AI & Tokenomics Governance

Overview

AI has emerged as a major technology investment category, and it behaves unlike anything that came before it. It is consumption-based, scales rapidly with adoption, and spans model APIs, GPU and accelerator compute, inference services, data platforms, and AI capabilities embedded across SaaS applications. Unlike traditional IT, AI costs scale with prompts, context windows, and tokens rather than fixed infrastructure.

Most organizations can track their AI spending. Far fewer can identify which models, teams, use cases, or customers are driving that spend, and whether the resulting business value justifies the investment. Without that visibility, AI budgets either expand without accountability or face broad cost-cutting measures. Both outcomes can undermine innovation and slow the initiatives that deliver the greatest business impact.

Key Features

Wipro SLICE 2.0 AI & Tokenomics Governance helps organizations govern AI economics at the point where costs are created, from tokens and prompts to inference and GPU consumption, while linking spend directly to business outcomes across any model, platform, and cloud environment.

It enables organizations to:

Discover AI consumption across model APIs, GPU infrastructure, inference platforms, data platforms and AI features inside SaaS
Meter and track tokens, prompts, context, inference calls and GPU hours at model level, including shared platform costs
Attribute cost to model, use case, team, product and customer, and establish cost per inference and cost per outcome
Govern spending through budgets, quotas, approval controls, model-routing policies, and anomaly detection, ensuring AI investments scale intentionally.
Optimize usage through model selection, prompt and context efficiency, caching, batching, scheduling, and improved GPU utilization.
Forecast AI demand as adoption grows, including capacity and commitment decisions for GPU infrastructure
Link AI cost to business outcomes, so leadership can scale what works and retire what does not

This marks a shift from reporting the AI bill to managing AI as a portfolio of measurable investments.

How It Works

A continuous feedback loop: outcome data refines attribution, policy and optimization.

Consume

Every AI spend source identified

Meter

Tokens and inference captured at model level

Attribute

Cost per model, use case, team, customer

Govern

Budgets, quotas, routing, alerts

Optimize

Right model, caching, GPU scheduling

Realize

Cost per outcome and return on AI

Benefits

Organizations can scale AI with confidence, control costs, and demonstrate measurable business value, delivering:

Every AI dollar traceable to a model, team and use case
Predictable AI economics, supported by forecasts that enable effective financial planning as adoption grows.
A lower cost per outcome without throttling innovation
Data-driven portfolio decisions to scale, optimize, or retire AI use cases based on measurable value.
Governance that holds across providers, with one policy applied across every model provider and cloud
Stronger commercial terms with model providers, backed by consumption evidence
Responsible, sustainable AI growth, with budgets, approvals and efficiency targets in place before scale

Outcomes You Can Measure

Share of AI spend attributed
Variance between AI forecast and actual
Trend in cost per inference and cost per outcome
Use cases scaled, refined or retired on measured value