{ "docId": "019dd923-5de0-76bd-a167-b6263dfec84f", "docSlug": "d97350c7b2b6040b", "documentTitle": "The front-runners’ guide to scaling AI Lessons from industry leaders", "authorId": "Accenture", "authorName": "Accenture", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 36, "pageCount": 39, "prevPage": 35, "nextPage": 37, "slideType": "appendix_methodology", "function": "summarize", "density": "dense", "nDataPoints": 4, "notes": "Describes hierarchical clustering (Ward's linkage, Gower's distance) and validation via latent class analysis, followed by financial correlation findings.", "elementsJson": [ "paragraph" ], "metadataConfidence": 1, "imagePath": null, "slideHref": "/slides/019dd923-5de0-76bd-a167-b6263dfec84f/36", "deckHref": "/decks/019dd923-5de0-76bd-a167-b6263dfec84f", "deckJsonHref": "/decks/019dd923-5de0-76bd-a167-b6263dfec84f.json", "deckAnchorHref": "/decks/019dd923-5de0-76bd-a167-b6263dfec84f#slide-36", "components": [ { "bbox": null, "kind": "callout", "text": "These results highlight the financial benefits of AI maturity and scaling strategic bets—though further research is needed to establish a causal link between AI adoption and superior financial performance.", "attrs": null, "subkind": null, "toolName": "Visual emphasis", "toolSlug": "visual-emphasis", "confidence": null, "componentId": "019dd951-a673-77f6-86b6-2b930149233b", "frameworkName": null, "frameworkSlug": null }, { "bbox": null, "kind": "metric", "text": "Revenue growth: 7%", "attrs": null, "subkind": "primary", "toolName": "Quantification", "toolSlug": "quantification", "confidence": null, "componentId": "019dd951-a673-77f6-86b6-2e819c74e734", "frameworkName": null, "frameworkSlug": null }, { "bbox": { "h": 0.45, "w": 0.42, "x": 0.51, "y": 0.18 }, "kind": "paragraph", "text": "In our statistical analysis, we examined the correlation between companies’ AI maturity levels, the extent to which they have scaled strategic bets and their financial performance. Our findings indicate a strong, statistically significant correlation, even after controlling for region and industry effects. Specifically, in 2023, front-runners with annual revenues of more than $10 billion saw their revenue grow 7 percentage points faster, on average, than did companies that are still experimenting with AI. Compared to the three other groups of companies, front-runners also earned a return on invested capital that was 4 percentage points higher, on average, as well as a total shareholder return that was 6 percentage points higher. These results highlight the financial benefits of AI maturity and scaling strategic bets—though further research is needed to establish a causal link between AI adoption and superior financial performance.", "attrs": null, "subkind": "paragraph", "toolName": null, "toolSlug": null, "confidence": null, "componentId": "69cb2b38-9b69-4a5e-9869-2e8fa511b54b", "frameworkName": null, "frameworkSlug": null }, { "bbox": { "h": 0.65, "w": 0.42, "x": 0.07, "y": 0.18 }, "kind": "paragraph", "text": "To identify distinct groups of companies based on the maturity of the 10 aforementioned capabilities, we applied “hierarchical clustering,” using maturity levels across those 10 capabilities as input variables. Gower’s distance was used to measure similarity, allowing us to effectively group companies with comparable levels of AI maturity. To determine how clusters were merged, we evaluated multiple linkage methods—single, complete and Ward’s. We ultimately selected Ward’s linkage for its ability to produce well-separated, interpretable clusters. The optimal number of clusters was identified using dendrogram analysis and the silhouette score, ensuring that the final segmentation captured meaningful differences in AI maturity across companies. To validate our clustering approach, we compared our results with latent class analysis, a probability-based classification method that assigns companies to clusters with a degree of uncertainty, rather than deterministic boundaries. The two methods exhibited an overall concordance of 85%, indicating strong alignment between the segmentation results. Notably, for companies classified as AI-reinvention ready, the concordance rate increased to 95%, suggesting that this group is particularly well-defined and consistently identified across different clustering techniques. This high level of agreement, moreover, reinforces the robustness of our classification methodology and confirms that front-runners exhibit distinct characteristics that make them easily identifiable, regardless of the clustering approach.", "attrs": null, "subkind": "paragraph", "toolName": null, "toolSlug": null, "confidence": null, "componentId": "ffa04394-759b-4984-a9a9-ae6cdc54cd88", "frameworkName": null, "frameworkSlug": null }, { "bbox": { "h": 0.03, "w": 0.15, "x": 0.07, "y": 0.13 }, "kind": "title", "text": "Clustering", "attrs": null, "subkind": "headline", "toolName": null, "toolSlug": null, "confidence": null, "componentId": "84b6daa2-7ff0-4d42-a5bb-bfee444ee1a0", "frameworkName": null, "frameworkSlug": null }, { "bbox": { "h": 0.03, "w": 0.22, "x": 0.51, "y": 0.13 }, "kind": "title", "text": "Financial performance", "attrs": null, "subkind": "headline", "toolName": null, "toolSlug": null, "confidence": null, "componentId": "8dcabf5a-6b02-49b9-b7af-2bcdc54bcbc9", "frameworkName": null, "frameworkSlug": null } ], "metrics": [ { "metricRaw": "Revenue growth", "numberRaw": "7%", "numberKind": "percent", "actionTitle": null, "calloutText": "These results highlight the financial benefits of AI maturity and scaling strategic bets—though further research is needed to establish a causal link between AI adoption and superior financial performance.", "numberScale": null, "numberValue": 7, "metricFamily": "growth_rate", "numberCurrency": null } ], "tools": [ { "name": "2x2 matrix", "slug": "matrix-2x2", "agent": null, "layer": "slide", "matchId": "b0da7af7-2fd1-4200-91e8-7e58afdfe11f", "evidence": "paragraph/paragraph: To identify distinct groups of companies based on the maturity of the 10 aforementioned capabilities, we applied “hierarchical clustering,” using maturity levels across those 10 capabilities as input", "confidence": 0.5 }, { "name": "Concrete Language", "slug": "concrete-language", "agent": "Storyteller", "layer": "slide", "matchId": "019dd95a-0f08-73d3-9881-22d3efa071f2", "evidence": "Methodology specifics on clustering and financial-performance link.", "confidence": 70 } ], "frameworks": [ { "name": "analytical-method", "slug": null, "matchId": "cd12dfa0-3adc-4ece-985d-ae76c1726454", "evidence": "Describes the use of hierarchical clustering and latent class analysis for segmentation.", "confidence": 0.9 } ], "arcBeats": [], "loops": [], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }