Problem-First AI Strategy: Automation That Delivers Business Value | Scott Litman

In this episode, Brendon Dennewill sits down with Scott Litman, SVP at Capacity and founder of Lucy AI (acquired by Capacity in 2024), to explore the reality behind AI adoption in enterprise organizations. Scott brings over 25 years of digital transformation experience and shares insights from guiding Lucy's integration into Capacity's AI platform, which now serves over 2,500 companies. 

The conversation cuts through AI hype to reveal why 95% of enterprise AI projects haven't reached production and what separates successful AI implementations from failed proof-of-concepts.

Scott emphasizes that while individual users have incredible AI superpowers at their fingertips, enterprise adoption faces unique challenges around data quality, security, and scale. He advocates for treating AI as automation rather than magic, focusing on solving specific business problems through strategic problem selection and rigorous testing. 

This episode is essential for RevOps professionals, enterprise leaders, and B2B growth teams who need practical guidance on moving from AI experimentation to production-ready solutions that deliver measurable business outcomes.

What You'll Learn

• Why enterprise AI adoption lags: Individuals leverage vast public datasets, while enterprises work with limited, sensitive data that demands unique methods and strict security.
• POC vs. production: Demos are easy; scaling to enterprise-grade solutions requires testing, governance, and discipline.
• Choosing the right problems: Target inefficient, error-prone, or time-consuming tasks where automation can turn days into minutes/hours.
• Framework for adoption: Begin with clear policies, targeted training, and a focus on automation over transformation to set realistic expectations.
• Driving user adoption: Offer persona-based training that solves real pain points and ensures early wins that delight users.
• Automation over hype: Treat AI like prior digital transformations—speed of execution matters more than novelty.
• Training and change management: Bridge skill gaps and standardize success through structured policies, approved tools, and clear use case guidelines.

Resources Mentioned

• EOS (Entrepreneurial Operating System)
• Capacity AI Platform
• HubSpot Inbound 2025
• MIT Enterprise AI Report 
• Microsoft Azure OpenAI Service
• Anthropic Claude