{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"AI-Ready Or Not?","title":"Why Bad Data Breaks AI with Heather Robinette","html":"","width":"100%","height":180,"duration":1894,"description":"You can build a dashboard that pulls together your forecast, call transcripts, and CRM. You can build an agent that handles one task surprisingly well. But if you don't trust the information underneath it, a confident answer doesn't get you very far.\n\n“So it's just a good reminder, too, that just 'cause it sounds confident doesn't mean it's right.”\n\nAbout This Episode\n\nHeather Robinette joined me to talk about what changed after she completed Overclock's 10-week AI for RevOps course. Before it, she mostly used AI through chat. By the end, she was connecting APIs, building applications, and working with agents. The useful part of that story is what she started doing with those skills. She describes pulling call information, Salesforce data, and weekly forecast discussions together to give leadership more context around a forecast. Her example of a risk-adjusted number is illustrative, not a reported business result.\n\nShe also walks through a marketing dashboard that started with sourced versus influenced pipeline. Then came campaign performance, conversion rates, goals, and the questions her team actually wanted to answer. She estimates putting about 20 hours into it through repeated iteration. Now the team has somewhere to start when it wants to understand a campaign, instead of asking her to pull every number. That is a practical change in how the work gets done, even if building it took more than a clever prompt.\n\nI share some of the smaller agents I've been building for my own work: triaging email, drafting replies, and finding duplicate tasks in Asana. We also get into the context problem. Someone helping with a project needs more than a task name. They need to understand the conversations and decisions behind it. I explain how I'm connecting meeting summaries, Google Docs, and NotebookLM so the people working with me can find that context themselves.\n\nThe conversation comes back to boundaries. Which systems should an agent be able to read? Which should it be...","thumbnail_url":"https://img.transistorcdn.com/zL8fVEwvoFrisrVJW2ZPszxP-Xj7xE1JfwF6UFNUBQo/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9hOWU4/NWE0NDRmM2VmYTBi/YWU2M2MwNDExOTk0/MWIxYy5wbmc.webp","thumbnail_width":300,"thumbnail_height":300}