{"id":90434,"date":"2026-03-05T00:15:54","date_gmt":"2026-03-05T08:15:54","guid":{"rendered":"https:\/\/rightwave.com\/rwi\/?p=90434"},"modified":"2026-03-05T00:33:56","modified_gmt":"2026-03-05T08:33:56","slug":"what-marketing-operations-teams-are-actually-using-for-ai-in-gtm-work","status":"publish","type":"post","link":"https:\/\/rightwave.com\/rwi\/what-marketing-operations-teams-are-actually-using-for-ai-in-gtm-work","title":{"rendered":"What Marketing Operations Teams Are Actually Using for AI in GTM Work"},"content":{"rendered":"\t\t
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Insights from the MOPS Slack Community<\/h3>\n

The conversation around AI in marketing often sounds theoretical. New models are launched every week, and vendors promise transformative productivity gains.<\/p>\n

But what are marketing operations professionals actually using day-to-day for GTM work?<\/strong><\/p>\n

To answer that, we analyzed a recent discussion in a Marketing Operations Professionals community<\/strong>, where practitioners shared their experiences using different LLMs for real marketing operations workflows.<\/p>\n

Responses from marketing operations professionals revealed a clear pattern: most teams are not committing to a single AI tool. Instead, they are building multi-model workflows based on each model\u2019s strengths.<\/strong><\/p>\n

Below are the key themes that emerged.<\/p>\n

1. Claude Is Emerging as the Preferred \u201cThinking Partner\u201d<\/h1>\n

One of the most consistent insights from the conversation was the shift toward Claude<\/strong> for complex thinking and strategic work.<\/p>\n

Several practitioners mentioned switching from ChatGPT to Claude recently, citing improvements in reasoning and response quality.<\/p>\n

Across the discussion, Claude was commonly used for:<\/p>\n

    \n
  • \n

    Strategic brainstorming<\/p>\n<\/li>\n

  • \n

    Long-form analysis<\/p>\n<\/li>\n

  • \n

    GTM planning<\/p>\n<\/li>\n

  • \n

    Writing frameworks and messaging<\/p>\n<\/li>\n

  • \n

    Reviewing complex documents<\/p>\n<\/li>\n<\/ul>\n

    One participant summarized the reasoning clearly (paraphrased):<\/p>\n

    \n

    Claude tends to think through problems more deeply, making it better for strategic work and complex reasoning.<\/p>\n<\/blockquote>\n

    This aligns with what many marketing operations teams are discovering: LLMs are not just content generators \u2014 they are increasingly used as thinking partners for strategic work.<\/strong><\/p>\n

    2. ChatGPT Still Dominates for Speed and General Utility<\/h1>\n

    Despite the rise of Claude, ChatGPT remains widely used across MOPS teams.<\/p>\n

    The primary reasons mentioned were:<\/p>\n

      \n
    • \n

      Faster output generation<\/p>\n<\/li>\n

    • \n

      Broad general-purpose capabilities<\/p>\n<\/li>\n

    • \n

      Strong ecosystem integrations<\/p>\n<\/li>\n<\/ul>\n

      Several practitioners described a dual-model workflow<\/strong>, where ChatGPT is used for:<\/p>\n

        \n
      • \n

        Fast content generation<\/p>\n<\/li>\n

      • \n

        quick summarization<\/p>\n<\/li>\n

      • \n

        operational tasks<\/p>\n<\/li>\n

      • \n

        personal productivity<\/p>\n<\/li>\n<\/ul>\n

        One practitioner noted that they rely on ChatGPT heavily outside of work as well, including tasks such as:<\/p>\n

          \n
        • \n

          analyzing fitness data<\/p>\n<\/li>\n

        • \n

          meal planning<\/p>\n<\/li>\n

        • \n

          home organization decisions<\/p>\n<\/li>\n

        • \n

          personal scheduling<\/p>\n<\/li>\n<\/ul>\n

          This reflects a broader trend: ChatGPT is becoming a general productivity assistant<\/strong>, not just a work tool.<\/p>\n

          3. NotebookLM Is Becoming the \u201cClient Brain\u201d<\/h1>\n

          Another interesting pattern from the discussion was the growing use of NotebookLM<\/strong> for managing client knowledge.<\/p>\n

          Marketing operations professionals described using NotebookLM to:<\/p>\n

            \n
          • \n

            upload client documents<\/p>\n<\/li>\n

          • \n

            centralize notes<\/p>\n<\/li>\n

          • \n

            ask contextual questions about projects<\/p>\n<\/li>\n

          • \n

            create an \u201cAI memory\u201d for complex engagements<\/p>\n<\/li>\n<\/ul>\n

            In large projects with significant documentation, this effectively turns NotebookLM into a project-specific AI knowledge base<\/strong>.<\/p>\n

            One practitioner described this workflow as:<\/p>\n

            \n

            Creating an \u201cAI brain\u201d for client work so you can query the entire project history instantly.<\/p>\n<\/blockquote>\n

            For agencies, consultants, and RevOps teams managing multiple accounts, this capability can dramatically improve knowledge retrieval and continuity across projects.<\/strong><\/p>\n

            4. Gemini Often Appears Through Enterprise Tooling<\/h1>\n

            Unlike ChatGPT or Claude, Gemini was rarely described as the primary model of choice.<\/p>\n

            However, it frequently appeared in environments where organizations already use Google Workspace.<\/p>\n

            Practitioners mentioned using Gemini primarily because:<\/p>\n

              \n
            • \n

              it comes bundled with other Google tools<\/p>\n<\/li>\n

            • \n

              it integrates with their existing workflows<\/p>\n<\/li>\n

            • \n

              it is available through enterprise subscriptions<\/p>\n<\/li>\n<\/ul>\n

              This highlights an important operational reality: enterprise adoption of AI models is often driven by ecosystem integration rather than model preference alone.<\/strong><\/p>\n

              5. Many Teams Are Running Multiple Models in Parallel<\/h1>\n

              Perhaps the most important takeaway from the conversation is that marketing operations professionals rarely rely on a single AI model.<\/strong><\/p>\n

              Instead, they combine multiple models depending on the task.<\/p>\n

              Examples shared in the thread included:<\/p>\n

                \n
              • \n

                Using Claude and another coding model together to cross-check outputs<\/p>\n<\/li>\n

              • \n

                Running models in parallel to catch errors or hallucinations<\/p>\n<\/li>\n

              • \n

                Integrating LLMs into tools like Clay for workflow automation<\/p>\n<\/li>\n<\/ul>\n

                This \u201cmulti-model validation\u201d approach helps mitigate one of the biggest risks in AI adoption: hallucinated or incorrect responses.<\/strong><\/p>\n

                What This Means for Marketing Operations Leaders<\/h1>\n

                The discussion reinforces an important lesson for GTM teams implementing AI:<\/p>\n

                AI adoption is not about choosing the best model.<\/strong><\/p>\n

                It\u2019s about designing workflows that combine models effectively.<\/strong><\/p>\n

                Across the responses, three emerging patterns stood out:<\/p>\n

                1. AI is becoming part of the MOPS stack<\/h3>\n

                LLMs are increasingly integrated into marketing operations workflows alongside CRM, automation platforms, and data tools.<\/p>\n

                2. Different models serve different purposes<\/h3>\n

                Teams are selecting models based on task specialization rather than loyalty to a single platform.<\/p>\n

                3. Knowledge management is the next frontier<\/h3>\n

                Tools like NotebookLM show that the real opportunity lies in turning company knowledge into queryable AI systems.<\/strong><\/p>\n

                \n

                The Bigger Opportunity: AI-Ready Marketing Operations<\/h1>\n

                These insights reinforce something we see consistently at RightWave:<\/p>\n

                AI delivers the most value when the underlying marketing operations foundation is strong.<\/strong><\/p>\n

                Clean data, structured processes, and connected systems determine whether AI becomes a productivity multiplier or just another experimental tool.<\/p>\n

                Without that foundation, AI outputs simply amplify existing chaos.<\/p>\n

                Final Thought<\/h1>\n

                The most mature marketing operations teams are no longer asking:<\/p>\n

                \u201cWhich AI tool should we use?\u201d<\/strong><\/p>\n

                Instead, they are asking:<\/p>\n

                \u201cHow do we design AI workflows that support our GTM processes?\u201d<\/strong><\/p>\n

                That shift in thinking will define the next phase of AI adoption in marketing operations.<\/p>\n

                Source:<\/strong>
                Insights aggregated and anonymized from a discussion in the MO Pros Marketing Operations Slack community<\/strong>, where practitioners shared their experiences using Claude, ChatGPT, Gemini, and NotebookLM for GTM work.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

                Insights from the MOPS Slack Community The conversation around AI in marketing often sounds theoretical. New models are launched every week, and vendors promise transformative productivity gains. But what are marketing operations professionals actually using day-to-day for GTM work? To answer that, we analyzed a recent discussion in a Marketing Operations Professionals community, where practitioners…<\/p>\n","protected":false},"author":45,"featured_media":90442,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[],"class_list":["post-90434","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-welcome"],"_links":{"self":[{"href":"https:\/\/rightwave.com\/rwi\/wp-json\/wp\/v2\/posts\/90434","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/rightwave.com\/rwi\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/rightwave.com\/rwi\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/rightwave.com\/rwi\/wp-json\/wp\/v2\/users\/45"}],"replies":[{"embeddable":true,"href":"https:\/\/rightwave.com\/rwi\/wp-json\/wp\/v2\/comments?post=90434"}],"version-history":[{"count":4,"href":"https:\/\/rightwave.com\/rwi\/wp-json\/wp\/v2\/posts\/90434\/revisions"}],"predecessor-version":[{"id":90439,"href":"https:\/\/rightwave.com\/rwi\/wp-json\/wp\/v2\/posts\/90434\/revisions\/90439"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/rightwave.com\/rwi\/wp-json\/wp\/v2\/media\/90442"}],"wp:attachment":[{"href":"https:\/\/rightwave.com\/rwi\/wp-json\/wp\/v2\/media?parent=90434"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/rightwave.com\/rwi\/wp-json\/wp\/v2\/categories?post=90434"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/rightwave.com\/rwi\/wp-json\/wp\/v2\/tags?post=90434"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}