[VISIONCAST] The New Era of Enterprise AI is Here l Nov 12, 2026 · 12 PM EST · Virtual Register now
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On-demand webinar
Four years into the AI boom, most enterprise deployments still never reach production. Industry analyst Alan Pelz-Sharpe explains why fluent models are not the same as reliable ones, and why governed data, clear processes, and deterministic systems are what separate AI that works from AI that only sounds right.
Enterprises have been seduced by the fluency of large language models, mistaking a plausible guess for actual reasoning. But business runs on arithmetic, rules, and regulations, not hallucinations. When AI is bolted onto fragmented data and inflexible legacy processes, the result is “Garbage In, Gospel Out”: confident answers built on unreliable foundations. It is the main reason most organizations fail to deploy.
In this session, industry analyst Alan Pelz-Sharpe dissects the last four years of enterprise AI and cuts through the marketing gloss. He exposes the “Smart Guess Trap”, shows what it really costs in compliance exposure and operational risk, and makes the case that deterministic systems and rigorous governance of data and processes are the only reliable path to genuine value. His conclusion is direct: in the enterprise, certainty is a competitive advantage. Probability is just an expensive excuse for bad data. The organizations that succeed treat their own knowledge as the foundation, so AI outputs can be trusted, defended, and acted on where it matters most.
The warning signs that stall enterprise AI before production, and how to spot them before they sink your initiative.
How confident-sounding fiction turns into compliance exposure, missed SLAs, and business decisions you cannot defend.
How to diagnose whether your AI is solving the underlying problem or simply papering over data chaos.
A clear framework for moving beyond experimentation to production-ready AI built on rules, governance, and trusted data.
Why your organization's real knowledge, not a bigger model, is what makes AI answers trustworthy, auditable, and actionable.
Practical actions to make real headway if you are actively working to move AI from pilot to production.
