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Adopting AI inside an organization is rarely smooth. Most people are not natural tinkerers, and it takes more than enthusiasm to change how teams prepare, run, and follow up on meetings.
For Aydin Mirzaee, CEO and co-founder of Fellow, the turning point was realizing that AI could take the work only the most organized people were willing to do and make it accessible to everyone. He calls this an AI “chief of staff”, a system that prepares meaningful follow-ups, captures decisions and actions, and threads context across meetings so teams can focus on higher-value work.
In this episode, Daniel and Aydin explore what it looks like to move from manual meeting hygiene to AI-first workflows. They discuss reasoning-driven pre-meeting briefs, role-specific templates, and writing back into systems like Salesforce and Jira without extra human effort. Aydin also reflects on adoption realities: how to create room for experimentation, why surfacing high-value workflows matters more than tinkering, and what it means to build for models that don’t exist yet.
🔑 What You Will Learn This Episode
🔗 Resources & Links
👉 If this conversation sparks an idea, share it with a colleague! It’s a grounded look at how AI reshapes work inside real teams.
By Daniel ManaryAdopting AI inside an organization is rarely smooth. Most people are not natural tinkerers, and it takes more than enthusiasm to change how teams prepare, run, and follow up on meetings.
For Aydin Mirzaee, CEO and co-founder of Fellow, the turning point was realizing that AI could take the work only the most organized people were willing to do and make it accessible to everyone. He calls this an AI “chief of staff”, a system that prepares meaningful follow-ups, captures decisions and actions, and threads context across meetings so teams can focus on higher-value work.
In this episode, Daniel and Aydin explore what it looks like to move from manual meeting hygiene to AI-first workflows. They discuss reasoning-driven pre-meeting briefs, role-specific templates, and writing back into systems like Salesforce and Jira without extra human effort. Aydin also reflects on adoption realities: how to create room for experimentation, why surfacing high-value workflows matters more than tinkering, and what it means to build for models that don’t exist yet.
🔑 What You Will Learn This Episode
🔗 Resources & Links
👉 If this conversation sparks an idea, share it with a colleague! It’s a grounded look at how AI reshapes work inside real teams.