From AI Pilots to Revenue Impact: What MIT Research Reveals About Making AI Deliver

AI pilots are everywhere, but predictable revenue impact is rare.
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Many RevOps teams are experimenting with AI, yet most initiatives stall before they ever influence forecasting accuracy, pipeline generation, or operational efficiency.
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So what separates AI experiments from real revenue outcomes? 🤔
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In this session, Celigo will break down new research from MIT Technology Review Insights, Bridging the AI Gap, which explores why AI initiatives struggle to scale and what organizations need operationally to turn AI into measurable business impact.
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We’ll explore the operational realities behind AI adoption, from data readiness and integrations to governance and workflow orchestration, and why these factors determine whether AI drives real pipeline growth or remains stuck in experimentation.
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You’ll also see how these principles translate into action through real revenue workflows, including how AI agents can coordinate prospecting activity and turn fragmented data into consistent pipeline generation.
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🔑 Key Takeaways

• Insights from MIT Technology Review research on why most AI initiatives stall before scale
• The operational gaps preventing AI from impacting forecasting and pipeline performance
• A practical blueprint for scaling AI across revenue workflows
• How AI agents can transform disconnected data into coordinated prospecting execution
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🎁 Bonus for Attendees

All attendees will receive the full MIT Technology Review Insights research report, shared in coordination with Celigo.

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