Topics Covered
AI efficiency vs. AI opportunity in modern B2B orgs
Use cases across Claude, Gemini, Copilot, and ChatGPT
Claude for Excel outperforming native plugins
AI-powered brand visibility audits (AIO, GEO)
Building MVPs from product demos using Gemini
Automating reporting and funnel analysis with ChatGPT & Gemini
Custom GPTs for keyword analysis and lead quality reviewsAI system design in regulated or high-security environments
Framework-based AI prompting for repeatable results
Testing rigor and prompt engineering for trustable AI output
Questions This Video Helps Answer
How can B2B marketing teams use AI to save time and create net-new strategic opportunities?
What LLM (Claude, ChatGPT, Gemini, Copilot) is best for specific tasks like Excel, brand analysis, or creative reviews?
How do I know if AI-generated reporting is accurate enough to trust?
What’s a good prompt structure to consistently get usable output from ChatGPT or Gemini?
How can I explain AIO (AI Optimization) visibility and results to executives?
Jobs, Roles, and Responsibilities Mentioned
Marketing teams
Sales teams
Finance and accounting departments
HR and admin functions
Demand gen strategists
Operations and supply chain leaders
AI consultants and systems integrators
Creative and copywriting leads
IT and cybersecurity teams
Executive and portfolio leadership
Key Takeaways
AI tools should be evaluated by outcome, not branding—Claude may outperform Copilot in Excel.
Reframing workflows to be AI-native rather than AI-assisted unlocks transformational gains.
Demand marketers can use LLMs to streamline reporting, extract funnel insights, and improve creative alignment.
Framework-driven prompting (like SPEC) helps generate consistent, high-trust outputs.
Custom AI workflows (e.g., for lead scoring, brand checks) can scale across clients and teams without deep coding.
Generative AI is a tool for internal enablement, not just public content.
