We curate most relevant posts about ABM on LinkedIn and regularly share key take aways
This episode synthesizes recent LinkedIn insights from CW 21 and 22, exploring the profound impact of AI on B2B marketing and sales in 2025.
A key theme is the fundamental shift in B2B buyer behaviour driven by AI. Enterprise buyers are increasingly turning to Generative AI tools for research, sometimes even more than traditional search engines like Google. AI is no longer seen as optional in B2B software but as "Always Included", leading nearly half of enterprise buyers to switch vendors for better AI capabilities. This alters the landscape of discoverability significantly; AI search tools provide direct answers by reading and interpreting content, making being quoted or referenced more crucial than simply ranking highly in traditional search results. This may lead to a decline in traditional search traffic, necessitating that marketers create content that is source-worthy, structured for AI answers (e.g., using headers, lists), shows authority, and answers niche-specific queries. Buyers are also taking longer to make decisions and search engines are rewriting queries based on intent. The B2B sales cycle could flatten into a single dialogue within chat interfaces, allowing buyers to schedule demos, request quotes, get comparisons, and evaluate ROI without leaving the chat. There's also a significant trend towards B2B marketing leveraging personal insights for more intimate, person-based targeting, akin to B2C marketing, by connecting disparate data points through AI. Buyers are doing most of their product-fit analysis before visiting a vendor's website.
AI is consistently highlighted as a powerful engine for efficiency, automation, and scaling marketing and sales operations. It can automate numerous tasks, including lead generation and enrichment, email communication, content creation and repurposing, customer support via chatbots, workflow management, internal knowledge management, meeting transcription/summarization, and analytics/decision intelligence. Specific examples include automating link-building outreach, optimizing cold email campaigns, and automating lead prioritisation and routing. Predictive AI in email marketing can significantly boost open and conversion rates, leading to substantial revenue increases with less manual effort. AI tools are estimated to save lean marketing teams significant annual amounts, potentially $74K–$115K per year for a 3-6 person team, by automating tasks across content, insights, campaign optimisation, engagement, and reporting. AI-driven sales automation aims to reimagine the sales journey, focusing on predictive insights, hyper-personalized outreach at scale, and faster sales cycles. AI can enable companies to scale campaigns dramatically, from 60 to 2,400 per year in one case, and drive revenue increases through hyper-personalization.
Crucially, the insights underline that AI functions as an augmenting tool and does not replace human expertise, strategy, or creativity. While AI excels at data analysis, pattern finding, and handling repetitive tasks, it often struggles with creative tasks like taglines, lacks genuine brand voice, creativity, and deep customer understanding. Human marketers provide the essential strategic thinking, creative direction, and "secret sauce" that algorithms cannot replicate. Quality output from AI requires quality, expert-driven input; "lazy input" results in bland, generic content. AI is seen as a co-pilot or an AI teammate that works alongside humans, amplifying their capabilities.
These changes necessitate significant strategic adaptations. The traditional B2B marketing playbook, especially its focus on MQLs and traditional marketing automation, is considered outdated. Recommended shifts include moving from lead capture to early-stage influence, from marketing automation to AI augmentation, and from an MQL obsession to meaningful engagement. Some suggest AI will transform B2B marketing towards more intimate, person-based targeting by leveraging personal insights, similar to B2C. Marketers face a real risk of relevance loss, not just job loss, if they don't strategically evolve. This involves creating content that is source-worthy and AI-answerable, building authority beyond one's own website, and potentially rethinking attribution models. The martech stack is shifting towards a more modular approach, integrating with cloud data platforms. AI requires agile marketing strategies due to its speed of change.
Practical considerations for B2B CMOs include adopting a "Strategy before tools" approach, benchmarking time-consuming tasks for potential AI automation, and prioritizing training team members on AI tools before broad license distribution to ensure adoption and productivity. CMOs should also actively use these tools themselves and develop prompt engineering skills. Utilizing AI focus groups offers a cost-effective way to gather market insights. Challenges highlighted include navigating privacy and compliance issues with generative AI, being wary of AI SDR solutions that overpromise based on generic outreach, and ensuring foundational data quality before attempting advanced AI personalization strategies. Companies need to prepare their content structure and data for AI-powered buyer journeys where interactions might increasingly occur within chat interfaces. The profitability of AI in B2B marketing is now evident due to advancements in understanding messy B2B data. Specific AI tools and LLMs like GPT-3/4o/4o-mini, Claude, Perplexity, and Gemini are being used for various tasks from research to content creation and strategy refinement. Google has also introduced AI tools for marketing within its platforms. Generic AI tools may fall short in B2B context; custom GPTs are seen as a solution. AI is also explored as a way to bridge the historical disconnect between marketing and sales. Measuring the value of AI implementations requires moving beyond simple ROI to comprehensive frameworks covering data quality, model performance, output effectiveness, and fairnessAI is also presented as a powerful driver of efficiency and automation. It can automate numerous marketing and sales operations, including lead generation, email communication, content creation, customer support, workflow management, and analytics. Automating tasks like link-building outreach is achievable using AI. Predictive AI in email marketing can significantly boost open and conversion rates, leading to increased revenue with less manual effort. It's estimated that AI tools could save lean marketing teams substantial amounts annually through task automation.
Crucially, the insights underscore that AI functions as a tool and does not replace human expertise or strategy. While AI excels at data analysis and identifying patterns, it may struggle with creative tasks like taglines and lacks human elements like brand voice, creativity, and deep customer understanding. The human marketer provides essential strategic thinking and creative "secret sauce". Quality output requires quality, expert-driven input, as "lazy input" results in poor AI content. AI is seen as augmenting human teams, handling repetitive tasks and assisting with ideation.
These changes necessitate significant strategic adaptations. The traditional B2B marketing playbook, particularly its focus on MQLs and traditional marketing automation, is becoming outdated. The recommended shifts include moving from lead capture to early-stage influence, from marketing automation to AI augmentation, and from MQL obsession to meaningful engagement. Some suggest AI will transform B2B marketing towards more intimate, person-based targeting by leveraging personal insights, akin to B2C. Marketers face a risk of relevance loss if they don't strategically adapt.
Practical considerations include the need for B2B CMOs to adopt a "Strategy before tools" approach, benchmark time-consuming tasks for potential AI automation, and prioritize training team members on AI tools before broad license distribution. CMOs should also actively use these tools and develop prompt engineering skills. AI focus groups offer a way to gather insights cost-effectively. There's a trend towards more modular martech stacks that can integrate with data platforms. Companies need to prepare their content and structure for AI-powered buyer journeys where interactions might occur within chat interfaces. Challenges highlighted include navigating privacy and compliance in the age of generative AI, being wary of AI SDR solutions that overpromise based on generic outreach, and ensuring foundational data quality before implementing advanced AI personalization strategies. Various specific AI tools and LLMs were mentioned as valuable for different marketing and sales tasks.
