B2B marketing is at the most significant inflection point since the emergence of inbound marketing fifteen years ago. Language models are not only changing how content is produced and how campaigns are managed: they are fundamentally changing the B2B buying process and, with it, all the strategies we build on that process.
This is the reading of the landscape for the next two years.
What’s already happening
The B2B buyer of 2025 arrives at the first contact with the supplier better informed than ever. Before speaking to any sales representative, they have consulted three or four LLMs about their problem, read AI-generated or AI-curated comparisons, viewed personalised case studies, and formed an initial preference based on information that AI systems have presented to them.
This has direct implications: 60-70% of the B2B purchase decision process occurs before the first commercial contact, according to Gartner’s latest report. LLMs have accelerated that trend because they make prior research more efficient. A buyer can obtain in thirty minutes with an LLM the intelligence that previously required three hours of manual research.
What does this mean for B2B marketing? The content you produce does not only need to rank on Google: it needs to be good enough for LLMs to cite it when buyers in your sector ask them about the problem you solve. GEO (Generative Engine Optimization) — optimisation for generative engines such as ChatGPT, Claude or Perplexity — is the SEO of the next three years.
The most significant changes ahead
Personalisation at a scale of one
Until now, personalisation in B2B marketing meant segment-level personalisation: the industry, company size, job title. With LLMs properly integrated into the marketing stack, personalisation can be truly individual: website content, email, the proposal and the sales rep‘s pitch all adapt to the specific profile of that company, that sector and that decision-maker.
This is not science fiction. It is what a well-fed CRM system, a language model, and the right data make possible. The company that implements this well will have a conversion advantage that is hard to match.
Autonomous agents in the sales cycle
AI agents are starting to manage parts of the B2B sales cycle autonomously: lead qualification, responses to technical queries, preparation of basic proposals, follow-up on opportunities. Within two years, the percentage of sales cycle interactions managed fully or partly by agents will rise from 20% to 50% for most mid-sized B2B companies.
This does not eliminate the sales team. It frees the sales rep from low-value tasks (basic qualification, routine follow-up) so they can focus on what truly requires relational intelligence: negotiation, managing multiple stakeholders, and understanding the client’s internal political context.
Multimodality in marketing content
Multimodal models — which process and generate text, image, audio and video simultaneously — are reaching commercial maturity. This means that complex content production (interactive product demos, visual proposals, success cases with dynamic data) will be achievable at a fraction of the current cost and time.
The most immediate impact: the quality threshold expected of B2B content is going to rise because the cost of producing high-quality content is going to fall. Companies that don’t raise that threshold will be visually displaced by those that do.
What to do today to be prepared
Audit your presence in LLMs: Ask Claude, ChatGPT and Perplexity about the problems you solve. Do you appear? How do they present you? What do they say about the competition? That is the base intelligence for your GEO strategy.
Produce content that LLMs want to cite: LLMs favour content with original data, well-argued expert opinions and direct answers to specific questions. Generic content with information that already exists on a thousand sites does not get cited. Content with a unique perspective and its own data does.
Start implementing segment-level personalisation now: Don’t wait for individual personalisation. Personalisation by sector and job title is achievable with current tools and is the necessary prior step towards more granular personalisation.
Build the structured data you’ll need: The predictive models and autonomous agents of the future need structured, quality data. Every lead not recorded correctly, every interaction not captured, is data debt you’ll have to pay later.