1、THE TOP 10 AI IMPERATIVES FOR 2025:HOW CXOS CAN TRANSFORM ENTERPRISE AI AND BUSINESS STRATEGYThe top 10 AI imperatives for 2025|3External Document 2024 Infosys Limited Knowledge InstituteCONTENTSExecutive summary 41.Smaller and more focused models 72.Agentic AI as the future of enterprises 83.Evolvi

2、ng processes from static to adaptive 114.Flexibility and leverage through poly AI 125.Innovation through an AI foundry and factory 146.Greener,lower-cost hardware and models 157.Fingerprinted data for better governance 178.Value chain-to-AI capability mapping 189.AI competency through upskilling and

3、 product-centricity 2010.AI for business growth 224|The top 10 AI imperatives for 2025External Document 2024 Infosys Limited Knowledge InstituteExecutive summaryIn 2024,we introduced the top 10 imperatives for AI.We wrote about the importance of data over models;hallucinations as features and not bu

4、gs;the growing importance of fine-tuned models,and responsible data management to ensure increased performance doesnt entail increased liability and risk.Now as we move into 2025,we have learned a few more things about AI business strategy.Its simply not that easy to derive value from AI.Clients we

5、speak to have been trapped in pilot purgatory,with success factors like improved productivity,lower cost,and risk mitigation hard to achieve.Part of this is because some critical foundations arent in place yet.Recent research in the Enterprise AI Readiness Radar by the Infosys Knowledge Institute fo

6、und that just 2%of enterprises globally are fully prepared for enterprise AI across the five foundational elements of strategy,governance,talent,data,and technology(Figure 1).Knowledge InstituteThe top 10 AI imperatives for 2025|5External Document 2024 Infosys Limited Knowledge InstituteIn the next

7、year,as enterprise moves from AI experimentation to expecting business value from AI,they will need an AI strategy that considers technical,organizational,and humanfactors.Because the AI landscape is very fast-moving,enterprises will need to avoid vendor lock-in and adopt a poly AI architecture.This

8、 allows businesses to deploy a range of AI services and tools that can be easily changed or updated while offering a consistent interface between the tools and the users.This reduces fragmentation and increases efficiency.Finally,for meaningful change,technology must be built with both speed and sca

9、le;experimentation and final rollout of AI products require a two-stage approach,leveraging an AI foundry for experimentation and an AI factory model for industrializing the product at scale.The first five imperatives below tell this story.The final five imperatives this year give further clues abou

10、t how to bring this technology to life,both internally to the organization,and for business growth.Enterprises need to address challenges as well as seek out opportunities to maximize the benefits of AI.Generative AI models can be power-hungry,emitting hundreds of tons of carbon.With nearly half of

11、companies on the Forbes 2000 list aiming for net zero,finding ways to create energy-efficient AI models is a clear priority.Similarly,with so few enterprises data-ready,now is the time to institute data governance and hygiene measures,ensuring the data that feeds the AI is privacy-and regulatory-com

12、pliant.We also consider how to build the right implementation strategy,choosing use cases based on high-impact areas in the value chain.Choice of AI model is all important,as different models offer varying levels of performance,accuracy,scalability,and regulatory compliance.So is the ability to give

13、 the AI independence to accomplish business goals with minimal human involvement a behind-the-scenes workhorse of sorts.This“agentic”AI is an evolution from knowledge-based,generative AI-powered tools to systems that use foundation models to execute complex,multistep workflows,independently interact

14、ing in a dynamic world.Strategy(23%)Technology(9%)Data(17%)Talent(35%)Governance(21%)Proportion ofenterprisesreporting theirreadiness for eachfoundationalelementOnly2%arereadyacrossallfivedimensionsFigure 1.Enterprises lag on readiness Source:Infosys Knowledge InstituteExternal Document 2024 Infosys

15、 Limited Knowledge InstituteWhen scaling enterprise AI,its important to bring your people along with you,and to ensure that what you do doesnt just replace jobs but uses AI to improve productivity and work moreeffectively.For this,in our last two imperatives,we introduce a three-tiered competence fr

16、amework,and we recommend better change management,a product-centric operating model,and using AI to expand existing business operations.Enterprise AI is here to stay,and a well-formed business strategy is synonymous with an AIstrategy.Smaller and more focused modelsAgentic AI as the future of enterp

17、riseEvolving processes from static to adaptiveFlexibility and leverage through poly AIInnovation through an AI foundry/factoryGreener,lower-cost hardware and models Fingerprinted data for better governanceValue chain-to-AI capability mappingAI competency through upskilling andproduct-centricityAI fo

18、r business growth12345678910The 10 AI imperatives for 2025 are 6|The top 10 AI imperatives for 2025Knowledge InstituteThe top 10 AI imperatives for 2025|7External Document 2024 Infosys Limited Knowledge Institute1.Smaller and more focused modelsMany enterprises began their AI journey last year by us

19、ing closed-access large language models(LLMs),defined as having more than 40 billion parameters,given their capabilities in text and image generation.However,as we move into 2025,small and medium-sized language models,with between 2 billion and 7 billion parameters,will dominate the enterprise lands

20、cape(Figure 2).Infosys has created two of these models for finance and operations,and there are similar offerings from IBM and Meta,among others.They provide greater accuracy,lower latency,and higher throughput than their larger siblings,and importantly,consume far less energy and Often based on ope

21、n-source architectures,these models offer a balance of performance,efficiency,and cost-effectiveness,and excel in task-specific pute.They also provide a foundation for companies to tune them to their specific requirements.Lower costs,lower risk,data containment,and greener compute all stack up to ma

22、ke this trend a growing force in business.Figure 2.The sweet spotSource:InfosysCost100 xModelsContext 100 xAccuracyAI agentsClosed-access modelsOpen-access modelsSmall languagemodels(40B parameters)8|The top 10 AI imperatives for 2025External Document 2024 Infosys Limited Knowledge InstituteEnterpri

23、ses,their employees,and their jobs started to see the impact of AI in 2024.Some businesses began to use AI tools to help employees with tasks and to boost productivity,and to offer enhanced content intelligence and other domain-specific use cases.However,according to our AI readiness research,only 1

24、2%of enterprises provided their employees with training on how to use AI tools effectively.The workforce of 2025-26,however,will be increasingly guided by AI agents,with human supervision and oversight(Figure 3).This approach is known as agentic AI and is made up of systems that have a degree of aut

25、onomy and act on their own to achieve specific goals.They can make decisions,plan actions,and learn from experiences.In the past Source:Infosys ConsultingFigure 3.AI agents will reshape workyear alone,Google,Microsoft,OpenAI,and others have invested in software libraries and frameworks to support ag

26、entic functionality.Unlike traditional assistive tools,these intelligent agents function as behind-the-scenes workhorses.For example,in software development,one agent might be responsible for writing code,another for testing,and another for critiquing.A virtual assistant,for example,could plan and b

27、ook a complex personalized travel itinerary,handling logistics across multiple travel platforms.Leaders should use agentic AI to increase productivity,transform processes,and ensure agents play a pivotal role in making complex workflows faster and more efficient through a natural language interface.

28、2.Agentic AI as the future of enterprisesThe workforce of the future is hybridMachines and humans working together to fulfill the missionHumansAI-augmentedhumansAutonomousAI agentsSome roles and employees will see limited change with AIAI-enabled tools and workspaces will empower employees,amplify t

29、heir potential,and increase productivity and satisfactionSome work will be handled by AI agents with humansupervision and oversightTodayTomorrowThe top 10 AI imperatives for 2025|9External Document 2024 Infosys Limited Knowledge InstituteKnowledge Institute10|The top 10 AI imperatives for 2025Extern

30、al Document 2024 Infosys Limited Knowledge InstituteKnowledge InstituteThe top 10 AI imperatives for 2025|11External Document 2024 Infosys Limited Knowledge InstituteAI adoption is happening in waves,with AI software engineering and enterprise AI assistants happening now this is the first wave.In th

31、e second wave,coming in 2025,AI will transform customer service,operations,sales and marketing.The third wave will be business process and experience reimagination.2025 will see leading enterprises embed AI,including generative AI interfaces,deeply into business operations,preparing the ground for m

32、ore valuable AI implementations(Figure 4).Embedding AI in this way is the bedrock of large-scale AI adoption,where workflows evolve from static processes to adaptive AI systems.Adaptive systems allow for real-time AI-driven decision-making as they continuously learn from new data,help users access i

33、nsights in real time,and adjust workflows as needed.To succeed,businesses must be prepared to view AI not as a tool but as an integral part of their long-term strategy and drive meaningful change in how they operate and make decisions.They must also ensure appropriate governance of key processes.Fig

34、ure 4.AI must be integrated into the businessSource:Infosys Knowledge Institute3.Evolving processes from static to adaptiveLaggardsLearnersLeaders15%53%33%27%55%18%49%38%13%High integration of AI solutionsLow integration of AI solutionsModerate integration of AI solutionsReadiness to support large s

35、cale AI adoptionRespondentsThe sample sizes for Laggards,Learners and Leaders are 698;1,559;and 343,respectively12|The top 10 AI imperatives for 2025External Document 2024 Infosys Limited Knowledge InstituteMost enterprises will likely end up having a mix of AI deployments across domain-specific use

36、 cases and wider customer journeys.Enterprises should look to a poly AI architecture,which provides an interface layer between users and AI tools and services(Figure 5).This reduces the issues of vendor lock-in and provides the flexibility to use the best-fit option and cross-leverage AI capabilitie

37、s across user experiences and business processes.A poly AI architecture also reduces fragmentation,increases efficiencies,and ensures consistent performance across deployments.Figure 5.Implement poly AI for efficiencySource:Infosys4.Flexibility and leverage through poly AIUsing this architecture wil

38、l put the enterprise ahead of the 98%of organizations that arent ready across all five dimensions,according to our AI readiness research.LLMSelectionLLMhub modelswappabilityNarrowtransformersFew shotlearning/selfhostedmodelPromptoperationsPromptevaluationContentmanagementModelevaluationModelperforma

39、nce/managementLLM Hub-Poly AIPrompt managementEvaluation chassisAWS BedrockPrompt operation lifestyleLLM as judgeLLM as evaluatorDashboardsand flowsMetricsand logsModel inference/UI optimizationCachingframeworkAI gateway&API managementPromptevaluationStreaming AIinteractionsConsolidation ofmultiple

40、models Identical inputs Reduced generation Inproved content Model graded metrics Deterministic metricsAI stream U framework Model agnostic interactions Cohesive interactionsPromptgenerationPromptexperimentationPromptevaluation&refinementPromptfinalizationA poly AI approach also ensures various tooli

41、ng and processes are transparent,measured,and monitored across multiple hyperscalers.The top 10 AI imperatives for 2025|13External Document 2024 Infosys Limited Knowledge InstituteKnowledge Institute14|The top 10 AI imperatives for 2025External Document 2024 Infosys Limited Knowledge InstituteThe fi

42、rst four imperatives smaller and focused models,agentic AI,reimagined processes and experiences,and poly AI architecture can only be built if the talent is available.However,this is a challenge for many:just 35%of enterprises are AI-ready on the talent dimension,according to AI ReadinessRadar.Enterp

43、rises must build a team that can rapidly build,test,and implement AI tools,processes,and services.Companies should invest in an internal development platform that provides software teams with all the tools and infrastructure they need to start piloting,building,and deploying solutions.Some companies

44、 will take a two-stage approach,first establishing an AI foundry to experiment with and incubate new technologies,develop new patterns,and try different use cases,after which an AI factory will turn the learnings from the foundry into products(Figure 6).This approach helps balance and manage the ris

45、ks associated with AI while scaling adoption within the enterprise.Figure 6.Innovate at speed and scaleSource:Infosys5.Innovation through an AI foundry and factoryEvolve responsibleAI frameworkEvolve autonomousOps&measurementOperationalize AI use caseIdeas and best practices sentback to AI Foundry s

46、tep 1Seed MVP goalDefinemeasurementframeworkEvolve AIfoundation123456AI FactoryInnovation atscale12345AI FoundryInnovation atspeedHypothesis creationRisk identificationTest hypothesis through experimentsCompile learningsIterateOnce complete,moveto AI Factory step 1The top 10 AI imperatives for 2025|

47、15External Document 2024 Infosys Limited Knowledge InstituteNow that weve got the innovation factory set up(Imperative#5),how can business leaders ensure were keeping costs low and compute as green as possible?Sustainability is a crucial element of responsible AI design,addressing the environmental

48、impact of AI systems.As AI models and infrastructure consume significant energy,its vital to develop more efficient,compact models that can run on less power-hungry hardware like CPUs instead of GPUs(Figure 7).Enterprises should prioritize the use of smaller,6.Greener,lower-cost hardware and models

49、more efficient models to reduce their carbon footprint while maintaining functionality.The optimization of data centers,including the use of renewable energy sources and advanced cooling systems,is also essential for sustainable AI operations.AI itself can contribute to sustainability efforts by opt

50、imizing power consumption in data centers and enhancing overall system efficiency without compromising service level agreements.Sustainable AI involves implementing lifecycle assessments for AI projects and considering environmental impact throughout all stages of development anddeployment.Source:In

51、fosys Knowledge InstituteFigure 7.LLMs challenge sustainability goalsModel energyintensityData centerworkloadsData centerenergy use100 days to train100-billion-parameter LLMs,usingtens ofthousands of GPUs+50%between2015 and 2022+340%between2015 and 202216|The top 10 AI imperatives for 2025External D

52、ocument 2024 Infosys Limited Knowledge InstituteKnowledge InstituteThe top 10 AI imperatives for 2025|17External Document 2024 Infosys Limited Knowledge InstituteIn prior research,we found that data accuracy,processes,and accessibility have a significant impact on successful AI development.However,t

53、he Infosys Knowledge Institutes AI Readiness Radar found that data is an area in which enterprises are least ready(just 17%have an effective data estate).In fact,30%of enterprises rated data governance processes used for AI as poor,increasing AI risks.Preparing data infrastructure includes having sy

54、stems cleanse and then fingerprint data so that it is transparent,and companies know what is present where(in each piece of data)across data stores.Additionally,companies must know how to handle structured and unstructured data,7.Fingerprinted data for better governanceincluding traditional analytic

55、al data,transactional data,synthetic data,and ecosystem data,generated by users or machines(Figure 8).Source:InfosysFigure 8.Data governance is paramountBring all data under management1.Organized and fingerprinted data2.Vertically integrated control functions3.Zero-trust privacy and regulatory compl

56、iance4.Governed consumptionNew data typesfor the Al eraERP pre-AI eraHistorical/analyticalEcosystemSynthetic andtraining dataUser generatedMachine generatedHistorical/AnalyticalTransactionalCompanies can use this fingerprint to establish data correlations and conduct governance activities.Enterprise

57、s should also establish a data or AI governance entity that is in charge of this data stewardship.Knowledge Institute18|The top 10 AI imperatives for 2025External Document 2024 Infosys Limited Knowledge InstituteWhile AI can boost productivity by as much as 40%,according to our AI Readiness Radar,it

58、s further potential lies in elevating the quality of work from good to exceptional,according to insights from a World Economic Forum 2024 study.CXOs should develop a strategic AI value map to identify high-impact business areas across the organization rather than pursuing siloed usecases.This approa

59、ch should consider the five AI evolutionary capabilities from AI as a general-purpose tool to AI as a collaborator to autonomous AI agents and map them Figure 9.AI evolutionary capabilitiesSource:Infosysto activities(Figure 9).Further analysis should consider factors such as value creation,cost mana

60、gement,data availability,and required skillsets for successful implementation.The primary use case and its supporting activities comprise the core business process.For example,in retail,this includes logistics,operations,and customer service.Enterprises should then map the level of automation needed

61、 to fulfill eachactivity.Strategic mapping and deployment of AI across the organization will help take the business to the next level.8.Value chain-to-AI capability mappingAI as ageneral-purpose toolAI as anassistantAI as acollaboratorAI-drivenprocessessAutonomousAI agentsThe top 10 AI imperatives f

62、or 2025|19External Document 2024 Infosys Limited Knowledge InstituteKnowledge InstituteExternal Document 2024 Infosys Limited Knowledge Institute9.AI competency through upskilling and product-centricityTo bring AI to life,every enterprise needs a talent strategy.An AI-first enterprise also needs a c

63、hange management strategy and a target operating model,increasingly viewed as product-centric.First,talent needs to go broad and deep on AI.This approach should aim to elevate human intelligence and capabilities across the organization,ensuring the company remains competitive in an AI-driven landsca

64、pe.For instance,Infosys three-tiered competence framework has:20|CMO radar report 2024The top 10 AI imperatives for 2025|21External Document 2024 Infosys Limited Knowledge Institute AI aware:Ensure all employees have a basic understanding of AI tools for effective collaboration and co-creation.AI bu

65、ilders:Develop people who can use AI skills to create AI-embedded or AI-reimagined solutions.Change management should include managing AI adoption and transition,leadership alignment,and a communications plan with regular channels and cadence.Figure 10.Deploy a product-centric modelSource:Infosys Kn

66、owledge InstituteTraditional modelProduct-centric operating modelStrategy and planningRetail bankingProduction supportInfrastructure,cloud,security,CoEsInvestment bankingCustomer journeyexperiencesBusinessproduct linesProducts e.g.pre-qualifcation,account opening,verifcation,loan processing,approval

67、 Squads(build,change,run)Shared services platform,microservices,cloud,infrastructure,information security,architecture,etc.Production supportInfrastructure,cloud,security,CoEsBusiness IT/domain CoEsArchitecture CoEPersonal banking home ownerMortgageProduct 1Product 2Product nEnterprise capabilitiesP

68、rojects teamsProjects teamsApplication portfolioApplication portfolio AI masters:Nurture experts who can design models and innovative methods that work at scale,reducing costs and adding value to the business.By adopting this approach,organizations can create a workforce equipped to collaborate effe

69、ctively with AI at various levels of sophistication.Third,the operating model should be designed so that teams can work in an agile,productcentric approach(Figure 10).In this organization model,AI products and services are created by cross-functional teams,grouped to orchestrate an end-to-end custom

70、er journey,which increases business velocity and experimentation.22|The top 10 AI imperatives for 2025External Document 2024 Infosys Limited Knowledge InstituteAI shouldnt be viewed simply as a cost-cutter orproductivity enhancer.Rather,it can be used to do things that werent possible before,leading

71、 tonew ways of operating and doing business.The financial services industry,being a fast mover in AI,often indicates how follower industries will use AI in business strategy.We can see from our banking index research that although reducing costs is still the biggest strategic priority,transforming b

72、usiness models is also growing in importance,and driving business growth isnt far behind(Figure 11).AI can process vast amounts of data to identify potential new growth markets.It can also identify new channels that might not have been considered,especially in an international context.10.AI for busi

73、ness growthAI can also be used for things that humans cant do,such as identifying patterns that humans miss.In research-intensive fields,it accelerates progress by enabling rapid exploration of complex problems.However,this transformation brings challenges,such as mismatches between existing skills

74、and emerging job requirements.To address these challenges and prevent the exacerbation of existing inequalities,prioritizing education on AI and its implications is crucial.Inclusive development practices,transparency,and ongoing education initiatives are essential for creating AI systems that not o

75、nly benefit individual users but also serve the greater good and build stakeholder trust.Figure 11.Transforming business models a key CXO prioritySource:InfosysAverage relative weight(Total=100)010203040Strategic priorityReduce costsTransform business modelSupport or drive business growthKeep the li

76、ghts onRespond to competition32161612122717141413Develop new innovations/new product oferings105Comply with regulatory requirements76Volume 3Volume 2Notes:1.N=396,where N is the number of banks surveyed in Volume 3.2.N=324,where N is the number of banks surveyed in Volume 2.3.Volume 2(conducted Janu

77、ary 2024),Volume 3(conducted April 2024).The top 10 AI imperatives for 2025|23External Document 2024 Infosys Limited Knowledge InstituteKnowledge InstituteExternal Document 2024 Infosys Limited Knowledge Institute24|The top 10 AI imperatives for 2025The top 10 AI imperatives for 2025|25External Docu

78、ment 2024 Infosys Limited Knowledge InstituteAuthorsRajeshwari Ganesan|Distinguished Technologist-InfosysHarry Keir Hughes|Principal Consultant-Infosys Knowledge Institute26|The top 10 AI imperatives for 2025External Document 2024 Infosys Limited Knowledge InstituteAbout Infosys Knowledge InstituteT

79、he 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 aids decision making on critical business and technology issues.To view our

80、 research,visit Infosys Knowledge Institute at or email us at .2024 Infosys Limited,Bengaluru,India.All Rights Reserved.Infosys believes the information in this document is accurate as of its publication date;such information is subject to change without notice.Infosys acknowledges the proprietary r

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