Live trusted AI session for enterprise buyers

An EY survey cited in the webinar puts adoption at roughly 54% of employees using generative AI at work, yet only about 5.4% of enterprises have AI running in production. Live polling on the call mirrored that: about 50% of attendees called out data and governance as their top concern, with hallucinations, visibility and 'where to start' close behind. The gap between pilot and production is a trust gap, not a capability gap.

The webinar lays out a short checklist: can the platform prevent hallucinations, and to what extent; what guardrails are deterministic versus probabilistic; what happens when the model is unsure; and can you replay any decision step by step in language a business user understands. If the answer to any of those is hand-wavy, the platform is not ready for regulated work.

Every step of every Kognitos run is captured in plain English: the inputs the platform read, the policy it applied, the decision it made, and any human exception that was raised. The business user does not need a developer or a JSON viewer; they read the trace the same way they would read a memo. This is what makes the platform usable for audit, SOX and regulator review.

Generative AI gives you fluent output. Trusted AI gives you fluent output plus a deterministic execution layer, a written policy that governs every decision, a human-in-the-loop for exceptions, and a full audit trail of what happened and why. Generative AI is a feature; trusted AI is a runtime, and only a runtime can carry workloads like AP, claims or close.

Peter describes the common baseline: with RPA plus IDP, customers are usually at a 60-70% straight-through-processing rate, and the remaining 30-40% drives most of the FTE cost because every exception requires a human. Adding governed AI on top frequently lifts STP into the 90%+ range, which is where the FTE math and the customer-experience math actually change.

Start with the highest-volume, most repetitive processes you already run. Talk to IT about the native automation already in your applications, then layer trusted AI on the exception-heavy parts that those tools cannot handle. Define success criteria in writing before you talk to vendors, then run a short 'prove it' pilot against one or two of your real pain points.

Nash and Peter address this directly: if a process is already well-served by a deterministic, in-product capability (a native ERP rule, a simple workflow tool) and the volume is low, adding AI is unlikely to pay back. AI is best applied where you have unstructured inputs, judgement, exception load, and meaningful volume; pick that intersection first.

CIOs, CISOs, automation and AI program leads, business process owners in finance, operations and customer service, and any executive sponsor evaluating an enterprise AI platform. It is intentionally vendor-agnostic in framing: the checklist works against Kognitos, against in-house builds, and against any competing platform you are considering.

Three deep-dive sessions follow this one: Trusted AI Procure-to-Pay, Trusted AI Order-to-Cash, and Trusted AI Record-to-Report. Together they apply the trust principles from this foundational session to each finance cycle. All four are on demand on the Kognitos webinars hub.