No consumption metering
Token bills arrive as undifferentiated cloud invoices — no owner accountability.
Governed AI adoption
AI Usage Value & Governance
Most enterprises are running AI without knowing what it costs, whether it's governed, or if it's creating value. Token bills arrive without explanation. Pilots survive on enthusiasm, not evidence. Governance reviews stall because nobody owns the risk classification. Productivity claims go unvalidated.
Kriviksha helps enterprises build AI that survives a governance review, runs on visible economics, and proves value against the baseline that justified the investment.
Fund AI on evidence, not enthusiasm.
Why programmes stall
The cost of ungoverned AI
These are the failure modes that show up before the model quality debate even starts.
No governance gate
Use cases reach production without risk classification or approval.
No benefit baseline
“AI is saving us time” can’t be validated — nobody recorded “before.”
No unit economics
A $40k/month workload might cost $0.08 or $4.50 per outcome — nobody knows which. Figures shown are representative scenarios, not measured client results.
Illustrative economics
From raw API spend to governed unit cost
Each step is a control lever — routing, caching and model choice — not a promised saving.
Raw LLM API
$0.27/outcome
Model routing
$0.14
Semantic caching
$0.09
Fine-tuned SLM
$0.05
Governed cost
Visible, owned
Illustrative progression — actual figures depend on model, volume and architecture. See AI-003 — Token metering and unit-cost intelligence and the AI Calculator.
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 Bases
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.
Illustrative scenarios
AI value in context
Illustrative industry scenarios — not named client engagements.
Financial services
Transaction monitoring
Compliance teams review thousands of flagged transactions. AI triages by assembling context and risk signals — analysts decide.
Governance
Identity-bound data access, human decision authority, full audit trail.
Indicative impact: review time reduced; analyst capacity redirected. Outcomes depend on your data quality, process design and control appetite.
Insurance
Policy document analysis
Underwriters check exclusions across hundreds of documents. AI extracts clauses and flags deviations — underwriters validate.
Governance
Page-level citation, no AI coverage decisions, extraction accuracy evaluation.
Indicative impact: cycle time reduced; audit confidence improved. Outcomes depend on document quality, citation discipline and review design.
Healthcare
Protocol screening
Clinical teams screen protocols against requirements. AI cross-references sections versus checklists and flags gaps — reviewers confirm.
Governance
PHI-aware handling, no AI clinical decisions, version-controlled knowledge base.
Indicative impact: screening shortened; gaps surfaced earlier. Outcomes depend on corpus quality, privacy controls and clinical review design.
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. Actual savings depend on your consumption profile.
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.
| 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.
Self-assessment
AI governance readiness check
Six diagnostic questions. Answers stay in your browser — nothing is collected or transmitted.
Indicative self-assessment, not a formal evaluation. Answers are not collected or transmitted.
Solution playbooks
See how each of these is actually delivered
Fifteen AI Value playbooks in our library set out the operating workflow, systems and data boundary, human controls, governance cadence and primary KPI for each enterprise scenario. Written as consulting deliverables, not overviews.
Economics and portfolio
- AI-003 — AI Gateway, Token Metering and Unit-Cost IntelligenceAttribution at the point of consumption, cost per workflow, routing and caching levers.
- AI-008 — AI Budget Control and Business-Unit ShowbackBudget envelopes, burn-rate alerting and showback before chargeback.
- AI-010 — AI Investment and Benefit Realisation DashboardThe board view: cost, adoption, risk, benefit and a recommendation per use case.
Governance and delivery
- AI-007 — Use-Case Intake, Risk Classification and ApprovalRisk-tiered gates, data permissions and an auditable authority to proceed.
- AI-015 — Production AI Control Monitoring and Incident ResponseDrift, quality and control monitoring with a defined incident path.
- AI-001 — Procurement Spend Leakage and Negotiation IntelligenceA worked generative-AI use case with human review governance throughout.