You will be provided with a research report. The body of the report will contain some citations to references.

Citations in the main text may appear in the following forms:
1. A segment of text + space + number, for example: "Li Qiang constructed a socioeconomic status index (SES) based on income, education, and occupation, dividing society into 7 levels 15"
2. A segment of text + [number], for example: "Li Qiang constructed a socioeconomic status index (SES) based on income, education, and occupation, dividing society into 7 levels[15]"
3. A segment of text + [number†(some line numbers, etc.)], for example: "Li Qiang constructed a socioeconomic status index (SES) based on income, education, and occupation, dividing society into 7 levels[15†L10][5L23][7†summary]"
4. [Citation Source](Citation Link), for example: "According to [ChinaFile: A Guide to Social Class in Modern China](https://www.chinafile.com/reporting-opinion/media/guide-social-class-modern-china)'s classification, Chinese society can be divided into nine strata"

Please identify **all** instances where references are cited in the main text, and extract (fact, ref_idx, url) triplets. When extracting, pay attention to the following:
1. Since these facts will need to be verified later, you may need to look for some context before and after the citation to ensure that the fact is complete and understandable, rather than just a simple phrase or short expression.
2. If a fact cites multiple references, then it should correspond to two triplets: (fact, ref_idx_1, url_1) and (fact, ref_idx_2, url_2).
3. For the third form of citation (i.e., where the citation source and link appear directly in the text), the ref_idx should be uniformly set to 0.
4. If the main text does not specify the exact location of the citation (for example, only the reference list is listed at the end of the article, without specifying the citation point in the text), please return an empty list.

You should return a JSON list format, where each item in the list is a triplet, for example:
[
    {
        "fact": "Text segment from the original document. Note that Chinese quotation marks should use full-width marks. And add a single backslash before the English quotation mark to make it a readable for python json module.",
        "ref_idx": "The index of the cited reference in the reference list for this text segment.",
        "url": "The URL of the cited reference for this text segment (extracted from the reference list at the end of the research report or from the parentheses at the citation point)."
    }
]

Here is the main text of the research report:
## Executive Summary

The major consulting firms in this evidence pool have announced substantial dollar AI commitments since 2023, with Accenture [1], PwC US [2], EY [4], KPMG [5], Deloitte [6], Capgemini [10], and IBM/Red Hat [3] all disclosing dollar commitments. The common output pattern is platformization: AI Navigator for Enterprise and a Center for Advanced AI at Accenture [1]; Azure OpenAI-based offerings at PwC [2]; EY.ai, EYQ, and EY Fabric at EY [4]; Microsoft 365 Copilot, Azure OpenAI, KPMG Clara, and Digital Gateway at KPMG [5]; a Global AI Simulation Center of Excellence at Deloitte [6]; Sage and other generative-AI tools at Bain [7]; Augmented Engineering and RAISE at Capgemini [8]; and an AI-assisted open-source security clearinghouse at IBM/Red Hat [3].

Talent scaling is equally prominent: 80,000 AI professionals at Accenture [1], 65,000 upskilled PwC US people [2], more than 100,000 EY AI/data credentials [4], a 265,000-person KPMG workforce including 85,000 audit professionals [5], more than 20,000 IBM/Red Hat engineers [3], 18,500 Bain teams equipped with tools [7], Capgemini training of more than 120,000 people [8], and a Capgemini plan to double Data & AI teams to 60,000 [13].

Client-facing examples include Accenture hotel and judicial-system projects [1], PwC insurance, aviation, and healthcare implementations [2], IBM/Red Hat financial-sector early adopters [3], an EY payroll chatbot and assurance capabilities [4], KPMG engagements with Coca-Cola EuroPacific Partners, Radiometer, and Xebia [5], Deloitte supply-chain and drug-discovery simulations [6], Bain internal Sage tools [7], and Capgemini work with Heathrow Airport [10] and engineering offerings [8].

Independent studies, however, caution that many enterprise AI initiatives stall: RAND cites estimates that more than 80% of AI projects fail [11], MIT NANDA reports 95% of organizations getting zero return from GenAI investment despite US$30–40 billion spent [14], and BCG's 2024 AI Radar finds 90% of surveyed leaders are observers or limited experimenters [9].

## The announced money: scale, scope, and comparability

The headline commitments are not directly comparable because they differ in geography, time horizon, and purpose. Accenture's US$3 billion is a three-year investment in its Data & AI practice [1]. PwC's US$1 billion is explicitly a PwC US commitment over three years [2]. EY's US$1.4 billion is described as the investment foundation for EY.ai [4]. KPMG's release states only that the Microsoft cloud and AI commitment is "multibillion dollar" over five years and projects potential incremental growth above US$12 billion [5]. Deloitte's US$3 billion is framed in a March 2025 release as a global GenAI investment through fiscal year 2030 [6]. Capgemini's €2 billion, reported as US$2.2 billion, is a three-year plan announced with first-half 2023 results [13] [10]. IBM and Red Hat's US$5 billion is tied to Project Lightwell, an open-source software security clearinghouse rather than a general consulting AI platform [3].

| Firm / scope | Headline commitment or status in evidence | Main commercial focus |
| --- | --- | --- |
| Accenture | US$3 billion over three years for its Data & AI practice [1] | industry accelerators, prebuilt models, AI Navigator for Enterprise, Center for Advanced AI [1] |
| PwC US | US$1 billion over three years [2] | Microsoft/Azure OpenAI offerings, internal platform modernization, upskilling [2] |
| EY | US$1.4 billion in investments behind EY.ai [4] | EY Fabric, EYQ, governance assets, alliance ecosystem [4] |
| KPMG | multibillion-dollar Microsoft cloud/AI commitment over five years; projected >US$12 billion incremental growth [5] | workforce modernization, audit, tax, advisory [5] |
| Deloitte | US$3 billion GenAI investment through fiscal year 2030 [6] | Global AI Simulation Center of Excellence, Converge, Quartz Atlas AI [6] |
| Capgemini | €2 billion over three years [13]; reported as US$2.2 billion [10] | industry-specific GenAI offers, Data & AI Campus, Google Cloud and Microsoft partnerships [13] |
| IBM/Red Hat | US$5 billion for Project Lightwell [3] | open-source AI security clearinghouse, commercial subscriptions [3] |

Several figures are plans or projections rather than completed spend. Accenture's release labels the investment and anticipated benefits as forward-looking statements [1]. KPMG's >US$12 billion is described as a potential incremental growth opportunity [5]. Deloitte's US$3 billion runs through fiscal year 2030 [6]. Capgemini's €2 billion was a three-year plan announced alongside H1 2023 results, where Cloud, Data & AI activities were cited as drivers of revenue growth [13]. As of 2026-09-13, the evidence also contains 2026 items, including IBM/Red Hat's May 2026 Project Lightwell [3], EY's July 2026 NVIDIA item [4], and Capgemini's May 2026 investment in the OpenAI Deployment Company [8], but it does not provide completion metrics for the 2023 commitments.

For Bain, the evidence gives tool and workforce details but no AI investment amount; Bain's release states that 12 generative-AI tools were rolled out since early 2023 and that the firm has a US$2 billion pro bono consulting pledge by 2035, but the latter is not described as an AI investment [7]. For BCG, the supplied evidence is survey-based rather than an investment announcement [9]. For McKinsey, the evidence available here does not provide a headline AI investment figure or product description; RAND's report only notes that McKinsey has conducted an annual AI survey [11].

## Platforms and delivery assets

The firms are not merely selling AI advisory hours; they are packaging AI into named platforms, internal assistants, and industrialized delivery assets. Accenture's AI Navigator for Enterprise is described as a generative-AI-based platform for business cases, decisions, architectures, algorithms, and responsible policies, while the Center for Advanced AI is dedicated to maximizing generative-AI value and R&D [1]. Accenture also says it will build accelerators for data and AI readiness across 19 industries and prebuilt industry and functional models [1], and it embeds AI in service delivery platforms such as myWizard, SynOps, and MyNav [1].

PwC US's investment is anchored in scalable offerings using OpenAI's GPT-4/ChatGPT and Microsoft's Azure OpenAI Service, plus modernization of internal platforms and a Responsible AI framework [2]. PwC's release also states that its AI capabilities have been recognized by Forrester, Gartner, and IDC [2]. EY.ai embeds AI into EY Fabric, which the release says is used by 60,000 EY clients and more than 1.5 million unique client users, and it introduces EY.ai EYQ, a secure large language model [4]. EY's governance assets include the EY.ai Confidence Index, Maturity Model, and Value Accelerator [4], and the release says EY Fabric powers 80% of the US$50 billion EY business [4].

KPMG's Microsoft alliance uses Microsoft 365 Copilot and Azure OpenAI Service across Audit, Tax, and Advisory [5]. In audit, KPMG says AI and Azure Cognitive Services are infused into KPMG Clara, and Microsoft Fabric integration allows teams to point to client data instead of ingesting it [5]. In tax, Azure OpenAI Service and Microsoft Fabric are integrated into KPMG Digital Gateway, and a generative-AI-powered virtual assistant is described [5]. In advisory, KPMG says it is developing an AI-enabled application development and knowledge platform on Microsoft Azure [5].

Deloitte's Global AI Simulation Center of Excellence couples GenAI with data to produce visualizations, simulations, scenario modeling, digital twins, and multi-agent systems [6]. It is organized around four domains: physical simulations for warehouse automation, logistics design, and infrastructure optimization; process simulations using agentic modeling and demand forecasting via Converge; people simulations for workforce planning and human-machine interaction; and strategic-options simulations for ROI modeling, including Quartz Atlas AI for molecular optimization in drug discovery [6]. The CoE is integrated into Deloitte Experience Centers and augments the Global GenAI Market Incubator [6].

Bain's Sage is a proprietary ChatGPT/GPT-4-powered platform developed in collaboration with OpenAI, designed to synthesize Bain's collective proprietary data and expertise [7]. It is one of 12 generative-AI tools deployed firm-wide, alongside Microsoft Copilot, an Expert Call Guide Draft Generator, and a dataset categorization and sentiment analysis tool [7]. Capgemini's Augmented Engineering portfolio includes Augmented R&D Discovery, Augmented Software Product Engineering, Augmented Product Support & Services, and Augmented Product Technical Publications [8]. The technical publications offering is described as a factory model and workflow assistant designed to reduce data retrieval time from hours to minutes and publication authoring time from weeks to days [8]. Capgemini also uses RAISE, Reliable AI Solution Engineering, to industrialize custom GenAI projects [8].

IBM and Red Hat's Project Lightwell is a trusted enterprise clearinghouse that uses advanced AI capabilities to validate and test fixes across open-source code, offered through commercial subscriptions [3]. It is intended to let enterprises report and resolve vulnerabilities, deploy validated patches, and coordinate upstream disclosures [3]. IBM and Red Hat say they already use more than 62,000 open-source packages with deep expertise in over 10,000, and that the clearinghouse extends that model to independent libraries, language toolchains, AI frameworks, and data streaming platforms [3].

## Where firms say AI is being used

Client-level evidence is thinner than platform evidence, and the named cases in this section rest mainly on vendor announcements rather than audited outcome reports [1] [2] [3] [4] [5] [6] [7] [8] [10].

| Firm | Client or use case | Application scenario | Evidence status |
| --- | --- | --- | --- |
| Accenture | a hotel group and a judicial system [1] | customer query management; synthesis of judicial process information across hundreds of thousands of complex documents [1] | described as current generative-AI projects [1] |
| PwC US | clients in insurance, aviation, and healthcare [2] | Azure OpenAI capabilities [2] | said to save time and costs and accelerate revenue, without detailed metrics [2] |
| IBM/Red Hat | Bank of America, BNY, Citi, Goldman Sachs, JPMorganChase, Mastercard, Morgan Stanley, Royal Bank of Canada, State Street, Visa, Wells Fargo [3] | open-source vulnerability identification, validation, and patching [3] | early adopters already collaborating [3] |
| EY | Intelligent Payroll Chatbot; Assurance capabilities; case-study titles for Novartis, Lion, and a food and pet care leader [4] | payroll questions; risk assessment, predictive analytics, content search, summarization, document intelligence; supply-chain risk; employee journeys [4] | piloted chatbot expected to reduce employer burden by more than 50%; case-study titles are listed without detailed outcomes [4] |
| KPMG | Coca-Cola EuroPacific Partners, Radiometer, Xebia [5] | back-office efficiency; intranet chatbot with Azure Cognitive Search and Azure OpenAI; data platform with Azure Synapse and KPMG One Data Platform [5] | client quote and described deployments [5] |
| Deloitte | warehouse/logistics, consumer demand forecasting, life-sciences drug discovery, power and utilities [6] | simulation, Converge demand forecasting, Quartz Atlas AI molecular optimization, real-time monitoring, preventive maintenance, hazard sensing [6] | described as recent work [6] |
| Bain | internal teams [7] | Sage, Microsoft Copilot, expert-call drafting, dataset categorization and sentiment analysis [7] | firm-wide rollout; the release describes internal tools rather than client cases [7] |
| Capgemini | Heathrow Airport [10]; engineering and R&D clients [8] | AI-based e-commerce and passenger services [10]; R&D discovery, software product engineering, product support, technical publications [8] | working with Heathrow [10]; offerings described as designed solutions [8] |
| BCG | Verizon, New York Life, Kingfisher, SAP executives [9] | efficiency gains, workforce repointing, promo optimization, markdowns, personalization, pricing, customer demand [9] | survey commentary, not BCG engagement case results [9] |

Capgemini's 2023 earnings-related reporting says the group had delivered generative-AI projects in life science, consumer products and retail, and financial services, and was working with Heathrow Airport on AI-based e-commerce and passenger services [10]. It also describes four customer-experience generative-AI assistants: a synthetic design assistant, personalized chatbots, a content and knowledge assistant for customer care, and a product and offers knowledge assistant for sales teams [10].

Independent enterprise evidence points to where value may be appearing. MIT NANDA's report says the biggest ROI is often in back-office automation, with best-in-class examples including BPO elimination of US$2–10 million annually, a 30% reduction in external creative and content agency spend, and US$1 million saved annually on outsourced risk checks in financial services [14]. It also reports front-office wins such as 40% faster lead qualification and 10% customer retention improvement through AI-powered follow-ups and messaging [14]. Mid-market top performers reported average timelines of 90 days from pilot to full implementation, while enterprises took nine months or longer [14].

## Partnerships, governance, and strategic direction

The strategic direction is alliance-led. Microsoft appears in several programs: PwC US's Azure OpenAI relationship [2], KPMG's multibillion-dollar Microsoft cloud and AI commitment [5], EY's early access to Azure OpenAI [4], Capgemini's partnerships with Google Cloud and Microsoft [13], and Bain's use of Microsoft Copilot alongside its OpenAI alliance [7]. Accenture frames its investment as ecosystem work across cloud, data, and AI partners [1]. Capgemini's 2024 release adds Salesforce, AWS, Mistral AI, and Liquid AI, and its 2026 related news describes an investment in the OpenAI Deployment Company [8]. EY's 2026 related news describes NVIDIA-powered enterprise AI capabilities with an industry-first validation of NVIDIA NemoClaw for LangChain [4].

IBM and Red Hat's Project Lightwell incorporates learnings from Anthropic's Project Glasswing and OpenAI's Trust Access for Cyber, and cites Anthropic's report that its Mythos Preview model identified nearly 3,900 high- or critical-severity vulnerabilities in open-source software [3]. PwC also announced an agreement with Harvey, which is backed by the OpenAI Startup Fund and built on OpenAI and ChatGPT technology [2]. EY's alliance ecosystem includes Dell Technologies, IBM, Microsoft, SAP, ServiceNow, Thomson Reuters, and UiPath [4]. KPMG's audit integration includes Microsoft Fabric, enabling teams to point to client data rather than ingest it [5]. Deloitte's CoE is integrated into Experience Centers and augments the Global GenAI Market Incubator [6].

Responsible AI and governance are recurring differentiators. Accenture says its responsible AI framework is part of delivery, its code of ethics, and its compliance program [1]. PwC says it drives governance through a Responsible AI framework and cites its Trust Survey finding that nearly all business leaders prioritize AI-related initiatives, but only 35% say their company will focus on improving governance, monitoring, and reporting of AI systems [2]. EY anchors EY.ai with the Confidence Index, Maturity Model, and Value Accelerator [4]. KPMG frames the alliance as responsible, trustworthy, and safe, and says ethics and security are at the core of advisory platform development [5]. Deloitte says the CoE combines technology-driven insights with human influence and oversight [6]. Capgemini says its engineering approach must manage precision, regulatory compliance, and risk tolerance [8]. BCG finds that organizations whose CEOs participate in responsible AI initiatives realize 58% more business benefits than those whose CEOs are uninvolved [9].

The partnership logic also shows a shift from single-vendor dependence to broader ecosystems. BCG's survey says only 3% of executives consider preexisting partnerships a priority when choosing AI solutions, and winners actively build ecosystems with software providers and GenAI startups [9]. MIT NANDA's report says external partnerships reached deployment about 67% of the time versus about 33% for internally built tools [14]. Fortune's reporting of the MIT report says purchased solutions delivered more reliable results, while companies were often hesitant to share failure rates [12]. IBM and Red Hat position their engineering capacity as a strategic asset at a time when many technology companies use AI to reduce technical headcount [3].

## Talent programs and the skills gap

Talent scaling is central to the announced programs. Accenture plans to double AI talent to 80,000 professionals through hiring, acquisitions, and training [1]. PwC US says it will upskill 65,000 people on AI tools and capabilities as part of My+ [2]. EY says it will roll out bespoke AI learning and development, building on more than 100,000 AI/data credentials awarded since 2018 and the EY Tech MBA launched in 2020 [4]. KPMG says Microsoft cloud and Azure OpenAI capabilities will empower its global workforce of 265,000, and that 85,000 audit professionals using KPMG Clara will be supported by AI and Azure Cognitive Services [5]. IBM and Red Hat will deploy more than 20,000 engineers augmented by advanced AI capabilities for upstream maintenance, vulnerability review, triage, prioritization, secure patch development, dependency hardening, and release engineering [3]. Bain says its AI tools equip 18,500 multi-disciplinary teams and that proper training was part of the rollout [7]. Capgemini says it has trained more than 120,000 team members on generative-AI tools through its Gen AI Campus [8], and its 2023 earnings release says it plans to double Data & AI teams to 60,000 [13].

The evidence also shows tension between firm-level training claims and enterprise adoption gaps. BCG's 2024 survey found that only 6% of companies had trained more than 25% of their people on GenAI tools, 45% of leaders lacked guidance or restrictions on AI use at work, and 59% of executives reported limited or no confidence in their executive team's GenAI proficiency [9]. BCG also says most leaders expect almost half of their workforce to need reskilling in GenAI over the next three years [9]. RAND's interviews report that many interviewees believe talent availability inhibits AI work, though relatively few identified it as the most frequent or impactful failure factor; interviewees said educational programs often focus on model development rather than data cleaning, identifying poor data, or deploying models to production [11]. MIT NANDA's report goes further, saying the core barrier is not infrastructure, regulation, or talent but the learning gap in tools that do not retain feedback, adapt to context, or improve over time [14]. These are different populations—executives in BCG's survey, technical practitioners in RAND's interviews, and enterprise deployments in MIT's report—so they should not be read as a single diagnosis [9] [11] [14].

Workforce impact is also described as uneven. MIT NANDA says most implementations do not drive headcount reduction, but organizations that cross the "GenAI Divide" are beginning to see selective impacts in customer support, software engineering, and administrative functions [14]. Fortune's reporting says companies are increasingly not backfilling positions as they become vacant, with changes concentrated in previously outsourced roles [12]. RAND notes that 84% of business leaders believe AI will have a significant impact on their business, but only 14% say they are fully ready to integrate AI [11].

## Failure evidence and what it means for consulting AI claims

Independent failure evidence complicates the announced-investment narrative. RAND's study, based on interviews with 65 experienced data scientists and engineers, says by some estimates more than 80% of AI projects fail, which it describes as twice the already-high failure rate of non-AI corporate IT projects [11]. It identifies leadership-driven failures as the most common category, with 84% of interviewees citing root causes such as leaders failing to communicate the problem, optimizing the wrong metrics, or changing priorities too rapidly [11]. Data-driven failures follow, with 30 of 50 interviewees discussing persistent data-quality issues, 16 of 50 describing bottom-up failures where teams chase new models rather than business problems, and 10 of 50 noting insufficient domain understanding [11]. The study also says underinvestment in infrastructure and immature technology can cause failure, while nearly all interviewees said compute power was not a limiting factor [11]. It excludes projects that simply use pretrained large language models, and notes that most interviewees were nonmanagerial engineers, which may bias findings away from executive perspectives [11].

MIT NANDA's July 2025 report, based on 300 public deployments, interviews with 52 organizations, and a survey of 153 senior leaders, says that despite US$30–40 billion in enterprise GenAI investment, 95% of organizations are getting zero return, while 5% of integrated pilots extract millions in value [14]. It finds that generic tools are widely adopted—over 80% of organizations have explored or piloted ChatGPT or Copilot and nearly 40% report deployment—but task-specific enterprise tools drop from 60% evaluated to 20% piloted and 5% implemented [14]. The report attributes the gap to a learning gap: tools that do not retain feedback, adapt to context, or improve over time [14]. It also says external partnerships reached deployment about 67% of the time versus about 33% for internal builds [14]. In a hypothetical allocation exercise, sales and marketing captured about 70% of budgets, though the report's notes say the broader ~50% allocation was consistent across interviews and that subcategory breakdowns are directional at best [14]. The report's own limitations note that pilot-to-production figures are directionally accurate based on interviews rather than official company reporting [14]. Fortune's reporting adds that companies surveyed were often hesitant to share failure rates and that the lead author, Aditya Challapally, emphasized flawed enterprise integration rather than model quality as the core issue [12].

BCG's 2024 AI Radar, surveying more than 1,400 C-suite executives in 50 markets and 14 industries, found that 89% rank AI and GenAI as a top-three tech priority for 2024 and 85% will increase AI/GenAI spending, but 90% are observers waiting for GenAI to move beyond the hype or running limited pilots [9]. It says 54% expect cost savings in 2024, with roughly half of those expecting more than 10%, and identifies five winner behaviors: investing in productivity and topline growth, systematic upskilling, vigilance about cost of use, building strategic relationships, and implementing responsible AI [9]. BCG also warns that only 6% of companies have trained more than 25% of their people, 45% lack guidance on AI use, and 19% view cost as the top solution-choice concern [9].

For consulting firms specifically, MIT's disruption index places Professional Services in a middle position: efficiency gains are visible, but client delivery remains largely unchanged, with scores ranging from 1.2 to 2.1 depending on weighting [14]. This does not prove that consulting engagements fail, but it cuts against any inference that announced platform investments automatically equal changed client delivery. The failure studies are not direct audits of consulting engagements: RAND interviews technical practitioners and excludes simple prompt-engineering uses [11], MIT assesses enterprise deployments and notes that its figures are directional rather than official company reporting [14], and BCG's findings are survey-based and self-reported [9].

## Where the evidence ends

The strongest supportable answer is that major consulting and professional-services firms have built AI businesses around announced capital commitments, proprietary or co-developed platforms, hyperscaler and model partnerships, and large-scale workforce programs. The evidence establishes dollar commitments for Accenture [1], PwC US [2], EY [4], KPMG [5], Deloitte [6], Capgemini [13], and IBM/Red Hat [3], and establishes tool rollouts for Bain [7] and market research for BCG [9]. It establishes application scenarios in audit and tax at KPMG [5], simulation in supply chain and life sciences at Deloitte [6], engineering and R&D at Capgemini [8], payroll and assurance at EY [4], open-source security at IBM/Red Hat [3], and internal knowledge work at Bain [7].

It does not establish realized return on these investments, firm-level client outcomes, or the status of 2023 three-year commitments by September 2026. The most consequential uncertainty is the gap between platform announcements and enterprise value capture. RAND, MIT, and BCG point to leadership, data, learning, governance, and workflow fit as determinants of value [11] [14] [9]. To change the judgment, one would need audited spend against commitments, client-level outcome metrics, post-2024 updates from Accenture, PwC, EY, KPMG, and Bain, and firm-specific evidence for McKinsey and BCG's own AI investment programs.

## References

[1] Accenture to Invest $3 Billion in AI to Accelerate Clients’ Reinvention — https://newsroom.accenture.com/news/2023/accenture-to-invest-3-billion-in-ai-to-accelerate-clients-reinvention
[2] PwC US makes $1 billion investment to expand and scale AI capabilities — https://www.pwc.com/us/en/about-us/newsroom/press-releases/pwc-us-makes-billion-investment-in-ai-capabilities.html
[3] IBM and Red Hat Commit $5 Billion to Redefine the Future of Open Source in the AI Era — https://newsroom.ibm.com/2026-05-28-ibm-and-red-hat-commit-5-billion-to-redefine-the-future-of-open-source-in-the-ai-era
[4] EY announces launch of artificial intelligence platform EY.ai following US$1.4b investment | EY - Global — https://www.ey.com/en_gl/newsroom/2023/09/ey-announces-launch-of-artificial-intelligence-platform-ey-ai-following-us-1-4b-investment
[5] KPMG and Microsoft enter landmark agreement to put AI at the forefront of professional services — https://kpmg.com/us/en/media/news/kpmg-microsoft-agreement-2023.html
[6] Deloitte drives impact and scale in new Global AI Simulation Center of Excellence to create real-time, seamless business insights and innovation — https://www.deloitte.com/global/en/about/press-room/global-gen-ai-simulation-center-of-excellence.html
[7] Bain & Company makes pioneering deployments of state-of-the-art AI tools worldwide | Bain & Company — https://www.bain.com/about/media-center/press-releases/2023/bain--company-makes-pioneering-deployments-of-state-of-the-art-ai-tools-worldwide/
[8] Capgemini announces ‘augmented engineering’ offerings powered by Gen AI - Capgemini — https://www.capgemini.com/news/press-releases/capgemini-announces-augmented-engineering-offerings-powered-by-gen-ai/
[9] From Potential to Profit with GenAI | BCG — https://www.bcg.com/publications/2024/from-potential-to-profit-with-genai
[10] IT Giant Capgemini Will Invest $2.2 Billion in Generative AI — https://news.bloomberglaw.com/artificial-intelligence/it-giant-capgemini-will-invest-2-2-billion-in-generative-ai
[11] The Root Causes of Failure for Artificial Intelligence Projects and How They Can Succeed: Avoiding the Anti-Patterns of AI | RAND — https://www.rand.org/pubs/research_reports/RRA2680-1.html
[12] MIT report: 95% of generative AI pilots at companies are failing | Fortune — https://fortune.com/2025/08/18/mit-report-95-percent-generative-ai-pilots-at-companies-failing-cfo
[13] Capgemini reports strong half-year performance:robust growth and improved operating margin - Capgemini — https://www.capgemini.com/news/press-releases/capgemini-reports-strong-half-year-performance-robust-growth-and-improved-operating-margin/
[14] https://vertical.com/wp-content/uploads/2025/11/v0.1_State_of_AI_in_Business_2025_Report.pdf — https://vertical.com/wp-content/uploads/2025/11/v0.1_State_of_AI_in_Business_2025_Report.pdf


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