CT Transformation Program - Phase 2 Approach

Products

Service Lines

~206

Total Team Size

Phase 2: Summary of Approach

Scope & Release Strategy

10 of 12 products are running partial releases (controlled pilots)
2 products (EY Fabric Next & Portal) are full-scope releases
1 product (Risk Radar) runs a simulated full release as a greenfield replay
Most pilots target non-prod environments; Tax SL products also include production
8 brownfield (existing products) vs 4 greenfield (new builds or replays)

AI Approach Distribution

5 products take a purely Agentic approach (Assurance, Consulting, EY Parthenon) — orchestrating AI agents with human oversight
2 products use a hybrid model (Risk Radar: Assisted+Agentic; EY Workforce: 60% Agentic / 40% Assisted)
5 products adopt an Assisted approach (Fabric & Tax) — enhancing existing workflows with AI prompts and co-pilots
Tax teams (ITTS, EYMP) have aspirational targets to evolve toward orchestration

Estimation & Expected Gains

Marketing.AI: 55 → 34 SPs (38% reduction)
EY.AI for Risk: 40 → 26 SPs (35% reduction)
EY Workforce: 70–100 → 40–60 person-days (~40% reduction)
Risk Radar: 9 months → 3 months (67% reduction)
Spotfire: 12 months/$1M → ~1 hour per migration (transformational)
Several Tax and Fabric products have yet to establish AI-specific ROM baselines

Tooling Landscape

Factory.AI — adopted by all 12 products as the primary AI platform
GitHub + Copilot — used by 11 of 12 products
Figma — used by 10 products
Azure DevOps (ADO) — system of record for Consulting and some Tax
Playwright — adopted by Consulting for automated QA
SonarQube / Artifactory — used by Fabric for quality gates

Program Objectives Alignment

Accelerate time-to-market — targeted by 10 of 12 products
Enhance software quality & security — targeted by 10 of 12 products
Increase efficiency — targeted by 11 of 12 products
Reduce total cost of delivery — targeted by 6 of 12 products
Enforce compliance — targeted by 7 of 12 products

Team Size by Service Line

Tax: 83 people across 4 products (largest SL)
Fabric: 70 people across 2 products
Consulting: ~13.5 people across 3 products
Assurance: ~9 people across 2 products
EY Parthenon: TBD (1 product)

All Products

Filter:

Helix Spotfire Refactoring

Assurance

Scope

Partial Non-prod
Migration tool (Spotfire to Power BI); development tool, not production release

Breakdown

Greenfield Built from scratch; involves migration and process changes

Estimation

Traditional: ~6 months, 8–10 people (uncertain feasibility)
AI: Migration reduced from 12 months/$1M to ~1 hour + automated QA

Benefits

Productivity Efficiency
Enabling work previously unfunded due to high cost

Team

~5 people (3 Architects + Eng Lead + BA)

Approach

Agentic Droids, Hooks, Skills; humans-in-the-loop

Tooling

Factory.AI

Objectives

Time-to-market Quality & Security Efficiency Reduce Cost

Risk Radar

Assurance

Scope

Simulated Full Single product environment (dev through prod)

Breakdown

Greenfield (Replay)

Estimation

Traditional: 9 months
AI: 3 months

Benefits

Productivity Doing more with less

Team

4 people (PM, UX, TL, CM, PDM, TA)

Approach

Assisted + Agentic

Tooling

GitHub Copilot Figma Factory.AI

Objectives

Time-to-market Quality & Security Efficiency Reduce Cost

Marketing.AI

Consulting

Scope

Partial 2 Sprints, Non-prod
One data integration + one E2E implementation. Success: QA Validation.

Breakdown

Greenfield

Estimation

Traditional: 55 Story Points
AI: 34 Story Points (38% reduction)

Benefits

Productivity Quality
Shift left on quality; reduce SME dependency (Dynamics, SAP)

Team

~6 people (0.5 Eng Lead, 0.25 PDM, 4 Dev, 1.25 QA)

Approach

Agentic Factory.AI Starter Kit; human orchestration

Tooling

Factory.AI GitHub Copilot Figma ADO Replit Playwright

Objectives

Time-to-market Quality & Security Efficiency Compliance (partly)

EY.AI for Risk - Internal Audit

Consulting

Scope

Partial 2 Sprints, Non-prod
Editable canvas feature. Success: QA Validation.

Breakdown

Brownfield

Estimation

Traditional: 40 Story Points
AI: 26 Story Points (35% reduction)

Benefits

Productivity Quality
Shift left on quality; reduce tech debt

Team

~3.5 people (0.25 PDM, 2 Dev, 1 QA, 0.25 Eng Lead)

Approach

Agentic Factory.AI Starter Kit; human orchestration

Tooling

Factory.AI GitHub Copilot Figma ADO Miro Playwright

Objectives

Time-to-market Quality & Security Efficiency Compliance (partly)

EY Workforce Platform

Consulting

Scope

Partial Non-prod (Dev + QA)
2–3 features E2E through all 7 PDLC phases across 2 services (Java + Data Pipeline)

Breakdown

Brownfield Mixed workload through AI-augmented PDLC

Estimation

Traditional: 70–100 person-days (2 sprints, 3 services)
AI (Factory.ai): 40–60 person-days

Benefits

Efficiency Quality
Target: 50% team reduction (8 to 4 FTEs); 50%+ defect reduction

Team

4 people (0.5 Eng Lead, 2.5 Dev, 1 QA)

Approach

Agentic 60% + Assisted 40% Factory.ai across all 7 PDLC phases

Tooling

Factory.AI GitHub Copilot ADO Playwright

Objectives

Efficiency Quality (case-by-case, not all pods)

Competitive Edge

EY Parthenon

Scope

Partial Monthly SAFe release schedule
Focus on intake, requirements, and selective SDLC improvements

Breakdown

Brownfield with limited greenfield; backlog optimisation, refactoring

Estimation

Traditional: Based on existing EVB boards
AI: No AI-derived metrics yet

Benefits

Quality Velocity
Improved story quality, reduced lead time, velocity increase per release

Team

TBD; existing team + BPMs, Architects, UX, Product/Delivery Managers

Approach

Predominantly Agentic Orchestrated agents across intake, requirements, sequencing, development

Tooling

GitHub Copilot Factory.AI Figma Copilot Studio

Objectives

Velocity Quality Reduce Friction Compliance

EY Fabric - Next

Fabric

Scope

Full Full EY Fabric Platform

Breakdown

Brownfield

Estimation

Traditional: 6 months

Benefits

Velocity Quality Efficiency

Team

50 people

Approach

Assisted

Tooling

SLA GitHub Figma Factory.AI SonarQube Artifactory GitHub Actions

Objectives

Time-to-market Quality

EY Fabric - Portal

Fabric

Scope

Full Sub-component of Fabric.ey.com

Breakdown

Greenfield

Estimation

Traditional: 3 months

Benefits

Velocity Quality Efficiency

Team

20 people (pods of 6)

Approach

Assisted

Tooling

SLA GitHub Figma Factory.AI SonarQube Artifactory GitHub Actions

Objectives

Time-to-market Quality

EYXP

Tax

Scope

Partial Production

Breakdown

Brownfield Product development operations

Estimation

Traditional: TBD

Benefits

Efficiency Quality

Team

22 people; all roles, entire team from participating pods

Approach

Assisted Enhance current ways of working

Tooling

AHA ADO Figma GitHub Copilot Factory.AI Figma Make

Objectives

Time-to-market Quality & Security Efficiency Reduce Cost Compliance

ITTS

Tax

Scope

Partial Non-prod / Prod
PDLC Scope (Lower Env); QA (Code Review / Bug Fixing)

Breakdown

Brownfield BDD / API Design & Contract Mgmt

Estimation

Traditional: Both (will compare with AI estimation)

Benefits

Velocity Quality Coverage Infra Costs Req Mgmt

Team

16 people; all roles, entire team from participating pods

Approach

Assisted Aspirational target: evolve to orchestration

Tooling

GitHub Copilot Factory.AI SLA Figma

Objectives

Time-to-market Quality & Security Efficiency Reduce Cost Compliance

Payroll

Tax

Scope

Partial Non-prod / Production
Operational Tools, Bugs, Documentation

Breakdown

Brownfield

Estimation

Traditional: Initially current approach

Benefits

Efficiency
Reduce manual/repetitive tasks, improve context mgmt, reduce rework

Team

12 people; all roles, entire team from participating pods

Approach

Assisted Reusable Instructions, Skills

Tooling

GitHub Copilot Factory.AI Figma

Objectives

Efficiency Quality & Security Time-to-market

EYMP

Tax

Scope

Partial Non-prod / Prod
Feature & user story health check, dev, testing, docs, release readiness

Breakdown

Brownfield

Estimation

Traditional: TBD

Benefits

Productivity Velocity Quality Efficiency

Team

33 people; all roles, entire team from participating pods

Approach

Assisted Identifying potential for orchestration

Tooling

GitHub Copilot Factory.AI SLA

Objectives

Quality (Requirements) Code Quality Efficiency Compliance

Service Line	Product	Scope	Breakdown	ROM (Traditional)	ROM (AI)	Team Size	Approach	Key Tooling	Key Objectives
Assurance	Helix Spotfire Refactoring	Partial, Non-prod	Greenfield	~6 months (8-10 ppl)	~1 hour per migration	~5	Agentic	Factory.AI	Time-to-market, Quality, Efficiency, Cost
Assurance	Risk Radar	Simulated Full	Greenfield	9 months	3 months	4	Both	GitHub, Copilot, Figma, Factory.AI	Time-to-market, Quality, Efficiency, Cost
Consulting	Marketing.AI	Partial, 2 Sprints, Non-prod	Greenfield	55 SPs	34 SPs	~6	Agentic	Factory.AI, GitHub, Copilot, ADO	Time-to-market, Quality, Efficiency
Consulting	EY.AI for Risk	Partial, 2 Sprints, Non-prod	Brownfield	40 SPs	26 SPs	~3.5	Agentic	Factory.AI, GitHub, Copilot, ADO	Time-to-market, Quality, Efficiency
Consulting	EY Workforce Platform	Partial, Non-prod	Brownfield	70-100 person-days	40-60 person-days	4	Agentic 60%+Assisted 40%	Factory.AI, GitHub, Copilot, ADO	Efficiency, Quality
EY Parthenon	Competitive Edge	Partial, Monthly SAFe	Brownfield	Based on EVB boards	No AI metrics yet	TBD	Agentic	Copilot, Factory.AI, Figma	Velocity, Quality, Compliance
Fabric	EY Fabric - Next	Full	Brownfield	6 months	—	50	Assisted	SLA, GitHub, Figma, Factory.AI	Time-to-market, Quality
Fabric	EY Fabric - Portal	Full	Greenfield	3 months	—	20	Assisted	SLA, GitHub, Figma, Factory.AI	Time-to-market, Quality
Tax	EYXP	Partial, Production	Brownfield	TBD	—	22	Assisted	AHA, ADO, GitHub, Copilot, Factory.AI	Time-to-market, Quality, Efficiency, Cost, Compliance
Tax	ITTS	Partial, Non-prod/Prod	Brownfield	Comparative	—	16	Assisted	GitHub, Copilot, Factory.AI, SLA	Time-to-market, Quality, Efficiency, Cost, Compliance
Tax	Payroll	Partial, Non-prod/Prod	Brownfield	Current approach	—	12	Assisted	Copilot, Factory.AI, Figma	Efficiency, Quality, Time-to-market
Tax	EYMP	Partial, Non-prod/Prod	Brownfield	TBD	—	33	Assisted	Copilot, Factory.AI, SLA	Quality, Efficiency, Compliance

Tooling Table

Tool	Description & Use
Factory.AI	AI-powered software engineering platform supporting agentic workflows (Droids, Hooks, Skills) and assisted prompting across the full PDLC. Used for code generation, test generation, requirements analysis, planning, and orchestration.
GitHub	Source control and collaboration platform. Manages code repositories, pull requests, and CI triggers. Foundation for version control and code review.
GitHub Copilot	AI-powered in-editor code completion and assisted coding tool. Provides real-time code suggestions during development.
Figma	Design and prototyping tool supporting design-to-code workflows and UI/UX collaboration. Translates designs into development-ready specifications.
Azure DevOps (ADO)	Work item tracking and system of record. Manages work items, phase artifacts, gate decisions, and audit trails across PDLC phases.
Playwright	End-to-end testing framework for automated QA testing. Supports shift-left on quality and reduces defects escaping to later stages.
SonarQube	Static code analysis and quality gate tool. Enforces code quality standards, identifies bugs, vulnerabilities, and code smells.
Artifactory	Binary repository and artifact management tool. Manages build artifacts, dependencies, and release packages across CI/CD pipelines.
GitHub Actions	CI/CD automation platform. Automates build, test, and deployment workflows triggered by code changes in GitHub repositories.
SLA	A comprehensive suite designed to streamline and integrate the workflow of software development teams, offering tailored functionalities for four key personas: Product Managers, Solution Architects, Developers, and Testers.
AHA	Product management and roadmapping tool. Supports feature planning, backlog management, and product development operations.
Figma Make	Design-to-code automation tool extending Figma capabilities to automate the translation of design assets into development-ready code.
Replit	Cloud-based development environment supporting rapid prototyping and collaborative coding in the browser.
Miro	Collaborative whiteboarding and visual planning tool. Supports brainstorming, workflow mapping, and team collaboration.
Copilot Studio	Microsoft's agent-building platform for creating custom AI agents for intake, requirements, and sequencing workflows.

Service Line Summaries

Assurance

2 Products • ~9 People • Agentic Focus

▼

Assurance is pursuing greenfield development with agentic AI workflows across both products in Phase 2. The service line is leveraging Factory.AI as the primary platform, with a focus on demonstrating significant efficiency gains — particularly with Helix Spotfire, which aims to compress what was a 12-month, $1M migration effort into approximately 1 hour using AI-driven automation.

Key themes: Greenfield builds, small highly-skilled teams, agentic orchestration with human-in-the-loop governance, significant ROI potential.

Summary Justification: click here for AI reasoning

Helix Spotfire Refactoring

Scope: Partial, Non-prod (Spotfire-to-Power BI migration tool)
Type: Greenfield • Team: ~5 (Architects + Eng Lead + BA)
Approach: Fully Agentic (Droids, Hooks, Skills)
ROM: Traditional 6 months → AI ~1 hour per migration
Key insight: Enables work previously unfunded due to prohibitive cost

Risk Radar

Scope: Simulated Full Release (dev through prod)
Type: Greenfield Replay • Team: 4 (PM, UX, TL, CM, PDM, TA)
Approach: Assisted + Agentic (mixed)
ROM: Traditional 9 months → AI 3 months (67% reduction)
Key insight: Replaying a full product build to measure AI impact end-to-end

Consulting

3 Products • ~13.5 People • Agentic Focus

▼

Consulting is running three focused pilots with small, lean teams and a predominantly agentic approach using Factory.AI Starter Kit. All three products have quantified ROM estimates showing 35–40% effort reductions with AI. The service line is particularly focused on the full PDLC lifecycle — notably EY Workforce Platform, which runs 2–3 features through all 7 PDLC phases and targets a 50% team size reduction (8 to 4 FTEs).

Key themes: Quantified AI gains (story points & person-days), Factory.AI Starter Kit adoption, agentic orchestration with human approval gates, Playwright for automated QA, shift-left on quality.

Summary Justification: click here for AI reasoning

Marketing.AI

Scope: Partial, 2 Sprints, Non-prod (data integration + E2E)
Type: Greenfield • Team: ~6
Approach: Agentic (Factory.AI Starter Kit)
ROM: 55 → 34 SPs (38% reduction)
Key insight: Reducing SME dependency on Dynamics/SAP systems

EY.AI for Risk - Internal Audit

Scope: Partial, 2 Sprints, Non-prod (editable canvas feature)
Type: Brownfield • Team: ~3.5
Approach: Agentic (Factory.AI Starter Kit)
ROM: 40 → 26 SPs (35% reduction)
Key insight: Targeting tech debt reduction alongside feature delivery

EY Workforce Platform

Scope: Partial, Non-prod; 2–3 features through all 7 PDLC phases
Type: Brownfield • Team: 4 (target 50% of original 8)
Approach: Agentic 60% + Assisted 40%
ROM: 70–100 → 40–60 person-days
Key insight: Most ambitious — AI augmenting every PDLC phase with human gates

EY Parthenon

1 Product • Team TBD • Agentic Focus

▼

EY Parthenon is entering Phase 2 with a single product (Competitive Edge) taking a predominantly agentic approach focused on the front end of the PDLC — specifically intake, requirements, and sequencing. Unlike other service lines, EY-P is constrained by a monthly SAFe release cadence, so AI gains target improved story quality and reduced lead time rather than raw speed-to-market. The team has not yet established AI-derived ROM metrics.

Key themes: Requirements-focused AI augmentation, agentic orchestration across intake and sequencing, role evolution (reduced BA reliance), Copilot Studio experimentation, quality over speed.

Summary Justification: click here for AI reasoning

Competitive Edge

Scope: Partial, aligned to monthly SAFe cadence
Type: Brownfield with limited greenfield • Team: TBD
Approach: Predominantly Agentic (orchestrated agents)
ROM: Based on EVB boards; no AI metrics yet
Key insight: AI cannot compensate for poor requirements; business engagement critical

Fabric

2 Products • 70 People • Assisted Focus

▼

Fabric represents the largest team footprint (70 people) with an assisted approach across both products. Unlike other service lines running partial pilots, Fabric is committing to full-scope releases. The two products share identical tooling stacks (including SonarQube for quality gates and Artifactory for artifact management) and target velocity, quality, and efficiency gains.

Key themes: Large-scale assisted adoption, full releases (not partial pilots), enterprise-grade tooling with quality gates (SonarQube, Artifactory, GitHub Actions), consistent approach across both products.

Summary Justification: click here for AI reasoning

EY Fabric - Next

Scope: Full release, full platform
Type: Brownfield • Team: 50
Approach: Assisted
ROM: Traditional 6 months
Key insight: Largest single team in the program

EY Fabric - Portal

Scope: Full release, sub-component of Fabric.ey.com
Type: Greenfield • Team: 20 (pods of 6)
Approach: Assisted
ROM: Traditional 3 months
Key insight: Greenfield opportunity to embed AI from day one

Tax

4 Products • 83 People • Assisted Focus

▼

Tax is the broadest service line in Phase 2 with 4 products and the largest combined headcount (83 people). All four teams are taking an assisted approach, focusing on enhancing existing workflows. Notably, Tax is the only service line where multiple products target production environments alongside non-prod. ITTS and EYMP have expressed aspirational goals to evolve toward agentic orchestration.

Key themes: Assisted-first with orchestration aspirations, production-inclusive pilots, all-roles engagement (not just developers), compliance as a key objective, operational efficiency focus.

Summary Justification: click here for AI reasoning

EYXP

Scope: Partial, Production
Type: Brownfield • Team: 22 (all roles)
Approach: Assisted
ROM: TBD
Key insight: Broadest tooling in Tax (AHA, ADO, Figma Make)

ITTS

Scope: Partial, Non-prod/Prod (PDLC scope, QA flows)
Type: Brownfield (BDD/API Design) • Team: 16 (all roles)
Approach: Assisted (aspiration to evolve to orchestration)
ROM: Comparative
Key insight: Unique focus on infra costs and requirements management metrics

Payroll

Scope: Partial, Non-prod/Production
Type: Brownfield • Team: 12 (all roles)
Approach: Assisted (Reusable Instructions, Skills)
ROM: Current approach
Key insight: Focus on reducing manual/repetitive tasks and rework

EYMP

Scope: Partial, Non-prod/Prod
Type: Brownfield • Team: 33 (all roles, largest Tax team)
Approach: Assisted (identifying orchestration potential)
ROM: TBD
Key insight: Emphasis on requirements quality and compliance enforcement

Each claim in the Assurance summary is traced to a specific statement in the source pre-requisite documents.

Claim	Source Evidence
“Greenfield + agentic across both products”	Spotfire docx: “Greenfield”; Risk Radar pptx: “Greenfield (replay)”. Both use agentic approaches.
“Both products pursuing greenfield”	Spotfire docx: “Greenfield – the work started from scratch”; Risk Radar pptx: “Greenfield (replay)”
~9 people total	Spotfire: 3 Architects + Eng Lead + BA = ~5; Risk Radar: 4 people. Total ~9
Spotfire: “12 months/$1M to ~1 hour”	Spotfire docx: “Migration that would have taken 12 months and $1M can potentially be done in about an hour”
Spotfire: Agentic (Droids, Hooks, Skills)	Spotfire docx: “Droids, Hooks, Skills, Agentic workflow with Humans in the loop”
Risk Radar: 9 months to 3 months (67%)	Risk Radar pptx: “9 Months (today’s approach), 3 Months (using AI)”
Risk Radar: Assisted + Agentic	Risk Radar pptx: “Assisted and Agentic”
“Enables work previously unfunded”	Spotfire docx: “enabling work that previously would not have been funded due to high cost and effort”

Each claim in the Consulting summary is traced to a specific statement in the source pre-requisite documents.

Claim	Source Evidence
“Three focused pilots”	CT Consulting xlsx has 3 product columns: Marketing.AI, EY.AI for Risk, EY Workforce Platform
~13.5 people total	Marketing.AI: 0.5+0.25+4+1.25=6; EY.AI Risk: 0.25+2+1+0.25=3.5; EY Workforce: 0.5+2.5+1=4. Total=13.5
“Quantified ROM estimates, 35–40% reductions”	Marketing.AI: 55→34 SPs (38%); EY.AI Risk: 40→26 SPs (35%); EY Workforce: 70–100→40–60 person-days (~40%)
“Factory.AI Starter Kit”	CT Consulting xlsx: “Agentic Predominantly leveraging Factory.AI leveraging Starter Kit”
EY Workforce: “all 7 PDLC phases”	CT Consulting xlsx: “runs 2-3 features end-to-end through all 7 PDLC phases”
EY Workforce: “50% team reduction (8 to 4)”	CT Consulting xlsx: “Team cost Engineering: 8 FTEs / 4 FTEs (50% reduction)”
Playwright for QA	CT Consulting xlsx: tooling lists “Playwright (for QA testing)” for all 3 products
“Shift-left on quality”	CT Consulting xlsx: Marketing.AI and EY.AI Risk both state “Shift left on Quality”

Each claim in the EY Parthenon summary is traced to a specific statement in the source pre-requisite documents.

Claim	Source Evidence
“Single product (Competitive Edge)”	Only one product listed in PDLC Transformation - EY Parthenon.pptx
“Predominantly agentic”	Pptx: “Predominantly agentic approach with prompt-based assistance”
“Front end of PDLC – intake, requirements, sequencing”	Pptx: “focus on intake, requirements, and selective SDLC improvements”
“Monthly SAFe release cadence”	Pptx: “Partial – aligned to existing monthly SAFE release schedule”
“Improved story quality, reduced lead time”	Pptx: “Improved story quality, reduced lead time into backlog, potential velocity increase per release”
“No AI-derived ROM metrics yet”	Pptx: “No AI-derived metrics yet; avoiding parallel metric systems”
Team TBD	Pptx: “Team size TBD; role evolution expected”
“AI cannot compensate for poor requirements”	Pptx: “AI cannot compensate for poor requirements; business engagement critical”
“Copilot Studio”	Pptx: “selective experimentation with Copilot Studio”
“Reduced BA reliance”	Pptx: “reduced reliance on traditional BA role”

Each claim in the Fabric summary is traced to a specific statement in the source pre-requisite documents.

Claim	Source Evidence
70 people total	EY Fabric xlsx: Next = 50 people, Portal = 20 people. Total = 70
“Assisted approach across both”	EY Fabric xlsx: both products list “Assisted”
“Full-scope releases”	EY Fabric xlsx: Next = “Full EY Fabric Platform”; Portal = “Full EY Fabric Next is a sub component”
“Identical tooling stacks”	Both list: SLA, GitHub, Figma, Factory.AI, SonarQube, Artifactory, GitHub Actions
Next: Brownfield, 6 months	EY Fabric xlsx: “Brownfield”, ROM “6 months”
Portal: Greenfield, 3 months	EY Fabric xlsx: “Greenfield”, ROM “3 months”
Portal: “pods of 6”	EY Fabric xlsx: “20 (group of pods 6 people in size)”
“Velocity, quality, efficiency”	EY Fabric xlsx: both list “Velocity, Quality, Efficiency” as benefit focus

Each claim in the Tax summary is traced to a specific statement in the source pre-requisite documents.

Claim	Source Evidence
83 people total	Tax xlsx: EYXP=22, ITTS=16, Payroll=12, EYMP=33. Total=83
“All four teams assisted”	Tax xlsx: all four list “Assisted” as approach
“Production environments”	Tax xlsx: EYXP=“Production”; ITTS=“Non-Prod/Prod”; Payroll=“Non-Prod/Production”; EYMP=“Non-Prod/Prod”
“ITTS and EYMP aspirational orchestration goals”	Tax xlsx: ITTS=“Aspirational/uncommitted target – evolve to orchestration by end of pilot”; EYMP=“identifying potential for orchestration implementation”
“All roles, entire team”	Tax xlsx: all four state “All roles; entire team from participating Pods”
“Compliance as key objective”	Tax xlsx: EYXP, ITTS, EYMP all list Compliance in objectives
EYXP: broadest tooling (AHA, ADO, Figma Make)	Tax xlsx: EYXP lists “AHA, ADO, Figma, GitHub, CoPilot, Factory.ai, Figma Make” – most tools of any Tax product
ITTS: infra costs and req mgmt metrics	Tax xlsx: ITTS benefits = “Velocity / Quality Coverage / Infra Costs / Req Management Metrics”
Payroll: “reduce manual/repetitive tasks, rework”	Tax xlsx: Payroll = “Operational efficiency, reduce manual and repetitive tasks, Improve project context management, Reduce Rework”
EYMP: “requirements quality and compliance”	Tax xlsx: EYMP = “Increased quality in requirements, Enhanced code quality, Compliance enforcement”

Phase 2: Summary of Approach

Scope & Release Strategy

AI Approach Distribution

Estimation & Expected Gains

Tooling Landscape

Program Objectives Alignment

Team Size by Service Line

All Products

Helix Spotfire Refactoring

Risk Radar

Marketing.AI

EY.AI for Risk - Internal Audit

EY Workforce Platform

Competitive Edge

EY Fabric - Next

EY Fabric - Portal

EYXP

ITTS

Payroll

EYMP

Tooling Table

Service Line Summaries

Assurance

Helix Spotfire Refactoring

Risk Radar

Consulting

Marketing.AI

EY.AI for Risk - Internal Audit

EY Workforce Platform

EY Parthenon

Competitive Edge

Fabric

EY Fabric - Next

EY Fabric - Portal

Tax

EYXP

ITTS

Payroll

EYMP

Assurance — Summary Justification

Consulting — Summary Justification

EY Parthenon — Summary Justification

Fabric — Summary Justification

Tax — Summary Justification