This episode explores how GenAI initiatives stall when customer feedback, model performance, risk, talent, and data readiness live in separate silos. It shows why a shared intelligence layer helps leaders spot what’s working, identify gaps faster, and make decisions that drive measurable business impact.
Episodes (7)
This episode explains why GenAI evaluation must be built into release discipline, not treated as a final check. It covers reusable release gates, scorecards, monitoring, and why evidence-based evaluation helps teams move faster with more trust and less risk.
This episode explores why GenAI products succeed only when they solve a real, high-value customer job inside an actual workflow. It covers how trust, friction, and clear use-case selection shape adoption more than model quality alone.
This episode explores why promising GenAI demos often stall when organizations hit issues like scattered knowledge, weak metadata, fuzzy permissions, and inconsistent sources. It breaks down the five pillars of enterprise data readiness and shows how to build a governed foundation that supports scalable, trusted retrieval use cases.
This episode explores how GenAI pilots can quietly accumulate security debt through inconsistent approvals, guardrails, and access controls that become painful at scale. The hosts discuss why baseline readiness, reusable controls, and a shared operating model help teams move faster without reinventing governance for every use case.
The hosts unpack what makes an AI agent different from a chatbot, and why the real test is whether it can safely handle bounded work with human checkpoints. They explore practical use cases like knowledge search, support triage, and draft-to-review workflows, while warning that poor data, weak governance, and unclear ownership can turn speed into chaos.
Steve and the team break down why GenAI has moved from experiments to an enterprise portfolio that needs clear prioritization, evidence, and governance. They outline what a decision-grade vision looks like, from selecting 2 to 4 value themes to setting boundaries, refresh cadences, and scaling criteria.