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Token IQ
Deck

Cost discipline at AI scale.

The tokens got cheaper. The bill got bigger. Token IQ turns enterprise AI from a runaway cost question into a governed value engine, diagnosed, optimized, and managed end-to-end, without slowing adoption.

24×
global token usage growth, 2026→2030, to 120 quadrillion tokens/month1
~95%
of enterprise AI usage runs on premium frontier models for tasks that don’t require them2
85%
of organizations miss their AI-cost forecasts by more than 10%3
The offeringOne unified service, diagnose → optimize → manage

Not token optimization, FinOps, and a managed run as three disconnected towers. One integrated offering that flexes to your maturity, your data availability, and your deployment pattern. Two chapters: the opportunity, then the approach.

01 · The Opportunity

The unit got cheaper. The bill got bigger.

The macroeconomics of compound AI have crossed from experimentation into the P&L. Each new frontier model lands at roughly twice the per-token price of the one it replaces, and much of the spend buys output that creates no marginal value. Annual AI budgets are being exhausted in months, not years, and leaders are now naming the trade-off openly as tokens or humans. This is not an adoption problem. It is a management-system problem: consumption fragments across seats, APIs, agents, context, and infrastructure, and standard cloud FinOps cannot see it.

24×token usage growth, 2026→20301 $106B→$255Binference market, 2025→20304 85%miss AI-cost forecasts by >10%3
The corporate AI journeyThree phases in a single year

The question has flipped from “can we adopt it?” to “can we afford to run it at scale?” Most enterprises move through three phases inside a single year, and most are now in reassessment, recognizing they don’t need frontier-level intelligence for every task.

PHASE 01

The board mandate

Top-down pressure to “do something about AI” quickly, ahead of any cost model. Budgets are approved before anyone can price the run.

PHASE 02

Tokenmaxxing

Every workload routed to premium frontier models regardless of cognitive difficulty, roughly 95% of usage runs on models the task never required.

PHASE 03

Reassessment & routing

The monthly invoice becomes a budget event. Leaders start to route the right task to the right model, which is exactly where a management system is needed.

Three failure modesCost · Governance · Adoption

The same root cause shows up three ways. Each one independently stalls enterprise AI.

COST

Spend is hard to predict

Agentic workflows, long context, repeated prompts, and premium-model overuse create volatility that standard cloud FinOps does not see. Spend is unpredictable and growing exponentially.

GOVERNANCE

ROI is hard to trace

Companion agents look like unbounded cost sinks. Most enterprises cannot tie AI cost to a workflow, a business transaction, an outcome, or an accountable owner.

ADOPTION

Controls lag usage

Budgets, routing rules, access tiers, and stop conditions are added after expensive usage patterns are already embedded. Scaling forces a default to "block" because the cost envelope at full deployment is genuinely unknown.

Why the old models failThe agentic Jevons paradox

Token costs behave differently than the budget and audit models assume. Lowering per-call cost or improving reasoning quality often increases total token throughput, because the system chooses to think more, branch more, and call more tools. Cheaper tokens, bigger bill.

×m
Driver 01
Agentic multiplier
Every business task triggers many model invocations, planning, tool calls, verification, retries. Cost scales with the multiplier, not the prompt.
hidden
Driver 02
Reasoning tokens
Extended-thinking tokens are billed as output even when their content is summarized or hidden. Billed output can far exceed visible output.
N²
Driver 03
Token inflation
A poor tokenizer that inflates sequences raises prefill attention cost roughly quadratically and decode KV traffic roughly linearly, multiplied across every agent loop.
elastic
Driver 04
Induced demand
Once marginal cost falls, more subagents, retries, eval passes, and users appear. Total spend rises even as unit price drops.

The correct design objective is not lowest tokens. It is highest information density per token, subject to hardware and workflow-reliability constraints. The fix is not to suppress capability, it is to meter it with budgets, routers, and value-of-failure thresholds.

Where the cost livesThree deployment patterns

The service meets the client where AI is already being consumed, built, or operated. Each pattern has a distinct cost-driver profile and a distinct set of levers.

PATTERN A

AI as end-user tool

SaaS copilots, coding assistants, productivity platforms.

  • License waste, dormant and mis-tiered seats
  • Token & context growth per session
  • Agent and tool sprawl
  • Model / plan selection and vendor pricing shifts
PATTERN B

AI as managed API

Production applications, agents, and workflows on frontier or managed APIs.

  • Output & reasoning tokens
  • Long-context surcharge
  • Agent loops and tool overhead
  • Model routing and cache-miss economics
PATTERN C

AI as self-hosted infrastructure

Open-source or fine-tuned models on private or cloud GPU.

  • GPU compute and utilization
  • KV cache and throughput efficiency
  • Model-size trade-offs
  • Inference stack, DevOps, reliability
Net effect

The tokens are cheaper, but the bill got bigger, not smaller. Why is this happening, and what can we do to manage our AI costs?

02 · The Approach, diagnose, optimize, govern, end to end

From a runaway cost question to a governed value engine, in three moves.

Optimization is a science, not a checklist, the multi-x gains come from non-obvious technique across model behavior, inference economics, and infrastructure. Start with the arc below, then click into any act for the engineering and the client proof behind it. No finding without a fact base; no recommendation without the engineering that deploys it.

The arc · click to drill inEvidence-led → Design-to-build → Value-realized

Three moves, in order. Each carries its own engineering depth and its own client proof. Open an act to follow it start to finish; close it to return here.

EnablersCross-cutting capabilities that run beneath all three acts, they make the arc deliverable.
The governance metric

The KPI is Cost per successful business action, not cost per million tokens. The moment agentic failures and retries enter the loop, raw token price stops being decisive. Every recommendation lands as a named standard procedure paired with the engineering component that executes it, deployable, not slides.

Internal Only · Practitioner Field Guide

Token IQ field guide & sales FAQ.

A go-to-market reference for Accenture practitioners: the narrative, the offering, who buys it and why, how to sell it, and how to handle the hard questions. Grounded in the Token IQ point of view. This is internal enablement, not a client deliverable. Validate figures against the latest engagement data before quoting to a client.

The 60-second narrativeWhat to say in one breath
  • The belief. The unit cost of intelligence keeps falling, yet the enterprise bill keeps rising. AI cost is now a management-system problem, not an adoption problem, and management systems are what we build.
  • The shift. The question flipped from “can we adopt it?” to “can we afford to run it at scale?” Roughly 95% of enterprise AI usage runs on premium frontier models for tasks that do not need them; many enterprises exhaust an annual AI budget in about three months.
  • Our position. Token IQ treats inference cost as an engineering and FinOps discipline, governed against one board-grade metric: cost per successful business action, not cost per million tokens.
  • The arc. Three moves: Evidence-led (diagnose where tokens leak), Design-to-build (engineer the fix), Value-realized (govern and prove it). Talent Navigator handles the workforce side.
  • The prize. Cost-out captures only a fraction of the value. The larger dividend is capacity freed for growth, when leaders redeploy it deliberately.
“Cost discipline at AI scale.”
“Cost per successful business action, not per million tokens.”
“Instrument, optimize, govern.”
“Humans in the lead.”
Headline numbersValidate before quoting
24×
token usage growth 2026 to 2030, to ~120 quadrillion/mo (Goldman Sachs)
~3 mo
to exhaust an annual AI budget at current run-rates
85%
of organizations miss AI-cost forecasts by more than 10% (Mavvrik + Benchmarkit, 372 cos)
The offering at a glanceThe arc, the assets, the diagnostic

Token IQ runs as three acts with a workforce enabler, entered through a low-commitment diagnostic.

Act 1 · Evidence-led

  • Instrument the token layer and baseline a cost-per-business-transaction
  • Five-dimension leakage diagnostic, anchored by Forensic IQ
  • Output: a dollar-ranked, prioritized plan

Act 2 · Design-to-build

  • Engineer the fix: routing, caching, compression, constrained decoding
  • Assets: Control IQ, Frontier IQ, Model IQ, Token Quotient
  • Recommendations land as deployable artifacts, not slides

Act 3 · Value-realized

  • Govern and prove it; transfer the operating model
  • Watchtower IQ run-governance suite
  • KPI: cost per successful business action

Enabler · Talent Navigator

  • The people side: decompose roles into tasks
  • Map each task to a future workforce mode
  • Quantify capacity freed and the skills to build
The diagnosticFive dimensions of leakage
  • User patterns. Persona-based license segmentation. In one estate, 77,000+ seats were segmented; the top 100 users drove 69% of consumption.
  • Workload design. Semantic disaggregation and context hygiene: which steps need generative AI at all, carrying only the context each step needs.
  • Compute strategy. The right task on the right model on the right hardware. Auto-routing cut inference cost about 90% at a global bank.
  • Governance & policy. Policy-as-code, shifted left. Retrofitting governance adds 2 to 4 months of rework; the average abandoned AI initiative sinks about $7.2M.
  • Observability plane. Full-stack attribution: every token tied to a user, workflow, team and transaction.
Buyer personasWho buys Token IQ

The five C-suite leadership moves each name their owners. Open a persona for the full profile: what they own, what keeps them up at night, the hook, the proof, and the move they own.

Also in the room: the board (capital allocation against the 24x macro), the FinOps lead (attribution and the observability plane), and engineering leadership (verification at scale).

How we sellEvidence first, size the prize before the build
  • Lead with the diagnostic. The Opportunity Diagnostic is a four-week, low-commitment entry: Day 1 stands up the data, then four phased weeks scored by Forensic IQ produce a dollar-ranked plan before any large build.
  • The honest diagnosis is the cheapest lever. Roughly 95% of usage runs on frontier for work that doesn't need it; the baseline tells the client which move pays first.
  • Recommendations are deployable. Every recommendation lands as a named standard procedure paired with the engineering that executes it.
  • Value and outcome-based deals are on the table. Yes, we offer them. We help the client capture the value the assessment identifies, then manage that cost in run, so commercial terms can tie to the savings and outcomes we deliver, not effort alone.
Old metric
Cost per million tokens

Rewards volume, hides the decisive driver, and can't be tied to a business outcome or an accountable owner.

Board metric
Cost per successful business action

CSBA = (token cost/call × calls/action × retry multiplier) / first-pass success rate + verification cost/action. Every lever maps to a term a finance team can model.

Three ways to startEach produces evidence, not a slide

Opportunity Diagnostic (4 wks)

  • Full five-dimension baseline scored by Forensic IQ
  • Dollar-ranked intervention backlog
  • The recommended onward path

Forensic IQ score (days)

  • Grade one estate across 135 inputs
  • Composite grade + viability band
  • The “fix these five first” shortlist

Targeted lever pilot (2 to 4 wks)

  • Caching or routing on one high-volume workflow
  • Before/after on a single use case
  • Proof the lever logic generalizes
Diagnose4-week entry
Builddesign-to-build, deployable
RunWatchtower IQ managed
Common FAQsThe questions clients actually ask

Grouped by what gets asked first. Click a question to expand the grounded answer.

The approach & economics
Cloud FinOps was built for VMs, storage and egress. It sees the invoice total, not which workflow, agent or prompt drove it. You cannot govern what you cannot attribute, so the first move is to instrument the token layer and tie every token to a user, workflow and transaction.
No. The objective is not the fewest tokens, it is the highest information density per token, metered with budgets, routers and value-of-failure thresholds. Suppressing reasoning is fragile; governing syntax, state and compute is durable. Budget for value, not volume.
A false binary. The model is humans in the lead: people set direction, guardrails and budgets; AI executes within them. Human judgment stays the premium asset, and freed capacity becomes growth only when it is deliberately redeployed.
The diagnostic starts from the telemetry you already have. Instrument before you optimize: the honest baseline is the cheapest lever in the paper, and it tells you which move pays first. Roughly 95% of usage runs on frontier for work that doesn't need it.
A gateway is a routing primitive, not an operating capability. Someone still owns the policy: budgets-as-code, caching strategy, routing rules and the diagnosis behind them. We bring the operating capability and size the prize before any build spend.
Routing, caching and compression change cost, not answers. Because first-pass success sits in the denominator of cost per successful business action, it compounds with every other lever. The 5% frontier tier is a floor set by consequence, not a target to minimize.
Optimization is a science across the model, serving and infrastructure layers. The figures are observed engagement ranges and vary by estate; we show the simple wins first and let the evidence set the expectation.
We are platform-agnostic and standards-first. Spend maps to emerging open standards: the FinOps FOCUS extension and the Tokenomics Foundation benchmarks launched under the Linux Foundation in June 2026, so the estate stays comparable as models and prices churn.
Cost-out captures only a fraction of the value. The larger prize is the capacity freed for growth; in most enterprises it is quietly reabsorbed into existing work unless a leader redeploys it on purpose.
How we run & offer itThe commercial and delivery questions
No. Token IQ is an accelerator our teams use within an engagement, not a formal SaaS product you buy a subscription to. The proprietary assets (Forensic IQ, Control IQ, Frontier IQ, Model IQ, Watchtower IQ) are accelerators applied during delivery, and they span the whole Token IQ offering rather than being sold as standalone software.
It deploys on cloud, public or in the client's private tenant, and it can also run on-prem for sovereign or air-gapped workloads. Owned, air-gap-capable inference is one of the controls, so the same governance applies wherever it runs.
An engagement, not a license. It starts with a four-week Opportunity Diagnostic scored by Forensic IQ, then design-to-build, then an optional managed run (Watchtower IQ). The work is anchored to outcomes against cost per successful business action.
Yes. Alongside standard time-and-materials, we structure deals around the value the assessment identifies and the cost we manage in run. We help the client capture the savings and outcomes Forensic IQ quantifies, and we can tie commercial terms to cost per successful business action, so the engagement is paid against value delivered rather than effort alone.
Across a four-phase lifecycle: Design (structural tokenization and vocabulary economics), Build (agentic systems engineering and pipeline optimization), Test (validation, disaggregation and caching), and Deploy & Run (AI FinOps and inference-fleet economics).
They are accelerators that span the whole offering: Forensic IQ diagnoses, Control IQ governs the control plane, Frontier IQ and Model IQ inform routing and model behavior, and Watchtower IQ runs the governed estate. Talent Navigator covers the workforce side. None is sold separately as a SaaS product.
The deeper scienceFor the technical buyer
Every token is a deterministic floating-point cost, consumes finite High Bandwidth Memory for the Key-Value cache, and occupies a slot in the self-attention matrix. Prefill (input) is compute-bound and scales with the square of context length; decode (output) is bandwidth-bound and scales linearly. The architectural lesson: keep prompts dense and contexts short.
The agentic Jevons paradox. As the marginal cost of a token falls, the system chooses to think more, branch more and call more tools, so total spend rises even as unit price drops. Model each token as a marginal allocation decision priced against compute, latency and the risk of an irreversible failure.
No. Roughly 57% of teams using that method fail once projects reach real production complexity. AI accelerates execution but cannot balance architectural trade-offs or enforce governance. The constraint simply moves downstream, from creation to verification.
Time saved drafting is now consumed reviewing. 96% of developers don't fully trust AI code; review now takes about 11.4 hours/week versus 9.8 writing. AWS CTO Werner Vogels calls the unreviewed, vulnerable backlog verification debt. The fix is to make verification a first-class, instrumented activity, not to suppress capability.
CSBA = (token cost/call × calls per action × retry multiplier) / first-pass success rate + human verification cost per action. Every lever in the offering maps to a term a finance team can model. Because success rate sits in the denominator, raising first-pass success compounds with every other saving.
Why AccentureFour ways we are different

Not a general claim. Four concrete differentiators, each carrying its own evidence. Most rivals can do one or two; the offering is built to do all four in a single motion.

01 · Credentials

Proven in production

  • Live engagements across telecom, banking, chemicals, pharma, healthcare, and professional services
  • Real estates and measured outcomes, not reference architectures
02 · Data & points of view

An authoritative Tokenomics point of view

  • Cost per successful business action: a unit-economics model a finance team can audit, term by term
  • Frontier IQ benchmarks 656 models across 100+ providers, refreshed continuously
  • A 14-lever architectural catalog with observed effect ranges, not a generic best-practice checklist
03 · Quantitative outcomes

Numbers, not adjectives

  • $12M to $3.8M a year at a telecom, input tokens down 70%; 842M tokens/day removed at a bank
  • $0.47 to $0.14 per matched transaction in a regulated estate
  • P1 incidents to zero; 99.3% PASS on evaluated AI output at a pharma leader
04 · Assets & accelerators

Fast-track value, do not build from scratch

  • Six IQ accelerators: Forensic IQ, Control IQ, Frontier IQ, Model IQ, Token Quotient, Watchtower IQ
  • Talent Navigator for the workforce side
  • Applied inside the engagement; recommendations land as deployable engineering, not slides
Versus the fieldWhere each alternative stops short

Clients have four alternatives. Each solves a slice and leaves the hard part, the part that actually moves the bill, undone. Pick a contender to see where it stops and where we win.

Cheat sheetThe five leadership moves and their owners
1
Instrument before you optimize
Stand up the observability plane and token-level attribution first.
Owner: CTO + CDAIO + FinOps lead
2
Shift governance left
Design budgets-as-code, routers and circuit breakers in, not retrofitted.
Owner: CTO + CDAIO + CISO
3
Re-anchor the metric
Adopt cost per successful business action; treat freed capacity as a growth input.
Owner: CFO + CDAIO
4
Put humans in the lead
People set direction and guardrails; AI executes within explicit budgets. Reskill to token-aware architecture.
Owner: CHRO + CDAIO + engineering leadership
5
Industrialize the run
Operate as a measured, budgeted token factory aligned to open standards.
Owner: COO + CDAIO + CTO
Proof pointsAnonymized, self-reported, validate before quoting

Financial services

  • 842M tokens/day removed via inter-agent context redesign in a KYC-AML estate
  • About $20K/mo on one use case, quality held to existing decisions

Telecommunications

  • $12M to $3.8M annual spend; input tokens down 70%
  • Caching, agent triage and dynamic chunking, business flow unchanged

Mining & insurance

  • ~40% off via two cache layers across two industries
  • About $5K/mo, zero quality impact
Highest-leverage leversEffect ranges (vary by estate)
-80 to -90%
KV prefix caching
-70%
model routing & cascading
-30 to -60%
structured generation & dense serialization
-50%
batch API offloading
Asset glossaryWhat each proprietary asset does
  • Forensic IQ. Token Cost Intelligence: scores the five dimensions across 135 inputs into a composite grade and a dollar-ranked plan.
  • Control IQ. The AI control plane: routing, budgets-as-code, circuit breakers and policy-as-code.
  • Frontier IQ. Continuous benchmarking across 656 models and 100+ providers.
  • Model IQ. Model behavior and tokenizer-level optimization.
  • Watchtower IQ. The run-governance suite across InfraOps, AgentOps, and ModelOps.
  • Token Quotient. The companion reference for the broader token economy.
  • Talent Navigator. Task-level workforce model: capacity freed and the skills to build.
UsageBefore you quote it to a client
  • In any client-facing derivative, use anonymized archetypes for clients and category names for competitors.
  • Engagement figures are single estates, self-reported, and vary by deployment. Validate before quoting to a specific client.
  • This guide is internal enablement. The client-facing chapters are The Opportunity and The Approach. Field Guide, Client References, Key Contacts & Operating Model, and Sources & Citations stay internal only.
The Approach  /  Act 1 · Evidence-led
01
Act one · See it · Evidence-led See where the money goes. You cannot govern what you cannot attribute.

Spend hides in SaaS invoices, license tiers, agent loops, and context payloads, invisible until someone audits the telemetry. Act one establishes the fact base: instrument the token layer, baseline a Cost per Business Transaction, and diagnose where the waste actually lives. No findings without evidence.

The diagnostic frameworkFive dimensions of leakage

Each dimension isolates a source of leakage, then translates it into controls, architecture changes, and operating routines. Not one lever, a configurable portfolio, selected by context. Open each to see the diagnostic question, the named levers, and the proven result.

Proprietary assetForensic IQ, Token Cost Intelligence

The diagnostic asset that scores the five dimensions on evidence. Forensic IQ grades an estate across 135 scored inputs and outputs a board-grade composite with a prioritized, dollar-ranked improvement plan, the rule-brick that turns telemetry into a defensible fact base. Open it as a live, page-within-a-page experience.

The diagnostic approachFour chapters, instrument, assess, attribute, prioritize

Not a one-phase assessment. The diagnostic sub-offering runs as four chapters, instrument, assess, attribute, prioritize, each a deliverable in its own right. Open any chapter for the methods, the engineering, and the output.

How you engageThe entry motion

You don’t buy a transformation up front. Act one is the low-friction way in: observe first, baseline fast, and let the evidence select the path.

Day 1data intake begins
4 wksto a baseline fact base
Proven in practiceFinancial Services, the diagnosis that found 842M wasted tokens a day

What evidence-led diagnosis surfaces: a cost driver no invoice line could name. A global bank ran KYC-AML through an agentic workflow handling 5,000 cases a day, and the bill was dominated by context the downstream agents never needed.

Diagnosed
A constellation of specialist agents

An orchestrator plus ~10 named agents, each receiving the full upstream context. Redundant token-passing, not reasoning, dominated the bill. Standard FinOps saw one rising invoice line; attribution at the token layer found the real driver.

5,000 cases / daylarge per-case context
Quantified
842M tokens/day, isolated to one fix

The diagnosis pinpointed inter-agent context handoffs as the lever, 840M input + 2M output tokens daily, about $20K/month on a single use case, before a line of the fix was built. Act two engineers it.

Generalizes to claims, underwritingclinical decision support
The Approach  /  Act 2 · Design-to-build
02
Act two · Fix it · Design-to-build Engineer the treatment. Recommendations land as deployable artifacts, not slides.

Findings become routing rules, caching patterns, prompt and context changes, policy-as-code, and dashboards. This is not generic technique applied blindly, each estate gets a client-specific treatment plan diagnosed from its own telemetry. That is the difference versus everyone selling a checklist.

The build approachFour chapters: design, harden, deploy

Not a single build SKU. The optimization sub-offering runs as four chapters, design the treatment, make the model deterministic, red-team it, then deploy in waves. Open any chapter for the methods, the engineering, and the output.

Proprietary assetsBuild & runtime accelerators

Reusable Accenture accelerators, adaptable to the client’s platform. The client gets a capability, tooling, monitoring, and governable patterns, not just a finding.

How you engageThe build paths

Once the diagnostic qualifies the prize, the client picks the build path that matches appetite, a fast sprint to prove savings, or a full implementation program.

4-8 wksto a deployed MVP
Path 1
Optimization sprint
Implement priority levers, tune controls, and prove early savings on the highest-cost workloads first.
Path 2
Implementation program
Deploy gateway rules, dashboards, routing, caching, and the operating routines that hold the gains.
Proven in practiceTelecommunications, $12M → $3.8M, business flow unchanged

The build levers, sequenced on a real estate. A large telecom operator’s annual token spend had climbed to $12M on an architecture never designed for agentic load.

Before
Agentic system not built for agentic load

Query patterns repeated, agent flows fanned out without triage, and context windows grew unchecked. On unit economics, this estate would have landed in breaks-case territory.

Large telecom operatormanaged-API tier
After
Query caching · agent triage · dynamic compression

Application-layer caching for semantically similar requests, triage routing of simple queries to lighter agents, and dynamic chunking/compression of context payloads. Input-token volume fell 70%; annual spend dropped to $3.8M.

Business behavior unchangedgeneralizes to Pattern B
Proven in practiceMining & Insurance, one insight, two caching layers, ~40% off

Same root insight, repeated semantics and static prompt prefixes create avoidable waste, solved two workloads in two industries with two cache layers, chosen by the shape of the estate.

Workload A · Mining
Application-layer caching

Employees ask semantically similar questions across shifts and regions. Semantically similar prompts served from a Redis cache, bypassing the LLM entirely.

Operational query workflows
Workload B · Insurance
Model-layer prefix caching

A long static pretext prompt was prepended to each unique transcript. Cached once at the model layer; only the unique transcript processed per call. Combined: ~40% reduction, ~$5K/mo, zero quality impact.

Generalizes to any static-prefix workload
The Approach  /  Act 3 · Value-realized
03
Act three · Keep it · Value-realized Govern it, prove it, run it. Cost-out and SLOs are governed, not promised.

Benchmark before and after, attribute savings to the lever that earned them, and transfer the operating model to the client. The KPI is Cost per successful business action, not cost per million tokens, because once agentic failures and retries enter the loop, raw token price stops being decisive. Run the live math and the case versus the field back on the approach page.

The run approachFour chapters: run, govern, prove, transfer

Not a hand-off at go-live. The managed sub-offering runs as four chapters, stand up the budgeted serving fleet, govern it continuously, prove the savings in a board-grade metric, then transfer or manage. Open any chapter for the methods and the output.

Proprietary assetWatchtower IQ, the run-governance suite

Watchtower IQ is the asset that makes the run measurable and governed, a continuously-watched control surface that turns AI serving from a black-box bill into budgeted, attributable spend, reported in cost per successful business action. Its operating surface is a single governed pane across infrastructure, agents, and models.

How you engageThe run paths

The savings only hold if someone runs the controls. The client either hands operations to a managed tier or takes the keys, the assets and playbooks transfer either way.

3-7 mosto managed operations
Path 3
Managed Token IQ
Run continuous visibility, governance, tuning, recommendations, and reporting as a managed annuity.
Path 4
Client enablement
Transfer assets, playbooks, governance, and CI routines so the client’s own team can run it.
The Approach  /  Act 1 · Evidence-led  /  Forensic IQ
IQ
Proprietary asset · Evidence-led Forensic IQ, Token Cost Intelligence.

The diagnostic asset that scores an AI estate on evidence, 135 scored inputs across the five dimensions, rolled up to a board-grade composite (A-F) with a dollar-ranked improvement plan. The full interactive experience runs embedded below.

Open full-screen ↗ Forensic IQ · embedded

The complete interactive Forensic IQ experience, embedded below, the rule-brick scorecard, the five dimension drill-downs, the Scenario Lab, and the dollar-ranked planner. Use the bar above to return to the approach at any time, or open it full-screen in a new tab.

The Approach  /  Enabler · Talent Navigator
Enabler · part of the Token IQ offering set Talent Navigator. The task, not the job.

Token IQ manages how AI runs end to end: instrument it, optimize it, and govern it against cost per successful business action. Talent Navigator is the people side: it breaks every role into tasks, maps each task to people or AI, and shows the capacity freed, the skills to build for the future, and where to reinvest. The two run together across all three acts.

Token IQHow AI runsInstrument, optimize, and govern it end to end.
Talent NavigatorThe peopleCapacity freed and the skills to build next.
One offering setOne operating modelThe machine side and the people side, planned together.
The premiseThe task is the unit of change

Most workforce decisions are still made at the job level. The change is happening at the task level. Talent Navigator starts at the task, then rolls back up to roles and functions.

The old way
Job-level, top-down

Job-level estimates, generic skills taxonomies, top-down programs. Too slow and too blunt to fund. The honest answer on ROI stays “we don’t really know.”

Months to a viewtoo blunt to act on
Task-level
Decompose, map, quantify, recombine

Break every role into tasks, map each to a workforce mode, then quantify the capacity freed and the skills to keep, build, or retire. A defensible view in days, not months.

Function by functiontied to real roles and cost
$2.3B→$14.2Bworkforce transformation market by 2033
Daysto a board-ready view, not months
The taskis the unit of design
The systemWhat it produces

From one task-level model, Talent Navigator produces three views. Open each.

The methodDecompose, map, quantify, recombine

A repeatable, data-driven sequence, rooted in the Art of Reinvention. Open any step for the inputs, the engineering, and the output.

By stakeholderWhere each leader starts

The CEO, CHRO, and CFO use the same task model to answer a different question.

CEO
One enterprise view

A sequenced, board-ready picture of AI’s workforce impact, so every function works from the same numbers.

CHRO
A funded workforce plan

Task-level visibility into which work is at risk and which skills are becoming critical, to move from scattered programs to one plan.

CFO
Proof of return

An investment case that traces freed capacity to actual cost, built bottoms-up rather than from macro estimates.

Proven in practiceWhere it has been used

Two engagements. Open either for the full case.

Sources & Citations

Sources & citations.

Market figures cited in this POV, with links to the primary sources. Superscript markers throughout the deck point here.

Cited in materialNumbered key figures
  1. Goldman Sachs

    Global token usage is forecast to multiply 24× between 2026 and 2030, reaching roughly 120 quadrillion tokens per month, as AI agents drive a step-change in inference demand.

    Goldman Sachs Research, “AI agents forecast to boost tech cash flow as usage soars,” May 2026. goldmansachs.com/insights/articles/ai-agents-forecast-to-boost-tech-cash-flow-as-usage-soars

  2. Accenture Token IQ analysis

    Roughly 95% of enterprise AI usage runs on premium frontier models for tasks that do not require them.

    Accenture Token IQ point of view and engagement analysis, 2026. Accenture’s own observation across client estates, not a third-party survey.

  3. Mavvrik with Benchmarkit

    Across a survey of 372 companies, 85% miss their AI-cost forecasts by more than 10%.

    Mavvrik with Benchmarkit, AI cost-forecasting survey of 372 companies. Figure is self-reported by respondents; validate before quoting to a specific client.

  4. MarketsandMarkets

    The AI inference market is projected to expand from about $106B in 2025 to $255B by 2030, a 19.2% CAGR.

    MarketsandMarkets, “AI Inference Market, Global Forecast to 2030,” Feb 2025. marketsandmarkets.com/Market-Reports/ai-inference-market-189921964.html

Industry & market context

  • Linux Foundation

    Announcement of the intent to launch the Tokenomics Foundation to establish open standards for AI cost management, with Accenture among the named supporting organizations.

    The Linux Foundation, 3 June 2026. linuxfoundation.org/press/linux-foundation-announces-the-intent-to-launch-the-tokenomics-foundation…

  • Sonar (SonarSource)

    State of Code developer survey: 42% of committed code is AI-generated (rising toward 65% by 2027), 96% do not fully trust it, and only 48% always verify before it ships.

    Sonar, “State of Code Developer Survey,” Jan 2026. sonarsource.com/state-of-code

  • Digital Applied

    Workflow reversal: developers now spend about 11.4 hours/week reviewing AI-generated code versus 9.8 hours/week writing new code.

    Digital Applied, “AI Coding Tool Adoption 2026: Developer Survey,” Q1 2026. digitalapplied.com/blog/ai-coding-tool-adoption-2026-developer-survey

  • Accenture Token IQ engagement data

    Observed cost, throughput, and quality results across client estates and Accenture’s own AI-as-a-Service platform, 2026. Figures are single estates, self-reported, and vary by deployment.

    Accenture Token IQ engagement data, 2026. Internal; validate against the latest figures before quoting to a client.

Methods & techniques

  • LLMLingua-2

    Task-agnostic prompt compression via data distillation, used for output trimming and token-flow reduction on high-cost prompts.

    Pan et al., “LLMLingua-2,” Findings of the ACL 2024. arxiv.org/abs/2403.12968

  • RadixAttention (SGLang)

    Automatic reuse of shared prompt prefixes via a radix tree, the basis for the prefix- and semantic-caching layer.

    Zheng et al., “SGLang: Efficient Execution of Structured Language Model Programs,” NeurIPS 2024. arxiv.org/abs/2312.07104

  • XGrammar

    Finite-state constrained decoding that masks invalid tokens during generation, so output conforms to the schema on the first pass and regeneration loops are eliminated.

    Dong et al., “XGrammar: Flexible and Efficient Structured Generation Engine for LLMs,” MLSys 2025. arxiv.org/abs/2411.15100

  • Splitwise

    Phase-splitting of compute-bound prefill from memory-bound decode, the basis for the disaggregated-serving lever.

    Patel et al., “Splitwise: Efficient Generative LLM Inference Using Phase Splitting,” ISCA 2024. arxiv.org/abs/2311.18677

  • Speculative decoding

    Lossless speculative decoding across heterogeneous vocabularies, accelerating generation without changing the answer.

    Timor et al., “Accelerating LLM Inference with Lossless Speculative Decoding,” ICML 2025. arxiv.org/abs/2502.05202

  • KVQuant

    KV-cache quantization that cuts cache memory and supports long-context inference, one of the inference-tuning quantization levers.

    Hooper et al., “KVQuant: Towards 10M Context Length LLM Inference with KV Cache Quantization,” NeurIPS 2024. arxiv.org/abs/2401.18079

  • TurboQuant

    Near-optimal vector quantization for KV-cache and weights, supporting the inference-tuning quantization levers.

    “TurboQuant.” arxiv.org/abs/2504.19874

Client References

Proof, across the portfolio.

Where the Token IQ asset family is taking real cost out of production AI, across global telecom, banking, pharma and healthcare leaders, grouped by asset.

Anonymize client names · on

External client engagements below, grouped by the lead asset. The toggle anonymizes client names only.

Model IQ· 2
T-Mobile
Global Telecom Operator
Managed-API workflow reinvention
Model IQ

Reinvented the managed-API workflow across routing, caching, agent rationalization, and context compression. Input-token volume fell 70%, cutting annual token spend from $12M to $3.8M, with no decrease in business-value outcomes.

$12M → $3.8M/yr · −70% input tokens
Morgan Stanley
Global Investment Bank
AI tokenomics as a governed utility
Model IQ

Transforming AI tokenomics from opaque API consumption into a governed enterprise utility, every token attributable, controlled, costed, and tied to business ownership. Gains the operating layer to manage model access, budgets, chargeback, optimization, and executive accountability across AI usage.

Governed token utility
Control IQ· 2
Tronox
Global Chemicals Manufacturer
Semantic & model-layer caching
Control IQ

Repeat work was being processed like net-new work. Applying semantic and model-layer caching, alongside a parallel insurance workload, cut token consumption ~40% with no quality degradation, for ~$5K/month in combined savings.

~40% tokens · ~$5K/mo
UOB
ASEAN Banking Group
KYC-AML agentic workflow · 5,000 cases/day
Control IQ

Redesigned inter-agent memory passing and improved cost attribution across the flow. The workflow stayed intact while saving 840M input and 2M output tokens per day, worth roughly $20K/month.

~$20K/mo · 5,000 cases/day
Watchtower IQ· 4
BCBS Michigan
Regional Health Plan
AI Environment on AWS · Membership Benefits
Watchtower IQ

Strands agents on Amazon Bedrock AgentCore now emit traces, latency, duration, token usage, and error rates into CloudWatch in a standard OTEL format, making every Membership Benefits interaction attributable down to the agent step. It is the business's first unified line of sight into agentic AI behavior across the environment.

Per-agent attribution · OTEL → CloudWatch
Pfizer
Global Pharmaceutical Leader
CMO division · 5 production AI apps
Watchtower IQ

A unified observability backbone across five production apps (Scout, CoCo, OCO, PromoMix360, Brand Onboarding) capturing span-level cost, latency, evaluation scores, and per-use-case attribution. Scout's LLM-as-a-Judge holds a 99.3% PASS rate with FAIL cases routed to a human compliance queue, and CoCo validates 8,900+ scored outputs a day.

99.3% PASS · 8,900+ scored/day
Mass General Brigham
Academic Health System
Enterprise DataOps · Azure / Databricks / Snowflake
Watchtower IQ

Brought a fragmented Azure, Databricks, Snowflake, ADF, and SQL/ML estate under a scaled Enterprise DataOps framework with proactive pipeline monitoring, clear SLAs, and CI/CD automation. P1 incidents fell to zero, P2 dropped 75%, mean time to resolve fell 81%, ticket reopen rate dropped to 0.5%, and CI/CD cut deployment time roughly 80% and errors about 95%.

P1 → 0 · MTTR −81% · P2 −75%
T-Mobile
Global Telecom Operator
Agentic operations · 18 agents across 7 platforms
Watchtower IQ

18 AI agents across 7 platforms automate ticket intake, RBAC provisioning, lifecycle management, compliance auditing, and self-healing, with multi-gate human approval. The team cut 300+ engineering hours a month, automated 380 RBAC requests a month, reduced ticket mis-routing by more than 90%, audited 29 workspaces daily at 99% SOX compliance, and dropped Azure VM repairs from hours to minutes.

300+ hrs/mo saved · mis-routing −90%
Draft
Internal Only · Operating Model

Key contacts & operating model.

How Token IQ is run, not who reports to whom. Seven workstreams carry the offering from an AI & Data core that builds the assets and owns the Tokenomics point of view, out through commercial launch, delivery at scale, workforce and finance reinvention, and internal adoption, all under GMC sponsorship. This is the broader team in formation, and the single place to find who owns what.

Executive Sponsor
GMC Sponsor
Lan Guan
Chief AI & Data Officer
lan.guan@accenture.com
Select a workstream to expand it. Each shows its mandate and named players, then opens to the full detail.
No workstream or person matches . Clear search
AI & Data core · authoritative on Tokenomics
Workstream lead
Leadership / cross-entity
Role to name
Key contributorsWider team in the Token IQ materials