TELUS Digital Agentic AI Delivery Playbook¶
Executive Summary¶
This playbook describes how I would lead agentic AI adoption without turning it into a tool-first experiment. The operating idea is simple: start with high-friction workflows, prove value with controlled pilots, measure business impact, and scale only when reliability, ownership, and governance are visible.
What This Artifact Is¶
This is not a fake transformation deck or a code demo wearing leadership clothes. It is a compact delivery packet meant to show how I would structure an Agentic AI and Automation practice: where to start, how to govern it, how to staff it, and how to decide whether it is working.
Delivery Principles¶
| Principle | Meaning |
|---|---|
| Workflow before model | Define the business process, handoffs, risks, and success criteria before choosing tooling. |
| Evidence by default | Every agent action should leave a trace: inputs, retrieved evidence, decision, action, and reviewer. |
| Human control at risk boundaries | Autonomy is acceptable for low-risk routing and drafting; high-impact actions require approval. |
| Platform consistency | Teams should share patterns for prompts, tools, evals, observability, and access control. |
| Measured scale | Expand from pilot to portfolio only when KPIs show time saved, quality preserved, and risk reduced. |
First Use Cases¶
These are selected for a telco/digital-services environment where customer trust, regulated data handling, and high-volume support operations are the operating reality.
- Customer support triage: route incoming service requests to the right queue with a draft response and severity flag, keeping a human reviewer in the loop for escalations.
- Internal knowledge routing: answer employee questions about policies, tools, and processes from a governed document corpus, with full retrieval evidence logged.
- Incident summarization: convert alert streams and on-call notes into structured incident summaries with suspected owner, impact scope, and recommended next check.
- Quality evidence generation: produce QA and data-quality reports for client-facing delivery milestones, with traceable inputs so audits are straightforward.
Governance Rhythm¶
| Cadence | Meeting | Output |
|---|---|---|
| Weekly | Delivery checkpoint | Pilot status, blocker removal, metric review |
| Biweekly | Risk and evaluation review | Failed cases, unsafe action attempts, policy updates |
| Monthly | Executive readout | KPI trend, adoption progress, scale/no-scale decisions |
One-Page Adoption Pattern¶
Select one workflow, baseline the current pain, build a narrow agentic pilot, instrument every step, compare against baseline, and graduate only when the workflow has a named owner, eval suite, runbook, and rollback path.