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1、IN THE AGE OF GENERATIVE AIProcurementTransformationCopyright 2023,Everest Global,Inc.All rights This document has been licensed to GEPProcurement Transformation in the Age of Generative AIShirley Hung,Partner Prateek Singh,Practice DirectorAkash Thunga,Senior Analyst Smarajeet Das,Senior Analyst C|
2、licensed to GEPIntroduction03Current state of digital adoption within procurement04Emergence of generative AI and its potential implications06Transforming procurement operations through generative AI 07Optimizing AI implementation through prioritization09Key considerations for implementing generativ
3、e AI 11Conclusion14I|licensed to GEPGenerative AI is garnering significant attention in the business world with its impressive human-like creative capabilities.Ever since the release of ChatGPT,organizations worldwide have been keenly exploring its applications.As technology evolves on an ongoing ba
4、sis,it continues to have a profound impact on business functions,and the procurement function is no exception.Over time,procurement has evolved from a back-office function to a strategic line of business,with its priorities extending beyond delivering traditional cost savings to establishing strateg
5、ic supplier relationships,managing risk dynamically and proactively,and ensuring business continuity.Technology plays a key role in enabling procurement organizations to deliver value beyond cost savings by leveraging data to improve efficiency,productivity,and accuracy across processes.However,proc
6、urement organizations are not at the forefront of adopting advanced digital solutions compared to business functions such as Finance and Accounting(F&A)and Human Resources(HR).While procurement has shown some interest in advanced technologies such as AI-/ML-based analytics and Intelligent Document P
7、rocessing(IDP),the overall adoption of cognitive solutions continues to be at a nascent stage.The emergence of AI-/ML-powered cognitive solutions that can assist in decision-making is opening new opportunities to transform procurement operations.Recent developments in the maturity of AI models,faste
8、r system computation power,and the availability of high-quality model training data are redefining technologies such as generative AI.These AI models can generate unique content in the form of text,images,videos,audios,code snippets,and synthetic data.Generative AI is expected to further augment the
9、 applicability and capability of existing AI-based solutions leading to potentially higher adoption and impact.With organizations across industries exploring ways to leverage generative AI capabilities in their operations,procurement leaders are also keenly tracking developments in this space to ide
10、ntify potential high-impact use cases across the Source-to-Pay(S2P)value chain as part of their transformation journeys.In this viewpoint,we explore digital transformation of procurement operations in the generative AI age,including:Current state of digital adoption in procurementUse cases of AI-/ML
11、-based solutions across the S2P value chainAdvent of generative AI and its implicationsUse cases of generative AI within procurement operations and their prioritizationKey considerations to ensure successful implementation of generative AI |licensed to GEPPROCUREMENT TRANSFORMATION IN THE AGE OF GEN
12、ERATIVE AITraditional technologies-25EBasic analyticsRobotic Process Automation(RPA)Basic chatbotsOptical Character Recognition(OCR)Current state of digital adoption within procurementMany organizations still rely on legacy systems that leverage traditional technologies to run their procu
13、rement operations.Thus far,procurement organizations have not adopted AI-/ML-based cognitive solutions at scale.While technology and service providers also play a key role in driving the digital transformation of most organizations procurement operations through their varied offerings,even in these
14、cases,the adoption of solutions with traditional technology capabilities is higher than advanced technology capabilities.Digitalization of procurement has traditionally focused on leveraging basic analytics,Robotic Process Automation(RPA),and rule-based chatbots.Basic analytics capabilities help in
15、streamlining data management,reporting,and dashboarding.RPA-enabled automation of repetitive processes reduces turnaround time and manual effort,allowing organizations to divert their resources to activities that are more strategic and judgment intensive.Rule-based chatbots assist in promptly answer
16、ing generic queries from various stakeholders.The adoption of these technologies is driven by factors such as rapid realization of benefits,low cost of implementation,and high ease of adoption.More mature procurement organizations have adopted advanced technologies but on a limited basis.Some organi
17、zations adopt advanced technologies such as IDP capabilities for the automated extraction of unstructured data from documents,which saves manual effort and time.In some situations,AI-based advanced analytics is used either through S2C and P2P suites or analytics tools to enable informed decision-mak
18、ing.Additionally,the adoption of AI-powered intelligent automation solutions is advancing RPA capabilities towards automating complex workflows and processes.Process mining solutions with cognitive capabilities are also complementing automation efforts by identifying process roadblocks.However,high
19、costs,lack of historic data,disparate systems,skill gaps,and lack of clarity on RoI are limiting the adoption of advanced technologies.Exhibit 1 illustrates this digitalization journey of procurement organizations and shows that adoption is high and gradually growing for traditional technologies but
20、 quite low despite gradual growth for advanced cognitive technologies.EXHIBIT 1Adoption of various technologies in procurementSource:Everest Group(2023)AI-/ML-based Cognitive technologies-25EAdvanced analyticsIntelligentautomationIntelligent virtual assistantsIntelligent Document Processi
21、ng(IDP)Low H|licensed to GEPPROCUREMENT TRANSFORMATION IN THE AGE OF GENERATIVE AIOpportunity to leverage AI/ML capabilitiesDespite low adoption,AI/ML technologies offer a wide range of applications within procurement to drive value acceleration.As Exhibit 2 shows,organizations should look to embed
22、AI capabilities across the S2P value chain for improved efficiency and accuracy.While AI capabilities can have immensely transformative applications within procurement,significant human intervention is still required to run procurement operations from end to end.This is due to the need for judgment
23、in activities such as the creation of documents for requisitions,purchase orders,invoices,RFXs,and contracts,customization of which depends on spend category,location,and decision-making processes(including approvals within P2P,sourcing strategy,negotiations,and category management).As AI models evo
24、lve,their ability to overcome current limitations and drive transformation will increase.EXHIBIT 2AI/ML use cases across the S2P value chainSource:Everest Group(2023)Spend analytics and insightsCategory management and sourcingContract management and administrationSupplier relationship managementSpen
25、d forecasting using predictive and prescriptive analyticsData-backed spend insights enabling spend optimizationGathering and analysis of location-and category-specific market intelligenceData-based insights to support in formulating the sourcing strategySupplier-specific market intelligence across p
26、erformance,risk,and sustainabilityAutomation of contract approval workflowsAutomated contract updates and data population from contractsAlerts on contract renewalCreation of contract templates based on historical contract dataMonitoring of contractual complianceContracts searchabilitySupplier assess
27、ment and segmentationSupplier historical performance analysis and ongoing performance managementPeriodic risk assessment on pre-defined criteriaAlerts on supplier non-complianceRequisition and PO processingAccounts payableTravel and expenseIdentification of process roadblocksStreamlining of workflow
28、s through intelligent automationIDP-based invoice data extractionand validationFraud detection and preventionFlagging up of irregular expense filingChatbots and cognitive virtual assistants to automate buy desks or guided buyingS2PS2CP2PTransactionintensiveJ|licensed to GEPPROCUREMENT TRANSFORMATION
29、 IN THE AGE OF GENERATIVE AIEmergence of generative AI and its potential implicationsGenerative AI models are trained on large amounts of data and built on Large Language Models(LLMs),which enable them to create content beyond their training data.This brings their creative and innovative problem-sol
30、ving and content-generation abilities very close to those of a human.Generative AIs uniqueness lies in its ability to create new data similar to,but not a copy of,the training data,enabling it to self-learn continuously.These models can be creative beyond predefined tasks,domains,and existing patter
31、ns to create new outputs not necessarily from historical data or associated patterns.While earlier AI solutions could also identify patterns,forecast trends,and help predict future outcomes,their outputs were primarily based on available historical data fed into the model,and they lacked the ability
32、 to generate creative outputs beyond the data set.Generative AI can act according to the uniqueness of the situation and solve new problems despite not having recorded data of the same situation or problem fed into the model.Traditional solutions follow a rules-based approach in which predefined cri
33、teria and rules across parameters drive outputs.They lack the innovative capabilities of generative AI,making them rigid and domain or purpose specific.Exhibit 3 depicts the superior capabilities of generative AI over previous AI/ML models.Furthermore,generative AI can enhance the applications of ad
34、vanced cognitive solutions,driving increased adoption,as shown in Exhibit 4.Outsourcing service and technology providers are expected to contribute to this trend by augmenting their existing solutions with generative AI capabilities.EXHIBIT 3Enhanced capabilities of generative AI vis-vis previous AI
35、 modelsSource:Everest Group(2023)AI capabilitiesGenerative AI capabilitiesAbility to identify patterns from historical dataRules-based predefined scope of operationsAbility to create per defined templatesIntelligence limited to small training datasetNatural language processing capabilitiesAbility to
36、 innovate beyond historical dataCreative problem-solving abilities beyond current scopeAbility to create per unique prompts beyond existing templatesEvolving intelligence due to its ability to create training data by itselfLLM |licensed to GEPPROCUREMENT TRANSFORMATION IN THE AGE OF GENERATIVE AIAI-
37、/ML-basedcognitive technologyEarlier estimate2023-25With generative AI2023-25Advanced analyticsIntelligent automationIntelligent virtual assistantIDPEXHIBIT 4Change in the adoption of cognitive technologies with the use of generative AISource:Everest Group(2023)EXHIBIT 5Applications of generative AI
38、 in procurementSource:Everest Group(2023)Tailored RFX creationContextualized contract creationRequisition and PO creation and customizationInvoice creationDocument creationSavings opportunity identificationNegotiation supportSourcing strategy formulationFraud detection(beyond historical patterns)Pro
39、cess optimization opportunities identificationDecision supportAutomated supplier onboardingSupplier relationship managementPayment schedule optimizationSophisticated virtual assistant offering end-to-end supportVirtual assistanceNOT EXHAUSTIVEGenerative AI use casesOrganizations are realizing that g
40、enerative AI can significantly accelerate the value driven by AI/ML solutions and improve the outcomes and RoI from such solutions.While still in an exploratory phase,generative AIs use cases show high potential throughout the S2P value chain,which includes many processes that require creative conte
41、nt generation and innovative problem-solving capabilities.Generative AI can play a key role in further transforming procurement operations,not merely as a sophisticated tool but as an integral element of the procurement digital ecosystem.Transforming procurement operations through generative AIOrgan
42、izations are exploring many potential use cases of generative AI technology across the S2P value chain.These use cases can be broadly classified into three categories,as Exhibit 5 shows.Low H|licensed to GEPPROCUREMENT TRANSFORMATION IN THE AGE OF GENERATIVE AIWe take a closer look at these applicat
43、ions below.Document creationS2P involves multiple transactions and associated documentation within its workflows.Key documents include requisitions,purchase orders,invoices,RFXs,and contracts.Generative AI can help automate the creation of these documents beyond predefined templates.It can create th
44、e desired type,format,and template of these documents almost immediately through appropriate prompts.Though the process may need human monitoring and quality approval,it reduces the time and effort required to manually create these documents repeatedly for different categories,suppliers,and location
45、s.Decision supportOrganizations can consider a comprehensive generative AI model built to assist in procurement operations as a round-the-clock subject matter expert,with a human evaluating and validating its outputs.They can use these models to identify suppliers based on parameters such as locatio
46、n,historical performance,financial health,service capability,pricing,languages supported,and shoring mix.Furthermore,the model can help assess and execute supplier onboarding with a high degree of automation.Generative AI can also guide and possibly drive supplier negotiations on behalf of organizat
47、ions.It can compute real-time pricing benchmarks and derive insights from market intelligence and historically recorded negotiations to help develop better negotiation strategies.Thanks to its human-like conversational style,coupled with its data processing and analytics capabilities,generative AI c
48、an be used to better manage supplier relationships.It can automatically ask for any additional data required from suppliers,run periodic performance evaluations through customized scorecards,detect and flag anomalies in the information submitted,and continuously monitor supplier risks.While it can h
49、elp automate these repetitive but value-building activities,it can also improve these activities by not just identifying the problem but offering recommendations to solve them.Similarly,it can evaluate interactions concerning a specific supplier and suggest corrective steps to improve the relationsh
50、ip.With its innovative abilities,generative AI can suggest ways to optimize processes across the value chain,including:Suggesting edits within the scorecard for more accurate evaluationIdentifying service areas with scope for innovationFlagging up bottlenecks in the supplier governance modelSuggesti
51、ng payment schedules to optimize invoice processingContextualizing RFXs to include specific requests based on the sourcing strategy Building contracts suitable to specific regions or locations to comply with local regulations by customizing clausesEvaluating workflows and suggesting process improvem
52、entsGenerative AI can act as a valuable assistant to leadership teams by helping formulate various strategies,keeping in mind the end objectives.So far,organizations only relied on AI-generated insights based on limited data for running strategic upstream procurement processes.With its ability to un
53、derstand multiple parameters and scenarios,generative AI can promptly help formulate a strategy considering organizational policies and priorities.During times of macroeconomic uncertainty and associated inflationary pressures,a key interest area for procurement stakeholders might be |licensed to GE
54、PPROCUREMENT TRANSFORMATION IN THE AGE OF GENERATIVE AIAIs ability to identify cost-saving opportunities based on its understanding of organization-specific requirements,the supplier landscape,regional nuances,and category intelligence.Virtual assistanceChatGPT created almost instant awareness of ge
55、nerative AIs text generation capabilities.In procurement,organizations are showing interest in leveraging generative AI as a virtual buying assistant that could replace a human-run buy desk and transform guided buying.Generative AI can also be used to automatically generate responses to common procu
56、rement-related queries from employees,suppliers,or other stakeholders.This would reduce the workload of helpdesk agents,improve response time,and ensure consistency and accuracy in the information provided.Optimizing AI implementation through prioritization Considering the many generative AI use cas
57、es and their ability to enhance existing AI applications,organizations should prioritize the deployment of these technologies across the S2P value chain to optimize outcomes.Adoption potential and impact are the key dimensions to consider when prioritizing the implementation of cognitive solutions i
58、n a phased manner.Exhibit 6 shows a framework that can be used to prioritize AI implementation.EXHIBIT 6A framework for prioritizing AI implementationSource:Everest Group(2023)Business criticalityCriticality of the activity influences the need for checks and balances through human intervention to en
59、sure quality and consistencyHighLowLowHighImpactAdoption potentialAccelerateOpportunisticDe-prioritizeVigilanceCategory management supportIdentifying savings opportunitiesNegotiation supportContract optimizationSupplier risk managementSourcing strategy formulation supportOptimizing supplier relation
60、shipsGuided buying supportCognitive virtual assistant(procurement helpdesk)Spend analyticsData constraintsLack of availability of initial training data and confidentiality of available data restricts adoption potential and applicabilityProcess complexityProcess complexity depends on the reasoning an
61、d judgment involved in the process lower complexity means higher adoption potentialFinancial impactImpact on spend savings and cost of procurement operations:the higher the savings,the higher the impact;the lower the costs,the higher the impactOperational impactImpact on the accuracy,efficiency,and
62、productivity of processes and resources;for example,turnaround time,speed-to-value,etc.Business impactImpact on risk and compliance,stakeholder experience,and satisfaction in terms of accuracy,value,ease,and accessibilitySupplier base insightsMarket intelligenceSupplier onboardingSupplier identifica
63、tionCustomized document creation(RFxs,POs,PRs,etc.)Process optimizationContractual complianceFraud detectionSupplier performance managementGenerative AI-specific use casesGenerative AI-augmented earlier AI use casesNOT EXHAUSTIVE|licensed to GEPPROCUREMENT TRANSFORMATION IN THE AGE OF GENERATIVE AIW
64、e have segregated example use cases across four quadrants based on their expected level of impact and adoption potential.Accelerate Use cases in this quadrant are the low-hanging fruits that the procurement ecosystem can quickly adopt to kick-off the AI-powered transformation journey.These use cases
65、 have high adoption potential and are expected to create high impact,making them cost-effective and easy to implement.For example,organizations can leverage generative AI-based cognitive virtual assistants to streamline their procurement helpdesk to drive impactful outcomes by improving efficiency,e
66、nsuring accuracy,and automating routine queries from internal and external stakeholders with limited human supervision.Generative AI-based solutions can also help in automating the creation of various procurement-related documents(PRs,POs,RFXs)as per specific requirements.Opportunistic Organizations
67、 can start planning for the implementation of these use cases due to the expected high impact offsetting increased effort for implementation.These use cases require more attention since they have lower adoption potential due to concerns related to data confidentiality,lack of streamlined processes,a
68、nd high business criticality.Use cases such as negotiation and identifying savings opportunities support using AI/ML solutions have significant financial impact leading to increased savings and improved efficiencies.However,the data needed to train the AI model for this purpose is highly sensitive a
69、nd requires careful implementation to ensure data security and confidentiality.Vigilance The nature of certain processes or their lack of strategic or operational importance may create low impact despite their high adoption potential.Organizations need to be vigilant in implementing these use cases
70、and prioritize them based on specific requirements.For example,generative AI can help in streamlining the supplier performance management process which involves periodic performance surveys and scorecards,ensuring compliance with SLAs/KPIs,and initiating improvement plans.De-prioritize Organizations
71、 should embrace a wait-and-watch attitude towards processes in this quadrant.Implementation of generative AI solutions for these processes is not worth the effort given the current maturity of generative AI solutions,process complexity,and the(un)availability of secure data practices.Generative AI-b
72、ased cognitive virtual assistants can potentially streamline procurement helpdesk to drive impactful outcomes by improving efficiency,ensuring accuracy,and automating routine queries from internal and external stakeholders with limited human |licensed to GEPPROCUREMENT TRANSFORMATION IN THE AGE OF G
73、ENERATIVE AIKey considerations for implementing generative AIWhen implementing AI,especially generative AI solutions,organizations need to overcome the challenges that market factors,internal technological limitations,talent constraints,and any apprehensions towards change pose.They should bear in m
74、ind the considerations illustrated in Exhibit 7 as they forge ahead on the generative AI path.Concerns with generative AI adoptionCost implicationsImplementing or developing AI capabilities can be expensive for organizations,especially for those looking to build generative AI capabilities in-house,a
75、s such capabilities rely on LLMs and specialized hardware with high computational power.However,organizations can optimize their RoI depending on the scale of operations and the ability to leverage across organizational functions.At their end,service providers and technology vendors are working to e
76、mbed generative AI into their existing tools and service offerings on top of their current AI capabilities.With economies of scale,they can absorb additional costs of leveraging generative AI within the solution/service pricing.As generative AI technology continues to evolve,we expect it to become c
77、heaper and more accessible.In addition to technology costs,the cost of running and maintaining the technology is also significant,especially given the high computational power and storage required for generative AI models.Notably,at a time of growing awareness of sustainability,a drastic increase in
78、 carbon footprint due to the adoption of this technology hinders organizational and larger societal goals to achieve carbon neutrality.This warrants a revamp of sustainability planning based on new energy demand forecasts considering usage of generative AI.EXHIBIT 7Key considerations for generative
79、AI implementationSource:Everest Group(2023)Concerns with generative AI adoptionCost implicationsTechnological OperationalEnvironmentalData security challengesConfidentialityData privacyEthical concernsAccountabilityBiasOrganizational preparedness considerationsSkill gapUpskillingEmployee apprehensio
80、nsData management practicesData governanceAvailability of quality dataIntegration with existing technologiesComputational powerRestructuring of technology |licensed to GEPPROCUREMENT TRANSFORMATION IN THE AGE OF GENERATIVE AIData security challengesIt has been challenging for governments,industry bo
81、dies,and individuals to ensure data privacy and security in the digital age.Despite significant global efforts to establish strong data privacy and security laws,achieving total compliance in the ever-changing digital world seems to be a struggle.Generative AI poses a new and significant risk to dat
82、a privacy and security concerns.While governments and industry bodies have built guardrails to prevent misuse,they have not yet developed a foolproof solution.Individuals continue to express concerns about how their publicly available data can be misused through generative AI solutions to create new
83、 content without any accountability.Organizations have similar concerns with regard to their highly confidential information.However,organizations can add data security layers to contain confidential information in trusted systems.Deploying solutions on secure cloud networks can efficiently control
84、the outward interaction of organizational solutions with the internet.Ethical concernsAt current maturity levels,generative AI poses ethical challenges as it lacks accountability and is prone to bias.To ensure appropriate checks and balances,organizations need to define accountability and responsibi
85、lity for the output created using generative AI solutions.Reliability and transparency of data sources is also an issue.Furthermore,high confidence in generative AI responses without checking for their credibility can lead to a hallucination bias.The evolution of generative AI technology can resolve
86、 some of these challenges.Explainable AI is an anticipated transformation of generative AI in which the tool offers justification for its outputs.In the meantime,organizations should adopt tools that assist in quality checking and establish good governance practices for human-machine interactions,wi
87、th human intervention and approval ensuring responsible data practices and quality.Organizational preparedness considerationsOrganizations are burdened with internal change management over and above the challenges that technology poses.They need to adopt comprehensive change management strategies to
88、 embrace and leverage new technologies effectively,including:Addressing the skills gapPlugging the talent gap should be of high priority as most domain-specific professionals lack the technical know-how to efficiently use newly deployed solutions.Employees might also be deeply skeptical of the techn
89、ology or adamantly opposed to adjusting to the transformative change in working.Incorporating upskilling and reskilling plans into the organizational talent management strategy can address employee concerns and ensure continuous improvement and speed-to-market with new technologies.Managing data The
90、 availability of quality data is critical for building training data sets.The lack of streamlined data management practices is a major roadblock,especially for internal procurement organizations where organization-specific data trains the solutions.Prioritizing data governance and embedding it into
91、the organizational culture helps overcome data-related challenges.Additionally,providers are using data captured from their broader experience as training data.They are also building protection layers to keep confidential data within the |licensed to GEPPROCUREMENT TRANSFORMATION IN THE AGE OF GENER
92、ATIVE AIPresentFutureTechnology stackComputation on GPUs/CPUs hardware Deployment on servers/cloudLimited interoperability Computation on TPUsSeamless integration with NLP,computer vision,and other AI techEdge-based deployment on IoT devices/smartphonesOrganizational structureHierarchical structure
93、Siloed teams Major focus on domain expertise Flat structure Collaborative teams Equivalent focus on domain expertise,technical skills,and stakeholder managementIntegrating with existing technologiesExisting organizational systems,including hardware,software,and governance mechanisms,may not necessar
94、ily align with the changes required for generative AI implementation.Organizations might needtechnology modernization,not just for the short-term implementation of AI solutions,but also keeping in mind their long-term impact.The adoption of AI-based solutions is going to drastically alter the way hu
95、man-machine interactions occur,thereby transforming the technology landscape and organizational structure,as Exhibit 8 shows.The technology stack design should serve the need for high computational power and integration across solutions to optimize the leverage of AI capabilities.Consequently,organi
96、zations will require restructuring to flatten hierarchies and establish collaborative teams to make AI an integral part of the organization.EXHIBIT 8Evolution of technology and organizational structureSource:Everest Group(2023)Lack of streamlined data management practices is a major roadblock for ma
97、ny internal procurement organizations.Prioritizing data governance and embedding it into the organizational culture would help overcome data-related challenges for implementing AI |licensed to GEPPROCUREMENT TRANSFORMATION IN THE AGE OF GENERATIVE AIConclusionThe transformation of procurement operat
98、ions is an ongoing process involving significant enhancement of process workflows and digital adoption resulting in associated with improved supplier relations and higher cost savings.While technology has made significant strides in transforming procurement operations through automation,analytics,an
99、d cognitive capabilities,the adoption of advanced technology solutions has remained low for a long time.However,generative AI with its advanced capabilities can potentially accelerate transformation and significantly improve efficiency and productivity.It can further drive digital adoption of cognit
100、ive solutions within procurement by augmenting existing AI capabilities across the S2P value chain.Generative AI offers a wide range of applications,including:Customized document creation across requisitions,purchase orders,invoices,RFXs,and contracts A comprehensive virtual assistant for answering
101、queries and enabling guided buying Identification of the scope for process optimization Automation of supplier relationship managementA strategic assistant to the leadership team Identification of cost-saving opportunities While the cost of implementation is difficult to control and influence,organi
102、zations can prioritize use cases and ensure the change management required to mitigate implementation risks.With changing technology,organizations may need to undergo a structural shift to sustain and maximize value NOTICE AND DISCLAIMERSIMPORTANT INFORMATION.PLEASE REVIEW THIS NOTICE CAREFULLY AND
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