positioned as catalysts for new business models and processes, alongside clear operational challenges

Real-world stacks showed agents orchestrated via Zapier or Make to automate research, enrichment, and handoffs

Success with agents depends on continuous training, testing, quality control, and explicit oversight

Field tests mapped agent categories across GTM, from research and reply to sourcing, scraping, and AI SDR roles

Teams reported that agents augment human reps rather than replace them when implemented with tight guardrailsSales and SDR Automation

Replacing human SDRs with AI led to lower meetings and poor conversion, prompting returns to human-led outreach in cases

Email outreach quality improved when human approval flows governed agent output and compliance before sending

Early productized AI SDRs were called ineffective without strong data and training, underscoring readiness gaps

Hybrid teams combining AI and humans were credited with outperforming traditional models on efficiency and outcomes

Data quality flaws created inbox chaos and fake LinkedIn profiles surged, reinforcing the need for governance and hygiene

Marketplace alt-text automation was noted as a concrete shift with direct implications for image SEO

Multi-draft AI writing was recommended to raise creative quality rather than accepting first outputs

Teams stressed disciplined measurement to link AI use with content performance

Guidance favored streamlined toolchains that accelerate production without eroding brand voice or controls

Go-to-market attention shifted from feature pushes to brand building as a durable growth driver in AI-heavy markets

Everyday search behavior was cited as shaping brand perception and buying decisions

Strategic vetting of AI use cases focused on brand protection, efficiency, and measurable ROI before scaling

Brand authority signals via consistent messaging, credible PR, and community presence were prioritized for AI-ranked exposure

Curated tool lists and lean stacks delivered material time savings for founders and small teams

Major suites like ChatGPT, Gemini, Claude, and Copilot were described as broadly comparable for custom AI capability

Evaluations spanned text, video, and visual domains to match tools with outcomes
• Agency case shares detailed compact five-tool stacks that accelerated delivery and client satisfaction

Treat AI as a team member with KPIs, ownership, and accountability tied to commercial goals

Address adoption fears by keeping human judgment in control and staging change

Break projects into smaller AI-supported tasks and use progressive prompting to improve reliability

Role-based prompting improved speed and confidence while maintaining human oversight

Transparency obligations, IP rights, and deepfakes featured as central governance topics for marketing teams

Safeguards for younger users in chatbot contexts reinforced responsible deployment norms

Leaders urged codifying policies and controls before scaling AI across customer-facing workflows

Teams linked governance to brand protection, legal compliance, and trust outcomes

Signals pointed to automated image alt text on marketplaces requiring asset and SEO adjustments

Perplexity’s momentum positioned it as a testable channel for early AI-search demand capture

Salesforce leadership emphasized practical enterprise AI over speculative narratives for revenue impact

Webinars showcased out-of-the-box integrations through Zapier and Make to operationalize AI across workflows