[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"node-by-slug-\u002Fen\u002Finsights\u002Fwhy-ai-time-savings-dont-create-business-value-en":3,"related-content-view":452},["Reactive",4],{"entity":5,"errors":-1},{"__typename":6,"status":7,"metatag":8,"id":202,"path":203,"title":204,"translations":205,"sharedHeaderRefMedia":213,"sharedTeaserTitle":13,"sharedTeaserText":240,"sharedTeaserRefMedia":242,"sharedParagraphs":244,"sharedTaxrefIndustries":440,"sharedTaxrefExpertises":444,"sharedIntroText":448,"sharedCtaLink":215,"sharedDate":450,"sharedAuthor":215},"NodeInsight",true,[9,14,18,21,25,30,34,38,42,46,50,54,58,62,66,69,73,76,79,81,84,87,91,95,99,103,107,111,115,119,123,127,131,135,139,142,145,148,151,154,157,160,163,166,170,174,177,181,185,189,192,196,200],{"tag":10,"attributes":11},"meta",{"name":12,"content":13},"title","AI business value: Why time savings don’t translate into ROI",{"tag":10,"attributes":15},{"name":16,"content":17},"description","Discover why AI time savings often fail to deliver real AI business value, and how enterprises can bridge the productivity gap to achieve measurable ROI, margin, and revenue impact.",{"tag":10,"attributes":19},{"name":20,"content":17},"abstract",{"tag":10,"attributes":22},{"name":23,"content":24},"icbm","51.525772, -0.082716",{"tag":26,"attributes":27},"link",{"rel":28,"href":29},"canonical","https:\u002F\u002Fcms.zuehlke.com\u002Fen\u002Finsights\u002Fwhy-ai-time-savings-dont-create-business-value",{"tag":10,"attributes":31},{"name":32,"content":33},"robots","index, 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Zühlke",{"tag":10,"attributes":67},{"property":68,"content":17},"og:description",{"tag":10,"attributes":70},{"property":71,"content":72},"og:image","https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Fmax_1300x1300\u002Fpublic\u002Fimages\u002Fpillar_1_pillar_page.png?itok=JF2GJTz-",{"tag":10,"attributes":74},{"property":71,"content":75},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fimages\u002Fzuhlke_innovation_social.jpg",{"tag":10,"attributes":77},{"property":78,"content":72},"og:image:url",{"tag":10,"attributes":80},{"property":78,"content":75},{"tag":10,"attributes":82},{"property":83,"content":72},"og:image:secure_url",{"tag":10,"attributes":85},{"property":83,"content":86},"https:\u002F\u002Fwww.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fimages\u002Fzuhlke_innovation_social.jpg",{"tag":10,"attributes":88},{"property":89,"content":90},"og:image:alt","image",{"tag":10,"attributes":92},{"property":93,"content":94},"og:updated_time","2026-06-25",{"tag":10,"attributes":96},{"property":97,"content":98},"place:location:longitude","7.438014",{"tag":10,"attributes":100},{"property":101,"content":102},"place:location:latitude","46.947944",{"tag":10,"attributes":104},{"property":105,"content":106},"og:street_address","80 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aren’t AI time savings translating into real AI business value?",[206,209],{"path":203,"langcode":207},{"id":208},"en",{"path":210,"langcode":211},"\u002Fde\u002Finsights\u002Fwarum-ki-zeitersparnis-keinen-geschaeftswert-schafft",{"id":212},"de",{"id":214,"sharedMediaLegend":215,"mediaImage":216,"name":239},"173153",null,{"url":217,"width":218,"height":219,"alt":220,"title":215,"size":221,"mime":222,"variations":215,"headerMinimalVariations":223},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fimages\u002Fpillar_1_pillar_page.png",1280,720,"Three colleagues collaborating around a table in an office; a woman with curly hair smiles while listening to a seated colleague who is gesturing, while a third colleague looks on, with a laptop and drinks visible, conveying a positive team interaction.",1038100,"image\u002Fpng",[224,229,230,235,238],{"name":225,"url":226,"width":227,"height":228},"FOCAL_POINT32_SMALL","https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_3_2_small\u002Fpublic\u002Fimages\u002Fpillar_1_pillar_page.png.webp?itok=30ltDe9X",800,533,{"name":225,"url":226,"width":227,"height":228},{"name":231,"url":232,"width":233,"height":234},"FOCAL_POINT169_MEDIUM","https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_16_9_medium\u002Fpublic\u002Fimages\u002Fpillar_1_pillar_page.png.webp?itok=0FMqrIX6",1200,675,{"name":236,"url":237,"width":218,"height":219},"FOCAL_POINT169_LARGE","https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_16_9_large\u002Fpublic\u002Fimages\u002Fpillar_1_pillar_page.png.webp?itok=srxfQpXH",{"name":236,"url":237,"width":218,"height":219},"pillar_1_pillar_page.png",{"processed":241},"\u003Cp>Discover why AI time savings often fail to deliver real AI business value, and how enterprises can bridge the productivity gap to achieve measurable ROI, margin, and revenue impact.\u003C\u002Fp>",{"id":214,"sharedMediaLegend":215,"mediaImage":243,"name":239},{"url":217,"width":218,"height":219,"alt":220,"title":215,"size":221,"mime":222,"variations":215},[245,252,258,266,310,316,322,328,339,401],{"__typename":246,"id":247,"ctaLink":215,"pgSharedText":248,"pgSharedTitle":215,"pgSharedCtaLink":215},"ParagraphPgText","34569",{"value":249,"processed":250,"format":251},"\u003Cp>After implementing AI programmes and developing the first projects supported by AI, many organisations can point to faster drafting, quicker analysis, or smoother support. Unfortunately, far fewer can show the effect in revenue, margin, or risk reduction.\u003C\u002Fp>\u003Cp>This can seem like a sign that AI is failing, but that is not necessarily true. In most cases, \u003Ca href=\"\u002Fnode\u002F26784\" target=\"_blank\" title=\"Discover why you should treat AI like any other enterprise investment\" data-entity-type=\"node\" data-entity-uuid=\"8dfa4e7f-8a17-46b1-a79d-2ebc73e8b3bc\" data-entity-substitution=\"canonical\">enterprise AI adoption stalls\u003C\u002Fa> not because the technology falls short, but because the organisation is getting faster at individual tasks without converting that speed into end-to-end outcomes.\u003C\u002Fp>\u003Cp>That conversion is the difference between adopting AI as a tool and integrating AI as part of an operating model.  \u003C\u002Fp>\u003Cp>And that’s the reason AI programmes can look promising without necessarily becoming valuable: the gap between \u003Ca href=\"\u002Fnode\u002F26507\" target=\"_blank\" title=\"Explore the often-overlooked aspect of AI adoption: ensuring that time saved translates into meaningful and value-driven activities\" data-entity-type=\"node\" data-entity-uuid=\"ef932e92-2f32-4705-89f2-ad4524de09f4\" data-entity-substitution=\"canonical\">time savings\u003C\u002Fa> and business outcomes is exactly where most AI programmes fail.\u003C\u002Fp>\u003Cp class=\"text-normal\">\u003Cem>This article is part of our 'Value from AI now' series on the three key challenges organisations face when scaling AI initiatives. \u003C\u002Fem>\u003Ca href=\"\u002Fnode\u002F26955\" title=\"Generate measurable value from AI now: how to turn AI into business transformation\" data-entity-type=\"node\" data-entity-uuid=\"4f28673c-5d7b-4d13-8034-1343b0427eea\" data-entity-substitution=\"canonical\">\u003Cem>Explore the full framework here\u003C\u002Fem>\u003C\u002Fa>\u003Cem>.\u003C\u002Fem>\u003C\u002Fp>","\u003Cp>After implementing AI programmes and developing the first projects supported by AI, many organisations can point to faster drafting, quicker analysis, or smoother support. Unfortunately, far fewer can show the effect in revenue, margin, or risk reduction.\u003C\u002Fp>\u003Cp>This can seem like a sign that AI is failing, but that is not necessarily true. In most cases, \u003Ca href=\"\u002Fen\u002Finsights\u002Fai-adoption-challenges-you-have-ai-pilots-but-do-you-have-ai-impact\" target=\"_blank\" title=\"Discover why you should treat AI like any other enterprise investment\" data-entity-type=\"node\" data-entity-uuid=\"8dfa4e7f-8a17-46b1-a79d-2ebc73e8b3bc\" data-entity-substitution=\"canonical\">enterprise AI adoption stalls\u003C\u002Fa> not because the technology falls short, but because the organisation is getting faster at individual tasks without converting that speed into end-to-end outcomes.\u003C\u002Fp>\u003Cp>That conversion is the difference between adopting AI as a tool and integrating AI as part of an operating model.  \u003C\u002Fp>\u003Cp>And that’s the reason AI programmes can look promising without necessarily becoming valuable: the gap between \u003Ca href=\"\u002Fen\u002Finsights\u002Fai-adoption-rethinking-time-and-purpose-in-the-workplace\" target=\"_blank\" title=\"Explore the often-overlooked aspect of AI adoption: ensuring that time saved translates into meaningful and value-driven activities\" data-entity-type=\"node\" data-entity-uuid=\"ef932e92-2f32-4705-89f2-ad4524de09f4\" data-entity-substitution=\"canonical\">time savings\u003C\u002Fa> and business outcomes is exactly where most AI programmes fail.\u003C\u002Fp>\u003Cp class=\"text-normal\">\u003Cem>This article is part of our 'Value from AI now' series on the three key challenges organisations face when scaling AI initiatives. \u003C\u002Fem>\u003Ca href=\"\u002Fen\u002Fexpertise\u002Fai-implementation\u002Fai-value-now-turn-ai-into-business-transformation\" title=\"Generate measurable value from AI now: how to turn AI into business transformation\" data-entity-type=\"node\" data-entity-uuid=\"4f28673c-5d7b-4d13-8034-1343b0427eea\" data-entity-substitution=\"canonical\">\u003Cem>Explore the full framework here\u003C\u002Fem>\u003C\u002Fa>\u003Cem>.\u003C\u002Fem>\u003C\u002Fp>","full_html",{"__typename":246,"id":253,"ctaLink":215,"pgSharedText":254,"pgSharedTitle":257,"pgSharedCtaLink":215},"34570",{"value":255,"processed":256,"format":251},"\u003Cp>Time saved matters, and it is often the first visible proof that AI is useful. Initial time savings are usually seen in daily tasks, such as drafts, research, and development. \u003Cstrong>But none of those gains is the same thing as business value.\u003C\u002Fstrong>\u003C\u002Fp>\u003Ch3>\u003Cstrong>The three layers: task acceleration, throughput, and economic impact\u003C\u002Fstrong>\u003C\u002Fh3>\u003Cp>There are three very different layers here:\u003C\u002Fp>\u003Cul>\u003Cli data-list-item-id=\"e9593986c1118800fedec593e2cc5b4eb\">\u003Cstrong>Task acceleration\u003C\u002Fstrong> means a person or team completes a piece of work faster\u003C\u002Fli>\u003Cli data-list-item-id=\"eac0feccffaa5cb7bfe77904b65702e28\">\u003Cstrong>Throughput change\u003C\u002Fstrong> means the end-to-end flow moves faster\u003C\u002Fli>\u003Cli data-list-item-id=\"e57e07f34185970b357319701f2ebf969\">\u003Cstrong>Economic impact\u003C\u002Fstrong> means revenue, margin, resilience, or risk actually improve\u003C\u002Fli>\u003C\u002Ful>\u003Cp>Most early AI wins sit in the first category, while executive teams, understandably, care about the third.\u003C\u002Fp>\u003Cp>Gartner’s research (2025, Gartner CFO Leadership Series) proves this reality, showing that \u003Cstrong>74% of CFOs are already seeing time savings from GenAI, but only 5% report cost reductions and 6% see revenue or profit uplift\u003C\u002Fstrong>.\u003C\u002Fp>\u003Cp>In regulated and safety-critical environments there’s an extra nuance: time saved often gets reinvested into assurance, through verification, traceability, and documentation. The problem is when this happens without being planned, measured, or linked to the outcomes it protects.\u003C\u002Fp>","\u003Cp>Time saved matters, and it is often the first visible proof that AI is useful. Initial time savings are usually seen in daily tasks, such as drafts, research, and development. \u003Cstrong>But none of those gains is the same thing as business value.\u003C\u002Fstrong>\u003C\u002Fp>\u003Ch3>\u003Cstrong>The three layers: task acceleration, throughput, and economic impact\u003C\u002Fstrong>\u003C\u002Fh3>\u003Cp>There are three very different layers here:\u003C\u002Fp>\u003Cul>\u003Cli>\u003Cstrong>Task acceleration\u003C\u002Fstrong> means a person or team completes a piece of work faster\u003C\u002Fli>\u003Cli>\u003Cstrong>Throughput change\u003C\u002Fstrong> means the end-to-end flow moves faster\u003C\u002Fli>\u003Cli>\u003Cstrong>Economic impact\u003C\u002Fstrong> means revenue, margin, resilience, or risk actually improve\u003C\u002Fli>\u003C\u002Ful>\u003Cp>Most early AI wins sit in the first category, while executive teams, understandably, care about the third.\u003C\u002Fp>\u003Cp>Gartner’s research (2025, Gartner CFO Leadership Series) proves this reality, showing that \u003Cstrong>74% of CFOs are already seeing time savings from GenAI, but only 5% report cost reductions and 6% see revenue or profit uplift\u003C\u002Fstrong>.\u003C\u002Fp>\u003Cp>In regulated and safety-critical environments there’s an extra nuance: time saved often gets reinvested into assurance, through verification, traceability, and documentation. The problem is when this happens without being planned, measured, or linked to the outcomes it protects.\u003C\u002Fp>","Time saved is a leading indicator, not AI business value",{"__typename":259,"id":260,"pgEmbededCode":261,"fullWidth":7,"edgeToEdge":265},"ParagraphPgCustomCode","34732",{"value":262,"processed":263,"format":264},"\u003Ch2 class=\"title\">\r\n    What value CFOs are seeing from GenAI\u003Cbr>\r\n\u003C\u002Fh2>\r\n\u003Cdiv style=\"width: 100%;\">\u003Cdiv style=\"position: relative; padding-bottom: 33.33%; padding-top: 0; height: 0;\">\u003Ciframe title=\"Time saved is a leading indicator\" frameborder=\"0\" width=\"1200\" height=\"400\" style=\"position: absolute; top: 0; left: 0; width: 100%; height: 100%;\" src=\"https:\u002F\u002Fview.genially.com\u002F69cbcad08127a6d6522ef646\" type=\"text\u002Fhtml\" allowscriptaccess=\"always\" allowfullscreen=\"true\" scrolling=\"yes\" allownetworking=\"all\">\u003C\u002Fiframe> \u003C\u002Fdiv> \u003C\u002Fdiv>\r\n\u003Ch5 class=\"title\">\r\n    Source: 2025 Gartner CFO Leadership Series — Drive Finance Productivity and Performance With AI Webinar Poll\r\n\u003C\u002Fh5>","\u003Ch2 class=\"title\">\n    What value CFOs are seeing from GenAI\u003Cbr>\n\u003C\u002Fh2>\n\u003Cdiv style=\"width: 100%;\">\u003Cdiv style=\"position: relative; padding-bottom: 33.33%; padding-top: 0; height: 0;\">\u003Ciframe title=\"Time saved is a leading indicator\" frameborder=\"0\" width=\"1200\" height=\"400\" style=\"position: absolute; top: 0; left: 0; width: 100%; height: 100%;\" src=\"https:\u002F\u002Fview.genially.com\u002F69cbcad08127a6d6522ef646\" type=\"text\u002Fhtml\" allowscriptaccess=\"always\" allowfullscreen=\"true\" scrolling=\"yes\" allownetworking=\"all\">\u003C\u002Fiframe> \u003C\u002Fdiv> \u003C\u002Fdiv>\n\u003Ch5 class=\"title\">\n    Source: 2025 Gartner CFO Leadership Series — Drive Finance Productivity and Performance With AI Webinar Poll\n\u003C\u002Fh5>","custom_code",false,{"__typename":267,"id":268,"title":269,"lead":270,"tabs":271},"ParagraphTab","34582","The five AI implementation challenges that stop productivity from becoming business value","In practice, we see five recurring  AI implementation challenges across organisations in different sectors.",[272,279,287,294,302],{"infoText":273,"pgSharedParagraphs":274},"1. Productivity gains stay trapped within the team",[275],{"id":276,"ctaLink":215,"pgSharedText":277,"pgSharedTitle":273,"pgSharedCtaLink":215},"34572",{"value":278,"processed":278,"format":251},"\u003Cp>One of the most common patterns we see is what you might call local optimisation.\u003C\u002Fp>\u003Cp>Here’s an example: a team becomes faster at writing, analysing, coding, or summarising  but that improvement remains confined to its own part of the workflow.\u003C\u002Fp>\u003Cp>The wider business outcome still depends on multiple functions working together, so a faster team does not automatically create a faster result. The organisation gets more efficient at one stage, without increasing the rate at which value is actually delivered.\u003C\u002Fp>",{"infoText":280,"pgSharedParagraphs":281},"2. Speed creates pressure elsewhere in the system",[282],{"id":283,"ctaLink":215,"pgSharedText":284,"pgSharedTitle":280,"pgSharedCtaLink":215},"34574",{"value":285,"processed":286,"format":251},"\u003Cp>Sometimes the gain does move beyond the team, but instead of improving the overall outcome, it overloads the next stage. When AI increases the volume or pace of work upstream, downstream teams and \u003Ca href=\"\u002Fnode\u002F26999\" target=\"_blank\" title=\"Discover why trust now matters more than features in deciding which AI initiatives will actually succeed\" data-entity-type=\"node\" data-entity-uuid=\"99261afe-9497-4593-865b-53f7800ff96b\" data-entity-substitution=\"canonical\">governance processes\u003C\u002Fa> have to absorb more decisions, checks, and exceptions. This often results in growing pressure in the system.  \u003C\u002Fp>\u003Cp>There are four patterns that tend to appear in these cases:  \u003C\u002Fp>\u003Cul>\u003Cli data-list-item-id=\"e87bbc61d55694f0eb27a52cf17075a4a\">\u003Cstrong>Cycle times improve far less than expected:\u003C\u002Fstrong> the slowest remaining process step still governs the end-to-end pace\u003C\u002Fli>\u003Cli data-list-item-id=\"e7db7a1477518c543555bef50b30a19d0\">\u003Cstrong>Escalations increase:\u003C\u002Fstrong> People seek cover when accountability or risk boundaries are unclear\u003C\u002Fli>\u003Cli data-list-item-id=\"ea384e7c0be09a5f0927b3476a97e46cf\">\u003Cstrong>Waiting increases:\u003C\u002Fstrong> Queues build at security, data access, architecture review, change-control windows\u003C\u002Fli>\u003Cli data-list-item-id=\"e7166122f488adeae4207f37208aac7a9\">\u003Cstrong>Rework increases:\u003C\u002Fstrong> Downstream teams spend more time validating, reconciling, and documenting\u003C\u002Fli>\u003C\u002Ful>\u003Cp>This is why teams can feel they are increasing speed, but that speed rarely translates into faster results overall: the system is revealing where it cannot absorb speed.\u003C\u002Fp>","\u003Cp>Sometimes the gain does move beyond the team, but instead of improving the overall outcome, it overloads the next stage. When AI increases the volume or pace of work upstream, downstream teams and \u003Ca href=\"\u002Fen\u002Finsights\u002Fscaling-ai-initiatives-why-trust-determines-enterprise-success\" target=\"_blank\" title=\"Discover why trust now matters more than features in deciding which AI initiatives will actually succeed\" data-entity-type=\"node\" data-entity-uuid=\"99261afe-9497-4593-865b-53f7800ff96b\" data-entity-substitution=\"canonical\">governance processes\u003C\u002Fa> have to absorb more decisions, checks, and exceptions. This often results in growing pressure in the system.  \u003C\u002Fp>\u003Cp>There are four patterns that tend to appear in these cases:  \u003C\u002Fp>\u003Cul>\u003Cli>\u003Cstrong>Cycle times improve far less than expected:\u003C\u002Fstrong> the slowest remaining process step still governs the end-to-end pace\u003C\u002Fli>\u003Cli>\u003Cstrong>Escalations increase:\u003C\u002Fstrong> People seek cover when accountability or risk boundaries are unclear\u003C\u002Fli>\u003Cli>\u003Cstrong>Waiting increases:\u003C\u002Fstrong> Queues build at security, data access, architecture review, change-control windows\u003C\u002Fli>\u003Cli>\u003Cstrong>Rework increases:\u003C\u002Fstrong> Downstream teams spend more time validating, reconciling, and documenting\u003C\u002Fli>\u003C\u002Ful>\u003Cp>This is why teams can feel they are increasing speed, but that speed rarely translates into faster results overall: the system is revealing where it cannot absorb speed.\u003C\u002Fp>",{"infoText":288,"pgSharedParagraphs":289},"3. AI is added to existing workflows, not fully integrated",[290],{"id":291,"ctaLink":215,"pgSharedText":292,"pgSharedTitle":288,"pgSharedCtaLink":215},"34576",{"value":293,"processed":293,"format":251},"\u003Cp>AI is often added on top of existing workflows (the thought process being: 'let’s use AI to speed up\u002Fsimplify this process), when in reality it should work the other way around ('we have this process. Could AI be useful here? If so, what would need to change in the process to achieve those gains?')\u003C\u002Fp>\u003Cp>When the first case is the reality and AI is being added without proper integration, the value has to cross into real workflows through friction. Some examples are manual handoffs, regular copy\u002Fpaste, and inconsistent adoption.\u003C\u002Fp>\u003Cp>We regularly see 'shadow AI workflows' appear: people use AI where it’s easy, then translate outputs back into formal processes. The benefits depend on individual behaviour; which means that when workload spikes or people rotate, the gains tend to collapse.\u003C\u002Fp>",{"infoText":295,"pgSharedParagraphs":296},"4. AI ROI measurement breaks down when impact cannot be proved",[297],{"id":298,"ctaLink":215,"pgSharedText":299,"pgSharedTitle":295,"pgSharedCtaLink":215},"34578",{"value":300,"processed":301,"format":251},"\u003Cp>Sometimes value exists, but finance, risk, audit, or leadership cannot see it with enough confidence.\u003C\u002Fp>\u003Cp>This is where AI ROI measurement becomes a critical discipline, and where most organisations lack a structured approach.\u003C\u002Fp>\u003Cp>Typical failure modes are predictable:\u003C\u002Fp>\u003Cul>\u003Cli data-list-item-id=\"e45d0c5f406ec3b4fe321cc0155d7f6ef\">usage metrics replacing outcomes\u003C\u002Fli>\u003Cli data-list-item-id=\"e0341948166aff072641a905fd4a22884\">no stable baseline\u003C\u002Fli>\u003Cli data-list-item-id=\"e489e91cd16720dd5fec67446f1650a4f\">double-counted time savings\u003C\u002Fli>\u003Cli data-list-item-id=\"e87760dfa898ed4738dbeb3511b718eaf\">risk reduction described without measurable proxies\u003C\u002Fli>\u003C\u002Ful>\u003Cp>If an organisation cannot defend the impact, it will never scale the investment.\u003C\u002Fp>","\u003Cp>Sometimes value exists, but finance, risk, audit, or leadership cannot see it with enough confidence.\u003C\u002Fp>\u003Cp>This is where AI ROI measurement becomes a critical discipline, and where most organisations lack a structured approach.\u003C\u002Fp>\u003Cp>Typical failure modes are predictable:\u003C\u002Fp>\u003Cul>\u003Cli>usage metrics replacing outcomes\u003C\u002Fli>\u003Cli>no stable baseline\u003C\u002Fli>\u003Cli>double-counted time savings\u003C\u002Fli>\u003Cli>risk reduction described without measurable proxies\u003C\u002Fli>\u003C\u002Ful>\u003Cp>If an organisation cannot defend the impact, it will never scale the investment.\u003C\u002Fp>",{"infoText":303,"pgSharedParagraphs":304},"5. Efficiency creates more demand, and complexity absorbs the gain ",[305],{"id":306,"ctaLink":215,"pgSharedText":307,"pgSharedTitle":309,"pgSharedCtaLink":215},"34580",{"value":308,"processed":308,"format":251},"\u003Cp>When AI makes it cheaper to create code, content, analysis, or prototypes, organisations usually create more of them.\u003C\u002Fp>\u003Cp>That sounds positive, but it often expands the backlog, increases integration pressure, and creates more operational complexity.\u003C\u002Fp>\u003Cp>Without portfolio management, AI can increase demand faster than the business can absorb it.\u003C\u002Fp>","5. Efficiency creates more demand, and complexity absorbs the gain",{"__typename":246,"id":311,"ctaLink":215,"pgSharedText":312,"pgSharedTitle":315,"pgSharedCtaLink":215},"34583",{"value":313,"processed":314,"format":251},"\u003Cp>Once leaders understand where value leaks, the next question is where it can reliably reappear (and under what conditions).\u003C\u002Fp>\u003Cp>In organisations seeing measurable outcomes, AI is embedded into the decisions and workflows that move real outcomes: customer journeys, product delivery, and operational\u002Frisk workflows.\u003C\u002Fp>\u003Cp>These are the pathways where AI investment returns become visible and where the economic impact of AI transitions from a slide deck claim into a P&L line.\u003C\u002Fp>\u003Cul>\u003Cli data-list-item-id=\"ef16d14484a7a42803f53ad3dfc39647b\">\u003Cstrong>Revenue impact \u003C\u002Fstrong>appears when AI improves commercial decision-making: qualification, conversion, pricing, next-best action, time-to-approval, or resolution speed in customer journeys.\u003C\u002Fli>\u003Cli data-list-item-id=\"e6c6892dd3d10c195e11295dc1589a76e\">\u003Cstrong>Margin impact \u003C\u002Fstrong>appears when the total cost of delivering an outcome reduces end-to-end. That usually requires redesigning work, reducing hand-offs, controlling exceptions, and removing rework. Faster output alone is not a margin strategy.\u003C\u002Fli>\u003Cli data-list-item-id=\"e178db7dccbc7030f1305b6b59b8728c9\">\u003Cstrong>Risk impact \u003C\u002Fstrong>appears when AI is tied to measurable operational outcomes: fewer incidents, reduced fraud leakage, lower loss drivers, faster detection, faster response, or fewer audit findings.\u003C\u002Fli>\u003C\u002Ful>\u003Cp>In other words, AI business value appears when AI changes the economics of work, not just the speed of individual tasks.\u003C\u002Fp>","\u003Cp>Once leaders understand where value leaks, the next question is where it can reliably reappear (and under what conditions).\u003C\u002Fp>\u003Cp>In organisations seeing measurable outcomes, AI is embedded into the decisions and workflows that move real outcomes: customer journeys, product delivery, and operational\u002Frisk workflows.\u003C\u002Fp>\u003Cp>These are the pathways where AI investment returns become visible and where the economic impact of AI transitions from a slide deck claim into a P&L line.\u003C\u002Fp>\u003Cul>\u003Cli>\u003Cstrong>Revenue impact \u003C\u002Fstrong>appears when AI improves commercial decision-making: qualification, conversion, pricing, next-best action, time-to-approval, or resolution speed in customer journeys.\u003C\u002Fli>\u003Cli>\u003Cstrong>Margin impact \u003C\u002Fstrong>appears when the total cost of delivering an outcome reduces end-to-end. That usually requires redesigning work, reducing hand-offs, controlling exceptions, and removing rework. Faster output alone is not a margin strategy.\u003C\u002Fli>\u003Cli>\u003Cstrong>Risk impact \u003C\u002Fstrong>appears when AI is tied to measurable operational outcomes: fewer incidents, reduced fraud leakage, lower loss drivers, faster detection, faster response, or fewer audit findings.\u003C\u002Fli>\u003C\u002Ful>\u003Cp>In other words, AI business value appears when AI changes the economics of work, not just the speed of individual tasks.\u003C\u002Fp>","Where AI business value does show up: three impact pathways",{"__typename":246,"id":317,"ctaLink":215,"pgSharedText":318,"pgSharedTitle":321,"pgSharedCtaLink":215},"34584",{"value":319,"processed":320,"format":251},"\u003Cp>Across sectors, the organisations that make this transition tend to have four things in common.\u003C\u002Fp>\u003Col>\u003Cli data-list-item-id=\"e2c5c232a07f3078322f5f66d9a1af0f1\">\u003Cstrong>First\u003C\u002Fstrong>, they define measurable KPIs early. They do not stop at hours saved; they actually track the business metric that matters.\u003C\u002Fli>\u003Cli data-list-item-id=\"e2076850957ee58bc4a6f42f62e28e046\">\u003Cstrong>Second\u003C\u002Fstrong>, AI is embedded into the critical path of work, not bolted on at the side.\u003C\u002Fli>\u003Cli data-list-item-id=\"e8a422f534e9b6477e440463d77e88802\">\u003Cstrong>Third\u003C\u002Fstrong>, they are deliberate about the method. They choose generative AI, agentic patterns, or classical machine learning to make the solution as effective, simple and robust as possible. They make this selection based on the workflow, and the risk profile, instead of following the latest hype cycle.\u003C\u002Fli>\u003Cli data-list-item-id=\"ef9ce66598bf308a82aa7a0829c88184a\">\u003Cstrong>Fourth\u003C\u002Fstrong>, they keep portfolio focus. They back fewer initiatives, with clearer ownership and a more credible path to measurable results. This is what \u003Ca href=\"\u002Fnode\u002F26994\" target=\"_blank\" title=\"Discover how to choose AI initiatives that will actually create value\" data-entity-type=\"node\" data-entity-uuid=\"1d488a1e-b0fb-4332-a9b1-6221ccebd74e\" data-entity-substitution=\"canonical\">effective AI portfolio management\u003C\u002Fa> really looks like.\u003C\u002Fli>\u003C\u002Fol>","\u003Cp>Across sectors, the organisations that make this transition tend to have four things in common.\u003C\u002Fp>\u003Col>\u003Cli>\u003Cstrong>First\u003C\u002Fstrong>, they define measurable KPIs early. They do not stop at hours saved; they actually track the business metric that matters.\u003C\u002Fli>\u003Cli>\u003Cstrong>Second\u003C\u002Fstrong>, AI is embedded into the critical path of work, not bolted on at the side.\u003C\u002Fli>\u003Cli>\u003Cstrong>Third\u003C\u002Fstrong>, they are deliberate about the method. They choose generative AI, agentic patterns, or classical machine learning to make the solution as effective, simple and robust as possible. They make this selection based on the workflow, and the risk profile, instead of following the latest hype cycle.\u003C\u002Fli>\u003Cli>\u003Cstrong>Fourth\u003C\u002Fstrong>, they keep portfolio focus. They back fewer initiatives, with clearer ownership and a more credible path to measurable results. This is what \u003Ca href=\"\u002Fen\u002Finsights\u002Fhow-to-choose-ai-initiatives-that-create-real-business-value\" target=\"_blank\" title=\"Discover how to choose AI initiatives that will actually create value\" data-entity-type=\"node\" data-entity-uuid=\"1d488a1e-b0fb-4332-a9b1-6221ccebd74e\" data-entity-substitution=\"canonical\">effective AI portfolio management\u003C\u002Fa> really looks like.\u003C\u002Fli>\u003C\u002Fol>","What truly integrated AI looks like in practice",{"__typename":246,"id":323,"ctaLink":215,"pgSharedText":324,"pgSharedTitle":327,"pgSharedCtaLink":215},"34585",{"value":325,"processed":326,"format":251},"\u003Cp>If your teams are getting faster, but revenue, margin, or risk indicators are not moving, you’re probably facing an AI conversion gap — and it is likely driven by one or more of the AI adoption challenges described above.\u003C\u002Fp>\u003Cp>The first step is to identify where value is leaking: in workflow integration, operating model, measurement, governance, or \u003Ca href=\"\u002Fnode\u002F27000\" target=\"_blank\" title=\"Explore how to scale AI from proof-of-concept to production\" data-entity-type=\"node\" data-entity-uuid=\"71c4a910-7cd2-44f5-b2b7-e5afbede6efe\" data-entity-substitution=\"canonical\">foundations\u003C\u002Fa>.\u003C\u002Fp>\u003Cp>The second step is to decide which initiatives are worth backing further, and which should be paused or stopped before they consume more budget and attention. That’s what we cover in our next blogpost, '\u003Ca href=\"\u002Fnode\u002F26994\" target=\"_blank\" title=\"Learn how to choose AI initiatives that will actually create value\" data-entity-type=\"node\" data-entity-uuid=\"1d488a1e-b0fb-4332-a9b1-6221ccebd74e\" data-entity-substitution=\"canonical\">How to choose AI initiatives that will actually create value\u003C\u002Fa>'.\u003C\u002Fp>","\u003Cp>If your teams are getting faster, but revenue, margin, or risk indicators are not moving, you’re probably facing an AI conversion gap — and it is likely driven by one or more of the AI adoption challenges described above.\u003C\u002Fp>\u003Cp>The first step is to identify where value is leaking: in workflow integration, operating model, measurement, governance, or \u003Ca href=\"\u002Fen\u002Finsights\u002Fscaling-ai-from-proof-of-concept-to-production\" target=\"_blank\" title=\"Explore how to scale AI from proof-of-concept to production\" data-entity-type=\"node\" data-entity-uuid=\"71c4a910-7cd2-44f5-b2b7-e5afbede6efe\" data-entity-substitution=\"canonical\">foundations\u003C\u002Fa>.\u003C\u002Fp>\u003Cp>The second step is to decide which initiatives are worth backing further, and which should be paused or stopped before they consume more budget and attention. That’s what we cover in our next blogpost, '\u003Ca href=\"\u002Fen\u002Finsights\u002Fhow-to-choose-ai-initiatives-that-create-real-business-value\" target=\"_blank\" title=\"Learn how to choose AI initiatives that will actually create value\" data-entity-type=\"node\" data-entity-uuid=\"1d488a1e-b0fb-4332-a9b1-6221ccebd74e\" data-entity-substitution=\"canonical\">How to choose AI initiatives that will actually create value\u003C\u002Fa>'.\u003C\u002Fp>","Bridging the AI productivity gap: where to start",{"__typename":329,"id":330,"pgTitle":331,"text":332,"link":333,"coloredBackground":7,"compactMode":7,"actionVariant":26,"buttonText":215,"pgEmbededCode":336},"ParagraphPgCtaDefault","35440"," Discover how to choose AI initiatives that will actually create value","If your teams are getting faster but revenue, margin, or risk indicators are not moving, the next step is deciding which initiatives are worth backing further and which should be parked or stopped. That's what we cover in our next blogpost.",{"title":334,"url":335,"internal":7},"Read what's next","\u002Fen\u002Finsights\u002Fhow-to-choose-ai-initiatives-that-create-real-business-value",{"value":337,"processed":337,"format":338},"","code",{"__typename":340,"id":341,"columns":342,"listItems":343,"text":215,"title":399,"variant":400},"ParagraphPgContentList","34928","2col",[344,376],{"id":345,"icon":215,"link":346,"logo":215,"media":349,"text":372,"title":375},"34926",{"title":347,"url":348,"internal":7},"Explore this topic","\u002Fen\u002Finsights\u002Fscaling-ai-initiatives-why-trust-determines-enterprise-success",{"id":350,"sharedMediaLegend":215,"mediaImage":351,"name":371},"173108",{"url":352,"width":218,"height":219,"alt":353,"title":215,"size":354,"mime":222,"variations":215,"listItemVariations":355},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fimages\u002Fai_trust_pillar_2_1.png","Profile silhouette of a person facing a glowing digital brain, representing human interaction with artificial intelligence.",1122721,[356,361,362,366,370],{"name":357,"url":358,"width":359,"height":360},"FOCAL_POINT31_SMALL","https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_3_1_small\u002Fpublic\u002Fimages\u002Fai_trust_pillar_2_1.png.webp?itok=1IxQuq9g",600,200,{"name":357,"url":358,"width":359,"height":360},{"name":363,"url":364,"width":233,"height":365},"FOCAL_POINT52_MEDIUM","https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_5_2_medium\u002Fpublic\u002Fimages\u002Fai_trust_pillar_2_1.png.webp?itok=pwht3IhI",480,{"name":367,"url":368,"width":218,"height":369},"FOCAL_POINT52_LARGE","https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_5_2_large\u002Fpublic\u002Fimages\u002Fai_trust_pillar_2_1.png.webp?itok=MPGqLJl8",512,{"name":367,"url":368,"width":218,"height":369},"ai_trust_pillar_2_1.png",{"value":373,"processed":373,"format":374},"\u003Cp>If your biggest challenge is not identifying value, but building the control, accountability and evidence needed to scale AI credibly, start here.\u003C\u002Fp>","content_list","Scaling AI safely starts with trust",{"id":377,"icon":215,"link":378,"logo":215,"media":380,"text":396,"title":398},"34927",{"title":347,"url":379,"internal":7},"\u002Fen\u002Finsights\u002Fscaling-ai-from-proof-of-concept-to-production",{"id":381,"sharedMediaLegend":215,"mediaImage":382,"name":395},"173146",{"url":383,"width":218,"height":219,"alt":384,"title":215,"size":385,"mime":222,"variations":215,"listItemVariations":386},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fimages\u002Fai_foundations_pillar_3_1_2.png","Close-up of a hand typing on a laptop, illuminated by screen light, representing hands-on development work.",971324,[387,389,390,392,394],{"name":357,"url":388,"width":359,"height":360},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_3_1_small\u002Fpublic\u002Fimages\u002Fai_foundations_pillar_3_1_2.png.webp?itok=04O8iROw",{"name":357,"url":388,"width":359,"height":360},{"name":363,"url":391,"width":233,"height":365},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_5_2_medium\u002Fpublic\u002Fimages\u002Fai_foundations_pillar_3_1_2.png.webp?itok=SDDuk2va",{"name":367,"url":393,"width":218,"height":369},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_5_2_large\u002Fpublic\u002Fimages\u002Fai_foundations_pillar_3_1_2.png.webp?itok=clWsuAjb",{"name":367,"url":393,"width":218,"height":369},"ai_foundations_pillar_3_1.png",{"value":397,"processed":397,"format":374},"\u003Cp>If AI is struggling once it meets live data, platforms or operational complexity, explore what it really takes to move from proof of concept to production.\u003C\u002Fp>","Promising pilots need stronger foundations","If a different challenge is your real constraint","media",{"__typename":267,"id":402,"title":403,"lead":215,"tabs":404},"34677","Frequently Asked Questions (FAQs)",[405,412,419,426,433],{"infoText":406,"pgSharedParagraphs":407},"What is AI business value and why is it hard to measure?",[408],{"id":409,"ctaLink":215,"pgSharedText":410,"pgSharedTitle":406,"pgSharedCtaLink":215},"34667",{"value":411,"processed":411,"format":251},"\u003Cp>AI business value refers to the tangible improvements AI delivers to revenue, margin, resilience, or risk reduction — as opposed to task-level time savings. It is hard to measure because most organisations track AI adoption through usage metrics rather than end-to-end business outcomes. Without a stable baseline and clear ownership of outcome measures, impact remains invisible to finance, audit, and leadership teams.\u003C\u002Fp>",{"infoText":413,"pgSharedParagraphs":414},"Why aren't AI time savings translating into revenue or margin improvements?",[415],{"id":416,"ctaLink":215,"pgSharedText":417,"pgSharedTitle":413,"pgSharedCtaLink":215},"34669",{"value":418,"processed":418,"format":251},"\u003Cp>Time savings are a leading indicator, not a business outcome. The problem is the AI conversion gap: individual tasks become faster, but the business outcome still depends on downstream processes, approvals, and integrations that AI has not yet touched. Without end-to-end workflow redesign and deliberate measurement, productivity gains remain local and never reach the P&L.\u003C\u002Fp>",{"infoText":420,"pgSharedParagraphs":421},"What are the most common AI adoption challenges when scaling to enterprise?",[422],{"id":423,"ctaLink":215,"pgSharedText":424,"pgSharedTitle":420,"pgSharedCtaLink":215},"34671",{"value":425,"processed":425,"format":251},"\u003Cp>The five most common AI adoption challenges are: (1) gains staying local while outcomes are end-to-end; (2) bottlenecks moving downstream when AI speeds up upstream work; (3) AI workflow integration failing because AI is added on top of processes instead of embedded in them; (4) AI roi measurement breaking down because impact cannot be proved to finance or leadership; and (5) efficiency creating more demand than the organisation can absorb.\u003C\u002Fp>",{"infoText":427,"pgSharedParagraphs":428},"How do you bridge the AI productivity gap in an enterprise organisation?",[429],{"id":430,"ctaLink":215,"pgSharedText":431,"pgSharedTitle":427,"pgSharedCtaLink":215},"34673",{"value":432,"processed":432,"format":251},"\u003Cp>Bridging the AI productivity gap requires three steps: first, identify where value is leaking: in integration, measurement, operating model, or governance. Second, embed AI into the critical path of real workflows rather than adding it as an overlay. Third, define outcome measures early and track the business metric that matters, not just usage metrics or hours saved.\u003C\u002Fp>",{"infoText":434,"pgSharedParagraphs":435},"What does effective AI ROI measurement look like in practice?",[436],{"id":437,"ctaLink":215,"pgSharedText":438,"pgSharedTitle":434,"pgSharedCtaLink":215},"34675",{"value":439,"processed":439,"format":251},"\u003Cp>Effective AI ROI measurement starts with a stable baseline before deployment, clear ownership of the metric being targeted, and a direct line between AI output and the business outcome it is meant to move. It avoids proxies like time saved or volume processed in favour of revenue impact, margin change, risk reduction, or measurable operational improvement.\u003C\u002Fp>",[441],{"id":442,"name":443},"855","All industries",[445],{"id":446,"name":447},"110","AI",{"processed":449},"\u003Cp>Most organisations implementing AI are seeing time savings, but the \u003Ca href=\"https:\u002F\u002Fwww.zuehlke.com\u002Fen\u002Finsights\u002Fais-tangible-business-impact-5-key-learnings-from-zuhlke-eth-zurich\">AI business value\u003C\u002Fa> gap is real: productivity gains are not translating into revenue, margin, or risk reduction.\u003C\u002Fp>\u003Cp>The root cause is rarely the model. It is the five AI implementation challenges that cause productivity to leak before it reaches business outcomes.\u003C\u002Fp>\u003Cp>Closing the gap starts with identifying where value leaks and embedding AI into the critical path. That is what bridging the AI productivity gap actually requires. \u003C\u002Fp>",{"timestamp":451},1777464000,{"data":453},{"relatedContentView":454},{"id":455,"pageInfo":456,"results":459},"3dad2dac-900c-48b3-9380-0814b1c1f4f1",{"page":457,"pageSize":458,"total":458},0,3,[460,499,524],{"id":461,"path":462,"sharedTeaserTitle":463,"sharedTeaserRefMedia":464,"sharedTaxrefIndustries":481,"sharedTaxrefExpertises":488},"5877","\u002Fen\u002Finsights\u002Ffive-stumbling-blocks-becoming-a-data-driven-company","Using data effectively – five stumbling blocks on the road to becoming a data-driven company",{"id":465,"sharedMediaLegend":215,"mediaImage":466,"name":480},"4888",{"url":467,"width":233,"height":227,"alt":468,"title":215,"size":469,"mime":470,"variations":215,"relatedContentVariations":471},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fimages\u002Fdata-driven-companies-ai.jpeg","Data Driven Companies AI",565640,"image\u002Fjpeg",[472,474,475,478,479],{"name":225,"url":473,"width":227,"height":228},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_3_2_small\u002Fpublic\u002Fimages\u002Fdata-driven-companies-ai.jpeg.webp?itok=_JXPVmfr",{"name":225,"url":473,"width":227,"height":228},{"name":476,"url":477,"width":233,"height":227},"FOCAL_POINT32_MEDIUM","https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_3_2_medium\u002Fpublic\u002Fimages\u002Fdata-driven-companies-ai.jpeg.webp?itok=IvTf9ipq",{"name":476,"url":477,"width":233,"height":227},{"name":476,"url":477,"width":233,"height":227},"data-driven-companies-ai.jpeg",[482,485],{"id":483,"name":484},"9","Commerce & retail",{"id":486,"name":487},"10","Industrial sector",[489,490,493,496],{"id":446,"name":447},{"id":491,"name":492},"875","Data",{"id":494,"name":495},"877","Data engineering",{"id":497,"name":498},"876","Data strategy",{"id":500,"path":501,"sharedTeaserTitle":502,"sharedTeaserRefMedia":503,"sharedTaxrefIndustries":521,"sharedTaxrefExpertises":522},"6942","\u002Fen\u002Finsights\u002Fhow-chatgpt-is-changing-business","ChatGPT: generate revenue not just text!",{"id":504,"sharedMediaLegend":215,"mediaImage":505,"name":520},"6240",{"url":506,"width":507,"height":508,"alt":509,"title":215,"size":510,"mime":470,"variations":215,"relatedContentVariations":511},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fimages\u002Fchatgpt.jpg",1089,680,"Eisberg im Wasser, mit viel grösserem Volumen unter Wasser",152957,[512,514,515,518,519],{"name":225,"url":513,"width":227,"height":228},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_3_2_small\u002Fpublic\u002Fimages\u002Fchatgpt.jpg.webp?itok=fbcnYqJV",{"name":225,"url":513,"width":227,"height":228},{"name":476,"url":516,"width":517,"height":508},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_3_2_medium\u002Fpublic\u002Fimages\u002Fchatgpt.jpg.webp?itok=yM-aYviV",1020,{"name":476,"url":516,"width":517,"height":508},{"name":476,"url":516,"width":517,"height":508},"chatgpt.jpg",[],[523],{"id":446,"name":447},{"id":525,"path":526,"sharedTeaserTitle":527,"sharedTeaserRefMedia":528,"sharedTaxrefIndustries":544,"sharedTaxrefExpertises":548},"22404","\u002Fen\u002Finsights\u002Fhow-data-and-ai-can-solve-insurances-strategic-dilemma","How data and AI can solve insurance’s strategic dilemma",{"id":529,"sharedMediaLegend":215,"mediaImage":530,"name":543},"167284",{"url":531,"width":359,"height":532,"alt":533,"title":215,"size":534,"mime":470,"variations":215,"relatedContentVariations":535},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fimages\u002Finnovation-in-insurance.jpg",400,"abstract sphere",141684,[536,538,539,541,542],{"name":225,"url":537,"width":359,"height":532},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_3_2_small\u002Fpublic\u002Fimages\u002Finnovation-in-insurance.jpg.webp?itok=K0he7ky_",{"name":225,"url":537,"width":359,"height":532},{"name":476,"url":540,"width":359,"height":532},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_3_2_medium\u002Fpublic\u002Fimages\u002Finnovation-in-insurance.jpg.webp?itok=kn_MYeCz",{"name":476,"url":540,"width":359,"height":532},{"name":476,"url":540,"width":359,"height":532},"innovation-in-insurance.jpg",[545],{"id":546,"name":547},"11","Insurance",[549],{"id":446,"name":447}]