What happens when a Chief Data & AI Officer tells the board "I'm not going to talk about AI" on day two of the job? At Zayo Group, the largest independent connectivity company in the United States with around 145,000 route miles, it sparked a systematic approach that generated tens of millions in value while building enterprise AI foundations that actually scale.

David Sedlock inherited a company with zero data strategy and a single monolithic application running the entire business. His counterintuitive move: explicitly refuse AI initiatives until data governance matured. The payoff came fast—his organization flipped from cost center to profit center within two months, delivering tens of millions in year one savings while constructing the platform architecture needed for production AI.

The breakthrough insight: encoding all business logic in portable Python libraries rather than embedding it in vendor tools. This architectural decision lets Zayo pivot between AI platforms, agentic frameworks, and future technologies without rebuilding core intelligence, a critical advantage as the AI landscape evolves.

Topics Discussed:

Implementing "AI Quick Strikes" methodology with controlled technical debt to prove ROI during platform construction - Sedlock ran a small team of three to four people focused on churn, revenue recognition, and service delivery while building foundational capabilities, accepting suboptimal data usage to generate tens of millions in savings within the first year.
Architecting business logic portability through Python libraries to eliminate vendor lock-in - All business rules and logic are encoded in Python libraries rather than embedded in ETL tools, BI tools, or source systems, enabling seamless migration between AI vendors, agentic architectures, and future platforms without losing institutional intelligence.
Engineering 1,149 critical data elements into 176 business-ready "gold data sets" - Rather than attempting to govern millions of data elements, Zayo identified and perfected only the most critical ones used to run the business, combining them with business logic and rules to create reliable inputs for AI applications.
Achieving 83% confidence level for service delivery SLA predictions using text mining and machine learning - Combining structured data with crawling of open text fields, the model predicts at contract signing whether committed timeframes will be met, enabling proactive action on service delivery challenges ranked by confidence level.
Democratizing data access through citizen data scientists while maintaining governance on certified data sets - Business users gain direct access to gold data sets through the data platform, enabling front-line innovation on clean, verified data while technical teams focus on deep, complex, cross-organizational opportunities.
Compressing business requirements gathering from months to hours using generative AI frameworks - Recording business stakeholder conversations and processing them through agentic frameworks generates business cases, user stories, and test scripts in real-time, condensing traditional PI planning cycles that typically involve hundreds of people over months.
Scaling from idea to 500 users in 48 hours through data platform readiness - Network inventory management evolved from Excel spreadsheet to live dashboard updated every 10 minutes, demonstrating how proper foundational architecture enables rapid application development when business needs arise.
Reframing AI workforce impact as capability multiplication rather than job replacement - Strategic approach of hiring 30-50 people to perform like 300-500 people, with humans expanding roles as agent managers while maintaining accountability for agent outcomes and providing business context feedback loops.

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