AI Usage Value & Governance | AI FinOps and Token Economics

Build AI that survives a governance review

Kriviksha helps enterprises adopt AI responsibly — combining automation, generative AI, governance frameworks and measurable outcomes while containing operational, legal and compliance risk.

AI Strategy & Readiness

Decide what to build before you build it, and know whether your data can support it.

Use-case discovery and scoringBusiness impact, delivery effort, data readiness, run cost, risk and measurement confidence.
Data readiness assessmentQuality, access, lineage and retention constraints for each candidate use case.
Governance foundationsDecision rights, risk tiering, privacy expectations and review points.
Execution roadmapSequenced by value and readiness, not by novelty.

AI Automation for Business Processes

Workflow automation and decision support across IT, procurement, finance and operations.

Process selectionHigh-volume, rule-bounded workflows where quality can be measured.
Human-in-the-loop designEscalation and review thresholds defined before deployment.
IntegrationITSM, ERP, procurement and identity systems already in place.
Quality monitoringAccuracy, escalation rate and cost per completed task tracked continuously.

Custom AI Assistants & Knowledge Platforms

Secure, role-based assistants over internal knowledge, policies and operational playbooks.

Role-based accessRetrieval scoped to what the user is already entitled to see.
Governed content sourcesCurated, versioned corpora with owners — not an uncontrolled file-share crawl.
Citation and traceabilityAnswers link back to source documents for review and audit.
Adoption measurementUsage by team and workflow, and whether it displaced the manual path.

Generative AI Applications

Enterprise copilots, document summarisation and intelligent knowledge management on governed data.

Copilots for defined rolesScoped to a job, with success criteria agreed up front.
Document summarisation and extractionContract, policy and evidence workflows with human sign-off.
Model selection and routingMatched to the quality, latency and cost profile each task actually needs.
Evaluation harnessRegression testing so a model or prompt change does not quietly degrade output.

AI FinOps

Token economics, in business language

Token economics connects model consumption to business outcomes. The useful question is not how many tokens you consumed — it is what each use case costs, whether that cost is forecastable, and whether the value justifies continued investment.

Cost attributionBy use case, product, team, customer or workflow — with showback or chargeback.
Unit economicsCost per request, per output, per active user and per completed business outcome.
LeversModel routing, prompt and retrieval optimisation, caching, batching and rate controls.
ControlsForecast versus actual, budget thresholds and anomaly detection on consumption spikes.

What drives AI cost

Model choice, input and output token volume, context size, request frequency, retrieval infrastructure, orchestration overhead and human review time.

The goal is not fewer tokens. It is better control over the trade-off between value, quality, latency, risk and adoption.

Forecast vs actual Budget thresholds Anomaly detection Showback / chargeback

AI use-case lifecycle

Every use case moves through the same gates, with a named owner at each one.

1

Idea

Captured with a business sponsor and a stated outcome.

2

Assess

Scored on impact, effort, data readiness, cost and risk.

3

Approve

Risk-tiered and funded, or declined with a reason on record.

4

Pilot

Bounded scope with quality and cost baselines captured.

5

Produce

Promoted with monitoring, ownership and rollback in place.

6

Monitor

Cost, quality, adoption and incidents on a fixed cadence.

7

Optimise

Routing, prompts, retrieval and caching tuned against unit cost.

8

Scale or retire

An explicit decision, made on evidence, on a schedule.

Illustrative — sample data, not a client estate

What an AI portfolio view looks like

This is the reporting artefact leadership signs off against. Figures below are constructed for illustration.

Illustrative AI use-case portfolio with cost, benefit, risk and recommendation
Use case	Owner	Model / provider	Monthly tokens	Monthly cost	Cost / outcome	Realised benefit	Risk tier	Adoption	Recommendation
Service desk summarisation	IT Operations	Hosted LLM	14.0M	$2,600	$0.31	Moderate — handling time down	Medium	Growing	Optimise
Sales proposal drafting	Commercial	API model mix	6.0M	$1,300	$4.20	Strong — cycle time reduced	Low	Healthy	Scale
Policy assistant	Risk & Compliance	Private retrieval flow	2.0M	$720	$1.80	Early — evidence access improved	High	Limited	Continue pilot
Contract clause extraction	Legal	Fine-tuned small model	0.9M	$210	$0.95	Weak — high escalation rate	High	Falling	Redesign

Figures are illustrative and do not represent any client environment. See Terms of Service on illustrative value examples.