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<reference>
IBM as Client Zero:
How IBM built an enterprise
AI orchestration model at
global scale

JULY 2026

Executive Summary
By deploying its own technology and workflow transformation methodology internally first – in a
program called “Client Zero” – IBM has been able to capture $4.5B in annualized productivity savings
over three years, more than double its original $2B target.
Ashley AuBuchon, Partner at IBM Consulting’s Client Zero Center of Competence, served as the program’s operational lead.
The challenge she and her team took on was straightforward: transform IBM’s own workflows using the same AI platform and
methodology IBM brings to enterprise clients. AuBuchon’s core principle throughout the project was “You have to eliminate and
simplify before you automate. If you automate a broken process, you just get a faster broken process.”
The program, led by Joanne Wright, SVP of Transformation and Operations, spanned HR, IT, finance, procurement, supply chain,
sales, sustainability, and customer support across IBM’s approximately 270,000 employees (Table 1). Key results include:
z

Employee Productivity: HR operating costs reduced 40%; AskHR resolved 94% of 16 million annual employee
inquiries without human intervention.

z

IT Support: AskIT reduced IT call and chat volume by 74% year-over-year; 92% of IT service sessions required no
human assistance.

z

IT Modernization: Infrastructure costs reduced 30%, generating more than $600 million in savings.

z

Customer Support: AI-powered customer support generated approximately $191 million in annualized
operational savings.
Table 1: IBM’s Client Zero Transformation at a Glance

Transformation Area

Primary AI Capabilities

Workflow Focus

Operational Objective

Employee Productivity

AskHR, AskIT, AskSales

HR, IT, employee support

Reduce transactional
workload

Finance and Operations

AskQ2C, AskEPM, Touchless
Financial Forecasting &
Journal Automation

Collections, planning,
procurement

Increase automation
and throughput

IT Modernization

Concert, Turbonomic,
Instana, Apptio and Bob

Infrastructure operations

Reduce infrastructure
complexity

watsonx Orchestrate,
Discovery

Customer support

Lower support cost
and escalation

Customer
Experience

IBM as Client Zero: How IBM built an enterprise AI orchestration model at global scale

1

About This Case Study
Futurum Research developed this case study based on
interviews with IBM stakeholders, executive briefings, and
supporting materials provided by IBM. Primary research
included interviews with Ashley AuBuchon, Partner, Client Zero
Center of Competence at IBM Consulting, who helped lead
the Client Zero transformation, as well as members of IBM’s
transformation leadership and Analyst Relations teams. IBM
commissioned the study and provided supporting information.
Futurum Research conducted the interviews and analysis
independently and retained full editorial control. The findings
presented reflect Futurum’s independent assessment of the
business and operational value generated through IBM’s
Client Zero transformation.
The Business Economic Value (BEV) framework is Futurum’s
methodology for evaluating the business and operational
value organizations derive from technology investments.
Traditional BEV studies emphasize financial outcomes such
as costs, benefits, return on investment, and payback periods.
Because IBM’s Client Zero transformation remains active and
continues to expand, many long-term financial outcomes are
still evolving. Accordingly, this case study focuses on leading
operational indicators of value, together with the measurable
business outcomes IBM has already achieved through
production deployments.
Futurum Research separately conducted a Business Economic Value
study on the ROI of watsonx Orchestrate for independent customers,
excluding IBM-reported data, and found similar themes and
examples of productivity gains.

IBM as Client Zero: How IBM built an enterprise AI orchestration model at global scale

2

Program Overview
IBM designed Client Zero around a deliberate sequencing principle: eliminate complexity, simplify end-to-end workflows, and
then automate. This ensured AI was applied to redesigned processes. IBM's AI transformation methodology reimagines business
processes from a blank slate before applying AI. That discipline ran through every domain AuBuchon's team touched. In HR,
employee interactions shifted from ticket-driven support to AI-agent resolution at the point of contact. In IT support,
IBM redesigned large portions of the resolution path around self-service, reserving human intervention for complex or
judgment-intensive issues. Each workflow transformation builds on those that came before it. AskIT moved from concept to
production in about 100 days, leveraging lessons learned from AskHR.
Executive oversight was built into the governance structure from the start. IBM CEO Arvind Krishna chaired a biweekly
steering committee with IBM's Executive Leadership Team, keeping productivity transformation a standing priority at the
highest level of the organization. The program operated under a four-layer governance model and was supported by only
13 staff members across the Transformation Steering Committee, the Transformation Project Office, and the Productivity
Discovery Team (Table 2).
Table 2: IBM Client Zero Governance Structure
Governance Layer

Role

Primary Function

Transformation
Steering Committee

Executive leadership
and oversight

Established enterprise priorities, allocated investment, and
governed program direction; chaired by CEO Arvind Krishna
on a biweekly basis with ELT members

Transformation
Project Office

Program coordination
and execution

Coordinated deployments across business functions and
managed operational dependencies and timelines

Productivity
Discovery Team

Opportunity identification
and prioritization

Identified workflow transformation opportunities and
evaluated initiatives before execution

Productivity
Catalysts

Embedded business-unit
transformation leaders

Drove AI adoption, workflow redesign, and operational
implementation within individual business functions

AuBuchon and her team recognized that transformation stalls when employees closest to day-to-day workflows are excluded
from redesigning those workflows. The Productivity Catalyst model, together with the IBMer watsonx Challenge, is an internal
program inviting employees to design and submit AI agents for real operational workflows, distributed ownership and
accelerated experimentation across the enterprise.
IBM also pursued a deliberate platform rationalization strategy in parallel, eliminating thousands of third-party applications
over several years, consolidating onto strategic platforms such as SAP, Salesforce, and Adobe, and integrating end-to-end
workflows with watsonx. The broader technology stack also included IBM Maximo for asset and operations management and
Apptio for technology business management. Together, these investments reflected IBM's strategy to consolidate onto strategic
enterprise platforms.

IBM as Client Zero: How IBM built an enterprise AI orchestration model at global scale

3

Transformation Results
Employee Productivity
AskHR, IBM's AI-powered HR agent built on watsonx, handled roughly 16 million employee interactions annually, fully resolving
94% without routing inquiries to a human. It automated more than 1.1 million transactions annually across more than 80 task
types, including payroll, benefits, performance management, employee transfers, and separations. IBM reported a 40%
reduction in HR operating costs.

“AskHR is actually automating tasks. I can transfer or
promote an employee all within this agent, without ever
going into our SAP SuccessFactors platform…[IBM now
has fewer] people doing transactional work and more
strategic advisors in HR.”
Ashley AuBuchon, IBM

AskIT extended the same model to IT support, serving approximately 270,000 employees across more than 40 languages
and with 815 trained intents. IT call and chat volume dropped 74% year-over-year. Out of the sessions, 92% required no
human assistance, and support ticket volume fell by more than 50%. The resolution model evolved, moving beyond simple
call deflection. IBM deployed the same AI-agent framework across additional functions, including AskSales for sales
operations, AskQ2C for quote-to-cash workflows, AskEPM for enterprise planning, and AskSustainability for environmental
reporting (Table 3).

IBM as Client Zero: How IBM built an enterprise AI orchestration model at global scale

4

Table 3: AI Agent Performance at Scale
Agent

Operational Scope

Primary Outcomes

AskHR

HR inquiries and employee transactions across payroll,
benefits, performance management, transfers, and
separations; 16M annual interactions and 1.1M automated
transactions across 80+ task types

94% inquiry resolution without human
involvement; 40% reduction in HR operating
costs

AskIT

Enterprise IT support across 40+ languages with 815
trained intents; ~270,000 IBM employees

92% of sessions require no human
assistance; 74% reduction in IT call/chat
volume; 50%+ reduction in support tickets

AskQ2C

Quote-to-Cash workflows, including collections,
accounts receivable management, invoice processing,
and order tracking

Reduced manual collections and
invoice processing; improved AR
workflow efficiency

AskEPM

Enterprise planning, forecasting, and financial
analysis workflows

Reduced manual data preparation;
improved planning and financial analysis
efficiency across finance operations

Finance, Operations, and IT Modernization
IBM extended its transformation to finance, procurement, and the supply chain by targeting workflows with high volumes of
manual processing. These workflows were redesigned to prioritize automation and standardized execution paths. AskQ2C
automated quote-to-cash collections, invoice processing, and order tracking. IBM Planning Analytics with Watson reduced
manual data assembly for forecasting and financial analysis.
IBM's IT modernization program delivered more than $600 million in infrastructure savings and a 30% reduction in infrastructure
costs by leveraging Apptio, IBM Concert, Turbonomic, and Instana for observability and automated resource optimization.
AuBuchon described the objective as "taking complexity out of the environment" and creating "more visibility and automation
across the infrastructure stack." In customer support, IBM deployed watsonx Assistant and related conversational AI, generating
approximately $191 million in annualized operational savings and enabling support volume to grow without proportional
increases in staffing.

IBM as Client Zero: How IBM built an enterprise AI orchestration model at global scale

5

The Productivity Flywheel
The $4.5 billion outcome stemmed from a series of transformations that reduced costs, freed capacity, and built organizational
capabilities that accelerated subsequent deployments, rather than from a single large deployment. AskIT's ability to move from
concept to production in about 100 days, using frameworks, governance structures, and lessons learned from AskHR, illustrates
how that compounding effect worked in practice.
IBM designed Client Zero as an iterative operating model rather than a static initiative. The flywheel runs in three
stages (Figure 1):
z

AI-driven workflow automation reduces the cost and complexity of a business function

z

IBM reinvests the resulting savings into additional AI deployment and infrastructure modernization

z

Each subsequent deployment expands the automation base and increases the organization’s capacity to identify and
implement the next transformation
Figure 1: The Productivity Flywheel

Automate workflows

Expand and repeat

AI agents handle standardized
tasks across business functions

Each cycle shortens the
path for the next deployment

$4.5B
annualized
savings

Reinvest savings
Savings fund the next
deployment cycle

Proof point: AskIT moved from concept to production in ~100 days, leveraging frameworks built for AskHR
That dynamic explains why the program exceeded its original target by a wide margin. The original $2 billion goal reflected
expectations based on a traditional linear deployment model. Instead, each transformation was built on those that came
before it. Each deployment shortened the path for the next, and the Productivity Catalyst network continuously surfaced new
opportunities. Together, these factors drove the outcome beyond $4.5 billion. IBM's leadership also sought to build workforce
capability for continuous workflow redesign. The IBMer watsonx Challenge supported that objective by encouraging employees
to identify new opportunities for AI across the enterprise.

IBM as Client Zero: How IBM built an enterprise AI orchestration model at global scale

6

What This Means for Enterprise Leaders
IBM treats Client Zero as operational evidence rather than a reference story. IBM used the same AI platform, governance
structures,transformation, and orchestration approaches internally that it now offers to enterprise clients. IBM Consulting also
reports delivering AI-first workflow transformations in less than 90 days, including 2 weeks for discovery, 2 weeks for solutioning,
and 4 to 8 weeks for MVP development.
From Futurum's perspective, the more important question is what conditions enabled these results. Several stand out:
z

Executive Commitment Was Structural: Arvind Krishna's biweekly steering committee kept the transformation on the
Executive Leadership Team's agenda throughout the program.

z

Sequencing Discipline Was Enforced: The eliminate → simplify → automate methodology ensured AI was applied only
after workflows had been redesigned.

z

Distributed Ownership Accelerated Adoption: The Productivity Catalyst model embedded accountability within
business units.

z

Platform Rationalization Created the Foundation for AI: Eliminating thousands of third-party applications and
consolidating onto strategic platforms established a consistent enterprise environment for AI deployment.

IBM operates in a unique environment. A few organizations operate at this scale, develop their own AI platform, or possess the
internal transformation consulting capabilities that IBM does through IBM Consulting. Even so, the underlying operating model
offers useful guidance for enterprise leaders. Visible executive governance, process-first discipline, embedded business-unit
ownership, and an iterative deployment model can help organizations scale AI transformation in a structured, sustainable way.

Takeaways
IBM's Client Zero demonstrates that enterprise AI transformation can be coordinated, governed, and scaled across a complex
global organization when the organizational architecture supports the technical ambition. The program shows that successful
AI transformation depends on more than technology. It requires a methodology that begins with process redesign, governance
structures that sustain momentum, and distributed ownership that embeds accountability across the organization.
As an IBM interviewee told Futurum, "Technology is usually not the hardest part. Scaling organizational adoption and
governance across the enterprise is where most transformation efforts succeed or fail."

IBM as Client Zero: How IBM built an enterprise AI orchestration model at global scale

7

Important Information About This Report
AUTHORS
Donald Jin
Research Director, Business
Economic Value | The Futurum Group

PUBLISHER
Futurum Research

INQUIRIES
Contact us if you would like to discuss this report and
The Futurum Group will respond promptly.

CITATIONS
This paper can be cited by accredited press and analysts,
but must be cited in context, displaying author’s name,
author’s title, and “The Futurum Group.” Non-press and
non-analysts must receive prior written permission by The
Futurum Group for any citations.

LICENSING
This document, including any supporting materials, is
owned by The Futurum Group. This publication may not be
reproduced, distributed, or shared in any form without the
prior written permission of The Futurum Group.

DISCLOSURES

ABOUT IBM CONSULTING
IBM Consulting helps organizations become smarter
businesses by embedding AI into the systems, workflows,
and decisions that matter most. As the only global
consultancy at scale within a major technology company,
we combine deep industry and domain expertise with
technology leadership and AI-enabled delivery. Powered
by IBM Consulting Advantage, a first-of-its-kind AI delivery
platform, we architect data, orchestrate platforms and
partners, and operationalize AI and people working together.
With trust, security and governance built-in, we help move
organizations from fragmented AI pilots to enterprisewide transformation. Learn more at ibm.com/consulting

ABOUT THE FUTURUM GROUP
The Futurum Group is an independent research, analysis,
and advisory firm, focused on digital innovation and marketdisrupting technologies and trends. Every day our analysts,
researchers, and advisors help business leaders from around
the world anticipate tectonic shifts in their industries and
leverage disruptive innovation to either gain or maintain a
competitive advantage in their markets.

The Futurum Group provides research, analysis, advising,
and consulting to many high-tech companies, including those
mentioned in this paper. No employees at the firm hold any
equity positions with any companies cited in this document.

CONTACT INFORMATION: The Futurum Group LLC I futurumgroup.com I (833) 722-5337
© 2026 The Futurum Group. All rights reserved.
</reference>

<statements>
1. IBM has implemented AI extensively within its own operations under the "Client Zero" program, unlocking $4.5 billion in annualized productivity savings over three years by embedding AI, hybrid cloud, and automation into HR, IT, finance, procurement, supply chain, sales, and customer support workflows. AskHR, an AI‑powered HR agent built on watsonx, handles roughly 16 million employee interactions annually, fully resolving 94% without human intervention and reducing HR operating costs by 40%. AI‑enhanced IT support and modernization have reduced infrastructure costs by about 30% and delivered over $600 million in savings, while AI‑powered customer support generated approximately $191 million in annualized operational savings.
2. Across these firms, headline investments are substantial: Accenture’s $3 billion Data & AI program; PwC US’s $1 billion generative‑AI commitment; Deloitte’s multibillion technology learning and development spend; and IBM’s internal Client Zero transformation delivering $4.5 billion in productivity gains. These figures signal a strategic view of AI as a core growth and productivity engine rather than a peripheral capability.
3. All major firms are running large‑scale talent programs: Accenture’s gen‑AI and agentic‑AI fundamentals for hundreds of thousands of employees; Deloitte’s AI Academy and Academy for AI; PwC’s My AI; EY’s AI Academy; IBM’s watsonx AI Labs and internal training under Client Zero; and Capgemini’s embedded generative‑AI curricula. These programs combine technical training with business context, governance, and responsible‑AI principles, reflecting a view that AI fluency must be pervasive across roles, from executives to developers.
4. Strategically, firms are emphasizing responsible AI—governance frameworks, ethics, bias mitigation, data privacy, and regulatory compliance—often as a distinct advisory offering. Agentic AI (autonomous or semi‑autonomous AI agents that act across systems) is emerging as a focal point in Accenture’s agentic‑AI investments, Capgemini’s Resonance/RAISE frameworks, Deloitte’s and PwC’s advanced automation offerings, and IBM’s orchestration models. Over the next several years, advisory services around AI regulation, auditing, and compliance are expected to become significant revenue streams for these firms.
5. Global consulting firms are converging on a model where AI—particularly generative and agentic AI—underpins both how they serve clients and how they run their own businesses. Large capital investments, proprietary platforms, extensive ecosystems, industry‑specific solutions, and workforce‑scale upskilling programs together position these firms as central orchestrators of AI adoption in enterprises worldwide. For organizations engaging these firms, the key differentiators lie in depth of proprietary tooling, strength of ecosystem partnerships, maturity of governance frameworks, and demonstrated case‑study outcomes across relevant industries.
</statements>

Begin the assessment now. Output only the JSON list, without any conversational text or explanations.