ai-automation

AI, Automation & Scale Thinking

The SPM leverages AI and automation to reduce support demand, lower cost, and scale outcomes.

Overview

This skill covers identifying, building, and governing AI-augmented workflows for supportability — from automated agenda generation and action tracking (already implemented in spm-prototype) to future copilot enablement, self-healing systems, and prompt libraries for support engineers.

Capabilities

1. Leverage AI and automation to reduce support demand and cost at scale.
2. Build self-healing and self-help ecosystems that resolve issues without human intervention.
3. Apply product + data thinking to scale supportability outcomes beyond linear staffing.
4. Drive innovation in diagnostics, copilots, and support tooling.
5. Evaluate supportability workflows for AI-augmentation opportunities (classification, summarization, routing).
6. Develop and maintain agent-based automation for agenda building, action tracking, and recap generation.
7. Implement AI governance guardrails: source traceability, hallucination prevention, human-in-the-loop checkpoints.
8. Operationalize Copilot-for-Service-style enablement for support engineers (prompt libraries, scenario playbooks).
9. Track AI-initiative adoption metrics and report on realized vs. projected value.
10. Maintain a backlog of automation candidates prioritized by effort/impact ratio.
11. Ensure all AI-generated outputs are flagged as such and validated before external distribution.

Workflow

Step 1 — Identify Automation Candidates

Evaluate SPM workflows against the automation matrix:

Workflow	Current State	Automation Opportunity	Effort	Impact
Signal collection	Automated (collect_signals.py)	✅ Done	—	High
SAP mover computation	Automated (compute_movers.py)	✅ Done	—	High
Agenda drafting	Automated (draft_agenda.py)	✅ Done	—	Medium
ADO reconciliation	Automated (reconcile_ado.py)	✅ Done	—	High
HTML report generation	Automated (generate_report.py)	✅ Done	—	High
Meeting transcript parsing	Manual	AI summarization	Medium	High
Follow-up email drafting	Manual	Template + AI	Low	Medium
Escalation brief creation	Manual	Template + AI	Low	Medium
Case classification	Manual	AI classification	High	High
GTS gap detection	Manual	Kusto + rules engine	Medium	High

Step 2 — Build the Automation

For each candidate, follow the pattern established in spm-prototype:

1. Define inputs/outputs — What data goes in, what artifact comes out
2. Write the module — Python script in src/ with clear function boundaries
3. Integrate into pipeline — Add as a step in run_cycle.py
4. Add config — Product-specific settings in config/settings.yaml
5. Test end-to-end — Run full cycle and validate output

Step 3 — Implement Governance Guardrails

Every AI-generated output must:

• Cite data sources (Kusto query, ADO item, case ID)
• Flag confidence level (High / Medium / Low)
• Include human-in-the-loop checkpoint before external distribution
• Be marked as AI-generated in metadata or footer
• Not contain PII or customer-identifying information

Step 4 — Build Prompt Libraries

For support engineer enablement, maintain scenario-specific prompts:

Scenario	Prompt Pattern	Output
Case triage	"Classify this case by product, severity, and likely root cause: {{case_description}}"	Product + severity + RCA hypothesis
RCA analysis	"Given these symptoms: {{symptoms}}, what are the top 3 most likely root causes for {{product}}?"	Ranked RCA list
Customer response	"Draft a customer response for: {{issue_summary}}. Resolution: {{resolution_steps}}"	Email draft
Knowledge article	"Create a troubleshooting guide for: {{issue_pattern}}. Include: symptoms, cause, resolution, prevention."	GTS draft

Step 5 — Track Adoption & Value

Measure AI initiative outcomes:

Metric	Definition	Target
Automation coverage	% of SPM workflows automated	Track over time
Time saved per cycle	Hours saved vs. manual process	Measure and report
Accuracy	% of AI outputs requiring no human correction	> 90%
Adoption rate	% of team using AI tools regularly	Track over time
Value realized	Cost/time savings from automation	Report quarterly

Current Implementation Status

Component	Status	Location
SPM Cycle Pipeline	✅ Live	spm-prototype/src/run_cycle.py
Kusto Signal Collection	✅ Live	spm-prototype/src/collect_signals.py
SAP Mover Computation	✅ Live	spm-prototype/src/compute_movers.py
IPD Forecasting	✅ Live	spm-prototype/src/compute_forecast.py
Carry-Forward Tracking	✅ Live	spm-prototype/src/carry_forward.py
Agenda Drafting	✅ Live	spm-prototype/src/draft_agenda.py
ADO Reconciliation	✅ Live	spm-prototype/src/reconcile_ado.py
HTML Report Generation	✅ Live	spm-prototype/src/generate_report.py
Auto-Auth (Azure CLI + PAT)	✅ Live	spm-prototype/src/run_cycle.py
GitHub Repo	✅ Live	CloudNet-Supportability/SPM-Agent
Meeting Transcript Parsing	🔲 Planned	—
Copilot Prompt Library	🔲 Planned	—
Self-Healing Diagnostics	🔲 Planned	—

Integration Points

• SPM Prototype: spm-prototype/ — the living implementation of this skill
• GitHub: https://github.com/CloudNet-Supportability/SPM-Agent
• Agent Skills Framework: Work/skills-main/skills/ — skill definitions
• MCP Builder: Work/skills-main/skills/mcp-builder/ — for building new integrations

Quality Criteria

• All AI outputs cite data sources
• Human-in-the-loop checkpoint before external distribution
• AI-generated content flagged in metadata
• Automation backlog maintained with effort/impact prioritization
• Adoption metrics tracked and reported quarterly
• No PII in AI-generated outputs