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1、ENTERPRISE AI READINESS RADAREnterprise AI Readiness RadarExternal Document 2024 Infosys Limited Knowledge Institute2Enterprise AI Readiness RadarExternal Document 2024 Infosys Limited Knowledge Institute3CONTENTSExecutive summary 4AI optimism but apprehension 6Readiness gaps fuel concern 10Culture-

2、mindset alignment influences AI approach 16The case for enterprise AI readiness 18From apprehension to adoption 20Appendix:Research methodology 28Enterprise AI Readiness RadarExternal Document 2024 Infosys Limited Knowledge Institute4EXECUTIVE SUMMARYThe stakes are high in the race for AI dominance.

3、Executives view this technology as the next industrial revolution,poised to build on digital and cloud investments to transform business models,drive new AI products and services,and shape the new economy.Beyond developing AI-based products and services,it is evident that AI will become a general-pu

4、rpose technology embedded in the enterprise technology stack.This ultimate integration of AI into an enterprises daily operations is what we define as enterprise AI.Because AI holds so much potential,many companies are now experimenting with it.They are eager to implement enterprise AI but most are

5、nowhere near embedding AI in their businesses,products,services and daily work required if they are to be ready for success in the second half of this decade.However,there are warnings that AI is overhyped and that we are in an AI bubble.Despite AIs clear potential,many companies are apprehensive ab

6、out potential reputational,legal,and compliance risks,plus whether AI will generate enough value to outweigh the cost and risks.Enterprise AI Readiness explores executive approaches and attitudes toward AI and assesses how prepared companies are to Enterprise AI Readiness RadarExternal Document 2024

7、 Infosys Limited Knowledge Institute5transition from their experimentation and point solutions to system-wide change and adoption.Infosys surveyed 1,500 companies globally and interviewed more than 30 executives to determine whether companies have the foundational building blocks in place and how cu

8、lture and leadership mindset influence AI adoption.Our major findings are outlined below to help leaders understand the challenges of achieving enterprise AI and consider how to apply these insights in their business context.Executives are confident about AI.However,Infosys research indicates that t

9、hey believe enterprise AI is three to five years from becoming a reality.Despite this experimentation and optimism,executives are not certain their companies will achieve all that AI promises.Additionally,many leaders worry about the many risks AI poses to their company and to their careers.Gaps in

10、AI readiness fuel apprehension Underscoring the unease surrounding this hyped yet promising field,most companies lack the required foundational building blocks.We found that only 2%are ready across the five readiness dimensions we identify.However,companies show readiness in some individual areas:ta

11、lent(35%are ready),strategy(23%),governance(21%),data(17%),and technology(9%).From apprehension to adoption To capture the mix of optimism and hesitation about enterprise AI,we classified leaders into four behavioral groups based on the intersection of company culture and leadership mindset:Ambitiou

12、s Innovators,Cautious Explorers,Frustrated Mavericks,and Worried Conformists.By establishing a foundational framework and making the case for AI tailored to each company,leaders can reduce organizational friction and influence how their company approaches AI.Steps to AI readiness Enterprise AI is th

13、e gold at the end of the rainbow.By laying the foundations and making a case for AI,we believe companies will transform their culture and leaders mindset from passive and skeptical to opportunistic and optimistic,achieving system-wide change instead of isolated point solutions.These are the five ste

14、ps:1.Develop a comprehensive AI strategy.2.Establish responsible AI governance.3.Upskill and build the workforce.4.Prepare data infrastructure for AI.5.Cultivate a culture of tech-powered innovation.Knowledge Institute6AI OPTIMISM BUT APPREHENSIONOptimism:Productivity and valueAlthough companies hav

15、e experimented with artificial intelligence(AI)since at least the early 2000s,the launch of generative AI in 2023 proved the watershed that focused leaders on AIs potential.As we found in our Generative AI Radar reports,companies are experimenting with use cases to understand how the technology migh

16、t benefit their businesses.Of the executives we interviewed for this research,most(80%)now expect a productivity boost from current AI projects(Figure 1).In fact,some believe that productivity benefits from current projects will be significant(as much as 30%to 40%for some respondents).On average,our

17、 respondents expected a 15%increase.Figure 1.AI set to boost productivitySource:Infosys Knowledge Institute?Enterprise AI Readiness RadarExternal Document 2024 Infosys Limited Knowledge Institute7?“I think the productivity boost from AI will be around 35%to 40%,because many companies are just prepar

18、ing the ground and are in the nascent stages of AI,”says Atul Srivastava,chief information officer at Trident Global,an e-commerce,retail,and manufacturing company.Our study also found that companies are beginning to measure AIs value delivery(return on investment;ROI),with almost half(47%)consideri

19、ng it the primary metric for AI success.Other metrics include how quickly and how many AI projects are delivered(Figure 2).Figure 2.How AI projects are measuredBumpy road to enterprise AISource:Infosys Knowledge InstituteThe executives we spoke to share a common conceptual understanding of enterpris

20、e AI:AI capabilities integrated and available organization-wide,with full interoperability,learning and improving business functions over time(Figure 3).This approach embeds AI into the organizations tech stack and into the organizations mindset,in contrast to a piecemeal,ad hoc approach that applie

21、s AI as experiments or a collection of individual usecases.This ad hoc approach arises when employees make individual decisions about using consumer AI tools from the web,such as Midjourney or ChatGPT,rather than being provided with tools by the business that have been evaluated for ethical and prac

22、tical purposes and integrated into the business.Further,enterprise AI demonstrates the hallmarks of good technology:It is secure,with risks mitigated and well-defined governance in place.Enterprise AI Readiness RadarKnowledge Institute8late 1990s,and are wary of a similar AI crash.The head of global

23、 equity research at Goldman Sachs,Jim Covello,suggests that the financial risks from AI are different from those during the dotcom boom because the cost of AI is higher and the long-term benefits and ROI are more unclear.“The idea that the transformative potential of the internet and smartphones was

24、nt understood early on is false.The roadmap on what those technologies would eventually be able to do also existed at their inception.No comparable roadmap exists today”,he says.Our interviewees shared this apprehension:Even executives who are the most optimistic and eager to implement AI are concer

25、ned about the uncertain road ahead.Together,these risks and institutional battle scars from digital transformation make many executives wary of putting their company or their career at risk to push an AI agenda.As one tech leader in the UK explains:“Im championing AI;I want to make sure its successf

26、ul,and sometimes investment in tech doesnt translate in positive impact,so I have to be careful because Im associating my credibility with it.”However,most respondents say AI is in its infancy,and believe their company is between three and five years from enterprise AI being a reality.Figure 3.Defin

27、ing enterprise AI Source:InfosysApprehension:Risks and rewardHowever,executives are not merely starry-eyed about AIs potential.They are also well aware of AIs legal,compliance,and reputational risks.In addition,investors are starting to see signs of an AI bubble.Manyexecutives lived through,or have

28、heard horror stories from,the dotcom bubble of the?Enterprise AI Readiness Radar9The idea that the transformative potential of the internet and smartphones wasnt understood early on is false.The roadmap on what those technologies would eventually be able to do also existed at their inception.No comp

29、arable roadmap exists today.Jim Covello Head of global equity research,Goldman Sachs Enterprise AI Readiness RadarExternal Document 2024 Infosys Limited Knowledge Institute10READINESS GAPS FUEL CONCERNEnterprise AI is complex and multifaceted,with much to sort out as companies move to implement it.A

30、s a US insurance company CIO says:“The CEO and COO are saying go ahead and do anything that we need to do,but it brings with it a lot of prep and consideration for security,increasing compute power in the cloud and on-premise.Having these conversations can be challenging.”But these technological con

31、siderations are just one dimension for businesses preparing to implement AI.Companies must also understand and implement new yet unclear considerations and processes for workforce,culture,governance,leadership,and data every aspect of their operating model.Through desk research,interviews and input

32、from AI practitioners and executives at Infosys and other companies,we developed a framework that simplifies and deconstructs Enterprise AI Readiness RadarExternal Document 2024 Infosys Limited Knowledge Institute11?areas for companies to build foundations for enterprise AI success.This framework id

33、entifies and assesses company readiness for enterprise AI on five dimensions(Figure 4),assessed as follows:Strategy:Alignment with resources and policies;value of each use case confirmed before initiation.Governance:Rigorous processes established to reduce risks from AI,including ethical,brand,and l

34、egal risks.Talent:Employees trained,knowledgeable,and have good understanding of AI.Data:Robustness and quality across all data types.Technology:The foundational technological capabilities are in place and readily accessible.Figure 4.Five dimensions of AI readinessSource:Infosys Knowledge InstituteW

35、e found that only 2%of leaders are confident that they are ready on all five of these dimensions.However,companies are ready in some of these areas in isolation:talent(35%are ready),strategy(23%),governance(21%),data(17%),and technology(9%).We discuss more details in each of the subsections.Enterpri

36、se AI Readiness RadarExternal Document 2024 Infosys Limited Knowledge Institute12?12%43%45%?13%45%42%?16%40%44%?14%53%33%?14%42%44%?StrategyThis dimension includes how prepared companies are to sign off on AI projects with a validated business case and ROI,comply with policies,ensure resource availa

37、bility,define clear use cases with target customers,and ensure sign-off with a governance council or similar.In our study,we found that only 15%of respondents were confident that their AI projects have these elements in place(Figure5).An AI strategy that includes these elements ensures that use case

38、s are desirable,feasible,and viable before they are initiated.Because the hype surrounding AI makes it difficult to separate valuable opportunities from unrealistic promises,it is difficult to quantify the potential impact of use cases and justify AI investments.The approach we propose here strength

39、ens the case for AI investment and builds consensus and transparency across units.Figure 5.Strategic AI readiness is lackingFigure 6.Confidence in governance is lowSource:Infosys Knowledge InstituteSource:Infosys Knowledge InstituteGovernance This dimension measures how well companies define governa

40、nce processes to reduce risks from AI.These risks include hallucinations,bias,misuse of data,and security.This dimension also measures their legal,ethical,and brand governance processes for example,IP theft,inappropriate model usage,harmful content,and defamation.It also includes mitigation of secur

41、ity threats,user privacy violations,and unauthorized access.We found that only about 10%of respondents are confident that governance processes are well-defined,and similarly,only 10%are confident in their companys ability to protect against security and privacy risks(Figure 6).AI in the enterprise h

42、as the potential to create significant business value,but comes with risks the elements of governance listed below guard against risks and are essential for enterprise AI.?13%58%29%?12%62%26%?12%65%23%?Enterprise AI Readiness RadarExternal Document 2024 Infosys Limited Knowledge Institute13?12%?57%?

43、31%Figure 7.Employees short on readinessFigure 8.Confidence in AI trainingSource:Infosys Knowledge InstituteSource:Infosys Knowledge InstituteTalentThis dimension considers employee proficiency with AI tools and techniques in their work,along with available access to training and AI expertise.Althou

44、gh 35%are confident that their employees are ready for AI,a full 57%are not,with the remaining 8%somewhere in between.Looking deeper at the readiness of employees,only 21%of companies say their workers have the knowledge to adopt AI,and only about 20%of companies indicate employee proficiency in AI

45、tools and access(Figure 7).Upskilling and company expertise in AI are also problem areas.When asked about the maturity of readily accessible training,only 12%of respondents are confident they have adequate AI training and expertise in place(Figure 8).Companies that are ready for enterprise AI train

46、employees to work effectively with AI systems and they communicate company values about AI to increase AI awareness among their workers.Newer roles in the digital sphere span data,engineering,and design as well as a stronger emphasis for business and IT to collaborate throughout projects and process

47、es.AI evangelists(or champions)who have expertise and knowledge also contribute meaningfully to a skilled environment of human-machine collaboration.?28%53%19%?21%51%28%?21%45%34%?18%51%31%?Enterprise AI Readiness RadarExternal Document 2024 Infosys Limited Knowledge Institute14?12%55%33%?13%57%30%?

48、12%52%36%?10%53%37%?Data This dimension measures company readiness to locate and access data,along with data accuracy and access,including permissions,ability to track regulations,and synthetic data versioning.In prior research,we found that data accuracy,processes,and accessibility have the most si

49、gnificant impact on successful AI development.We term this data health,and in the current research,we found it to be low across the board(Figure 9):30%rate data accuracy and data governance processes used for AI as closer to poor,increasing AI risks.Only about 10%of respondents say it is relatively

50、easy to locate data within their company.Likewise,only 10%say data is readily accessible for AI projects.For enterprise AI,companies require these building blocks for all data types structured and unstructured,including audio data,visual data,and text.Data health for enterprise AI also extends beyon

51、d traditional analytical and transactional data to include user-generated content,synthetic data,machine data,ecosystem data,and third-party data.This data has historically not been managed with the same rigor and governance as structured corporate data.Enterprise AI needs a data architecture for th

52、is proliferation of data and data types,with safeguards for bias,security,privacy,and regulations.Figure 9.Data health low and most struggle to locate and access dataSource:Infosys Knowledge InstituteEnterprise AI Readiness RadarExternal Document 2024 Infosys Limited Knowledge Institute15?13%62%25%?

53、2%?11%?31%?40%?17%Figure 10.Foundational preparedness Source:Infosys Knowledge InstituteSource:Infosys Knowledge InstituteTechnologyOur research showed the biggest gaps are evident in the technology dimension of enterprise AI readiness.We found that only 9%were ready on this dimension,which includes

54、 access to critical AI capabilities such as machine learning frameworks,prebuilt algorithms,and dynamic compute,along with automation within software development teams.We evaluated four foundational technology capabilities:Dynamically provisioned infrastructure and compute.Security and rights manage

55、ment.Flexible infrastructure for open-and closed-source AI models Integration with AI frameworks.Only 2%had all four of these capabilities,but more than half had either none(17%)or one(40%)(Figure 10).Further,we found that most companies(62%)AI development processes are only partly automated,and 25%

56、indicate they are mostly manual(Figure 11).Automation relegates repetitive tasks to software,which means a better experience for customers,reduced error rates,and improved compliance.While getting the other dimensions in place is vital,a poor technology and engineering shop limits the speed and scal

57、e of AI projects.AT&T is an example of a company that puts this excellence at the heart of its operating model.Software engineers,data scientists,and thousands of citizen developers build enterprise applications as the telecoms company becomes AI-first.With technology a key pillar of its AI strategy

58、,AT&T combines AI and a platform engineering approach to software development to optimize its networks,upgrade legacy software,and enhance contact centers.The company also uses generative AI to upskill employees around the three pillars of human-centricity,responsible AI,and secure/ethical by design

59、.Figure 11.Levels of automation in placeEnterprise AI Readiness RadarExternal Document 2024 Infosys Limited Knowledge Institute16CULTURE-MINDSET ALIGNMENT INFLUENCES AI APPROACH Along with the quantitative 1,500-person study into these five readiness dimensions,qualitative interviews were conducted

60、with 30 executives in the US and UK to understand the needs,motivations,emotions,and tensions about enterprise AI.Companies were classified into four behavioral groups(or archetypes)based on corporate culture and leader mindset(Figure 12).Half of the executives interviewed were Cautious Explorers,le

61、aders taking baby steps in firms trying to move quickly.Onefifth were Ambitious Innovators,placing big bets on transformation through enterprise AI.Both groups had medium levels of AI Figure 12.AI leader archetypes?Source:InfosysEnterprise AI Readiness RadarExternal Document 2024 Infosys Limited Kno

62、wledge Institute17implementation.The rest struggled in risk-averse environments that lack a“burning platform”priority.These laggards,Worried Conformists and Frustrated Mavericks,had implemented few AI projects.Ambitious Innovator:These companies are already making big decisions and are willing to ta

63、ke risks.They currently have medium levels of AI implementation.To move faster and more safely,they should assess readiness and identify gaps across the five dimensions.Cautious Explorer:Like ambitious innovators,these companies have medium levels of AI implementation but are wary of the risks and s

64、o have fewer pilots than Ambitious Innovators.Tomove toward,achieve success through emphasizing automation,which our interviewees indicated is something that the culture at this archetype is sold on.Frustrated Maverick:These companies have made strides toward AI in small,safe steps,and have low leve

65、ls of AI implementation.CIOs at these companies should communicate AI successes to the rest of the company and get buy-in for moving ahead with AI.Worried Conformist:These companies have low levels of AI implementation,sometimes with employees just experimenting with ChatGPT,and the company as a who

66、le discouraging use of AI.CIOs in these companies should find low-or zero-cost ways to gather facts to convince other leaders for instance,sharing public case studies or attending an event and talking with other companies about AI successes.Enterprise AI Readiness RadarExternal Document 2024 Infosys

67、 Limited Knowledge Institute18THE CASE FOR ENTERPRISE AI READINESSExpectations for AI are high,but executives believe they are years away from achieving enterprise AI,our research found.Regardless of their pursuit of AI projects,companies do not have the foundational building blocks in place across

68、strategy,governance,talent,data,and technology.Yet they must forge ahead.Even those apprehensive about AI believe their companies will move to enterprise AI eventually and they need to be on board.In the words of a banking industry CIO:“I need to be able to adapt otherwise Ill become obsolete.I need

69、 to do it.”Enterprise AI Readiness RadarExternal Document 2024 Infosys Limited Knowledge Institute19This plays out at the enterprise level as well:“Big tech companies have no choice but to engage in the AI arms race right now given the hype around the space and FOMO,”says Jim Covello of Goldman Sach

70、s.Our research on generative AI over the past year shows that even smaller companies,including those with revenues of only$500 million,are in the same situation and so are experimenting with AI and ramping up investments.A recent article in Harvard Business Review argued that“palpable fear of missin

71、g out”spurred companies to explore generative AI,but companies are struggling to realize cost and efficiency benefits.In the face of uncertainty,companies should establish an AI readiness framework as discussed in this research.This means when they decide to press ahead with specific AI projects,the

72、y can be confident in their AI strategy,governance,talent,technology,and data.Our research also sheds light on the complexity of the cultural dimension of AI readiness.Specifically,executive mindsets about AI dont always match company culture,which impacts progress on AI.For instance,in the case of

73、Cautious Explorers,the company may push to implement AI,but tech leaders often want to move slower.We believe that a strong foundation for AI readiness,as outlined in this report,will provide leaders the confidence to move forward,aligned in working to transform the enterprise.Companies ready for en

74、terprise AI will differentiate themselves in this new business era,which is defined by innovation,uncertainty,and disruption.It is also defined by a degree of delayed gratification.While leaders take the time to identify AI technologies and projects to adopt in pursuit of productivity gains and ROI,

75、the business must press on to establish readiness building blocks,and lay a firm foundation for enterprise AI.Enterprise AI Readiness RadarExternal Document 2024 Infosys Limited Knowledge Institute20FROM APPREHENSION TO ADOPTIONWe found that most companies are not ready for enterprise AI because the

76、y do not have the foundational building blocks in place.They believe enterprise AI is three to five years from being a reality at their company.But by putting the work in now,they can ensure they dont become tomorrows laggards.Companies face a duality of significant risk and reward as they adopt AI,

77、as well as uncertainty about how it will impact all aspects of their enterprise,from the workplace to R&D to their workforce talent mix.In addition to building the case for AI fitted to company culture and leadership,companies need to close gaps in the five dimensions of our framework:strategy,gover

78、nance,talent,data,and technology.Five specific steps will reduce apprehension about AI,close gaps,and garner more benefits from AI.Embedding these practices will help companies join the 2%of their peers that demonstrate readiness across all five dimensions.1.Develop a comprehensive AI strategy.2.Est

79、ablish responsible AI governance.3.Upskill and build the workforce.4.Prepare data infrastructure for AI.5.Nurture a culture of tech-powered innovation.Enterprise AI Readiness RadarExternal Document 2024 Infosys Limited Knowledge Institute21?A comprehensive AI strategyCompanies should create a clear

80、AI strategy that aligns with their business objectives and outlines how AI can drive value.This strategy should encompass technology investments,talent acquisition,and ethical considerations.Our previous research found that an effective AI strategy can add nearly$467 billion in profit growth collect

81、ively.And according to recent research,companies with a well-defined AI strategy achieve three times the revenue growth compared to peers.The importance of having an AI strategy cannot be overstated:“If youre a corporation and you dont have an AI strategy,you dont have a strategy,”says Mike Mayo,hea

82、d of Wells Fargos large-cap bank research.The primary challenge is ensuring alignment across diverse business units,which can be overcome by involving cross-functional teams in the strategy development process.Our research found that less than a quarter of companies positively rate the ability of IT

83、 and business teams to work together(Figure 13).Companies should create an AI value office,responsible for structure and to govern AIs impact on the modern data estate,emerging technology;generative AI experimentation;integrating AI into operations;and overseeing responsibility,regulations,culture,a

84、nd ethics.It uses value to prioritize investments and project greenlighting,with outcomes tied to reputational,legal,and compliance risks.“We use a strategic value framework to launch AI projects,”says Bradley Schaufenbuel,chief information security officer at Paychex,a human capital management comp

85、any.“It assesses AI projects for viability,feasibility,desirability,compliance,and governance.And it helps with the success of the implementation.”Figure 13.Business and IT collaborationSource:Infosys Knowledge InstituteEnterprise AI Readiness RadarExternal Document 2024 Infosys Limited Knowledge In

86、stitute22?Responsible AI governanceResponsible AI is not a monolithic approach,but a tailored set of guardrails based on industry and geographic regulatory considerations.Companies should create a centralized responsible AI team to safeguard technical,legal,ethical,and reputational risks.Indeed,our

87、research indicates that only 10%of companies establish governance that provides these guardrails(Figure 14).Governance guardrails for AI are vital in the data-driven landscape.Data models become secure as companies institute responsible-AI-by-design principles into their processes.Secure-by-design p

88、rinciples establish processes that reduce risks of hallucinations,bias,misuse of data,IP theft,inappropriate usage,and defamation.At Infosys,the Responsible AI Office is the custodian of AI governance and facilitates collaboration across functions.This office establishes and maintains a governance f

89、ramework and defines policies,procedures,and decision-making processes related to enterprise AI.While the office serves many functions,these can be grouped into the broad categories of scan,shield,and steer.Schaufenbuel describes the importance of this type of office:“In the long run,with help from

90、a governance office,we want to put every AI governance issue through an automated AI governance review.This would provide insights on bias and explainability issues.Wed love to build a real AI governance platform that assesses things like bias and explainability for all AI models used in our enterpr

91、ise,tracked that over time.At present,our mechanism is more like an approval gate,and we cannot go back to the model or track changes over time.”Figure 14.Preparedness of governanceSource:Infosys Knowledge InstituteEnterprise AI Readiness RadarExternal Document 2024 Infosys Limited Knowledge Institu

92、te23If youre a corporation and you dont have an AI strategy,you dont have a strategy.Mike Mayo Head of Wells Fargo large-cap bank researchEnterprise AI Readiness RadarExternal Document 2024 Infosys Limited Knowledge Institute24?63%58%26%31%?12%11%?Figure 15.Banking relies on upskilling Source:Infosy

93、s Knowledge InstituteUpskill and build the workforceTalent is the AI arbitrage dimension all companies have employees,yet few develop their workforce strategically to close gaps and maximize ROI for the other dimensions such as data and tech.Talent is more than skills motivation leads to adoption an

94、d institutional capabilities.To make this happen,executives should communicate the benefits for employee career development and job security,along with the benefits that technology brings.The AI upskilling opportunity is unprecedented.As AI knowledge is disseminated,tool proficiency and access incre

95、ases,and business and IT professionals will speak each others language:“A real skill gap in our organization is finding people that can translate geek-speak into business terminology this skillset is pretty hard to find,”according to one executive we spoke to.Our research found that only 12%of Forwa

96、rd-thinking companies are creating AI skill categories and providing learning paths to build these skills and ensure readiness.For example,Infosys has classified generative AI skills across a ladder of capabilities,starting with AI Aware(nearly all employees),AI Builder(larger number of practitioner

97、s),and AI Master(small number of specialists).companies are confident that they are providing employees with sufficient training opportunities.However,our recent research on technology,skill,and AI adoption trends showed signs that banking firms now rely more on upskilling than hiring or outsourcing

98、.Figure 15 shows that for key technologies such as machine learning,large language models,and cybersecurity,companies expect to close skills gaps through upskilling in the next six months.Our future research willexplore whether this trend is pervasive across industries.Enterprise AI Readiness RadarE

99、xternal Document 2024 Infosys Limited Knowledge Institute25Prepare data infrastructure Data quality and accessibility remain a perennial challenge for companies,and respondents rated it as the biggest barrier to AI success.The relationship between data quality and data infrastructure is key,and in t

100、he words of one of our interviewees,“Data is nothing more than an outcome of how your internal systems are arranged.”Companies should assess current systems,including hardware,applications,and integrations.Assessments should include whether hardware is fit to run an AI application,interoperability,a

101、nd infrastructure complexity.Part of this assessment also includes where data is stored it should all be brought together.To bring all data under management could require changing the data ecosystem.Preparing data infrastructure includes having systems to cleanse and then fingerprint data so it is t

102、ransparent,and companies know what is present where(in each piece of data)across data stores.Companies can use this fingerprint to establish data correlations and conduct governance activities.Additionally,companies must know how to handle structured and unstructured data,including traditional analy

103、tic data,transactional data,synthetic data,and data from the ecosystem,generated by users or machines.Only about one in 10 said it was easy to locate or access this data in their company,while 3%said they could both easily locate and access data(Figure 16).“We spent millions on master data managemen

104、t and governance,”says Kendall Knight,chief information officer at Loomis,a Swedish financial services company.“If you dont manage your data in a structured way across the portfolio,AI is just going to exacerbate the problem.If you have a data governance team,then they can become your AI governance

105、team as well.”Figure 16.Locating and accessing dataSource:Infosys Knowledge Institute?Enterprise AI Readiness RadarExternal Document 2024 Infosys Limited Knowledge Institute26Tech-powered innovationOrganizational culture often lags behind process changes,such as the quick move to remote work in 2020

106、 and again now with the rapid implementation of AI.Culture,often defined as“the normative glue”that holds a company together,evolves through behaviors repeated over time that eventually come to be seen as“how we do things around here.”A culture of innovation is built through repeated experimentation

107、 with AI that values both failures and successes.Our research indicates that this culture is not yet present at most companies,as only 43%of respondents indicate that they have fully prepared their employees for AI.Further,only 21%of our respondents said that their employees have the requisite knowl

108、edge to adopt AI tools and techniques(Figure 17).Figure 17.Low readiness on AI knowledge To facilitate experimentation and provide employees with the knowledge to adopt AI tools and techniques,companies should enable access to these tools.They can establish policies for and access to generative AI t

109、ools.“It is about getting the technology into peoples hands so that they understand what it is and how to work with it.This enables the technology teams to work on bigger applications that can create much more impact down the road,”says Cameron Gwinn,a senior director at KPMG.Source:Infosys Knowledg

110、e InstituteThe most effective access can be through a repository of self-service tools.This access can be as simple as an internal development marketplace,or a full-blown platform engineering squad that curates the user interface with important AI capabilities,including prebuilt algorithms,machine l

111、earning frameworks,and templates for developing agents and chatbots.?Enterprise AI Readiness RadarExternal Document 2024 Infosys Limited Knowledge Institute27It is about getting the technology into peoples hands so that they understand what it is and how to work with it.This enables the technology t

112、eams to work on bigger applications that can create much more impact down the road.Cameron Gwinn Senior director,KPMGEnterprise AI Readiness RadarExternal Document 2024 Infosys Limited Knowledge Institute28APPENDIX:RESEARCH METHODOLOGY Infosys commissioned a survey of 1,505 executives at companies i

113、n Australia,France,Germany,New Zealand,the United Kingdom,and the United States during June and July 2024 to gauge the level of readiness for enterprise AI.The survey looked at 12 sectors:automotive;consumer packaged goods;energy,mining,or utilities;financial services;healthcare;high tech;insurance;

114、life sciences;logistics or supply chain;manufacturing;retail or hospitality;and telecommunications.To enrich insights,the research also included phone interviews with 40 industry practitioners,executives,and subject matter experts.We asked respondents about factors that indicate readiness for enterp

115、rise AI,the challenges they face in implementing AI,and what outcomes they expect from their AI projects.Many of the questions about readiness involved asking about sentiment.To mitigate individual bias in these responses,we compared each response to an individuals most common response across sentim

116、ent questions.We then arrived at a relative sentiment for each question asked,and classified each response as some form of positive,neutral,or negative and aggregated those categories for reporting.Enterprise AI Readiness RadarExternal Document 2024 Infosys Limited Knowledge Institute29Revenue of co

117、mpanies in the surveySource:Infosys Knowledge InstituteSource:Infosys Knowledge InstituteSource:Infosys Knowledge Institute?53%?17%?13%?8%?8%$5bn$3bn to$5bn$1bnto$3bn?12%?6%?9%?12%?9%?11%?5%?6%?4%?11%?7%?7%Respondents by industryRespondents by regionEnterprise AI Readiness RadarExternal Document 202

118、4 Infosys Limited Knowledge Institute30About Infosys Knowledge InstituteThe Infosys Knowledge Institute helps industry leaders develop a deeper understanding of business and technology trends through compelling thought leadership.Our researchers and subject matter experts provide a fact base that ai

119、ds decision making on critical business and technology issues.To view our research,visit Infosys Knowledge Institute at or email us at .AuthorsJeff Kavanaugh|Dallas Harry Keir Hughes|London Nikki Seifert|FrankfurtAnalysis and productionKate Bevan|London Snehal Bhosale|Bengaluru Isaac LaBauve|Dallas

120、Pramath Kant|Bengaluru Pragya Rai|BengaluruInfosys Topaz is an AI-first set of services,solutions and platforms using generative AI technologies.It amplifies the potential of humans,enterprises,and communities to create value.With 12,000+AI assets,150+pre-trained AI models,10+AI platforms steered by

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