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1、AI BUSINESS VALUE RADAR:LIFE SCIENCES EDITION#BreakthroughsForLife2AI Business Value Radar:Life Sciences Edition|External Document 2025 Infosys LimitedKnowledge InstituteKnowledge Institute2AI Business Value Radar:Life Sciences Edition|External Document 2025 Infosys Limited3 External Document 2025 I

2、nfosys Limited|AI Business Value Radar:Life Sciences EditionKnowledge InstituteTable of contentsKnowledge InstituteIntroduction 4Where AI works 6AI requires transformation,investment 10The workforce opportunity 12Spend,transform,train 16Appendix A:Use case types used in the survey 17Appendix B:Resea

3、rch approach 20Contributors 213 External Document 2025 Infosys Limited|AI Business Value Radar:Life Sciences Edition4AI Business Value Radar:Life Sciences Edition|External Document 2025 Infosys LimitedKnowledge InstituteLife sciences companies are ahead of the pack in achieving value from artificial

4、 intelligence(AI),according to a new Infosys study.We surveyed 3,798 business leaders for the Infosys AI Business Value Radar and found that about 20%of AI use cases deliver on all business objectives,and 30%of use cases deliver on some objectives.(See Appendix A for methodology and more details on

5、use cases studied.)Focusing specifically on life sciences operations,we found that 28%of AI use cases deliver on all business objectives(vs.19%across all industries)and an additional 37%(vs.32%)have delivered on some objectives.Life sciences organizations have achieved more positive impact from AI,c

6、anceled fewer AI projects,and had fewer AI use cases in the sandbox(Figure 1).Thats better than practically all other industries.Life sciences companies have typically lagged in adopting new technologies.Extensive regulatory approvals for new software and processes limit how pharmaceutical organizat

7、ions can try new tools.But the strictly regimented process-focused nature of their work matches well with whats required to implement generative AI tools,such as having data thats already clean and in a fit state to use with generative AI.In contrast,the healthcare industry performed slightly below

8、average in achieving value from AI.Healthcare organizations,of course,Knowledge InstituteIntroduction5 External Document 2025 Infosys Limited|AI Business Value Radar:Life Sciences EditionKnowledge InstituteFigure 1.Stage of use case deployments across the research sampleSelectPlanPilotDeploy2%No pla

9、ns6%Still inplanning6%Still inpilot3%CanceledPre-deployment13%Canceled postdeployment6%No value achieved37%Partial value achieved28%Most value achievedSource:Infosys Knowledge Instituteare in the business of healing and caring,but struggle with many competing definitions,standards,and stakeholders.L

10、ife sciences and pharmaceutical organizations focus heavily on their core mission of drug discovery,and on product delivery and deployment.Their core business involves processing and analyzing proprietary data,over which they have strong controls and deep understanding.And regulatory requirements an

11、d safety standards require that drugmakers follow structured and detailed processes from testing to marketing to manufacturing.“AI has been used in life sciences to accelerate established processes,maintain standards and comply with regulators,”says Subhro Mallik,Infosys executive vice president for

12、 life sciences.Mallik also believes drug discovery is an area where AI has much promise and can potentially deliver substantial benefit.Standard technology infrastructure in life sciences organizations has enabled companies to quickly apply artificial intelligence.Infosys clients have applied AI and

13、 generative AI use cases across the value chain,particularly in Phase I to Phase III drug trials and in regulatory review.Infosys and its clients are actively using AI to translate clinical trial information and to create plain-English summaries of clinical trial outcomes.Clients also are using AI t

14、o identify tumors in radiology scans,conduct biostatistical analysis,and draft regulatory documents.Infosys subsidiary BASE life science has deployed a generative AI platform that uses AI agents that interface with legacy systems and enhance existing workflows ranging from clinical trials and regula

15、tory processes to marketing and customer relationship management functions.6AI Business Value Radar:Life Sciences Edition|External Document 2025 Infosys LimitedKnowledge InstituteAI delivers value in life sciences for most industry-specific use cases as well as broad functional use.Figure 2 shows th

16、e top industry-specific use cases with higher“viability”that is,that are most likely to generate business value.Three life science-specific uses AI for image and voice processing and diagnosis,AI for clinical trials,and AI for regulatory and submissions ranked in the top 10 most viable uses of AI am

17、ong 77 studied.A fourth life sciences use is AI for personalized and digital medicine scored 14th overall(Figure 2).We also considered AI use cases in functional areas,that is,AI used for general business functions such as marketing,customer services,and human resources that are not necessarily tail

18、ored to an industry or specialization.In life sciences,respondents indicated that they use AI more in software development and IT operations than in other functional categories(Figure 3).Overall,life sciences organizations outpace other industries in successful AI use in functional areas of business

19、(Figure 4,which shows the viability scores for the use case ranking in Figure 3).But life sciences survey respondents said they do not frequently achieve business value on the most frequent functional use for AI:software development.Overall across all industries,AI for software development delivers

20、positive outcomes,our survey found.Knowledge InstituteWhere AI works7 External Document 2025 Infosys Limited|AI Business Value Radar:Life Sciences EditionKnowledge InstituteThis is tied into the standardization and legacy nature of life sciences tech.With strict regulation and only a few vendors,man

21、y life science functions run on commercial,off-the-Use caseViabilityrankIndustryClaims processingInsurance8.Client advisory servicesProfessional services9.Building electrifcationEnergy,mining,or utilities10.Energy tradingEnergy,mining,or utilities11.Automated warehousingLogistics12.Self optimizing d

22、atacentersHigh tech13.Personalized anddigital medicineLife sciences14.Loyalty programsConsumerpackaged goods15.Project/case management1.Professional services2.Network securityTelecommunications3.Image and voiceprocessing and diagnosisLife sciences4.Clinical trialsLife sciences5.Smart network operati

23、ons(predictive maintenance,self-healing,digital twins)Telecommunications6.Customer onboardingor registrationTelecommunications7.Regulatory andsubmissionsLife sciencesFigure 2.AI for life sciences scores very highSource:Infosys Knowledge Instituteshelf systems,which limits the impact of AI on softwar

24、e development.While this limits the opportunity to create business value via software development,it means life sciences or pharmaceutical businesses can focus their tech budgets on business problems,says Infosys vice president Surya Duvvuri.“Theyre pouring all their energy into using technology to

25、solve real business problems,”he adds.Infosys has worked with a pharmaceutical client to build a platform for staffers to develop AI use cases.The platform,called Cortex,manages security and regulatory considerations,and links to select curated data sources.This creates a development environment tha

26、t follows regulatory,explainable AI and responsible AI standards.With that in place,workers can choose AI models and large language models to develop AI use cases and integrate them with existing processes.While our survey found that AI lags at delivering business value for software development in l

27、ife sciences,Infosys leaders expect that to change.Gurdeep Singh Rooprai,an Infosys associate vice president,says he and client companies are developing effective ways to put AI to work with legacy systems and across the software development life cycle.But in one critical area,the effort being put i

28、nto getting AI to generate value is not bearing fruit.Using AI for drug discovery was the sole life sciences-specific use of 8AI Business Value Radar:Life Sciences Edition|External Document 2025 Infosys LimitedKnowledge InstituteAI scored by executives as not currently generating value.This is a cor

29、e mission for pharmaceutical organizations,but also a complex task.Big pharmaceutical businesses have invested heavily in using AI for drug discovery but have achieved limited success.One particularly enticing promise from AI is its ability to find new insights from large quantities of data.Major ph

30、armaceutical companies have decades of drug development and testing data in their archives.Most potential compounds dont end up as safe and effective for the condition that they target.But imagine if AI systems could re-visit the test data and molecules with scaled up training models,Duvvuri says:AI

31、 could identify a new use for a therapeutic that failed to deliver on its intended use.Infosys survey data and industry developments offer some hope that AI for drug discovery will become more viable.First,our survey shows that AI for product development is going well in a broad sense.Next,rapid cha

32、nge and advances in AI s have potential to create new tools for drug discovery.This spring,researchers used AI to develop a new method for protein sequencing,which has application in health and life sciences.And newly developed LLM agents for biologic and medicine uses have potential to expand capab

33、ilities and build even more specific models for new therapeutics or new uses for therapeutics.Figure 3.Top AI functional use cases in life sciences40%Software development32%IT,operations,and facilities21%Marketing20%Product development20%Content intelligence19%Cybersecurity and resilience19%Sales an

34、d revenue18%Workforce18%Manufacturing17%Fraud risk and compliance16%Sustainability15%Finance14%Customer service12%Supply chainSelection rateSource:Infosys Knowledge Institute9 External Document 2025 Infosys Limited|AI Business Value Radar:Life Sciences EditionKnowledge InstituteFigure 4.Life science

35、s frequently achieve good outcomes across AI use categories,with one exception1.47Cybersecurity and resilience1.45IT,operations,and facilities1.37Fraud risk and compliance1.30Workforce1.30Marketing1.30Customer service1.23Sales and revenue1.22Content intelligence1.22Supply chain1.22Manufacturing1.18P

36、roduct development1.16Sustainability1.15Finance0.80Software development0.00.20.40.60.81.01.21.41.6Relative viabilitySource:Infosys Knowledge InstituteLife sciences and pharma businesses are pouring all their energy into using technology to solve real business problems.Surya DuvvuriVice president,Inf

37、osys10AI Business Value Radar:Life Sciences Edition|External Document 2025 Infosys LimitedKnowledge InstituteAI requires transformation,investmentThere is a very clear prerequisite to finding business value with AI.Successful life sciences use cases all require changes to operating models and data a

38、rchitecture to make them viable(Figure 5).This is true across all sectors:You have to do the transformation to unlock business value with AI.Tim Skeen,CIO at Sentara Health,said that his organizations cloud-driven tech transformation is an advantage as it begins to apply AI to data and its operation

39、s.The Virginia-based healthcare organization has developed a data store for sharing clinical trial information and research supported by AI-driven data integrations.“Our operational systems that are generating data and usage,we have that connected to understand that across the spectrum,”he told Venk

40、y Ananth,Infosys vice president,in a video interview.Our research finds a strong correlation between the amount spent on each use case and its relative viability.The more spent,the more successful a specific life sciences use case is likely to be.However,there are a number of opportunistic use cases

41、,including image and voice processing and diagnosis that achieve high viability for relatively little spend(Figure 5).These are good bets for companies looking for lower cost AI wins.That said,with the falling costs of AI models,the calculus of AI viability may change in future.Use cases that did no

42、t score highly in our viability model,including drug discovery,could well start to become more viable as companies are able to invest more in them as Knowledge Institute11 External Document 2025 Infosys Limited|AI Business Value Radar:Life Sciences EditionKnowledge Institutethe fundamental cost of d

43、oing AI falls.Further drug discovery is simply a more intricate task than automating a regulatory process.The more time,effort and expertise life sciences devote to it,the more we expect results to improve.Figure 5.70%of the more successful use cases in life sciences require more tech and operationa

44、l transformationFigure 6.Using AI for drug discovery has not frequently delivered business value,thus far at leastTransformational use caseshave high viability and levelsof spendSome highly viable use casesare not attracting muchspend but could be easierto implementRed vs.green clusters=AI use cases

45、 requiring more transformation and higher spend to succeed vs.thosethat do not.Adjusted average spending is adjusted for company size,where a value of 1 is a use case typewith an average amount of spending per implementation.A viability score of 1 indicates an average likelihoodto deliver business o

46、bjectives.A required transformation score of 1 indicates an average amount of changesneeded to operating model and technical architecture for implementation.Required transformationHighLow1.200.800.500.600.700.800.901.001.101.201.301.401.50Viability0.650.700.750.800.850.900.951.001.051.101.151.201.25

47、1.301.351.40Adjusted average spendingTelemetryDisruption management of fightsEnterprise resiliencemonitoringClaims processingCustomer onboardingor registrationSecurity managementAutonomous driving agents AI orchestrated processesSmart buildings/smartwarehouse automation Project/case management More

48、viableLess viableRisk assessment and underwritingSource:Infosys Knowledge InstituteSource:Infosys Knowledge InstituteSelectPlanPilotDeploy4%No plans10%Stillin planning4%Still in pilot8%CanceledPre-deployment18%Canceledpostdeployment8%No value achieved42%Partial value achieved8%Most value achieved12A

49、I Business Value Radar:Life Sciences Edition|External Document 2025 Infosys LimitedKnowledge InstituteThe workforce opportunityBeyond tech transformation,our research finds that those whose organizations prepare their staff for AI perform even better in achieving business value from AI.Across all in

50、dustries,the top tier of companies in workforce preparation achieves significantly better success with AI and business value.To come to this finding,we asked respondents to select which definition in Figure 5 best matched their own companys employee engagement with AI.We then categorized them into f

51、our archetypes (Figure 7).Trailblazers,those who are fully engaged with AI,represent the top 16%of all respondents.About two-thirds of all companies fall into the middle two tiers(Explorers or Pathfinders)and the bottom tier of 17%we classify as Watchers(Figure 8).Given their capability to extract b

52、usiness value from AI,it would be easy to assume that life science companies do a strong job of preparing their workforce for AI and have a disproportionate share of Trailblazers.Thats just not true.Most life sciences businesses fall into the middle two tiers and break out at roughly the same ratios

53、 as the whole survey.Infosys industry specialists say pharmaceutical businesses do well with AI because they already have strict processes and high-quality data.Enhancing workforce readiness with AI is a tremendous opportunity for life sciences Knowledge Institute13 External Document 2025 Infosys Li

54、mited|AI Business Value Radar:Life Sciences EditionKnowledge Institutebusinesses.Life sciences organizations can improve their probability of success by 19 percentage points by advancing from the Pathfinder tier to Trailblazer,and a full 31 percentage points by moving from tier 3 Explorer status to

55、Trailblazer.Further,the 16%of life sciences businesses that have prepared their workforces to be Trailblazers have the highest levels of viability(Figure 9).Acceptance scores how easily customers or users adopted and accepted the outputs from a specific AI use case.Making the most of AI is critical

56、and timely Figure 7.Workforce readiness archetypesArchetypeDefnitionWatchersMinimal engagement with employees on AI.Limited or no training,education,orchange management initiatives are in place.Initial steps taken to address AI.Limitedchange management practices;employeeshave minimal involvement or

57、support inunderstanding AIs role.ExplorersRegular training and educational programson AI,with growing employee engagement.Change management practices are startingto support employees in adapting to AIchanges,though some uncertainty persists.PathfndersFully engaged in continuous AI training,education

58、,and change management.Employees are fully supported inunderstanding and adapting to AI,and theirfeedback actively informs AI deploymentstrategies.Trailblazersfor life sciences businesses,given scarcity and growing labor challenges in the sector.A 2024 report by Manpower Group found that life scienc

59、es has the greatest challenges with labor scarcity.And industry executives flagged replacing retiring employees as their top workforce challenge,according to a 2024 KPMG report.“Pharma businesses are recruiting AI data scientists to their organizations,and pairing them with company veterans with bus

60、iness knowledge,and giving the business experts basic training in how artificial intelligence works,”Duvvuri says.Bridging the gap between data science and drug discovery is where the work becomes difficult.Both the data scientist and business expert have deep expertise,but both also need to have be

61、tter understanding of the Source:Infosys Knowledge InstituteFigure 8.Workforce readiness and AI16%16%36%31%30%35%17%17%TrailblazersWatchersPathfndersExplorersLife sciencesOverallSource:Infosys Knowledge Institute14AI Business Value Radar:Life Sciences Edition|External Document 2025 Infosys LimitedKn

62、owledge InstitutePharma businesses are recruiting AI data scientists to their organizations,and pairing them with company veterans with business knowledge,and giving the business experts basic training in how artificial intelligence works.Surya DuvvuriVice president,Infosysothers domain in order to

63、generate value.The pharma experts receiving AI-driven insights are skeptical,Duvvuri says.“Are they telling me what I want to hear?”,he adds.32%46%19%27%14%15%21%27%TrailblazersWatchersPathfndersExplorersLife sciencesOverallLife sciencesOverallLife sciencesOverallLife sciencesOverall“The translation

64、 from business to IT how to look at the data and understanding different aspects is where it is becoming difficult.”Figure 9.Workforce readiness enhances AI successSource:Infosys Knowledge Institute15 External Document 2025 Infosys Limited|AI Business Value Radar:Life Sciences EditionKnowledge Insti

65、tute15 External Document 2025 Infosys Limited|AI Business Value Radar:Life Sciences EditionKnowledge Institute16AI Business Value Radar:Life Sciences Edition|External Document 2025 Infosys LimitedKnowledge InstituteSpend,transform,trainAI suits the life sciences sector,and these companies lead the w

66、ay in achieving value from it.But there remains more value to unlock.The spend is worth it.Companies who invest in AI and in transforming their digital systems will have the advantage even in an industry with strong AI results.The cost and effort involved in transforming operating models and data ar

67、chitecture seems daunting,but the clear value that AI delivers is more compelling.From protein folding to mRNA vaccine innovations,AI has already demonstrated good value in life sciences.Our research finds that putting AI to work and getting results can be costly in life sciences,but the return on i

68、nvestment is worth it.That ROI can be amplified through workforce engagement.Preparing workers for AI through training,education and positive change management leads to better acceptance and use of AI.And the more workers use AI to augment their efforts,the sharper their use cases will become.AI age

69、nts AI systems that can operate autonomously and across platforms are being used by large pharmaceutical operations for a range of purposes,including drug discovery,personalized medicine,and management of regulatory processes.Our research found that deploying AI agents to orchestrate systems is a lo

70、w-cost,low-effort and highly viable use of AI.With the right transformation,workforce training,AI agents could unleash even greater business value from AI in life sciences.At this next frontier,business value could come even more readily.Knowledge Institute17 External Document 2025 Infosys Limited|A

71、I Business Value Radar:Life Sciences EditionKnowledge InstituteAppendix A:Use case types used in the surveyFigure A1.Functional categories and their use case typesBased on interviews with subject matter experts and desk research,we collated 55 use case types across 14 categories(Figure A1).We simila

72、rly collated 77 industry-specific use case types across 15 industry sectors (Figure A2).All these use case types are themselves at a level of abstraction higher than a specific use case,to make the survey manageable but they are also relevant for all respondents.CapabilityUse case typeCapabilityUse

73、case type1.Contentintelligence Generate content Manage content Analyze content Content performance8.Fraud,risk,and compliance Fraud detection and prevention Risk modelling and analytics Compliance2.Customerservice Improve/support customer service agents Automated self-service/AI assistants Customer

74、service performance and analysis Personalized customer service9.Softwaredevelopment Legacy code migration and modernization Developer code assistant Automating code development Testing code/QA3.Cybersecurityand resilience Threat and anomaly detection Enterprise resilience monitoring10.Sustainability

75、 Supply chain transparency Energy optimization Material reuse,circular economy/products4.Marketing Customer segmentation Optimizing marketing strategy Marketing asset creation Personalized marketing11.Workforce Performance management Talent acquisition and management Assist employee workfow Personal

76、ized onboarding and employeeexperience Workforce management and scheduling5.Sales andrevenue Find cross-sell/up-sell opportunities Churn prevention Optimizing sales strategy Supporting sales executives E-commerce product recommendations12.Procurementand supply chain Supplier risk assessment Supply c

77、hain optimization Supply chain forecasting Procurement and contract management Protecting the bid process6.IT,operations,and facilities Asset management AI-orchestrated processes Smart buildings/smart warehouseautomation Incident management and ticketing13.Manufacturing Smart,connected factory Preve

78、ntive maintenance for assets Quality assurance with autonomousdecision making(defect detection)Safety,security,and risk assessment Demand forecasting and inventory7.Productdevelopment Product design and innovation Product testing Personalized product development14.Finance Invoice and payment process

79、ing Expense management Cashfow forecasting/optimization Automated fnancial reportingSource:Infosys Knowledge Institute18AI Business Value Radar:Life Sciences Edition|External Document 2025 Infosys LimitedKnowledge InstituteThe survey asked respondents to select up to five functional categories out o

80、f 14(Figure A1)where their companies are pursuing AI.Respondents provided details on these categories,the top five that their company is already interested in.As such,this is a self-selecting sample.In Figure 1,for example,we would typically expect far more projects that had failed,been canceled,or

81、been in pilots for what is an early stage and experimental technology.Each category had between two and six common use case types(for example,product recommendation use cases in the sales and retail category).For each use case type within a category,respondents were asked about the stage of implemen

82、tation of their initiative(s).Options for this question were:No plans to implement;Planning;Created proof of concept or pilot;Canceled before deployment;Deployed,not generating business value;Canceled after deployment;Deployed,generating some business value;Deployed,achieving most or all objectives.

83、Respondents were then asked about the amount of spending for that use case type to date(from any start date).This was followed by questions about the amount of operational or business model change as well as the amount of change in data structures and technical architecture needed for each use case

84、type.Finally,respondents were asked about the proportion of their user base that accepted and used the AI tool deployed(if any)for each use case type.The same series of questions was asked of industry-specific use case types for the industry of the respondent(Figure A2).Figure A2.Industry-specific u

85、se case typesIndustry Industry-specifc use caseIndustry Industry-specifc use case1.Automotive Autonomous driving agents Immersive vehicle infotainment withvoice assistant AI-powered navigation systems with feetmanagement Vehicle diagnostics and predictive maintenance Vehicle usage analysis for usage

86、-based insurance9.Logistics Delivery route optimization Returns management Automated warehousing Capacity management Autonomous delivery vehicles Predictive maintenance2.Consumerpackagedgoods Price pack architecture Recipe creation Loyalty programs Visual merchandising Smart and sustainable packagin

87、g AI-driven new product launch execution10.Manufacturing Streamlined product development and design Parts procurement and contract management Smart,automated factory with preventivemaintenance Digital supply chain and logistics Quality assurance with autonomous decisionmaking3.Energy,mining,andutili

88、ties Environmental impact modeling Carbon capture,utilization,and storage(CCUS)Energy trading Building electrifcation Predictive maintenance Exploration11.Retail Physical retail experience E-commerce retail experience Staf scheduling Virtual try-on Consumer research Returns management19 External Doc

89、ument 2025 Infosys Limited|AI Business Value Radar:Life Sciences EditionKnowledge InstituteSource:Infosys Knowledge Institute8.Life sciences Drug discovery Clinical trials Image and voice processing and diagnosis Regulatory and submissions Personalized and digital medicine4.Financialservices Reconci

90、liations Dispute prediction KYC Pretrade analytics Trade fnance12.Travel andhospitality Disruption management of fights Ofer bundling Staf planning Security management RFP management for large hotel events5.Healthcare Radiology Patient triage Personalized treatment and care AI-enhanced telemedicine1

91、3.Telecommunications Network life cycle management(planning/design/optimization/slicing)Smart network operations(includes predictivemaintenance,self-healing,an digital twins)Network security Wireless channel modeling Customer onboarding or registration6.High tech Silicon design Process optimization

92、for better yield Self-optimizing data centers Digital twins of complex systems14.Professionalservices Staf utilization Market/client research and strategic planning Project/case management Client advisory services Ethics,compliance,and reporting7.Insurance Application approval,policy management,and

93、renewals Claims processing Risk assessment and underwriting Telemetry 15.Public sector Decision management Personalized benefts counseling Eligibility determination Case management Regulatory compliance Accessibility20AI Business Value Radar:Life Sciences Edition|External Document 2025 Infosys Limit

94、edKnowledge InstituteAppendix B:Research approachExpert analysis and interviewsInterviewed AI experts to formulate and validate which AI use cases and use case types are most salient to each category or industry,and to gain additional insights into the findings.SurveySurveyed 3,798 senior executives

95、(250 in life sciences),representing more than 3,200 companies(210 in life sciences),between December 2024 and January 2025 about AI use cases being pursued at their companies.Respondents represent businesses with more than$1 billion in annual revenue across 14 industries in the US,Canada,UK,France,G

96、ermany,Nordics,Australia,and New Zealand.We also included public sector organizations with budgets of$1 billion or more from the US and Canada.See Appendix A for specific survey methodology for use case types.ModelCreated scores for viability(based on probability of success),required transformation,

97、and acceptance of AI tools for each use case type.Viability scores were weighted to favor use cases that had achieved most or all business objectives.Each of the three scores is normalized relative to a mean of 1 for more meaningful visualization.Adjusted average spending is the spending adjusted fo

98、r company size(those with larger revenues tend to spend more on use cases and those with smaller revenues tend to spend less),where 1 represents the average spending per implementation($1.96 million).Source:Infosys Knowledge Institute21 External Document 2025 Infosys Limited|AI Business Value Radar:

99、Life Sciences EditionKnowledge InstituteChad Watt|Infosys Knowledge Institute,DallasContributorsAuthorKate Bevan|Infosys Knowledge Institute,LondonIsaac LaBauve|Infosys Knowledge Institute,DallasPramath Kant|Infosys Knowledge Institute,BengaluruPragya Rai|Infosys Knowledge Institute,BengaluruAnalysi

100、s and productionInfosys 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-plus AI assets,more than150 pre-trained AI models,and more than 10 AI platforms steered b

101、y AI-first specialists and data strategists,and a responsible-by-design approach,Infosys Topaz helps enterprises accelerate growth,unlock efficiencies at scale,and build connected ecosystems.Connect with us at .22AI Business Value Radar:Life Sciences Edition|External Document 2025 Infosys LimitedKno

102、wledge InstituteAbout 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 aids decision making on c

103、ritical business and technology issues.To view our research,visit Infosys Knowledge Institute at or email us at .23 External Document 2025 Infosys Limited|AI Business Value Radar:Life Sciences EditionKnowledge Institute23 External Document 2025 Infosys Limited|AI Business Value Radar:Life Sciences E

104、ditionKnowledge Institute 2025 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 rights of other companies to the trade

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