G2, Notion, and Deloitte CMOs Reset Brand, Content, and AI Strategy for LLM-First Buyers
Buyers now ask LLMs for vendor picks before visiting you. CMOs are rebuilding brand, structuring content, and tuning sites to continue the AI-started conversation.
AI Search Is Changing B2B Marketing. Here's How CMOs Are Adapting
Buyers are asking large language models for vendor recommendations before they ever hit your site. Panelists from G2, Notion, and Deloitte at HubSpot's INBOUND made one thing clear: brand and owned content need a reset for an AI-first path to purchase.
G2 data says four out of five people now research products with LLMs. That means your brand has to be recognizable, consistent, and present across the surfaces AI pulls from-not just your homepage.
The Return of Brand (Because Decisions Are Emotional)
Logic justifies. Emotion decides. That's as true in B2B as it is in consumer. If buyers form their shortlist inside an AI chat, your brand has to carry the weight of trust, clarity, and preference before a comparison chart ever appears.
The takeaway: you can't "buy demand" and ignore brand anymore. Invest in signal: distinct POV, consistent naming, proof assets, and formats LLMs can digest.
Rethink Owned Content for AI Indexing
Deloitte's CMO put it plainly: content must be discoverable and attributable. Structure matters-clear taxonomies, FAQs, glossaries, product specs, citations, and schema that teach answer engines who you are and what you do.
Your website's role shifts down the path. Traffic may dip, but intent goes up. Optimize less for education-at-scale and more for conversion, proof, and deep evaluation.
Make Your Site a Conversation Continuation
Prospects start the conversation with an LLM. Your site should pick it up without friction. Build prompt-style UX: Q&A modules, comparison tools, interactive demos, and "ask a question" patterns supported by human chat where it counts.
Some brands are even testing "shadow sites" that feed clean, structured content to LLMs while the public site focuses on experience. Expect very different websites within a year.
Operating Model: Be a Fast Experimenter
Chasing "first mover" can burn you. "Fast follower" is too slow. The move is fast experiments with guardrails-pilot, measure, scale, kill. Do it with Risk, Legal, Security, and Data in the room from day one.
- Stand up an AI working group with Marketing, Data, Legal/Risk.
- Audit content and data: accuracy, freshness, permissions, PII handling.
- Define guardrails: prompts, approvals, model access, logging, watermarking.
- Run small pilots with clear success metrics and rollback plans.
- Upskill the team and publish internal playbooks.
Use Cases That Win Now
Start where humans are slow and inconsistent. Repurpose long-form assets into social, email, briefs, and region-specific versions. That's a no-regrets move for speed and consistency.
Next, scale the work you never had bandwidth for: long-tail lead nurturing, micro-segmentation, content QA, sales enablement variants, A/B test ideation. Treat AI as another capable teammate you can call on for repeatable tasks.
Agents: From Copilots to Systems
Agentic AI requires clean data and clear objectives. Think of a gradient: simple copilots for creation and research on one end; coordinated agents handing off tasks on the other. Pick the level that matches risk and value.
Sophisticated systems exist in other domains (think autonomous driving). Marketing will get there, but start small: defined workflows, sandboxed data, human approval at key steps.
Your 90-Day Plan
- Week 1-2: Content/data audit. Prioritize high-intent pages and proof assets.
- Week 2-3: Add structured Q&A, glossaries, schemas, citations to top pages.
- Week 3-6: Pilot derivative content at scale for social and email. Measure output speed and engagement.
- Week 4-8: Build a prompt-style site module (FAQ → answer snippets → CTAs).
- Week 6-10: Launch a nurture agent pilot with strict guardrails and human review.
- Week 8-12: Report on lift: time-to-asset, pipeline influenced, conversion rate on high-intent pages.
Metrics That Matter
- Brand: direct traffic quality, branded search, recall in third-party surveys, mention quality in external answer engines.
- Content: coverage of key questions, citation rate, freshness, structured data completeness.
- Conversion: demo/start-trial rate on high-intent pages, sales cycle time, win rate vs. lookalike cohorts.
- Ops: time-to-first-draft, review cycles, error rate, compliance exceptions.
Pitfalls to Avoid
- Over-optimizing for classic SEO while ignoring how LLMs summarize and attribute.
- Launching agents without data governance and human fail-safes.
- Publishing unstructured content that LLMs can't parse or cite.
- Measuring activity, not outcomes (volume of posts vs. pipeline and revenue impact).
Helpful Resources
Level Up Your Team
If you're building AI skills across marketing, these resources can help your team move faster with confidence:
