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The conversation begins with a sobering statistic from an MIT report: 95% of generative AI pilots fail to deliver measurable impact on P&L. But as Stuart points out, that doesn’t mean they’re worthless. Many projects drive internal process improvements, enhance compliance, and streamline operations—benefits that don’t always show up on a balance sheet but are critical to business success.

This discussion highlights a recurring theme: back-office automation consistently delivers higher ROI than customer-facing AI. Internal processes are well-documented and easier to optimize, while public-facing AI carries reputational risks. Stuart shares examples of chatbots behaving badly on social media, underscoring the importance of starting with internal use cases.
Key Takeaways:

• Most AI pilots fail to impact P&L—but still deliver internal value.
• Data readiness is essential; clean, structured data is the foundation.
• Back-office automation offers higher ROI and lower risk than customer-facing AI.
• Targeted, repeatable solutions outperform one-off proofs of concept.
• Avoid vendor lock-in by deploying in customer tenants and delivering code.
• Success lies in purposeful design, tailored agents, and a steady approach to innovation.
  
  

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