Some marketing teams are stuck in a documentation trap. They won’t build AI teammates because they think they need perfect internal knowledge first.
You can build custom versions of ChatGPT, Claude, Gemini, or Copilot trained for specific tasks. Upload your best practices, templates, and examples of good work, and the AI gets better at handling that task for your team.
But many teams never start building custom GPTs, projects, gems, or agents because of what they don’t have.
Some have nothing written down. Others have scattered or outdated playbooks. And those with established best practices assume it’s still the best way forward because it worked before.
You don’t need perfect internal knowledge to build effective AI teammates. You can use external research to fill gaps, validate what you have, or challenge assumptions you didn’t know you were making.
Deep research features in tools like ChatGPT, Claude, Gemini, and Perplexity gather industry benchmarks, best practices, and frameworks in minutes. You can use that research as knowledge for your custom AI teammate, then refine it based on your team’s situation.
I used this approach for my own digital twin. I asked the AI to research publicly available information about me, my work, and my frameworks. The research report became part of the knowledge base, giving my digital twin context about how others see my work and what’s being said about my ideas.
Below are examples of what marketing teams can research to build stronger AI teammates, whether starting from scratch or questioning what they already have.
Build your AI teammates by blending external research with your situation. Start with deep research, test it in practice, and let your team improve it over time. That becomes knowledge worth keeping.