{ "docId": "019dd923-5ca1-7489-b636-83d9001ae71a", "docSlug": "1f4828d675dd67cf", "documentTitle": "Insights from the leading edge of generative AI adoption", "authorId": "Deloitte", "authorName": "Deloitte", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "strategy_consulting", "sourceTypeLabel": "Strategy consulting", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.778, "pageNumber": 18, "pageCount": 34, "prevPage": 17, "nextPage": 19, "slideType": "risk_register", "function": "diagnose", "density": "overcrowded", "nDataPoints": 13, "notes": "The slide uses a custom bubble-path diagram to visualize survey data on risk management actions.", "elementsJson": [ "paragraph", "bubble_chart", "headline_text" ], "metadataConfidence": 1, "imagePath": null, "slideHref": "/slides/019dd923-5ca1-7489-b636-83d9001ae71a/18", "deckHref": "/decks/019dd923-5ca1-7489-b636-83d9001ae71a", "deckJsonHref": "/decks/019dd923-5ca1-7489-b636-83d9001ae71a.json", "deckAnchorHref": "/decks/019dd923-5ca1-7489-b636-83d9001ae71a#slide-18", "components": [ { "bbox": null, "kind": "callout", "text": "Some of the surveyed organizations were already actively managing generative AI implementation risks through actions such as monitoring regulatory requirements and ensuring compliance (47%).", "attrs": null, "subkind": null, "toolName": "Visual emphasis", "toolSlug": "visual-emphasis", "confidence": null, "componentId": "019dd951-ba8c-7422-8543-7c7a0539e0b2", "frameworkName": null, "frameworkSlug": null }, { "bbox": { "h": 0.7, "w": 0.35, "x": 0.6, "y": 0.15 }, "kind": "chart", "text": "Bubble chart showing percentages of organizations taking specific risk management actions.", "attrs": null, "subkind": "bubble", "toolName": null, "toolSlug": null, "confidence": null, "componentId": "eb47a08b-5c1e-4bcf-8bdd-64bf999ddc35", "frameworkName": null, "frameworkSlug": null }, { "bbox": null, "kind": "metric", "text": "Percentage of organizations: 47%", "attrs": null, "subkind": "primary", "toolName": "Quantification", "toolSlug": "quantification", "confidence": null, "componentId": "019dd951-ba8c-7422-8543-839a59a07ed1", "frameworkName": null, "frameworkSlug": null }, { "bbox": { "h": 0.35, "w": 0.45, "x": 0.05, "y": 0.1 }, "kind": "paragraph", "text": "Now: Key findings\nSpecific generative AI risks and concerns include inaccurate results and information (i.e., “hallucinations”); legal risks such as plagiarism, copyright infringement, and liability for errors; privacy and data ownership challenges; lack of transparency, explainability and accountability; and systemic bias. The latter exemplifies another category of risk in which AI amplifies and exacerbates a problem that already exists, such as propagating and systematizing existing social biases, facilitating and accelerating the spread of misinformation, helping criminals commit crimes, or fanning the flames of political divisiveness.", "attrs": null, "subkind": "paragraph", "toolName": null, "toolSlug": null, "confidence": null, "componentId": "5659f9d6-3d5b-4235-98a3-59420db08337", "frameworkName": null, "frameworkSlug": null }, { "bbox": { "h": 0.35, "w": 0.45, "x": 0.05, "y": 0.6 }, "kind": "paragraph", "text": "Some of the surveyed organizations were already actively managing generative AI implementation risks through actions such as monitoring regulatory requirements and ensuring compliance (47%), establishing a governance framework for generative AI (46%), and conducting internal audits and testing on generative AI tools and applications (42%) (figure 8). However, such organizations are in the minority and their actions barely scratch the surface of the challenge. This is especially true given that regulatory requirements typically lag behind the pace of technology innovation—although a US presidential executive order and the European Union’s ambitious Artificial Intelligence Act are clear signs government leaders in many parts of the world are taking the issue of AI risk very seriously.", "attrs": null, "subkind": "paragraph", "toolName": null, "toolSlug": null, "confidence": null, "componentId": "dfa1040a-4545-4623-bf3c-cfac379f45a5", "frameworkName": null, "frameworkSlug": null }, { "bbox": { "h": 0.15, "w": 0.45, "x": 0.05, "y": 0.45 }, "kind": "paragraph", "text": "According to the business and technology leaders we surveyed during fourth quarter 2023, the biggest concerns related to governance were: lack of confidence in results (36%), intellectual property issues (35%), misuse of client or customer data (34%), ability to comply with regulations (33%), and lack of explainability / transparency (31%).", "attrs": null, "subkind": "paragraph", "toolName": null, "toolSlug": null, "confidence": null, "componentId": "ee0fe906-7f8f-4cdf-b243-772f08a8a591", "frameworkName": null, "frameworkSlug": null }, { "bbox": { "h": 0.1, "w": 0.35, "x": 0.6, "y": 0.85 }, "kind": "source-note", "text": "Figure 8. Q: What is your organization currently doing to actively manage the risks around your generative AI implementations? (Oct./Dec. 2023) N (Total) = 2,835", "attrs": null, "subkind": null, "toolName": null, "toolSlug": null, "confidence": null, "componentId": "c58636a2-5a82-4da9-8ce0-076a338abe33", "frameworkName": null, "frameworkSlug": null }, { "bbox": { "h": 0.1, "w": 0.3, "x": 0.65, "y": 0.05 }, "kind": "title", "text": "Managing generative AI implementation risk", "attrs": null, "subkind": "headline", "toolName": null, "toolSlug": null, "confidence": null, "componentId": "fdfe334d-58f3-4474-818b-088a0ab18a16", "frameworkName": null, "frameworkSlug": null } ], "metrics": [ { "metricRaw": "Percentage of organizations", "numberRaw": "47%", "numberKind": "percent", "actionTitle": null, "calloutText": "Some of the surveyed organizations were already actively managing generative AI implementation risks through actions such as monitoring regulatory requirements and ensuring compliance (47%).", "numberScale": null, "numberValue": 47, "metricFamily": "survey_sentiment", "numberCurrency": null } ], "tools": [], "frameworks": [], "arcBeats": [ { "to": 20, "from": 13, "beatId": "1a670dc9-17c6-4a45-80e7-2c0b658fed5b", "arcName": "The Consultant's Gambit", "arcSlug": "consultants-gambit", "beatName": "Evidence & Proof", "beatSlug": "consultants-gambit-evidence-proof", "evidence": "Data tables, industry trends, and risk register", "position": 2, "confidence": 0.8, "parentBeatName": "Evidence", "parentBeatSlug": "evidence" } ], "loops": [ { "to": 22, "from": 11, "name": "Cost Of Inaction", "slug": "27-cost-of-inaction", "bestFor": "Urgent budget requests, compliance, risk mitigation", "matchId": "8dd0f86a-2675-4c7a-b842-0728e098e8e2", "evidence": "Slides discussing current efforts, societal impacts, and leader concerns", "position": 0, "objective": "Highlighting the risks of not adopting generative AI", "structure": "The Status Quo -> The Hidden Costs Accumulating -> The Future State of Inaction -> The Tipping Point", "confidence": 0.6, "description": "Quantify what happens if the audience does nothing" }, { "to": 20, "from": 14, "name": "Ladder Abstraction", "slug": "28-ladder-abstraction", "bestFor": "Training, thought leadership, making complex ideas accessible", "matchId": "a8072f27-eff0-49bb-9470-efca58d935fc", "evidence": "Slides on off-the-shelf solutions, talent, governance, and risk", "position": 1, "objective": "Showing the complexity of generative AI adoption", "structure": "The Concrete Example -> The Pattern It Represents -> The Universal Principle -> Back to Specific Application", "confidence": 0.6, "description": "Move between concrete examples and abstract principles to make ideas stick" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }