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FOR CEOS, COOS & CFOS · 150–5,000 EMPLOYEES · FINANCIAL SERVICES · HEALTHCARE · MANUFACTURING · LOGISTICS · PROFESSIONAL SERVICES · RETAIL

Your operations are carrying costs AI should already be eliminating.

Your Board wants AI efficiency gains. Your CFO wants proof it pays. The AI Cost Reduction Accelerator is a structured, two-stage system that identifies exactly where AI cuts your operational costs, calculates the ROI against industry benchmarks, and builds the CFO-grade business case.

No sign-up. No email. Instant results on screen.
⚡ The only AI diagnostic that shows you the estimated cost of every token — before you commit a single dollar to implementation.

Used by 400+ mid-market operators across US, UK, AU

Finance Healthcare Manufacturing Logistics Retail

AI pilots fail because businesses guess instead of modeling.

Most mid-market businesses spend capital on the wrong process optimizations, guessing at which tasks AI can fix, and watching pilots fail because no one modeled the business case first. The Cost Reduction Accelerator replaces speculation with proof: diagnosis before investment, evidence before commitment.

Why traditional approaches fail:

  • ❌ Guessing which operational processes will yield the highest ROI.
  • ❌ Ignoring runtime token consumption costs at production scale.
  • ❌ Failing to present CFO-compliant financial business cases.

If any of these sounds like the last 12 months, you're in the right place.

The companies who stay stuck here don't lack ambition. They lack a system. Here's the system.

PE-backed / Growth pressure

Your PE firm or board has asked you to demonstrate operational leverage before the next growth round. You need a defensible, CFO-ready ROI figure.

The tool graveyard

You have Microsoft Copilot, maybe Zapier, but no line item of savings. You automated a task without knowing what it was costing you first.

New CXO / Leadership change

A new CXO joined in the last 18 months, asking why processes still run on spreadsheets. Urgency without a framework creates expensive mistakes.

Too small for a CAIO

You're 200–800 people. You can't justify a full-time Chief AI Officer or an $800K Big-4 consulting engagement. You need implementers who transfer capability.

We don't ask you to build before you know it pays.

That's Stage 2. Stage 1 is the proof.

STAGE 1 — ASSESS & PROVE

The AI4PROFIT Accelerator

CFO-Grade ROI Analysis · 2–4 Weeks · Fixed Investment

Before a single line of automation is built, we map your operational processes, identify where AI creates measurable savings, benchmark those savings, and produce a formal CFO report.

  • Line-by-line OpEx savings breakdown
  • Full CapEx estimate & complexity rating
  • AI running cost projections & token economics
  • Print-ready CFO Report for budget approval
STAGE 2 — BUILD & TRANSFER

Offshore Build + L&D Transfer

Actual Automation Implementation · Your Team Owns It

If — and only if — Stage 1 proves the ROI case, we build the automations offshore at a fraction of local development cost, with a Learning & Development programme running alongside.

The Exit Strategy

The goal isn't dependency on us. The goal is capability transfer. You end Stage 2 with working automations, a team that understands them, and no reason to keep paying a consultant.

What goes into a CFO-grade AI business case

And why most companies can't build one alone.

01

Automation Intelligence Library

Select from pre-built AI use cases with documented financial outcomes for your specific sector.

Explore Module
02

Benchmark ROI Indicator

Compare your projected OpEx savings against documented industry benchmark data. Finance trusts this.

Explore Module
03

CapEx Estimation Assistant

Calculate one-time development costs including offshore rates that reduce build costs by 60–75%.

Explore Module
04

Token Cost Intelligence

Calculate real monthly API token costs and learn optimizations that cut those costs 40–60%.

Explore Module
05

CFO Report Generator

Everything feeds into a formal, print-ready business case structured exactly the way finance reads.

Explore Module
What AI diagnostics surface in practice.

Three modelled scenarios — each grounded in published industry research.

MODELLED SCENARIO — McKinsey HBR

MARKETING: Intent-Based Lead Gen

The challenge: Sales teams spending the majority of their week on non-selling activity — research, admin, follow-up — with no signal of which leads are most likely to convert.

What the research shows:

McKinsey partners writing in Harvard Business Review (2016) found that AI-pioneering sales teams reported "an increase in leads and appointments of more than 50 percent, cost reductions of 40 to 60 percent, and call-time reductions of 60 to 70 percent."

Source: Baumgartner, Hatami & Valdivieso, June 2016.
Evidence: Anecdotal self-report. Quoted verbatim.
What a CRA diagnostic examines:
  • Workflow steps from lead capture to first contact.
  • Current manual prioritization and lead scoring.
  • Outreach preparation time per sales rep.
CRA Output:

Ranked opportunities, difficulty ratings, projected OpEx saving range, and scale-up token costs.

MODELLED SCENARIO — McKinsey/Gartner

FINANCE: Reporting & Compliance

The challenge: Finance teams spending the majority of their reporting cycle on manual data consolidation, reconciliation, and compliance checks — leaving little time for analysis.

What the research shows:

McKinsey (2024), drawing on a survey of 102 CFOs, found that finance teams using AI spend "20 to 30 percent less time crunching data."

Source: McKinsey, 2024. Survey of 102 CFOs.
What a CRA diagnostic examines:
  • Data extraction, consolidation, and submit workflow.
  • Reconciliation and exception-handling process.
MODELLED SCENARIO — McKinsey 2022

PROCUREMENT: Demand Forecasting

The challenge: Procurement teams relying on spreadsheet-based or static rolling-average forecasts — causing stockouts, carrying overheads, and reactive ordering.

What the research shows:

McKinsey (2022) found that AI operations forecasting reduces errors by 20% to 50%, reducing product unavailability by up to 65%. Carrying costs drop by 5% to 10%.

Source: McKinsey, 2022. "AI-driven operations forecasting".
What a CRA diagnostic examines:
  • Current rolling average methods and forecasting workflows.
  • Inventory carrying costs and PO processing cycles.

We'll tell you straight: this isn't for everyone.

This is NOT for you if:

  • You're already engaged with Accenture, McKinsey, or BCG on an AI transformation programme. We don't compete there.
  • You have a Chief AI Officer in seat and Databricks or Vertex AI in production. You're past the stage we address.
  • You're a startup, pure-play tech company, or in financial distress. Our ROI model requires operational stability.
  • You want AI implemented without your team learning to run it. Dependency is not our model. Knowledge transfer is mandatory.

This is EXACTLY for you if:

You're 150–5,000 people, in the US, UK, or Australia, in a sector with significant manual process overhead, under board or investor pressure to show operational leverage...

...and you need a business case, not a pilot, not a strategy deck, not another SaaS tool that no one measures.

If that's you, Stage 1 is where you start.

Questions you probably have

Is this just another AI savings calculator? ▼
No. The Cost Reduction Accelerator maps actual process steps in a structured workflow. The savings estimate is calculated dynamically based on the specific steps, roles, tools, and cycle times you input — not from a generic industry multiplier.
What does the free version actually give me? ▼
You get a ranked list of your top 3–5 AI automation opportunities, an OpEx savings estimate calculated from your process context, implementation difficulty ratings for each opportunity, and an on-screen, scorecard. No signup or email is required.
How is the CFO report different from the free scorecard? ▼
The Tier 1 scorecard highlights opportunities. The Tier 2 report models the full financial business case: CapEx breakdown, three-scenario ROI, payback period tracking against the CFO 14-month benchmark, industry peer comparisons, and a detailed 90-day implementation plan designed to be walked directly into a boardroom.
Do I need technical knowledge? ▼
No. The diagnostic is designed for operations, finance, and business leaders, not software engineers. If you understand the basic steps of how the work is done in your department, you can complete the diagnostic.

The cost of not knowing your number is already accumulating.

Every quarter your manual processes run unmeasured is a quarter where the savings you could have realised stayed in a workflow instead of your margin.

There is no Stage 2 commitment in Stage 1. You get the report. You decide what to do with it.

No credit card required. Results on screen in minutes.
No credit card US/UK/AU Timezones Results in 2-4 weeks
AI Accelerator

We are not a SaaS platform. We are not a Big-4 consultancy. We are a fractional AI consulting firm that implements and then transfers. Our engagement model has a built-in exit.

US · UK · AU

AI Cost Reduction Accelerator

Free Diagnostic Tool

Map your current workflow steps below. Surfacing AI automation opportunities, difficulty ratings, and scaled token runtime costs instantly.

1. Business Context

₹

2. Workflow Steps

If blank, the CRA runs an instant calculation based on your process parameters. Free keys at aistudio.google.com

Analyzing Process Inefficiencies...

⌛ Step 1: Analysing current workflow...
⌛ Step 2: Designing AI-optimised workflow...
⌛ Step 3: Calculating financial impact...

This takes 20–40 seconds. Please wait.

CRA Diagnostic Scorecard

Invoice Processing

Headline Projected Savings
₹0 - ₹0 / yr
Diagnostic Summary: AI integration can automate approximately 0% of human touch time in this workflow, freeing up 0 annual staff hours.
Top AI Opportunities:
AI Token Consumption: ₹0/mo
VERIFIED OPPORTUNITY

CFO Report Package Upgrade

"Want the full CFO-ready financial model with CapEx, payback period, and implementation plan? Upgrade to the Report Package."

AI Cost Reduction Accelerator

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AI Accelerator
💡 Automation Library
My Processes
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User Management
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Name
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AQE Digital > My Processes

Organizational Dashboard

Total Users
0
Total Processes
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Org-wide Annual Savings
₹0

All Processes

Name Category Owner Status Est. Savings Action

User Management

Name Email Role Processes Owned Action

Automation Intelligence Library

Discover pre-configured, high-ROI AI use cases before mapping your processes.

Showing 0 of 0 use cases
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No use cases found

No use cases match your current filters. Try broadening your search or clearing some filters.

Library Management

Manage the master use case registry, taxonomy tags, and AI tools directory.

Use Case Name ⇅ Industry ⇅ Business Function ⇅ Value Metric AI System Type Custom Dev Last Updated ⇅ Actions
Tool Name ⇅ Category ⇅ Vendor/Provider Free Tier Custom Dev Impact Notes Status Actions

1. Industries

2. Business Functions

3. Sub-Functions

Model Name Input Rate ($ / M tokens) Output Rate ($ / M tokens) Last Updated Status Actions

My Processes

Process Name

AI Token Cost: ₹0
AI Token Consumption Cost ℹ️ Token costs are the per-use charges from AI tools like GPT-4, Claude, or Gemini. Unlike subscriptions, these scale with usage volume and must be included for an accurate OpEx calculation.
▼
Your token costs are included in your subscription fee above. No additional token cost entry is required.

Rates shown are indicative as of mid-2026. Verify current rates at your provider's pricing page before finalising.

💬
Simple Query / Chat
500–2,000 tokens
1× multiplier
📄
RAG / Doc Processing
2,000–8,000 tokens
2× multiplier
🛠️
Tool-Calling Agent
5,000–15,000 tokens
5× multiplier
🤖
Multi-Agent Automation
50k–200k tokens
15× multiplier

Combined input + output per execution

Number of process runs per month

In Out
Monthly Token Consumption
100,000 tokens
1× agentic multiplier
Total Estimated Monthly Token Cost
₹0
Base cost: ₹0

Volume Sensitivity — How Token Costs Scale With Usage Growth

Scenario Volume Level Monthly Token Cost
Conservative Current Volume ₹0
Realistic 1.5x Scaling ₹0
Stress 3.0x Scale Burst ₹0

Token costs grow with usage. The Stress scenario reflects typical growth patterns when automation scales across teams or transaction volumes increase. Source: AI4PROFIT Token Economics Research.

OpEx Saving before token cost: ₹0 | OpEx Saving including token cost: ₹0
⚠️ Token costs are significantly impacting your projected saving. Consider reviewing your model selection or process volume.

Workflow Steps

Analysing process...
No analysis data found. Run the AI analysis from the Setup tab.
Financial Impact
Before vs After Workflow
Process Health
🖨️ Full Report (PDF)
Conservative (70% Confidence)
₹0
0 mo payback
Realistic (Base Case)
₹0
0 mo payback
Optimistic (Ceiling Case)
₹0
0 mo payback

CFO Summary

KPI Comparison

Metric Current Conservative Realistic Optimistic

Feasibility Verdict

Total Monthly OpEx Saving
₹0
Annual OpEx Saving
₹0
Efficiency Gain %
0%

Stage 2: Final CFO Report

Carried Over from Stage 1 (Read-Only):
Monthly Savings: ₹0
Annual Savings: ₹0
Efficiency Gain: 0%
Total CapEx Investment
₹0
Total 1st-Year Cost of Ownership
₹0
CapEx + Maintenance + Automated OpEx
Net Year 1 ROI
0%
Net 3-Year ROI
0%

Payback Timeline

Payback Period: 0 months
M0 M3 M6 M9 M12 M15 M18 M21 M24

Cumulative Savings vs Investment (36 Months)

Transformation Summary
0 elim / 0 auto

Current Workflow Highlights

AI-Optimised Workflow

0

Process Health out of 100

A

Grade

Cost Leakage Report

Detailed Workflow Analysis Report

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Use Case Details
Function & Sub-Function

Description

Quantitative Data Requirements

Qualitative Data Requirements

Example Sources

Implementation Notes

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