Deflection does not eliminate L1 work; it eliminates the easy half and leaves the hard half — verification, escalations, AI administration — to whoever you prepared for it. This playbook assesses your team against this track, sequences who moves toward AI administration versus the cloud and security bridges, and redesigns L1 job content around verification before attrition does it for you.
Tier: Core · Workflow area: Communication & leadership · ~35 minutes to design; a quarter to run
This playbook is UC-55, written for the lead or manager (formal or de facto) who owns the question "what happens to my team?" It replaces the two failure modes — pretending nothing changes, and a scary all-hands about "upskilling" with no plan — with a skills matrix, individual paths, and job descriptions that match the actual new work.
Build the skills matrix from this track's spine. Rows are team members; columns are the track's eight capability areas: AI-assisted troubleshooting (m2), knowledge ops (m3), scripting with review discipline (m4), identity/endpoint/patch automation (m5), assistant & RAG administration (m6), governance & measurement (m7), communication (pb-47/48), and leadership/mentoring (m8). Score 0–3: 0 unaware, 1 aware, 2 practices with support, 3 owns and teaches. Use the track's assessment engine for a first pass where staff have taken it; confirm with observed work, not self-rating alone.
Map the demand side from your own deflection math. From pb-42's portfolio and pb-44's measurements, estimate what the queue looks like in 12 months: which contact types shrink, what grows (bot administration, conversation review, escalation handling, KB engineering, automation maintenance). Write the future org's work inventory — hours per week per activity — before touching anyone's title. The gap between the matrix and the inventory is the transition plan's exact shape.
Sequence people onto three broad paths — in conversations, not announcements. One 30-minute conversation each: here is the matrix row, here is where the work is going, here are the paths — which pulls you?
Sequence matters: send your strongest documenter to AI administration first — they build the training material everyone else uses.
Redesign the L1 job description around verification. The new L1 content, written as a job description you could post: reviews and grades bot conversations (pb-44's loop as rota duty); handles warm handoffs where context is pre-assembled (pb-40); runs and audits AI triage (pb-01); verifies AI-drafted comms and KB articles before publish (pb-47); executes approval steps for R3 agent actions (pb-41). Note what disappeared — password resets, status lookups — and what that implies: the entry-level task pipeline that used to train juniors is gone, so the JD must include deliberate shadowing and lab time, or you will hire people with no path to competence.
Draft individual development plans with the LLM as scaffolding.
Draft a 90-day development plan. Current role: L1 support agent.
Matrix scores: scripting 2, identity ops 2, RAG admin 0,
governance 1, communication 2. Chosen path: AI service-desk
administration. Available: 4 hrs/week protected learning time,
this track's modules m6-m7 and playbooks pb-40..46, one sandbox
tenant. Output: weekly plan with one produced ARTIFACT per
fortnight (not "read about X" — build/document/measure something),
and a named checkpoint conversation every 30 days.
You edit for reality (on-call rotas, seasonal load), the person edits for ambition, and the artifact-per-fortnight rule keeps it from becoming a reading list.
Protect the learning time or admit there is no plan. Four hours a week, on the calendar, defended against the queue — funded by the very deflection driving the change. Track it like an SLA: months where learning time collapses under load are months the transition slipped, and that goes in the pb-48 report's watch items.
Review quarterly against artifacts, not attendance. Each person's plan produces things — a deployed intent, a graded review batch, a lab write-up, an AZ-104 exam booking. The quarterly review walks the artifacts, rescores the matrix rows they touch, and re-sequences: paths are commitments to direction, not cages.
Skills matrices and development plans are sensitive personnel data — store them access-controlled, never paste named assessments into external AI tools, and keep LLM-drafted plans as drafts until the human conversations have shaped them.
Assess against the track, size the future queue from your own deflection data, sequence people through conversations onto administration, bridge, or deepened-frontline paths, and rewrite the L1 job around verification — the teams that plan this transition staff the new roles from within; the teams that don't, staff them from outside while their people read the writing on the wall.