{ "schemaVersion": "deck-intelligence.v1", "id": "019dd923-5de0-76bd-a167-b6263dfec84f", "slug": "d97350c7b2b6040b", "title": "The front-runners’ guide to scaling AI Lessons from industry leaders", "subtitle": "Accenture", "author": "Accenture", "pageCount": 39, "kind": "consulting-deck", "orientation": "landscape", "aspectRatio": 1.778, "document": { "id": "019dd923-5de0-76bd-a167-b6263dfec84f", "slug": "d97350c7b2b6040b", "title": "The front-runners’ guide to scaling AI Lessons from industry leaders", "rawTitle": "The front-runners’ guide to scaling AI Lessons from industry leaders", "authorId": "Accenture", "authorName": "Accenture", "authorDisplay": "Accenture", "kind": { "slug": "consulting-deck", "label": "Consulting deck" }, "sourceType": { "slug": "strategy_consulting", "label": "Strategy consulting" }, "fileName": "Accenture-Front-Runners-Guide-Scaling-AI-2025-POV.pdf", "sourceUrl": "https://www.accenture.com/content/dam/accenture/final/accenture-com/document-3/Accenture-Front-Runners-Guide-Scaling-AI-2025-POV.pdf", "sourceDate": null, "presentationDate": null, "ingestedAt": "2026-04-24 07:54:31+00", "status": "ready", "pageCount": 39, "orientation": "landscape", "aspectRatio": 1.778, "targetCompany": null, "targetTicker": null, "metadata": { "local_root": "/tmp/legacy-images/d97350c7b2b6040b", "origin_corpus": "consulting" } }, "coverage": { "pageCount": 39, "pagesIndexed": 39, "pagesWithImages": 39, "pagesWithMetadata": 39, "imageCoveragePct": 100, "metadataCoveragePct": 100, "componentCount": 224, "chartCount": 5, "metricSlides": 17, "toolMatches": 98, "documentToolMatches": 0, "frameworkMatches": 14, "arcMatches": 2, "beatMatches": 8, "loopMatches": 6, "patternMatches": 0, "storymakersMatch": true, "scoreAvailable": true, "reviewAvailable": true, "activistThesisAvailable": false, "pitchdeckMetadataAvailable": false, "pitchdeckProfileAvailable": false }, "blocks": [], "matches": { "arcs": [ { "arc": { "name": "The Consultant's Gambit", "slug": "consultants-gambit", "status": "active", "bestFor": "Business cases, project proposals, strategic recommendations", "canonId": "019dd956-5e0a-73c8-b305-81c38c959e9c", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Situation & Context -> Problem & Complication -> Solution & Approach -> Evidence & Proof -> Impact & Next Steps", "description": null, "familyLabel": null, "categoryName": "Strategy & Business", "categorySlug": "strategy-business" }, "matchId": "019dd95a-0542-7728-a78e-7e4d30eab6d0", "evidence": "Classic POV pacing: exec summary (p4) -> problem framing (pp7-9) -> what front-runners do (pp10-13) -> industry proof + cases (pp14-20) -> five imperatives + close (pp21-32).", "isPrimary": true, "confidence": 90, "extraction": { "at": "2026-04-29 13:07:28.977035+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null } }, { "arc": { "name": "The Triple Take", "slug": "triple-take", "status": "active", "bestFor": "Data-driven presentations, research findings", "canonId": "019dd956-6221-73ad-bc12-378f8eb1f018", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "The Facts (What) -> The Implications (So What) -> The Action (Now What)", "description": null, "familyLabel": null, "categoryName": "Analysis & Information", "categorySlug": "analysis-information" }, "matchId": "019dd95a-0542-7728-a78e-8168220c35dd", "evidence": "Macro shape also reads What (research findings pp4-19) -> So What (winning big p20) -> Now What (5 imperatives pp22-32).", "isPrimary": false, "confidence": 60, "extraction": { "at": "2026-04-29 13:07:28.977035+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null } } ], "beats": [ { "to": 6, "arc": { "name": "The Consultant's Gambit", "slug": "consultants-gambit", "status": "active", "bestFor": "Business cases, project proposals, strategic recommendations", "canonId": "019dd956-5e0a-73c8-b305-81c38c959e9c", "version": 1, "bodyDocId": "019df22a-2356-73eb-8061-d8c81dc0aa49", "structure": "Situation & Context -> Problem & Complication -> Solution & Approach -> Evidence & Proof -> Impact & Next Steps", "description": null, "familyLabel": null, "categoryName": "Strategy & Business", "categorySlug": "strategy-business" }, "from": 4, "name": "Situation & Context", "beatId": "019dd95a-0682-776c-8e32-e9cd14c0afd3", "matchId": "019dd95a-0542-7728-a78e-7e4d30eab6d0", "beatType": { "name": "Situation & Context", "slug": "consultants-gambit-situation-context", "status": "active", "canonId": "019dd9b8-05ae-741b-992d-65ee83c639b3", "version": 1, "description": null }, "evidence": "Executive summary + 'Get strategic' set the AI-scaling backdrop and financial upside.", "position": 1, "isPrimaryArc": true, "parentBeatType": { "name": "Setup", "slug": "setup", "status": "active", "canonId": "019dd9b8-020e-7505-af9b-d9b9a4679bcc", "version": 1, "description": "Where the deck grounds the reader before doing anything else. 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The exception (Sparkline) ends with new bliss which is functionally a resolution." }, "alignedBlockIds": null, "matchConfidence": 60 } ], "loops": [ { "to": 6, "from": 5, "loop": { "name": "03_aha_moment", "slug": "03-aha-moment", "status": "active", "bestFor": "Data-heavy sections, research findings, analytical arguments", "canonId": "019dd956-66f4-7668-ab25-cec9c996c0bc", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "The Problem/Question -> What the Data Shows -> The Insight", "description": "Establish a tension, present data, then deliver the insight that changes everything", "familyLabel": null, "categoryName": "Narrative", "categorySlug": "narrative" }, "matchId": "019dd95a-07fe-70ce-8d3a-99d2b071bafd", "evidence": "p5 sets up the 34% data point; p6 delivers the insight: 3x more likely to be delighted with financials.", "position": 1, "objective": "Reveal that scaling strategic bets correlates with outsized financial performance.", "confidence": 75, "extraction": { "at": "2026-04-29 13:07:29.547081+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null } }, { "to": 9, "from": 8, "loop": { "name": "34_segmentation_split", "slug": "34-segmentation-split", "status": "active", "bestFor": "Customer analysis, market research, resource allocation", "canonId": "019dd956-7398-77a8-9a5f-2e4d39589f42", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "The Aggregate View -> Segment A Behavior -> Segment B Behavior -> The Insight in the Difference", "description": "Divide a whole into meaningful segments to reveal hidden patterns", "familyLabel": null, "categoryName": "Consulting Framework", "categorySlug": "consulting-framework" }, "matchId": "019dd95a-07fe-70ce-8d3a-9f5ce7659397", "evidence": "Web-of-progress + 'Appreciate the Eight' stacked bar splits firms into 8/7/43/42% segments.", "position": 2, "objective": "Split the surveyed population into four AI-maturity segments to isolate front-runners.", "confidence": 90, "extraction": { "at": "2026-04-29 13:07:29.547081+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null } }, { "to": 13, "from": 11, "loop": { "name": "04_tale_two_worlds", "slug": "04-tale-two-worlds", "status": "active", "bestFor": "Competitive analysis, benchmarking, case for change", "canonId": "019dd956-674f-750b-b15d-f082e2717f4e", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "Current State -> Desired State / Benchmark -> The Gap & Implication", "description": "Show the gap between two states to drive urgency or highlight opportunity", "familyLabel": null, "categoryName": "Comparison", "categorySlug": "comparison" }, "matchId": "019dd95a-07fe-70ce-8d3a-a11f9f90d1a7", "evidence": "p11 names the gap (34% scaled bets), p12 waffles compare 4 segments, p13 quotes 19% vs 5% sponsorship.", "position": 3, "objective": "Contrast front-runners vs fast-followers across capability adoption and sponsorship.", "confidence": 80, "extraction": { "at": "2026-04-29 13:07:29.547081+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null } }, { "to": 17, "from": 15, "loop": { "name": "06_zoom_in", "slug": "06-zoom-in", "status": "active", "bestFor": "Technical deep-dives, case studies, detailed analysis", "canonId": "019dd956-6846-74db-82be-dff5b058b072", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "The Big Picture -> Key Area of Focus -> Specific Detail -> Implication", "description": "Start broad, then progressively focus on specific details that prove your point", "familyLabel": null, "categoryName": "Analysis", "categorySlug": "analysis" }, "matchId": "019dd95a-07fe-70ce-8d3a-a43b94e699d3", "evidence": "p15 industry table -> p16 top-3 bets per industry -> p17 QBE Insurance case study detail.", "position": 4, "objective": "Zoom from industry-level rankings to a single illustrative insurance case.", "confidence": 85, "extraction": { "at": "2026-04-29 13:07:29.547081+00", "model": "claude-legacy", "runId": null, "seconds": null, "promptVersion": null } }, { "to": 20, "from": 19, "loop": { "name": "39_benchmark_gap", "slug": "39-benchmark-gap", "status": "active", "bestFor": "Performance improvement, competitive analysis, target setting", "canonId": "019dd956-75a4-7522-8d1a-af94da4703e9", "version": 1, "bodyDocId": "019df22a-2420-77be-bc41-ded96d08cb21", "structure": "Our Performance -> Industry Average -> Best-in-Class -> The Gap = The Opportunity", "description": "Compare performance against best-in-class to quantify the opportunity", "familyLabel": null, "categoryName": "Comparison", "categorySlug": "comparison" }, "matchId": "019dd95a-07fe-70ce-8d3a-aadda05e1e10", "evidence": "p19 bump chart of 5 challenges across 4 segments; 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precedes Analysis, and the five imperatives arrive as explicit answers to the named challenges", "Replace 'Place your bets' (p32) with a concrete next-steps slide listing the 3-5 first moves a non-front-runner should make in the next 90 days, and promote the p30 'five imperatives' figure to be the penultimate recap" ], "closingScore": 55, "openingScore": 62, "topStrengths": [ "Specific, quantified claims anchor nearly every analytical slide — 8% front-runners (p9), 34% of bets scaled (p11), 19% vs 5% CEO sponsorship (p13), 85% vs 2% on 360° value (p20), 2.4x ROI uplift (p22)", "Three industry case studies (insurance p17, BMW p26, Allianz p28) are each tethered to a specific imperative they illustrate, not dropped in as decoration", "Clear analytical build-up from 'who are the front-runners' (p7-9) to 'what do they do differently' (p10-13) to 'where are they concentrated' (p14-17)" ], "topWeaknesses": [ "Five imperative titles (p22, p24, p25, p27, p29) are verb-phrase topic labels, not 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Today, generative artificial intelligence and other forms of AI have flipped the script, bringing the previously unattainable within reach. That’s why the world’s largest companies are investing heavily in data and AI. But reinventing the enterprise with generative AI (gen AI) isn’t simply a matter of deploying a few chatbots. Reinvention is about building advanced AI capabilities like “agentic architecture,” networks of AI agents that go beyond automating routine tasks to orchestrating entire business workflows. Endowed with sophisticated reasoning, AI agents collaborate autonomously to improve quality, productivity and cost-efficiency at scale. Agentic architecture is spreading fast: one-third of the companies we surveyed for this report are already using AI agents to strengthen their innovation capabilities. Reinvention thus requires integrating AI deeply into the core of a company’s strategy. 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A big reason for this, our research also shows, is low data “readiness”—which arises when all types of data, especially unstructured data, are not used to the max. Encouragingly, most business leaders recognize this challenge. For example, 70% of the companies we surveyed acknowledged the need for a strong data foundation when trying to scale AI. Data, of course, isn’t the only obstacle to enterprise reinvention with gen AI. Outdated IT systems, as well as workers’ lack of access to, respectively, gen AI tools, comprehensive training and clear guidance from leadership are significant barriers, too. At the same time, our research revealed that a small minority of companies (“front-runners”) are already achieving considerable success at reinventing their enterprises with gen AI. These companies consistently get one very important thing right: They combine what we call “table stakes” investments in gen AI with “strategic bets” (see below, “Get strategic”). Front-runners, for example, use agentic AI in their table stakes to boost efficiency. 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The micro view, however, shows that not all reinvention-ready companies are equally proficient at scaling strategic bets in gen AI. 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And companies that are experimenting have comparatively low levels of maturity (small webs).", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 18:42:20.075+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "bda27167-3cbf-4113-b77c-a91e1a28308c", "parentComponentId": null }, { "bbox": { "h": 0.09, "w": 0.35, "x": 0.074, "y": 0.253 }, "kind": "paragraph", "text": "Al reinvention-ready companies have, in short, developed strong digital cores, which are essential for scaling AI and data-driven initiatives, ensuring data accessibility, computing performance and security. 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After all, more fast-followers (89%) than front-runners (81%) have already developed the five AI foundational capabilities referenced in Figure 1. To understand why, look first to the new data and AI essential capabilities for gen AI. Here, front-runners have a clear edge: We found that 28% of front-runners have developed all five of these capabilities, compared to only 19% of fast-followers. The edge is also evident when front-runners are compared to other companies. 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For instance, they’re more likely than fast-followers to heavily use zero-party data (44% vs. 4%), second-party data (30% vs. 7%), third-party data (25% vs. 8%) and synthetic data (35% vs. 6%). Fast-followers, in contrast, are only slightly more likely than front-runners to heavily use first-party data (60% vs. 67%) and tacit knowledge (72% vs. 68%).", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 18:42:18.113+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "0dd777a8-7304-42bd-953f-2d74602387b9", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.28, "x": 0.36, "y": 0.13 }, "kind": "paragraph", "text": "Fast followers are nevertheless held back in this area, our research also revealed, because they mostly lack a centralized operating model—such as a “center of excellence” that serves as the focal point for a company’s AI strategy, development and deployment. For example, only 16% of fast-followers have a centralized operating model, while 57% of front-runners do.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 18:42:18.113+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "4f424e57-7d06-408e-8ff3-6bd05a9812fa", "parentComponentId": null }, { "bbox": { "h": 0.12, "w": 0.57, "x": 0.36, "y": 0.63 }, "kind": "paragraph", "text": "But in many other data-related practices, fast-followers lag far behind. For example, 17% of front-runners use “retrieval-augmented generation” to enhance their LLMs, while only 1% of fast-followers do. Similarly, front-runners are much more likely than fast-followers to do things like use “knowledge graphs” to structure and contextualize data (26% v. 3%) and manage data effectively over the entire data lifecycle (22% vs. 6%).", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 18:42:18.113+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "5944635c-acb2-4014-bf25-980761d25a9c", "parentComponentId": null }, { "bbox": { "h": 0.2, "w": 0.28, "x": 0.07, "y": 0.41 }, "kind": "paragraph", "text": "Front-runners prioritize people-centered change, too: They’re four times more likely than fast-followers to focus on cultural issues that impede change; three times more likely to emphasize talent alignment and transparent communication; three times more likely to use insights from behavioral science to continuously monitor the impact of AI-driven change; and two times more likely to offer structured training programs for employees.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 18:42:18.113+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "634f0233-4440-446b-bc55-59c95c97690f", "parentComponentId": null }, { "bbox": { "h": 0.08, "w": 0.28, "x": 0.36, "y": 0.53 }, "kind": "paragraph", "text": "When it comes to data, fast-followers do possess certain advantages. For example, 96% are very strong in data governance, compared to 83% of front-runners. Ditto for data platforms (98% vs. 90%, respectively).", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 18:42:18.113+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "9779376d-709a-48b5-83b5-bc56e8cc8738", "parentComponentId": null }, { "bbox": { "h": 0.1, "w": 0.28, "x": 0.07, "y": 0.63 }, "kind": "paragraph", "text": "To be sure, front-runners don’t have an edge at everything AI-related. Fast-followers, for example, are particularly strong at talent development; 96% of fast-followers focus on cultivating specialized AI talent (such as AI engineers), compared to 88% of front-runners.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 18:42:18.113+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "9d14dfe4-a39a-4898-bdc0-74c8d9b6c8d1", "parentComponentId": null }, { "bbox": { "h": 0.25, "w": 0.28, "x": 0.07, "y": 0.13 }, "kind": "paragraph", "text": "Consider other distinguishing traits of front-runners. These companies are more likely to have strong CEO and board sponsorship for their AI investments than fast-followers (19% vs. 5%, respectively, of surveyed companies). Front-runners are also more likely than fast-followers (59% vs. 36%) to have fully integrated their core AI strategy, critical processes and technology capabilities into a cohesive framework. More broadly, front-runners are three times more likely than other companies to have achieved a high level of maturity with their AI platforms.", "attrs": null, "canon": { "kind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "5f2affc0-941d-4d07-959d-4bfc1930cea4", "version": 1, "description": "Single prose block in the body of a slide." }, "tool": null, "subkind": { "name": "Paragraph", "slug": "paragraph", "status": "active", "canonId": "019de52c-fb80-75af-aa5c-a7a4bfac2433", "version": 1, "description": null }, "framework": null }, "subkind": "paragraph", "groupRole": null, "confidence": null, "extraction": { "at": "2026-05-02 18:42:18.113+00", "model": "google/gemini-3.1-flash-lite-preview-20260303", "runId": null, "seconds": null, "promptVersion": null }, "componentId": "a05ea85e-3e37-48be-a27f-35d17b57106a", "parentComponentId": null }, { "bbox": { "h": 0.15, "w": 0.28, "x": 0.36, "y": 0.33 }, "kind": "paragraph", "text": "Another important differentiator for front-runners is that they’re more likely to be skilled at using and continuously improving autonomous AI agents that are tailored to industry needs. For instance, 65% of front-runners are skilled in this area, compared to 50% of fast-followers. 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This content is provided for general information purposes only, does not take into account the reader's specific circumstances, and is not intended to be used in place of consultation with our professional advisors. Accenture disclaims, to the fullest extent permitted by applicable law, any and all liability for the accuracy and completeness of the information in this document and for any acts or omissions made based on such information. Accenture does not provide legal, regulatory, audit, or tax advice. Readers are responsible for obtaining such advice from their own legal counsel or other licensed professionals. This document refers to marks owned by third parties. All such third-party marks are the property of their respective owners. No sponsorship, endorsement or approval of this content by the owners of such marks is intended, expressed or implied. Copyright © 2025 Accenture. All rights reserved. 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Combining innovative research techniques, such as data-science-led analysis, with a deep understanding of industry and technology, our team of 300 researchers in 20 countries publish hundreds of reports, articles and points of view every year. Our thought-provoking research developed with world leading organizations helps our clients embrace change, create value and deliver on the power of technology and human ingenuity. 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We are a talent- and innovation-led company with approximately 801,000 people serving clients in more than 120 countries. Technology is at the core of change today, and we are one of the world's leaders in helping drive that change, with strong ecosystem relationships. We combine our strength in technology and leadership in cloud, data and AI with unmatched industry experience, functional expertise and global delivery capability. Our broad range of services, solutions and assets across Strategy & Consulting, Technology, Operations, Industry X and Song, together with our culture of shared success and commitment to creating 360° value, enable us to help our clients reinvent and build trusted, lasting relationships. We measure our success by the 360° value we create for our clients, each other, our shareholders, partners and communities. 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