1、Agentic AI the new frontier in GenAI An executive playbook Harnessing AI isnt just about technology its about unleashing unprecedented potential.In an era where speed,efficiency,and customer centricity dictate market leadership,organisations need to harness every tool at their disposal.Over the past

2、 couple of years,artificial intelligence(AI)has exploded onto the world stage,with companies and individuals across the globe rapidly adopting the technology.The GCC is playing a lead role in the space,with business leaders in the region exploring ways of integrating this rapidly developing technolo

3、gy into their operations.Generative AI(GenAI)is being recognised as a game-changer for innovation in the region,empowering enterprises by automating routine tasks,enhancing customer experiences and assisting in critical decision-making processes.Insights from our 27th Annual CEO Survey:Middle East f

4、indings have shown that 73%of CEOs in the Middle East believe GenAI will significantly change the way their company creates,delivers and captures value over the next three years1.GenAI is poised to make a significant economic impact,with estimates indicating that it could contribute between$2.6 tril

5、lion and$4.4 trillion annually to global GDP across various industries by 2030.In specific sectors,such as energy,investments in GenAI are expected to triple,from$40 billion in 2023 to over$140 billion by the end of the decade.This surge in investment reflects the transformative potential of GenAI,p

6、articularly in enhancing productivity,streamlining business processes,and reshaping value chains across industries2.Against this backdrop,multimodal GenAI agentic frameworks has emerged as transformative catalysts,enabling businesses to accelerate process automation at an unprecedented scale.This te

7、chnology involves multiple AI agents working together,each specialising in different tasks or data types,to solve complex problems and automate processes.By collaborating and constantly learning,these agents enhance decision-making,optimise processes,and drive innovation.It combines range of advance

8、d AI techniques to process diverse data types and automate complex tasks.The central question isnt whether to adopt this technology,but how swiftly organisations can integrate it to stay ahead of the competition.This executive playbook explores how organisations can leverage this technology to boost

9、 operational efficiency,enhance customer experience,and drive revenue growth.It provides real-world success stories spanning industry sectors and organisational functions,strategic insights,tactical blueprints,and best practices to guide your journey into this revolutionary landscape.Key insights Ag

10、entic AI,differentiated by its advanced human-like reasoning and interaction capabilities,is transforming the manufacturing,healthcare,finance,retail,transportation,and energy sectors,among others.Organisations AI strategies should leverage multimodal GenAI capabilities while ensuring ethical AI saf

11、eguards to drive autonomous process re-engineering and enhanced decision-making across all lines of business.Integrated effectively,agentic AI can enhance efficiency,lower costs,improve customer experience,and drive revenue growth.What is agentic AI?Agentic AI generally refers to AI systems that pos

12、sess the capacity to make autonomous decisions and take actions to achieve specific goals with limited or no direct human intervention3.Key aspects of agentic AIAutonomy:Agentic AI systems can operate independently,making decisions based on their programming,learning,and environmental inputs.Goal-or

13、iented behaviour:These AI agents are designed to pursue specific objectives,optimising their actions to achieve the desired outcomes.Environment interaction:An agentic AI interacts with its surroundings,perceiving changes and adapting its strategies accordingly.Learning capability:Many agentic AI sy

14、stems employ machine learning or reinforcement learning techniques to improve their performance over time.Workflow optimisation:Agentic AI agents enhance workflows and business processes by integrating language understanding with reasoning,planning,and decision-making.This involves optimising resour

15、ce allocation,improving communication and collaboration,and identifying automation opportunities.Multi-agent and system conversation:Agentic AI facilitates communication between different agents to construct complex workflows.It can also integrate with other systems or tools,such as email,code execu

16、tors,or search engines,to perform a variety of tasks.AutonomyGoal-oriented behaviourWorkflow optimisationEnvironment interactionLearning capabilityMulti-agent and system conversationEvolution to multimodal GenAI agentsIn AI,the only constant is changeembrace a culture of perpetual innovation.The jou

17、rney of agentic frameworks began as simple,rule-based systems designed to perform specific tasks.Over time,these systems have evolved into sophisticated,multimodal agents capable of processing and integrating information from various sources,such as text,images,and audio.Multimodality capabilities a

18、llow AI agents to understand,employ reasoning,and interact like humans,enhancing their effectiveness and versatility to solve a wide range of business problems4.Goal-oriented behaviourAI agentIntegration of ML(2000s)Learning from dataNLP enabled user interactionsIntegration of Machine Learning(2000s

19、)Learning from DataNatural Language Processing(NLP)Enabled User Interactions:Introduction of multimodality(2010s)Combining text,images,and audioEnhanced user interactionsIntegration of Machine Learning(2000s)Learning from DataNatural Language Processing(NLP)Enabled User Interactions:Advanced autonom

20、y and real-time interactions(2020s-present)Human-like reasoning and advanced autonomyUser interactions within an ethical and responsible AI-controlled environment The evolution can be broken down into three key phases:1.Integration of Machine Learning(ML)Learning from data:The integration of ML allo

21、wed agents to learn from large datasets,improving their ability to make decisions and perform tasks.This was a significant step forward from rule-based systems,as agents could now adapt to new information and improve over time.Natural Language Processing(NLP)enabled user interactions:Advances in NLP

22、 enabled agents to understand and generate human language more effectively,making interactions more natural and intuitive.2.Introduction of multimodalityCombining text,images,and audio:Multimodal agents emerged,capable of processing and integrating information from various sources.For instance,an ag

23、ent could analyse a text description,recognise objects in an image,and understand spoken commands.This multimodality made agents more versatile and capable of handling complex tasks.Enhanced user interactions:Multimodal agents could interact with users in more dynamic ways,such as providing visual a

24、ids in response to text queries or understanding context from a combination of spoken and visual inputs.3.Advanced autonomy and real-time interactionsAdvanced autonomy:Agents can operate independently,rationalise and set their own goals,develop path(s)to attain these goals,and make independent decis

25、ions without constant human intervention,leveraging data from multiple sources or synthetic datasets.In a multi-agentic orchestration system,the first set of agents focus on mimicking human behaviour(e.g.ChatGPT-4o),that is,thinking fast to come up with solution approach,while the second set of agen

26、ts focus on slow reasoning(e.g.ChatGPT-1o)to come up with a vetted solution5.Combining thinking fast and slow reasoning,agents can process information and make optimal decisions in real-time crucial for applications like autonomous vehicles,real-time customer service,and various mission-critical bus

27、iness processes.This autonomy makes agentic AI particularly powerful in dynamic and complex real-world environments.User interactions within an ethical and responsible AI-controlled environment:With increased capabilities,there has also been a focus on ensuring that agentic systems operate ethically

28、 and responsibly,considering factors such as bias,transparency,and accountability.(2000s)(2010s)2020s-presentWhy organisations should pay attention In the fast lane of technological evolution,missing the AI turn today means being outpaced tomorrow.Agentic AI offers significant advantages in efficien

29、cy,decision-making,and customer interaction.By automating routine tasks and providing intelligent insights,agentic AI can help organisations save time,reduce cost,and improve overall productivity.Moreover,organisations who adopt an agentic AI system can gain a competitive advantage by leveraging its

30、 capabilities to innovate and enhance their business operations.Lower cost to entry and economies of scale makes it favourable for organisations to fully harness the capabilities it offers compared to its predecessors like traditional ML and Robotic Process Automation(RPA)-driven automations.Agentic

31、 AI systems can significantly enhance an organisations competitive edge by automating complex workflows,reducing operational costs,and improving decision-making processes.These systems are designed to adapt to changing business environments,driving higher productivity and enabling organisations to s

32、tay competitive.For example,agentic AI can predict market trends and customer preferences,allowing businesses to tailor their strategies proactively.This adaptability not only improves efficiency but also fosters innovation,giving companies a significant edge over competitors6.Moreover,agentic AI sy

33、stems can handle large volumes of data and extract actionable insights,which can be used to optimise operations and enhance customer experiences.By automating routine tasks,these systems free up human resources to focus on more strategic initiatives,thereby increasing overall organisational agility

34、and responsiveness7.Agentic AI systems can analyse vast amounts of data quickly and accurately,providing valuable insights to inform better decision-making.Businesses can leverage these insights to optimise revenue and operations,identify market trends,and make data-driven decisions.For instance,in

35、the financial sector,AI can analyse market data to predict trends,inform investment strategies,and boost investment ROI.In retail,it can streamline inventory management by predicting demand and optimising stock levels.Agentic AI can significantly enhance business efficiency and productivity by autom

36、ating routine tasks and processes.This allows employees to focus on more strategic and creative activities.For example,in customer service,agentic AI can handle common inquiries,freeing up human agents to tackle more complex issues.In manufacturing,AI-driven robots can manage repetitive tasks with p

37、recision and consistency,reducing errors and increasing output.By integrating agentic AI,businesses can offer personalised and responsive customer experiences.AI-driven chatbots and virtual assistants can provide instant support,answer queries,and even recommend products based on customer preference

38、s and dynamic interactions.This improves customer satisfaction,builds loyalty,and drives sales.For example,e-commerce platforms use AI to recommend products based on browsing history and purchase behaviour.Enhanced decision-makingBoosted efficiency and productivityImproved customer experienceAgentic

39、 AI systems are redefining customer service centres and are gaining popularity as a game-changing capability for both government entities and private sector organisations.While traditional rule-based chatbots(software-as-a-service)provided basic 24/7 support,and Retrieval Augmented Generated(RAG)-ba

40、sed chatbots enhanced human-like interactions(enhanced software-as-a-service),agentic AI surpasses both in terms of accuracy,contextual coherence,and problem-solving ability.In terms of accuracy,rule-based chatbots are limited to programmed responses,causing inaccuracies when queries fall outside of

41、 predefined rules.RAG-based chatbots depend on retrieved data that may not match user intent.In contrast,the novel approach of agentic AI allows it to understand nuances in language,generating accurate responses even to complex or unseen queries.Its ability to learn from vast datasets enhances preci

42、sion and adaptability,making it superior for customer interactions.One of the biggest limitations of chatbots has been contextual coherence.Rule-based chatbots struggle to maintain context in extended interactions due to linear scripting,leading to disjointed responses that harm customer experience.

43、RAG-based chatbots may produce inconsistent replies if retrieval mechanisms dont consider previous interactions.Whereas agentic AIs orchestration capability helps it excel at tracking conversation history,understanding dialogue flow,ensuring responses remain contextually appropriate and coherent,sig

44、nificantly boosting customer engagement.Thus far,both rule-based and RAG-based chatbots have limited autonomous problem-solving ability.The former cant handle problems outside their scripts while the latter provide information but cant synthesise data and prepare the human-live problem-solving logic

45、 to solve complex issues across integrated sources such as CRMs,ERP,or IVR systems.The agentic AI performs dynamic reasoning and decision-making,leveraging a series of autonomous agents,analysing customer issues,considering multiple factors,and applying learned knowledge to resolve problems more eff

46、iciently.The outcome is quicker,solution-oriented,and fluid conversations that enhance customer experience and set new standards for efficiency and responsiveness in automated customer service.Micro-agentsCustomer support agentCustomer support agentUser experience agentFAQ agentIssue resolution agen

47、tStatus updates agentFeedback collection agentNth agentOrchestrator agentMaster agentHow to conceptualise agentic AI solutions for future business operations Agentic AI business imperativesOrganisations managing day-to-day operations stand to gain significantly from agentic AI systems,embracing the

48、emerging service-as-a-software model.This innovative approach transforms manual labour into automated,AI-driven services.Rather than purchasing traditional software licences or subscribing to cloud-based software-as-a-service(SaaS),businesses can now pay for specific outcomes delivered by AI agents.

49、For example,a company might employ AI customer support agents like Sierra to resolve issues on their websites,paying per resolution rather than maintaining a costly human support team.This model allows organisations to access a wider range of services whether its legal support from AI-powered lawyer

50、s,continuous cybersecurity testing by AI penetration testers,or automated CRM management at a fraction of the cost.This not only drives efficiency but also significantly reduces operational overheads.By leveraging the service-as-a-software model,businesses can automate both routine and highly specia

51、lised tasks that were once time-consuming,required skilled professionals,and typically involved expensive software licences or cloud solutions.AI applications with advanced reasoning capabilities can now handle complex tasks,from software engineering to running customer care centres,enabling compani

52、es to scale their operations without a proportional increase in cost.This transition expands the services available to organisations of all sizes,freeing them to focus on strategic priorities while AI systems manage the operational burden.Adopting these AI-driven services positions businesses to sta

53、y competitive in an ever-evolving marketplace8.Service-as-a-software represents an outcome-focused,strategic shift,enabling organisations to transition from their current state to operating in copilot and ultimately autopilot modes.Sierra,for instance,offers a safety net by escalating complex custom

54、er issues to human agents when necessary,ensuring a seamless customer experience.While not all AI solutions offer this built-in fallback,a common strategy is to initially deploy AI in a copilot role alongside human workers.This human-in-the-loop approach helps organisations build trust in AI capabil

55、ities over time.As AI systems demonstrate their reliability,businesses can confidently transition to an autopilot mode,where AI operates autonomously,enhancing efficiency and reducing the need for human oversight.GitHub Copilot is a prime example of this,assisting developers and potentially automati

56、ng more tasks as it evolves.For organisations with high operational costs,outsourcing specific tasks to AI services that guarantee concrete outcomes is an increasingly attractive option.Take Sierra,for example:businesses integrate Sierra into their customer support systems to efficiently manage cust

57、omer queries.Instead of paying for software licences or cloud-based services,they pay Sierra based on the number of successful resolutions.This outcome-based model aligns costs directly with the results delivered,allowing organisations to harness AI for specific tasks and pay solely for the outcomes

58、 achieved.This shift from traditional software licences or cloud SaaS to service-as-a-software is transformative in several ways:Targeting service profits:Traditional SaaS focused on selling user seats,whereas service-as-a-software taps into service profit pools,delivering solutions that focus on sp

59、ecific business outcomes.Outcome-based pricing:Instead of charging per user or seat,service-as-a-software adopts a pricing model based on the actual outcomes achieved,directly aligning costs with results.High-touch delivery models:Service-as-a-software offers a top-down,highly personalised approach,

60、providing trusted,tailored solutions that meet the specific operational needs of businesses.Transitioning from copilot to autopilot modelsOutsourcing work through AI servicesWhy should organisations consider early adoption and avoid being late movers?Late moversEarly adoptersMarket positionSet indus

61、try benchmarks and gain first-movermarket advantage.Struggle to catch up and miss out on creating competitive advantage.InnovationLeverage AI to innovate business processes,deploy the AI solutions effectively and create differentiation.Slow to innovate business processes and take full advantage of A

62、I solutions to create differentiation.Customer relationshipsBuild deeper customer relationships through personalised and newer experiences.Play catch-up to match the personalised services of early adopters.Operational efficiencyStreamline operations and reduce operational cost early on.Higher lost o

63、pportunity cost due to late entry and adoptions.Learning curveBenefit from the initial learning curve and shape industry standards.Miss out on early learning opportunities and industry influence.Market shareIncrease market share and profitability through early adoption.Struggle to achieve similar ma

64、rket share.Barriers to entryCreate barriers for competitors through deep AI integration.Face higher barriers to entry due to established competitors.Cost to entryPay relatively higher cost of entry and iterative test-and-learn due to new AI solutions.Pay relatively lower cost of entry and lower lear

65、ning and experiments.Manufacturing:Siemens AGSiemens transformed its maintenance operations by deploying AI models that analyse sensor data from machinery.The system predicts equipment failures before they occur,scheduling maintenance proactively.The multimodal framework processes data from various

66、sources vibration,temperature,and acoustic signals providing a holistic view of equipment health and proactive maintenance orchestrated by the agentic AI models.Technology stack:AI models:Regression and deep learning models Platforms:Siemens MindSphere9 Tools:Scikit-learn,TensorFlow,Keras,IoT sensor

67、sFinancial impact:Savings:Reduced maintenance costs by 20%Revenue growth:Increased production uptime by 15%Non-financial benefits:Enhanced equipment reliability Improved worker safetyHealthcare:Mayo ClinicBy integrating AI into its radiology workflows,Mayo Clinic allows for quicker and more accurate

68、 diagnoses.The multimodal AI processes imaging data alongside patient history and lab results,offering comprehensive insights that aid radiologists in decision-making,automating documentation and process automation across the radiology value chain.Technology stack:AI Models:Regression and Convolutio

69、nal Neural Networks(CNNs)models Frameworks:NVIDIA Clara platform10 Tools:Scikit-learn,PyTorch,Medical Imaging DataFinancial impact:Efficiency gains:Reduced diagnostic times by 30%Cost reduction:Lowered unnecessary procedures by 15%Non-financial benefits:Improved diagnostic accuracy Enhanced patient

70、outcomesFinance:JPMorgan ChaseJPMorgans Contract Intelligence(COiN)platform uses AI to analyse legal documents,extracting key data points in seconds.The multimodal framework interprets complex legal language,images,and tables,streamlining a process that once took thousands of human hours.Technology

71、stack:AI models:NLP with Generative Pre-trained Transformers(GPT)Frameworks:COiN platform11 Tools:Python,HadoopFinancial impact:Savings:Saved 360,000 hours of manual review annually Risk mitigation:Significantly reduced compliance riskNon-financial benefits:Enhanced accuracy in document analysis Imp

72、roved employee productivityReal-world success storiesCatalysing change across all industries Retail:AmazonAmazon leverages AI to analyse browsing behaviour,purchase history,and even visual preferences.Multimodal AI models generate personalised recommendations,orchestrate tasks across order fulfilmen

73、t value chains,and enhance the shopping experience to drive sales.Technology stack:AI models:Regression and deep learning Models Frameworks:Amazon Personalise12 and Amazon Order Fulfilment Tools:AWS SageMakerFinancial impact:Revenue boost:Increased sales by 35%through personalised recommendations an

74、d one-click order fulfilment Customer retention:Improved loyalty rates by 20%Non-financial benefits:Enhanced customer satisfaction Increased engagement time on the platformTransportation and logistics:DHLDHL utilises AI models to predict and orchestrate shipping demands,optimise routes,and manage wa

75、rehouse operations.The system processes data from various sources,including traffic patterns,weather conditions,and order volumes.Technology stack:AI models:ML models and route optimisation algorithms Frameworks:DHL Resilient supply chain platform13 Tools:IoT devices,ML modelsFinancial impact:Cost s

76、avings:Reduced operational costs by 15%Efficiency gains:Improved delivery times by 20%Non-financial benefits:Enhanced customer satisfaction Reduced carbon footprintEnergy:BP(British Petroleum)BP uses AI to analyse seismic data,generating 3D models of subterranean structures.The multimodal approach c

77、ombines geological,geophysical,and historical data to identify favourable drilling sites and orchestrate drilling equipment settings for optimal outcomes.Technology stack:AI models:Regression and GenAI models Frameworks:Azure cloud services14 Tools:Microsoft AIFinancial impact:Savings:Reduced explor

78、ation costs by 20%Revenue growth:Increased successful drilling operations by 15%Non-financial benefits:Reduced environmental impact Improved safety measuresEducation:PearsonPearsons AI models tailor educational content to individual learner needs,adjusting difficulty levels and content types based o

79、n performance and engagement data.Technology stack:AI models:Adaptive learning algorithms Frameworks:Multimodal content delivery systems15 Tools:Python,TensorFlowFinancial impact:Revenue increase:Boosted course enrollment by 25%Cost reduction:Lowered content development costs by 15%Non-financial ben

80、efits:Improved student outcomes Enhanced user engagementMedia and entertainment:NetflixNetflix uses AI models to recommend and orchestrate content by analysing viewing habits,ratings,and even visual content features.The multi-modal AI ensures that users find content that resonates with their prefere

81、nces,keeping them engaged.Technology stack:AI models:ML and GenAI models Frameworks:Netflix multimodal user interaction analysis16 Tools:AWS,Apache SparkFinancial impact:Subscriber growth:Increased retention rates by 10%Revenue boost:Enhanced engagement leading to higher subscription renewalsNon-fin

82、ancial benefits:Personalised user experiences Improved content strategyTelecommunications:AT&TAT&Ts AI models analyse and orchestrate network performance data and customer interactions to optimise network operations and personalise customer service through chatbots.Technology stack:AI models:ML for

83、network analytics Frameworks:Edge computing with multimodal data inputs17 Tools:AI chatbots,data analytics platformsFinancial impact:Cost savings:Reduced operational expenses by 15%Revenue growth:Improved upselling through personalised offersNon-financial benefits:Enhanced network reliability Improv

84、ed customer satisfactionGovernment and public sector:Singapore GovernmentSingapore utilises AI models to orchestrate and manage traffic flow,energy consumption,and public safety.The multi-modal system processes data from various sensors and citizen feedback mechanisms to make real-time decisions.Tec

85、hnology stack:AI models:ML and GenAI models Frameworks:Smart Nation platform18 Tools:IoT sensors,cloud computingFinancial impact:Efficiency gains:Reduced administrative costs by 25%Economic growth:Attracted US$12 billion in foreign investmentNon-financial benefits:Improved public services Enhanced q

86、uality of life for citizensReal-world success storiesInnovation within business functionsHuman resources:UnileverUnilever uses AI to screen candidates by analysing video interviews and responses,allowing recruiters to focus on the most promising applicants.Technology stack:AI models:NLP and facial r

87、ecognition algorithms Frameworks:Multimodal candidate assessment platforms19 Tools:HireVue AI platformFinancial impact:Cost reduction:Saved over US$1 million annually in recruitment costs Efficiency gains:Reduced hiring time by 75%Non-financial benefits:Enhanced diversity in hiring Improved candidat

88、e experienceCustomer service:Bank of AmericaErica,an AI virtual agent,handles over a million customer queries daily including snapshots of month-to-date spending and flagging recurring charges providing instant assistance and freeing human agents to tackle more complex issues.Technology stack:AI mod

89、els:GenAI for conversational interfaces Frameworks:Multimodal customer interaction platforms20 Tools:Erica,the virtual assistantFinancial impact:Cost savings:Reduced customer service costs by 10%Revenue growth:Increased product cross-selling by 5%Non-financial benefits:Improved customer satisfaction

90、 24/7 customer support availabilityMarketing:Coca-ColaCoca-Cola uses AI to generate marketing content,analyse consumer trends,and personalise advertising,resulting in more effective campaigns.Technology stack:AI models:Generative Adversarial Networks(GANs)Frameworks:Multimodal data analysis for cons

91、umer insights21 Tools:Custom AI platformsFinancial impact:Efficiency gains:Reduced content creation time by 50%Revenue increase:Boosted campaign ROI by 20%Non-financial benefits:Innovative marketing strategies Enhanced customer engagementSupply chain management:WalmartWalmart employs AI to predict p

92、roduct demand,optimise stock levels,and streamline logistics,ensuring products are available when and where customers need them.Technology stack:AI Models:Predictive analytics for demand forecasting Frameworks:Multi-modal data integration from sales,weather,and events22 Tools:Data lakes,Machine Lear

93、ning modelsFinancial impact:Cost Reduction:Decreased inventory costs by 15%Revenue Growth:Improved product availability leading to higher salesNon-financial benefits:Reduced waste Enhanced supplier relationshipsResearch and development:Insilico Medicine Insilico Medicine,a biotechnology company focu

94、sed on longevity,has developed inClinico,an AI platform that predicts phase II clinical trial outcomes to enhance drug discovery and development.Technology stack:AI Models:In-house-developed multimodal foundation model Platforms:Multi-modal integration of omics,text,clinical trials,small molecule pr

95、operties,and disease targets23 Tools:Transformer-based,in-house-trained AI model and platform Financial impact:Cost Reduction:35%nine-month ROI in an investment application Time Efficiency:Reduced drug development timeNon-financial benefits:Accelerated drug discovery and clinical trials process 79%a

96、ccuracy for clinical trialsLegal:Hogan LovellsThe AI platform analyses large sets of contracts and legal documents,extracting critical information,and identifying risks.Technology stack:AI models:NLP and ML Frameworks:Kira Systems platform with multimodal data processing24 Tools:Kira AIFinancial imp

97、act:Efficiency gains:Increased review speed by 40%Cost savings:Reduced billable hours for clientsNon-financial benefits:Improved accuracy Enhanced client satisfactionProcurement:CoupaCoupas AI-driven spend management platform optimises supplier selection,contract management,and spend analytics,trans

98、forming procurement processes into a strategic function.Technology stack:AI models:Predictive analytics,machine learning,and spend forecasting.Frameworks:Coupa Source-to-Pay,Coupa Business Spend Management(BSM).25 Tools:Cloud computing,advanced sourcing optimisation,real-time spend visibility.Financ

99、ial impact:ROI:Achieved an impressive 276%return on investment(ROI).Efficiency gains:Reduced procurement cycle and significantly enhancing process speed.Non-financial benefits:Increased compliance and risk management.Improved supplier performance and relationshipsIT Operations:MicrosoftMicrosoft use

100、s AI to monitor IT systems,predict failures,and automate support tickets,ensuring seamless operations.Technology stack:AI Models:Anomaly detection and predictive maintenance algorithms Frameworks:Azure AI with multi-modal data inputs26 Tools:AI chatbots,Monitoring toolsFinancial impact:Cost Savings:

101、Reduced IT support costs by 20%Efficiency Gains:Improved system uptime by 15%Non-financial benefits:Enhanced employee productivity Proactive issue resolutionSales:SalesforceSalesforces AI analyses customer interactions,market trends,and sales data to provide actionable insights for sales teams.Techn

102、ology stack:AI models:Predictive analytics with ML Frameworks:Salesforce Einstein with multimodal data processing27 Tools:CRM systemsFinancial impact:Revenue growth:Increased sales by 15%Efficiency gains:Reduced sales cycle times by 25%Non-financial benefits:Improved customer relationships Enhanced

103、decision-makingKey GenAI agentic tools and their differentiationCommercial solutionsOpen-source solutionsLangGraph28 Target audience:Startups and established enterprises Support:Offers robust customer support and professional services Integration:Seamlessly integrates with existing enterprise system

104、s Customisation:High level of customisation and control over workflows Features:Advanced features like statefulness(having a perfect memory or knowledge of previous calls or requests),streaming support,and moderation loopsCrewAI30 Target audience:Fortune 500 companies and large enterprises Ease of u

105、se:Provides no-code tools and templates for quick deployment Deployment options:Supports both self-hosted and cloud deployments Support:Comprehensive support and maintenance services Efficiency:Designed for handling complex,multi-agent tasks efficientlyAutoGen29 Target audience:Developers and resear

106、chers Open-source framework:Facilitates cooperation among multiple AI agents Simplification:Orchestrates,automates,and optimises complex LLM workflows Human-in-the-loop:Supports human-in-the-loop workflows for enhanced performance Community-driven:Encourages innovation and collaboration within the c

107、ommunityAutoGPT31 Target audience:AI enthusiasts and developers Autonomous AI agent:Executes tasks independently using GPT-4 architecture Task management:Breaks down complex goals into manageable sub-tasks Capabilities:Utilises internet access and code execution for task completion Versatility:Appli

108、ed in various domains like content creation and customer service Popularity:Rapidly growing open-source project with a strong communityWhen deciding between commercial vs open-source agentic AI tools,consider your organisations needs,upstream/downstream integration capabilities,and accessibility to

109、resources to build,deploy,and manage these solutions.Commercial solutions such as LangGraph and CrewAI offer robust support,seamless integration,and advanced features,making them suitable for complex,large-scale deployments.Conversely,open-source solutions like AutoGen and AutoGPT are excellent choi

110、ces for rapid prototyping and proof-of-concept development,providing flexibility,community-driven innovation,and low cost of entry for technology decision makers and developers.The agentic AI tools ecosystem is expected to witness a rapid surge over the next few quarters.Commercial solutions will li

111、kely continue to enhance their enterprise capabilities,focusing on a wide range of integration options,security,and developer-friendly features.Meanwhile,open-source tools will see increased community contributions,leading to rapid innovation in depth and coverage of agentic AI features and increase

112、d adoption.As commercial and open-source AI solutions evolve,organisations should stay agile,leveraging the strengths of both types to remain competitive and innovative.Formulating your GenAI strategy and crafting the AI capability roadmap that works for your businessA vision without execution is ha

113、llucinationalign your GenAI strategy with actionable plans and meticulous execution.Lets explore how to effectively integrate these principles into your AI roadmap:Define clear objectives:What do you aim to achieve cost reduction,revenue growth,customer satisfaction,or building an economic moat?Alig

114、n AI initiatives with business goals:Ensure that AI projects are underpinned by your companys strategic objectives.Whether its cost reduction,increasing revenue,customer satisfaction,or creating a competitive advantage,aligning AI efforts with business goals ensures relevance and maximises impact.Se

115、cure executive sponsorship:Having support from top management is crucial for securing resources and driving organisational change.Executive sponsorship can also help align AI initiatives with broader business strategies.Stakeholders buy-in:Ensure executive and departmental alignment.Start with high-

116、impact use cases:Identify areas where AI can deliver significant value quickly.Prioritise projects that address pressing challenges or offer substantial benefits,such as cost reduction or revenue growth,to demonstrate AI ROI early on.Seek expert advice:Consult with AI experts or hire consultants to

117、formulate your AI strategy and help you in making informed decisions.Technology infrastructure:Is your IT environment ready for AI integration?Platform options:Weigh-in commercial and open-source AI solutions and make build-vs-buy decisions based on your organisations requirements,budget,and technic

118、al expertise.Consider integration:Ensure the chosen platform can integrate seamlessly with your existing systems and workflows,both upstream and downstream.Data readiness:Do you have access to quality,multimodal data?Talent pool:Do you have the skills in-house,or will you need external expertise?Sta

119、rt small:Begin with small pilot projects to test the effectiveness of agentic AI in your business environment.Measure success:Define clear metrics for success and monitor the performance of the pilot projects.Gather feedback from stakeholders and make necessary adjustments.Agile methodology:Be flexi

120、ble,nimble and adaptive in your implementations.Iterate and improve:Use the insights gained from pilot projects to refine your approach and address any challenges.Assess capabilitiesMeticulous executionVision alignmentAssess capabilitiesAssess Meticulous executionScale upOrganisational changeRisk ma

121、nagementStep 1Step 2Step 3Step 4Step 6Step 5Vision alignment Gradual expansion:Once the pilot projects are successful,gradually scale up the implementation of agentic AI across more areas of your operations.Ensure support:Provide adequate training and support to your team to ensure a smooth transiti

122、on and adoption of the new technology.Monitor and optimise:Continuously monitor the performance of agentic AI systems and optimise them for better results.Ethical considerations:Address potential biases and compliance issues.Security protocols:Protect sensitive data and align AI governance with nati

123、onal and global standards.Educate and upskill:Begin by familiarising your workforce with the core concepts of data and AI.Understand what it is,how it works,and its potential applications in your organisation,business function and/or industry32.Foster innovation:Encourage a culture of innovation wit

124、hin your organisation by promoting experimentation and collaboration.Adapt and evolve:Be prepared to adapt your strategies and processes as the technology evolves and new opportunities arise.Stay informed:Keep up with the latest developments and trends in AI by reading industry reports,inviting expe

125、rts to all-hands sessions,attending conferences,and participating in webinars.Scale upRisk managementOrganisational changeTop 10 dos and donts for maximising ROI from AI investmentsAvoid the GenAI hype trapfocus on pragmatic steps that deliver real value.Dos Ensure a customer-centric approach:Always

126、 prioritise the end-user experience,eventually it pays off in both financial and non-financial results.Conduct thorough research:Before implementing AI solutions,research the available technologies to find the best fit for your business needs.Understand the capabilities and limitations of agentic AI

127、 to set realistic expectations.Start with small projects:Begin with pilot programmes to test the effectiveness of AI solutions.Small-scale implementations allow you to measure impact and make adjustments before a full-scale rollout.Monitor performance and iterate:Regularly track the performance of y

128、our AI systems using key metrics aligned with your business goals.Use this data to refine models,adjust strategies,and make data-driven improvements over time.Build cross-functional teams:Assemble teams that include members from various departments,such as IT,operations,finance,and marketing.Cross-f

129、unctional collaboration ensures that AI initiatives are well-rounded and consider different perspectives and expertise.Invest in employee training:Equip your team with the necessary skills to work alongside AI systems.Training ensures smooth integration and helps employees leverage AI tools effectiv

130、ely.Invest in quality data:High-quality data is the backbone of effective AI solutions.Invest in data cleaning,integration,and management processes to ensure your AI systems have accurate and reliable data to work with.Prioritise data security and privacy:Implement robust security measures to protec

131、t sensitive data.Ensure compliance with relevant regulations to maintain customer trust and avoid legal issues.Invest in scalable AI platforms:Choose AI platforms and tools that are scalable and can grow with your business needs.Scalable solutions allow you to expand AI capabilities without signific

132、ant additional investments.Invest in continuous learning:Stay curious and updated with AI advancements and industry trends.Ignore customer feedback:Pay attention to how your customers interact with AI solutions.Use their feedback to refine and enhance the user experience.Top 10 dos and donts for max

133、imising ROI from AI investmentsAvoid the GenAI hype trapfocus on pragmatic steps that deliver real value.Donts Underestimate complexity:AI projects are not plug-and-play.Rush implementation:Avoid hastily integrating AI without a clear strategy.A rushed implementation can lead to wasted resources and

134、 suboptimal results.Neglect human oversight:While AI can automate many tasks,human oversight remains crucial.Maintain a balance between automation and human input to ensure quality and accountability.Ignore user adoption:Ensure that the AI solutions are user-friendly and meet the needs of those who

135、will interact with them daily.High user adoption rates lead to better data input,more accurate outputs,and higher ROI.Overlook ethical considerations:Be mindful of the ethical implications of AI use.Ensure your AI systems are designed to prevent biases,respecting privacy laws,promote fairness and tr

136、ansparency.Adhere to ethical guidelines and legal regulations related to AI use.Ignore change management:Prepare your workforce for AI adoption through training and change management programmes.Educated employees are more likely to embrace AI tools,leading to better utilisation and ROI.Underestimate

137、 costs:Be realistic about the investment required for AI integration,including infrastructure,maintenance,and training costs.Plan your budget accordingly to avoid financial strain.Ignore partnerships:Collaborate with trusted technology providers,consultants,AI experts,and academic institutions.Exter

138、nal expertise can accelerate implementation,provide valuable insights,and help avoid common pitfalls.Overlook for long-term sustainability:Develop a long-term AI strategy that considers future needs and technological advancements.Sustainable planning ensures that your AI investments continue to deli

139、ver value over time.Looking aheadBy harnessing the unprecedented capabilities of agentic AI systems,both government entities and organisations can achieve significant efficiency gains,enhanced customer experiences,and superior business outcomes.While agentic AI will play a transformative role in bot

140、h sectors,their specific objectives,contexts,and goals will shape the distinct applications and their benefits.Government entities can prioritise large-scale initiatives such as policymaking,governance,public welfare,economic stability,and sustainability,leveraging agentic AI to orchestrate complex

141、systems.In contrast,organisations aiming for profitability growth,cost optimisation,and competitive advantage can focus on developing agentic AI point solutions to address specific challenges within defined domains.Most entities are expected to begin by experimenting with low hanging use cases.A sma

142、ller number will see the vast opportunity window with agentic AI solutions and adopt a strategic approach,recalibrating AI strategies to fully harness agentic AI solutions across a broader spectrum of business use cases and processes.Only a handfullike Amazon,Google,Meta etc.will embrace an AI-first

143、 mindset,reimagining products,services,and processes to redefine value creation mechanisms.In this transformation,agentic AI systems will take the lead role with humans as co-pilots,optimising speed,accuracy,contextual coherence,and cost-efficiency.Human oversight will evolve,shifting focus toward m

144、ore strategic planning and innovations rather than operational management.The shift from human-driven,labour-intensive processes to AI-managed operations will see autonomous agents handling tasks with unprecedented speed,precision,and adaptability.This transformation will not only reduce costs but a

145、lso unlock new revenue streams and growth opportunities,allowing businesses and governments to deliver services faster and at a much larger scale.As agentic AI systems become more integrated,they will redefine how we work,pushing the boundaries of possibility and enabling a smarter,more agile world.

146、The future of agentic AI is nearer than anticipated,propelled by rapid technological advancements.However,realising its full potential requires greater commitment and adoption across both government and industry.Leaders at the forefront of AI adoption arent just leveraging technologytheyre redefinin

147、g whats possible.Success in this arena wont be accidental;it demands strategic vision,meticulous planning,and relentless execution.For C-suite executives and senior leaders,embracing agentic AI is not just an option but a strategic imperative to stay ahead in an increasingly competitive and AI-drive

148、n world.The future belongs to those who prepare for it todaymake AI the cornerstone of your strategic arsenal.Get in touchAkif KamalPartnerTechnology ConsultingPwC Middle E Dr.Mohammad Tanvir AnsariDirectorTechnology ConsultingPwC Middle E Kaushal ChapaneriSenior AssociateTechnology ConsultingPwC Mi

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152、ool|Digital Learning Platforms|Pearson UK 16.Machine Learning Platform 17.AT&T Labs|Our Work|Analytics and AI-based Automation 18.Artificial Intelligence in Singapore|IMDA 19.The Amazing Ways How Unilever Uses Artificial Intelligence To Recruit&Train Thousands Of Employees|Bernard Marr 20.Erica-Virt

153、ual Financial Assistant|Bank of America 21.Coca-Cola Scaling GenAI Marketing Campaigns With Digital Twins|Consumer Goods Technology 22.Walmarts Element:A machine learning platform like no other 23.Prediction of Clinical Trials Outcomes Based on Target Choice and Clinical Trial Design with MultiModal

154、 Artificial Intelligence-Aliper-2023-Clinical Pharmacology&Therapeutics 24.Hogan Lovells Enhances Transactional Services with Kira 25.The Total Economic Impact Of Coupa For Source-To-Pay 26.Rethinking device management internally at Microsoft with AI-Inside Track Blog 27.Salesforce Einstein AI Solut

155、ions 28.LangChain Academy 29.AutoGen 30.CrewAI 31.AutoGPT 32.Agentic AI Overview,Applications and Use Cases About PwC At PwC,our purpose is to build trust in society and solve important problems.Were a network of firms in 152 countries with nearly 328,000 people who are committed to delivering quali

156、ty in assurance,advisory and tax services.Find out more and tell us what matters to you by visiting us at .Established in the Middle East for 40 years,PwC has 24 offices across 12 countries in the region with around 8,000 people.( refers to the PwC network and/or one or more of its member firms,each of which is a separate legal entity.Please see for further details.2024 PwC.All rights reserved