AI Ops
A service management design for AI economics and impact
Five disciplines that make AI consumption measurable, governable and safe to run at enterprise scale.
AI FinOps
SPEND BY BU
Tokens and cost per BU
TOKENS / $ CONSUMED BY
Employees, apps, agents
MODELS USED
LLMs / APIs per workload
OUTPUT
Outcome per token spent
ROI
Value generated vs. spend
AIOps
AGENT BEHAVIOUR
Monitored for anomalies
CONSUMPTION RATE
Tracked against budget
ALERTS & THROTTLING
Auto-alert and throttle
GATES & APPROVALS
Consumption checkpoints
MODEL SELECTION
Checks and controls applied
AUTONOMOUS AGENTS
Hard limits per agent
ModelOps
MODEL SELECTION
Best-fit model, optimised
MODEL ROUTING
Right model per request
PROMPT MANAGEMENT
Versioned and reusable
CONTEXT MANAGEMENT
Right context, every call
PRIVATE VS PUBLIC
Chosen by data sensitivity
RESPONSIBLE AI
Bias, safety, fairness
REGULATORY COMPLIANCE
Aligned to law and policy
ITSM
INCIDENT MANAGEMENT
AI-triaged, faster MTTR
PROBLEM MANAGEMENT
Root cause via AI
CHANGE MANAGEMENT
AI-assessed risk and impact
REQUEST FULFILMENT
Self-service, agent-led
SLA MANAGEMENT
Tracked and AI-flagged
KNOWLEDGE MANAGEMENT
AI-curated, up to date
Security
DATA TOKENIZATION
Masked before model use
ACCESS & IDENTITY
RBAC: users, apps, agents
PRIVACY & RESIDENCY
Residency and PII controls
THREAT MONITORING
Injection and leak detection
AUDIT & COMPLIANCE
Full trail, policy-aligned
INCIDENT RESPONSE
Playbook for AI incidents
Instrumented well, this model becomes the platform for an AI Ops service line staffed by agents and humans.