Now Assist (Generative AI)

Most Now Assist programmes stall for one of three reasons. The underlying data — knowledge base structure, catalogue alignment, incident classification — isn't clean enough for the model to produce useful output. The governance model around generative AI hasn't been agreed, so legal and security halt the rollout the week before it goes live. Or success metrics were never defined, so the platform team can't show whether the deflection rate, summarisation quality, or authoring assist is moving the numbers that justified the investment.

Done well, Now Assist is not a feature toggle — it's an operating discipline. A clean knowledge graph. A governed prompt and skill library. Role-based access to AI capabilities. Measurement against pre-baseline KPIs. A continuous-improvement cadence that keeps model output trustworthy as the business changes. That is the version MainStack implements, and the version that pays back across ITSM, CSM, HRSD, and ITOM rather than sitting in a single use case.

We start with a Now Assist readiness assessment: knowledge base structure, incident and case classification quality, catalogue alignment, integration points, and the governance gaps that will block production rollout. The output is a sequenced enablement plan that prioritises use cases by feasibility and impact, with the data and process remediation needed for each one. It is not a generic AI roadmap; it is a prioritised list of changes that get Now Assist into production safely.

Implementation covers skill configuration, prompt library design, Agent Workspace integration, Virtual Agent topic design, and assisted authoring rollout — alongside the governance scaffolding around them: AI usage policy, role-based access, audit logging, and human-in-the-loop review where the use case requires it. Every engagement ends with a trained platform team, a documented operating model for prompt and skill management, and the measurement cadence that proves Now Assist is moving the numbers that justified the spend.