PRESHai's enablement curriculum is role-based, hands-on, and tied to the agents being deployed. Engineers, operations, practice leads, and governance owners exit the engagement with documented proficiency, not a binder of slides.
THE DISCIPLINE
Most AI engagements end with a system that works and a team that does not yet own it. The platform runs, the agents perform, the dashboards are green, and then the consultant leaves and the team that needs to operate the new capability has to figure it out from documentation.
PRESHai builds the enablement curriculum into the engagement so that by launch, the team that owns the system has been trained on the system they own. Role-based tracks, hands-on labs, operational runbooks, and a knowledge-transfer plan structured around the agents going into production. The curriculum is specific to your stack, not generic AI training.
Engineering, operations, practice leads, and governance owners each get their own track. The tracks share a vocabulary so cross-role conversations stay coherent, but the depth and emphasis differ by what each role needs to own after launch.
For developers and AI engineers building and extending agents on the platform. Covers agent architecture, prompt engineering, evaluation rubric design, tool-and-connector authoring, and the runtime patterns that make agents debuggable in production.
For the operations team that runs agents day-to-day. Covers run-history reading, evaluation drift detection, on-call rotation patterns, incident playbooks, rollback procedures, and the difference between a model issue and a workflow issue.
For the practice and business leads who own the workflows the agents touch. Covers workflow-redesign methodology, decision-rights mapping, capability-review cadence, and reading the agent activity dashboard at the right altitude for business decisions.
For IT, security, legal, and compliance leads. Covers the agent-governance model, audit-log access patterns, scoped-credential management, approval-routing administration, and the documentation standards that make the program defensible under audit.
Each format is calibrated to a specific learning outcome. Hands-on labs build building skill. Runbooks build operating skill. Redesign sessions build judgment. Each is a real deliverable in the engagement, not a generic training motion.
Engineers build a real agent against your stack, with the platform, the integrations, and the evaluation harness all in scope. The lab outputs are reviewed against the same readiness checklist a production agent goes through.
Each agent ships with the runbook the on-call team needs: how to read its run history, how to invoke a rollback, how to escalate a drift alert, how to onboard a new on-call engineer to the agent. Authored during deployment, owned post-launch.
Co-led with practice leads, these are working sessions that produce decision-rights maps and the redesigned operating model the team will run after launch. Not a workshop in the abstract; the output goes straight into the SOP rewrite.
Documented, sequenced, time-boxed. Names the consultant deliverables that have to land before the engagement closes and the internal owners who pick them up. The post-engagement support window is structured into the plan.
Practitioners progress through proficiency tiers tied to demonstrated work: built an agent end-to-end, ran an on-call rotation, led a workflow-redesign session, owned a capability review. The tier model gives leaders a way to see practice-level capability without subjective inference.
A defined support window after the engagement closes where the PRESHai team is on call for questions, debugging, and the first few capability reviews. Office hours are how the team you trained becomes the team that can extend the system without us.
KNOWLEDGE TRANSFER
Knowledge transfer is the part of the engagement that decides whether the team you trained is actually self-sufficient when the engagement closes. It cannot be left as an implicit handoff. PRESHai writes the knowledge-transfer plan into the engagement: which deliverables land before close, who picks them up, what the first 90 days of internal ownership look like.
The plan includes a defined post-engagement support window. Office hours, on-call escalation paths into the PRESHai team for the first quarter, and named decision points where we either step back or extend. By the end of that window, the team that runs the system can build the next agent without us, and the proficiency tiers we mapped during the engagement are the evidence.
Training and Change Management are the two enablement practices in the AI Implementation engagement. Training builds skill; Change names the redesigned operating model. Most engagements run them in parallel.
Read about Change ManagementTraining is one phase. The full implementation engagement spans strategy, platform, integration, governance, and enablement.
Tour the implementation engagementINSIGHTS
How AI and automation are reshaping operations, workflows, and growth strategies across the IT channel.
Schedule an enablement scoping conversation. We will name the role tracks, the formats, the proficiency tiers, and the knowledge-transfer plan.
