Confidently Wrong: How AI Fails RevOps Teams (And How to Fix It)

AI is already changing how RevOps teams work.
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From building workflows to analyzing pipeline to answering ad hoc questions, tools like Claude are helping operators move faster than ever before.
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But there’s a catch.
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AI generates answers; confident answers. And without the right context, data, or guardrails, those answers can be… wrong.
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So how are the best teams actually using AI today, and what should you be careful about? 🤔
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In this session, Eli Portnoy (Founder & CEO at BackEngine) joins RevOps Co-op to walk through real-world ways operators are using AI tools like Claude to improve output, automate work, and move faster across revenue workflows.
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We’ll also unpack the less talked-about side of AI adoption: where things break, why hallucinations happen in operational contexts, and how teams can build the right guardrails to ensure outputs are actually trustworthy.
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If you’re experimenting with AI or already embedding it into your workflows, this session will help you separate what’s useful from what’s risky.
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🔑 Key Takeaways

• How to connect Claude to your stack without breaking compliance or cost
• Real examples with corresponding prompts for RevOps teams building workflows in AI tools like Claude
• The data and context conditions your AI needs to be reliable
• How to spot a hallucination before it reaches your CRO
• Common pitfalls when using AI with limited or disconnected data
• How to optimize queries and avoid wasted spend on Claude
• How to think about guardrails, context, and data quality to improve reliability

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