Most AI projects do not stall because the technology failed. They stall because the work was done in the wrong order, or because pieces were built in parallel before the foundations underneath them were stable. This session examines how to diagnose where your organization actually stands, identify the gaps that are invisible from the inside, and build a roadmap that sequences the work correctly and moves AI from pilot to production.
- The Sequencing Problem: Governance, information architecture, knowledge engineering, and content readiness are not parallel workstreams. Each one enables the next. Organizations that skip the sequence and go straight to model deployment produce results that are inconsistent, unscalable, and often unsafe.
- The AI Readiness Maturity Model: A five-level framework that evaluates organizational capabilities across four domains: Knowledge Readiness, Operational Readiness, Technical Readiness, and Governance Readiness. The composite score matters less than the gaps between domains. The weakest pillar is always the bottleneck.
- Why Organizations Misjudge Their Own Readiness: Infrastructure investment feels substantial, so technical readiness feels mature. Content and knowledge gaps do not surface until AI surfaces them, which is too late to fix cheaply. The diagnostic replaces perception with evidence-based questions that decompose broad claims into specific, verifiable conditions.
- Building the Roadmap: A useful AI readiness roadmap connects assessment to action to measurement. It defines phases, dependencies, governance checkpoints, and KPIs, and it produces a plan that can be executed and validated rather than one that goes out of date.
- The Operating Model: Getting to production is one problem. Staying there is another. Models drift, content ages, and governance atrophies without an operating model in place. The operating model is the long-term mechanism that keeps AI performing reliably at scale.
Speakers