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SAMI Webinar Recap: AI Strategies to Drive Demand Generation Revenue in 2025

by Ami Sao

 

In a recent session, Mark Goloboy, founder of Market Growth Consulting, explored how enterprises can strategically deploy AI, optimize Large Language Models (LLMs), and implement agentic Retrieval-Augmented Generation (RAG) systems to transform marketing and sales workflows for sustainable revenue growth.

Mark, a leading expert in AI for B2B marketing, argues that while experimentation with AI technologies is widespread, genuine strategic integration remains elusive for most organizations. This discussion outlines a clear, research-driven roadmap for how marketing leaders can move beyond fragmented AI adoption toward comprehensive transformation.

“AI won’t replace marketers, but marketers who know how to use AI will replace those who don’t.”

– Paul Roetzer, AI Academy

▶️ Watch the full conversation → Here

Why B2B Companies Struggle to Unlock AI’s Potential

Despite the proliferation of AI tools, most B2B enterprises underperform in AI-driven transformation. The root cause? Many organizations still treat AI as a peripheral add-on rather than a foundational enabler.

Rather than accelerating pipeline velocity or improving win rates, poorly integrated AI efforts tend to exacerbate existing inefficiencies. Mark emphasizes that achieving tangible results requires thoughtful system design, not mere tool acquisition.

*AI Implementation Activity: 2 Questions to Ask Yourself That Could Save You Hours

  1. What tasks on your calendar currently take more than an hour?
  2. How could you reduce them to 5–10 minutes using AI?

The Strategic Significance of Agentic Retrieval-Augmented Generation (RAG)

At the core of Mark’s approach lies agentic Retrieval-Augmented Generation (RAG): a type of AI that doesn’t just respond with what it already “knows”, it goes out and fetches real-time, external data, then uses that information to generate an informed and tailored response.

Unlike static LLM prompting, agentic RAG allows marketers to:

  1. Auto-gather competitive intelligence
  2. Refine audience segmentation
  3. Deliver personalized, timely content

This agentic capability transforms AI from a passive tool into an active collaborator, fundamentally reshaping the structure and potential of marketing and sales teams.

How Enterprises Can Prioritize Their Brand Content Within LLMs

An increasingly critical, and often overlooked, aspect of AI strategy involves ensuring that brand content is accessible, favored, and retrievable by major LLMs such as GPT-4, Claude, and Gemini.

Optimization Tactics:

  1. Use semantically rich, structured formats designed for machine comprehension.
  2. Prioritize topical authority and content clarity.
  3. Refresh content frequently to maintain continuous relevance.

*Staggering Statistics:

  1. The Forrestor Buyer Journey Survey (2024) shows “89% of B2B buyers now use AI at some stage of their journey”.
  2. Gartner predicts “search engine volume will drop 25% by 2026, due to AI chatbots and other virtual agents”.

Without deliberate optimization, brands risk becoming invisible to buyers who now rely heavily on AI-powered systems to guide their purchase decisions.

Real-World Applications of AI in Demand Generation Workflows

The operationalization of AI yields profound benefits across the marketing and sales value chain. Mark illustrates real-world implementations where AI autonomously conducts market research, develops nuanced customer profiles, and personalizes engagement strategies based on real-time behavioral data.

These applications do more than accelerate existing workflows; they reframe the strategic role of marketing teams. By offloading repetitive research and personalization tasks to AI, organizations can reallocate human expertise toward functions such as creative strategy, brand positioning, and relationship development.

*Actionable Tips:

  1. Repurpose Sales Call Recordings: Transform recorded sales calls into executive briefs or marketing content to maximize their utility.
  2. Utilize Voice Memos: Capture ideas and brainstorms on the go, then use AI tools to structure and develop these concepts efficiently.
  3. Leverage Customer Interactions: Analyze existing customer interactions to extract real-time insights, informing strategy and decision-making.

AI is changing the game, giving you the power to scale with fewer resources and do what only larger teams could once do. Ultimately, this reallocation results in faster deal cycles, reduced operational overhead, and an increase in overall marketing effectiveness.

Conclusion: AI as the New Foundation for Strategic Growth

Mark’s session challenges the conventional framing of AI as merely a disruptive force. Instead, he positions AI as a foundational pillar of modern B2B growth strategy.

Through the intelligent optimization of LLMs, the deployment of agentic RAG systems, and the strategic reengineering of marketing workflows, enterprises can achieve a level of operational intelligence and resilience previously unattainable.

Organizations that embrace this shift thoughtfully, aligning AI initiatives with core strategic goals rather than adopting tools opportunistically, will not simply adapt to the changing landscape. They will define it.