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Provocative thinking, transformative insights,tangible outcomesAI: BUILTTO SCALEFrom experimental to exponentialAchieve competitive agility

About the authorsKetan AwalegaonkarRobert BerkeyGreg DouglassAthena ReillyManaging DirectorAccenture Applied IntelligenceManaging DirectorAccenture Applied IntelligenceSenior Managing DirectorAccenture StrategyManaging DirectorAccenture StrategyKetan leads Strategy & Consulting acrossall Accenture Applied Intelligence industryand functional practices. Ketan partnerswith Fortune 500 C-suite executives andboard members to transform their digitaland analytics operating models by applyingintelligence through design-thinking,AI, data strategy and a cloud-basedplatform ecosystem.Robert has over 20 years of experienceshaping and delivering enterprise analyticsstrategy and transformation programs forFortune 500 clients, including valuetargeting, operating model & talent,analytic delivery models, data &technology architecture and employeeadoption programs.Greg leads Communications, Media andTechnology within Accenture Strategy.His role focuses on helping clientsworldwide achieve high performancethrough profitable growth, acceleratedinnovation, organizational agility, andoperational excellence.Athena helps C-suite leaders addresssome of their top challenges, includingchanging business models, managingdata volumes, addressing analyticsimmaturity and transitioning away fromlegacy technology.He teaches AI at both the Kellogg School ofManagement & McCormick School ofEngineering at Northwestern University.Ketan in based in Chicago, Illinois.He has co-authored several white papers onanalytics transformation including “TheInsight-Powered Enterprise” and “Preparingfor a Data Science Transformation”, andco-created Accenture’s AnalyticsDiagnostic (patent pending). Robert isbased in Portland, Oregon.Greg has over 25 years of consultingexperience across the telecom, media,technology and retail industries, havingfocused on new digital business launches,strategic digital planning, business growthstrategies and cost transformation. Greg isbased in Dallas, Texas.For 20 years, Athena has guided teams indeveloping actionable strategies andplans to create more value throughanalytics and define the optimaltechnology footprint. A frequentcommentator on digital trends in nationaland global media publications, Athena isbased in San Francisco, California.AI: BUILT TO SCALE2

THE NUMBERSTELL THE STORYA full 84% of C-suite executives believe they must leverageArtificial Intelligence (AI) to achieve their growth objectives.Nearly all C-suite executives view AI as an enabler of theirstrategic priorities. And an overwhelming majority believeachieving a positive return on AI investments requires scalingacross the organization.Yet 76% acknowledge they struggle when it comes to scalingit across the business. What’s more, three out of four C-suiteexecutives believe that if they don’t scale AI in the next fiveyears, they risk going out of business entirely.With the stakes higher than ever, what can we learn fromcompanies that successfully scale AI, achieving nearly 3x thereturn on investment and a 30% premium on key financialvaluation metrics?84%of executives believethey won’t achievetheir growth objectivesunless they scale AI76%of executivesstruggle with howto scale AI acrossthe business75%of executives believethey risk going outof business in 5 yearsif they don’t scale AIAI: BUILT TO SCALE3

Nail it, then scale itTo answer that question, Accenture conducted a landmark global study involving1,500 C-suite executives from organizations across 16 industries.The study focused on determining the extent to which AI enables the business strategy, the top characteristics required to scaleAI, and the financial results when done successfully. The aim: Help companies progress on their AI journey, from one-off AIexperimentation to gaining a robust organization-wide capability that acts as a source of competitive agility and growth. Threedistinct groups of companies with increasing levels of capability required to successfully scale AI emerged from the research.01 Proof of ConceptFactory02 Strategically ScalingIn our experience, most companies (80-85%) arestuck on this path.They conduct AI experiments andpilots but achieve a low scaling success rate and alow return on their AI investments. Their efforts tendto be siloed within a department or team and areoften IT-led. They lack a connection to a businessoutcome or strategic imperative. The time andinvestment it takes to scale is underestimated,leaving the full potential of AI untapped.What dowe mean?Artificial Intelligence (AI) encompassesmultiple technologies that enablecomputers to sense, comprehend, act, andlearn. AI includes techniques such asmachine learning, natural languageprocessing, knowledge representation,computational intelligence, among others.Only 15-20% of companies have made this leap. Thesecompanies have journeyed beyond proof of concept toachieve a much higher success rate scaling AI—nearlydouble. And a much higher return—nearly three timestheir counterparts. As a C-suite priority, thesecompanies have a clear AI strategy and operatingmodel linked to the company’s business objectives,supported by a larger, multi-dimensional teamchampioned by the Chief AI, Data or Analytics Officer.However, the scaled AI is generally across pointsolutions, e.g., personalization.Pilot: Rolling out a capability with real data,users and processes in a productionenvironment (using a subset of the relevantscope). The purpose is to test how thecapability performs with a limited scope andto make any needed modifications beforeexpanding to the full applicable scope.03 Industrializedfor GrowthVery few ( 5%) companies have progressed to this pointon their AI journey. These companies have a digitalplatform mindset and create a culture of AI with dataand analytics democratized across the organization.They have scaled thousands of models with a responsibleAI framework. They promote product and serviceinnovation and realize benefits from increased visibilityinto customer and employee expectations. Ourresearch indicates that industrializing AI will enablecompetitive differentiation which is correlated withsignificantly higher financial results.Scale: Extension of the piloted capabilityacross the full applicable scope with allrelevant data, end users, customers, andprocesses. Purpose is to maximize theapplication’s value to the organization.AI: BUILT TO SCALE4

Paying dividends:Proven premium valueThe C-suite executives surveyedreported positive ROI on their AIinvestments. We dug deeper.Was there any relationship between successfully scaling AI acrossthe enterprise and key market valuation metrics? What was the“premium” for being a leader?Using survey data combined with publicly available financial data,our team of data scientists created a model to identify the premiumfor companies in our sample that successfully scale AI, controllingfor various characteristics of the companies.We discovered a positive correlation between successfully scalingAI and three key measures of financial valuation: Enterprise Value/Revenue Ratio, Price/Earnings Ratio, Price/Sales Ratio. 35%Enterprise Value/Revenue Ratio 33%Price/EarningsRatio 28%Price/SalesRatioCompanies that were identified as Strategic Scalers realize a success rate of 70% or more intheir AI scaling initiatives and a return on their AI investment of 70% or higher.AI: BUILT TO SCALE5

The great divideUS 110m gapWhen the 1,500 companies in our research wereanalyzed collectively, US 306 billion was spent on AIapplications in the past three years. The ROI gapamongst them was significant. On average, itspanned US 110 million between companies stuck inProof of Concept and those who have progressed tobecoming Strategic Scalers.186%StrategicScalersOverall reported spend on Artificial Intelligenceinitiatives over the past three yearsOver 500 million 10 millionor less26%9%Between 101- 500 million14%Between 51- 100 million32%Proof ofConcept14%38% 11- 50millionThe difference in return on AI investments between companies in the Proof of Conceptstage and Strategic Scalers equates to an average of US 110 million.AI: BUILT TO SCALE6

Companies strategically scaling AI have nearly2x the success rateand 3x the returnfrom AI investments vs. companiespursuing siloed proof of conceptsAI: BUILT TO SCALE7

AI’s evolutionarypaths to growth03 Industrializedfor Growth Digital platform mindset and enterpriseculture of AI democratizing real-timeinsights to drive business decisions Clear enterprise vision, accountability,metrics, and governance breakingdown silos Multi-disciplinary teams of 200 specialists championed by Chief AI,Data or Analytics Officer‘What if’ analysis enabling improvedacquisition, service and satisfaction Responsible business practicesenhancing brand perception and trust Competitive differentiator and valuecreator driving higher P/E multiples02 Strategically Scaling CEO focus with advanced analytics anddata team solving big rock problems01 Proof of ConceptFactory Analytics buried deep and not aCEO focus Able to tune out data noise and focuson essentials Siloed operating model typically IT-led Unable to extract value from their dataIntelligent automation and predictivereporting Struggle to scale as unrealisticexpectations on time required Catch up on digital/AI/data asset debt Experimental mindset achieving scaleand returns Significant under investment, yieldinglow returnsAI: BUILT TO SCALE8

The research revealed three criticalsuccess factors that separate those thathave progressed to Strategically Scalingand those still in Proof of Concept.Strategic Scalers:010203Drive “intentional” AITune out data noiseTreat AI as a team sportRoadblocks toscalingWhen executives ranked their topchallenges for scaling AI, they placed“lack of budget” at the bottom of the list.A possible explanation: AI is a C-suitepriority. So, while it may be challenging todecide which initiatives to fund first, themonetary resources, overall, aren’t a problem.Among the top challenges? The inability to setup a supportive organizational structure, theabsence of foundational data capabilities, andthe lack of employee adoption. As the studyshows, it’s exactly these aspects whereStrategic Scalers outperform theircounterparts in Proof of Concept.AI: BUILT TO SCALE9

01Drive “intentional” AICreating value from AI requires leaders to anchorAI in C-suite objectives.Strategic Scalers pilot and successfully scale more initiatives than theirProof of Concept counterparts—at a rate of 2:1—and set longer timelines.They are 65% more likely to report a timeline of one to two years to movefrom pilot to scale. And even though they achieve more, Strategic Scalersspend less. At first glance it may seem paradoxical. But the data indicatethat these leaders are more intentional, with a more realistic expectationin terms of time to scale—and what it takes to do so responsibly.To successfully scale, companies need structure and governance inplace. And the Strategic Scalers have both. Nearly three-quarters ofthem (71%) say they have a clearly-defined strategy and operatingmodel for scaling AI in place, while only half of the companies in Proofof Concept report the same.Strategic Scalers are also far more likely to have defined processesand owners with clear accountability and established leadershipsupport with dedicated AI champions. Initiatives not firmly groundedin business strategy and lacking a governance construct to overseeand manage are slower to progress. Turf wars break out over who“owns” AI and data. And, regardless of the AI platforms used, or theknow-how recruited, misaligned efforts fall flat.For StrategicScalers, 8 of 10scaling initiativesare successfulTo all intentsand purposesMost global organizations today believepassionately in the value of data and analytics.But one life sciences company had beenstruggling to move from theory to execution inimplementing data and insights capabilitiesacross all its business divisions. And they had avision to scale a collaborative data-poweredservice delivery model to create an internalmarketplace for FAIR (findable, available,interoperable, reusable) data.Working with data and digital leadership, themulti-functional team designed and delivered aholistic data and analytics strategy whileachieving immediate value through targeteduse-cases in each of the key areas of the business.In addition to architecting and standing up thenew scalable data and analytics delivery model,they created a model to make data search easierand more intuitive and embedded a new datadriven culture within the organization.The result? The company’s digitaltransformation is speeding ahead, powered bydata analytics insights across its business.AI: BUILT TO SCALE10

Do the basics brilliantlyWhen it comes to scaling AI, Strategic Scalers do the basics brilliantly. Compared to companies inProof of Concept, they have a clearly defined strategy and operating model for AI, defined processand owners for measuring value from AI, clearly defined accountability, appropriate levels of funding,and flexible business processes with embedded AI. They also scale through reusable assets onplatforms, so successive AI programs are 3-5X faster to market at lower spend. 60%Clearly definedaccountabilityfor scaling 50%Clearly definedstrategy and operatingmodel for scaling 30%Defined process andowners for measuringvalue from AI 50%Appropriatelevel of funding 27%Packaged or custombuilt AI applicationsfor scaling 26%Flexible businessprocesses withembedded AISizing upthe situationThe “smaller” companies in our studygenerated revenues between US 1 and5 billion a year. The largest had revenues ofmore than US 30 billion. When it comes toscaling AI, are there any major differencesbetween these two groups of companies?Do the largest companies face lower scalingsuccess rates due to their organizationalco