Generative AI Success Requires a Strong Foundation: Insights From Seth Earley in Harvard Business Review Analytic Services
Harvard Business Review Analytic Services published a comprehensive report on what organizations must establish to adopt generative AI effectively. The study examines data readiness, governance, operating models, and organizational maturity, and features expert insights from Seth Earley on why so many initiatives fail to scale.
Seth highlights a central challenge across industries: generative AI cannot produce consistent or trusted results without a disciplined information foundation.
“If you skip the foundational work such as clean data, integrated systems, and governance, you are not building AI. You are building chaos.” - Seth Earley
Key themes from Seth’s interview
Data and content quality shape AI performance
Generative AI depends on structured, accessible, and well governed knowledge assets. Inconsistent metadata, fragmented systems, and outdated content remain major barriers to accuracy and reliability.
Successful AI requires operational and cultural readiness
Seth emphasizes the importance of clear roles, decision rights, and cross functional coordination. Organizations that excel in AI treat information as a managed business asset.
Governance enables safe and scalable AI
Policies, standards, and ongoing oversight help teams mitigate risk, reduce bias, and maintain trust in AI generated outputs.
AI strategy must connect to business results
Seth reinforces that true value comes from aligning generative AI efforts with measurable business outcomes rather than isolated experimentation.
“Generative AI without a plan is like building on sand. Success only comes when the foundation is sound.” - Seth Earley
For organizations aiming to move beyond early pilots, Seth’s insights offer a clear roadmap for strengthening data foundations, improving information architecture, and implementing scalable AI governance.
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