Topics CoveredAI efficiency vs. AI opportunity in modern B2B orgsUse cases across Claude, Gemini, Copilot, and ChatGPTClaude for Excel outperforming native pluginsAI-powered brand visibility audits (AIO, GEO)Building MVPs from product demos using GeminiAutomating reporting and funnel analysis with ChatGPT & GeminiCustom GPTs for keyword analysis and lead quality reviewsAI system design in regulated or high-security environmentsFramework-based AI prompting for repeatable resultsTesting rigor and prompt engineering for trustable AI outputQuestions This Video Helps AnswerHow 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 MentionedMarketing teamsSales teamsFinance and accounting departmentsHR and admin functionsDemand gen strategistsOperations and supply chain leadersAI consultants and systems integratorsCreative and copywriting leadsIT and cybersecurity teamsExecutive and portfolio leadershipKey TakeawaysAI 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.

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