1、IBM Institute for Business Value|Benchmark InsightsAIs quantified impact on the finance functionImproving process quality,cost,and efficiency2We help transform finance organizations from simply improving the efficiency of their finance processes to creating smart functions with intelligent workflows

2、.These workflows can find,connect,and analyze data,uncovering deep insights that can inform intelligent decisions.Our financial consultants partner with clients to advise and manage end-to-end processes.To learn more,please visit IBM can help1AI provides quantified operational benefits across the fi

3、nance function.Key takeawaysAI is an untapped opportunity in finance.Only roughly one-fifth of surveyed organizations are operating or optimizing AI in financial planning and analysis,record to report,order to cash,or procure to pay.AI significantly impacts finance processes and applications.Top AI

4、use cases being implemented are key performance indicator(KPI)selection and monitoring,close and consolidations,collections,financial forecasting,and market performance comparison.AI is driving operational benefits.The adoption of AI has a positive impact on finance function costs and finance area-s

5、pecific productivity metrics.2Introduction For the CFO and finance function,the expectation to be guardian of stability and agent of transformation has elevated both whats at stake and opportunity.To tackle this paradox of responsibilities and drive value through the organization,finance needs new a

6、pproaches,new tools,new perspectives,new organizational constructs,and skillsespecially related to data.Yet,CFOs are ambiguous about how well their own finance organizations fulfill their duties(see Figure 1).1 In the IBV 2022 C-suite CFO Study,executives state they are most effective at executing t

7、raditional finance tasks(transaction processing);still,two in five are not effective.Only 47%think they excel at measuring and managing performancea task that has not seen improvement since 2013.And only 38%of respondents say they are effective in planning and executing strategy,a dramatic 25%drop o

8、ver the same period.Control and risk management effectiveness has declined even more:31%since 2013.To enhance effectiveness,finance needs to choose the right course of action with speedfed by having the right data at the right timeenabling execution decisions to proceed unencumbered.And finance shou

9、ld spend the right amount of time and effort on decision-making,increasing efficiency without compromising quality.3Traditional finance tasksPerformance measurement and managementControl/risk managementStrategy planning and executionQ:How effective is your organizationss finance function at the foll

10、owing?Source:IBV 2022 C-suite CFO Study.50%47%51%57%47%64%44%38%31%less25%lessFIGURE 1 Finance effectiveness A decline in strategy and control effectiveness over time2013 20212013 20212013 20212013 20214AI can be a key enabler,changing the way work gets done to drive business outcomes.It can be appl

11、ied to improve transactional activities and decision support.And replacing manual work with AI automation can help streamline financial processes while also enhancing business partnerships through better-informed decision-making.In the record-to-report(R2R)general accounting and reporting area,AI-po

12、wered workflow and data models could include a reconciliation module that aggregates subledger transactions and performs risk-based reconciliations and cognitive forecasting.Automating procure to pay(P2P)with AI has been shown to increase productivity and permit finance to detect more fraudulent inv

13、oices.2 AI-driven innovations in order to cash(O2C)help with credit scoring,pricing decisions,and the prevention of payment frauds.AI and advanced analytics rank as key components of the financial planning and analysis(FP&A)process,galvanizing and orchestrating planning and performance management.AI

14、 refers to next generation information systems that understand,reason,learn,and interact.These systems do this by continually building knowledge and learning and understanding natural language.And AI can reason and interact more naturally with human beings than traditional programmable systems.Persp

15、ectiveAI defined45FIGURE 2 AI adoption by finance areaRoughly half of organizations report adopting AI in each functionGiven this immense opportunity for finance,the IBM Institute for Business Value(IBV),in partnership with the American Productivity&Quality Center(APQC),surveyed 1,000 senior finance

16、 personnel.The research covered their AI adoption in four key finance areas(R2R,P2P,O2C,FP&A)and finance organization performance.This survey was conducted in 2021 with the goal of quantifying the impact of AI on operational level metrics.AI adopters,defined as those piloting,implementing,operating,

17、or optimizing AI in any of the four finance areas,provided estimations of how AI influenced their scores on common finance metrics.Across respondents,roughly half report adopting AI in each area,with only one-fifth operating or optimizing AI(see Figure 2).For more than 80%of AI adopters,AI has been

18、incorporated into business-as-usual environments for less than two years.15%13%14%15%14%15%14%14%11%13%13%13%11%8%7%8%PilotingImplementingOperatingOptimizing51%50%49%48%Financial planning and analysisOrder to cashRecord to reportProcure to pay6Case studyMedia company:Optimizing the record-to-report

19、close cycle3For a media company,the close process was complicated by:Resources from all the subprocesses involved in journal preparation and reconciliations Disparate distribution of workload among the team Lack of standard templates,process,and uniform technology,resulting in errors and escalations

20、.AI provided an opportunity to interpret the large volume of data and conduct process redesign to standardize and automate processes.AI systems helped recognize personnel who could do the same work faster and accurately and recommended redistribution of workload.As a result,the company experienced a

21、 reduction in turnaround time by 2.2 hours and a reduction in errors to less than 0.5%.There was also a 24%increase in efficiency.67Across respondents,roughly half report adopting AI across four finance functions.8A statistical analysis of the data provides empirical evidence that positively correla

22、tes the adoption of AI with a positive impact on finance function costs and finance area-specific productivity metrics.This correlation does not imply causation since other factors,such as the use of other exponential technologies,operating model,finance skills,and lean processes,can contribute to f

23、inances performance.Looking at the total annual finance function cost as a percentage of revenue,half of AI adopters credit AI with a decrease of 7%or more and one-quarter credit AI with a decrease of 14%or more(see Figure 3).Clearly,AI helps these companies streamline transaction processing by decr

24、easing labor-intensive,repetitive tasks through intelligent automation.For the bottom quartile,their finance function cost increased at least 2%,perhaps indicating unsuccessful implementations of AI.AI impact on finance metricsClearly,AI helps these companies streamline transaction processing by dec

25、reasing labor-intensive,repetitive tasks through intelligent automation.9FIGURE 3AI impact on finance function cost as a percentage of revenueHalf of AI adopters credit the technology with a decrease of 7%or moreTotal annual finance function cost as a percentage of revenue(median)=1.28%Estimated AI

26、impact*on metric25th percentileMedian75th percentile*AI impact:Computed at respondent level.AI contribution as a percentage of respondents current value.Source:IBV Performance Data and Benchmarking Program.2021.Higher with AILower with AI-7%-14%2%010This AI benefit can also be seen in the number of

27、finance function FTEs redeployed in the past 12 months as a percentage of total finance function FTEs(see Figure 4).The median of AI adopters attribute 40%or more of this change to AI,which allow these employees to refocus on strategic activities.FIGURE 4AI impact on finance function FTEs redeployed

28、The median of adopters attribute 40%or more of this change to AINumber of finance function FTEs(including contractors and outsourced resources)that were redeployed in the past 12 months as a percentage of total finance function FTEs(median)=6%Estimated AI impact*on metric25th percentileMedian75th pe

29、rcentile*AI impact:Computed at respondent level.AI contribution as a percentage of respondents current value.Source:IBV Performance Data and Benchmarking Program.2021.Higher with AILower with AI030%40%50%11Record to reportRespondents tell us they are implementing AI in a number of record-to-report a

30、ctivities(see Figure 5).A companys strategy execution is guided by its KPIs.Finance can help the enterprise make the difficult choices required to set and execute strategy,including supporting AI-influenced KPIs.Using AI in KPI selection and monitoring helps determine not only the outcomes that need

31、 to be measured,but also the measurement and prioritization of those outcomes.With close and consolidations,AI assists with automating journal entries and completing account reconciliations.A journal entry AI advisor provides qualitative reviews using historical behavior and rules that enforce organ

32、izational policies and generates early insights into performance and business impact.Machine learning provides actionable insights on reconciliation anomalies.Risk insights highlight transactional anomalies period over period.With risk reporting,AI technologies can identify and manage emerging risks

33、.For instance,they can aggregate storylines from news and social media to identify potential risks.The solution can then project possible scenarios with business implications based on selected risk drivers.FIGURE 5 Implementation of AI use cases:Record to reportMore than 40%of respondents are implem

34、enting AI in this area40%41%KPI selection and monitoringClose and consolidationsAccounting regulationsRisk reporting47%45%Percentage of respondents selecting the category as a finance-related AI use case being implemented today.1112In terms of AIs impact,respondents estimate a reduction in cycle tim

35、e in days to perform monthly close at the business entity level(see Figure 6).This metric indicates the speed at closing the books at the end of each month,leading to accelerated reporting of financial information.Half of AI adopters credit AI with a decrease of at least 25%and one-quarter credit AI

36、 with a decrease of at least 67%.25th percentileMedian75th percentile*AI impact:Computed at respondent level.AI contribution as a percentage of respondents current value.Source:IBV Performance Data and Benchmarking Program.2021.Higher with AILower with AI0FIGURE 6AI impact on monthly close cycle tim

37、eHalf of AI adopters credit the technology with a decrease of at least 25%.0Average cycle time in days to perform monthly close at the business-entity level(median)=10 daysEstimated AI impact*on metric-25%-67%13Procure to payImplementing AI in procure to pay streamlines the process and enhances deci

38、sion-making(see Figure 7).An AI-powered workflow,underpinned by data models,optimizes touchless processing and provides a unified interface for buyers,suppliers,procurement,and finance staff.Invoices are validated against business rules,coded,and matched to the purchase order automatically.Spend and

39、 pricing intelligence enables insights during sourcing.AI automates procurement operations and manages inquiries from buyers and suppliers.FIGURE 7Implementation of AI use cases:Procure to payRoughly 40%of respondents have adopted AI in this areaPercentage of respondents selecting the category as a

40、finance-related AI use case being implemented today.42%40%41%41%39%Master data managementExpense managementFraudPurchase ordersAccounts payable processing1314For the cycle time in days from receipt of invoice until approved and scheduled for payment(see Figure 8),half of AI adopters credit AI with a

41、 decrease of 33%or more and one-quarter credit AI with a decrease of 100%or more.An invoice in the approval phase creates cost.Shortening this KPI indicates improved efficiency in the P2P process.And finance can capture additional savings from early-payment discounts and favorable payment terms as w

42、ell as avoid late fees.25th percentileMedian75th percentile*AI impact:Computed at respondent level.AI contribution as a percentage of respondents current value.Source:IBV Performance Data and Benchmarking Program.2021.Higher with AILower with AI0FIGURE 8AI impact on cycle time from receipt of invoic

43、e until approved and scheduled for payment Half of AI adopters credit the technology with a decrease of 33%or more.0Cycle time in days(including weekends)from receipt of invoice until approved and scheduled for payment(median)=5 daysEstimated AI impact*on metric-33%-100%15Order to cashRespondents re

44、port implementing AI for invoicing/billing,collections,and payments(see Figure 9).Across these activities,as well as order management and disputes and deductions,AI collects,prepares,and distributes data from documents and unstructured data.This automation eliminates the need to validate and manuall

45、y populate data to the order-to-cash systems.AI advisors surface insights at critical moments in the order-to-cash process to optimize decision-making and enable greater accuracy in data extraction and matching.For example,AI can help create and authenticate invoices,and analyze and process disputes

46、.Collections from high-risk customers can be prioritized using AI-based customer segmentation,and organizations can leverage AI technology to predict the payment timelines.Finance staff can then follow up on valid deductions and help ensure customer satisfaction.Intelligent workflows with AI can aut

47、omate cash applications on the same day against invoices and remittances and automatically manage scenarios,such as parent-child relationships and prepayments.4FIGURE 9Implementation of AI use cases:Order to cashMore than 40%of respondents report adopting AI in this area43%44%42%CollectionsPayment m

48、atchingInvoice/billingPercentage of respondents selecting the category as a finance-related AI use case being implemented today.1516Major consumer products company:Increasing collections efficiency and improving customer experience5Disparate systems associated with collections created siloed process

49、es and imposed actions on workers that result in suboptimal outcomes for both the company and their customers.The organization leveraged a cognitive collections platform with AI that can learn from customers purchases,payment patterns and trends,and recommend changes,including workflow reconfigurati

50、ons.The agile and intelligent solution enabled collection process practitioners to make better-informed decisions on their own.As a result,the company generated a 35%increase in collection practitioner efficiency,30%reduction in delinquent receivables,and 25%increase in customer satisfaction.Case st

51、udy1617Respondents tell us that AI has impacted the total annual value of uncollectable balances as a percentage of revenue(see Figure 10).This KPI measures efficiency in O2C and helps companies optimize their collections process by reducing waste and refining resource consumption.6 Half of AI adopt

52、ers credit AI with a decrease of at least 2%and one-quarter credit AI with a decrease of at least 8%.FIGURE 10AI impact on the value of uncollectible balances as a percentage of revenueOne-quarter of adopters credit the technology with a decrease of at least 8%.Total annual value of uncollectible ba

53、lances,as a percentage of revenue(median)=.65%Estimated AI impact*on metric25th percentileMedian75th percentile*AI impact:Computed at respondent level.AI contribution as a percentage of respondents current value.Source:IBV Performance Data and Benchmarking Program.2021.Higher with AILower with AI03%

54、-2%-8%18For days sales outstanding(DSO)(see Figure 11),half of AI adopters credit AI with a decrease of 13%or more and one-quarter credit AI with a decrease of 24%or more.DSO measures the average number of days it takes an organization to collect payments from its customer.7 Decreasing DSO provides

55、greater liquidity.FIGURE 11AI impact on days sales outstandingHalf of AI adopters credit the technology with a decrease of 13%or more Average days sales outstanding(median)=42 daysEstimated AI impact*on metric*AI impact:Computed at respondent level.AI contribution as a percentage of respondents curr

56、ent value.Source:IBV Performance Data and Benchmarking Program.2021.25th percentileMedian75th percentileHigher with AILower with AI00-24%-13%19Financial planning and analysisA significant amount of data on the market,company performance,competitor information,pricing,and operations needs to be aggre

57、gated and assessed by FP&A teams.The implementation of AI(see Figure 12)can partially automate the labor-intensive process by parsing through this data to identify anomalies,enhance forecasting,optimize pricing,and provide recommendations.AI applies trend analysis,correlation analysis(including patt

58、ern and anomaly detection),and neural networks for financial forecasting.With neural networks,AI determines the relationship among data and uses it to predict new data,resulting in higher forecast accuracy.8Application of AI for market performance comparison allows FP&A teams to factor in more varia

59、bles and internal/external influences.9 This facilitates producing scenarios on the industry and industry peer actions and developing ROI projections.With pricing optimization,an AI analytics model can balance win probability against pricing to optimize expected revenue and profit and increase win r

60、ates.Analysis can factor in historical success and transaction characteristics(offering,configuration,deal size,customer)to enable more informed pricing decisions.Deals priced at or better than optimal price guidance can be set to“auto approve”or follow a streamlined approval path.FIGURE 12 Implemen

61、tation of AI use cases:Financial planning and analysisMore than 40%of respondents report adopting AI in this area44%42%44%43%Financial forecastingMarket performance comparisonPricing optimizationFinancial planningPercentage of respondents selecting the category as a finance-related AI use case being

62、 implemented today.1920Case studyNukissiorfiit:Reaping the rewards of AI in FP&A10Nukissiorfiit is a government-owned Greenland energy company dedicated to supplying water and energy to residents without using fossil fuels.The company was challenged with providing accurate financial data and forecas

63、ts.Previously,70 people worked to generate just one forecast a year.Nukissiorfiit used machine learning and analytics solutions to automate,accelerate,and share intelligent forecasts and results.Now it can make faster,more informed decisions on capital projects.Planning and forecasting processes wer

64、e streamlined from 70 people contributing to an annual plan to just nine people creating monthly plans.Business visibility,forecast reliability,and agility were improved by providing the latest,most accurate information to stakeholders.Time spent on forecasting decreased from 1,000 hours annually ac

65、ross many roles to well below 200 hours.With the solutions,the company can now use the insights to set thresholds and be alerted if the forecasts are outside of the ranges;it can also override the alerts based on experience or additional information.The result:Nukissiorfiit is more agile,and its fin

66、ancial planning is more accurate.AIs natural language processing can capture and analyze the intent of human language data to recommend a way of working that improves the effectiveness of the financial planning process.Automated machine learning capabilities create“what-if”modeling scenarios based o

67、n the incoming data and helps finance staff to better understand and plan for future outcomes.11 By constantly comparing current plan data to historical data and trends,AI assists planners in understanding variances.And AI allows FP&A teams to factor in economic data,organizational benchmarks,and op

68、erational data.12With the implementation of these use cases,AI provides both an impact on efficiency through intelligent automation as measured by cycle time and quality indicated by forecasting accuracy.2021With days to complete the financial forecast(see Figure 13),half of AI adopters credit AI wi

69、th a decrease of at least 25%in the cycle time and one-quarter credit AI with a decrease of at least 50%.By reducing cycle duration,finance can focus on performing analysis and making recommendations to adjust organizational direction.In addition,AI adopters see a decrease in cycle time in hours to

70、develop a short-term cash flow forecast.Half of adopters credit AI with a decrease of 50%or more and one-quarter credit AI with a decrease of 140%or more.25th percentileMedian75th percentile*AI impact:Computed at respondent level.AI contribution as a percentage of respondents current value.Source:IB

71、V Performance Data and Benchmarking Program.2021.Higher with AILower with AIFIGURE 13AI impact on on financial forecast cycle timeHalf of AI adopters credit the technology with a decrease of at least 25%in cycle time 0-25%-50%Average cycle time in calendar days(including weekends)to complete the fin

72、ancial forecast(median)=12 daysEstimated AI impact*on metric022To provide high quality products and services to their customers,Finland baker Vaasan relied on very short planning cycles that were informed by various data sources from across the organization.When the COVID-19 pandemic first occurred,

73、Vaasans demand doubled overnight,putting significant pressure on their supply chain.With the help of predictive analytics,the company was able to operate with less excess capacity,as well as predict energy consumption and costs and build long-term product plans.As a result of their planning solution

74、 with AI,Vaasan realized higher profits and customer satisfaction.Vaasan is currently testing a model that can analyze cost center trends,saving planners time spent sifting through data manually at months end.Case study Vaasan:Upgrading planning1323Half of AI adopters credit AI with a decrease of 20

75、%or more in typical overall forecast error and one-quarter credit AI with a decrease of 50%or more(see Figure 14).As forecast error goes down,the risks decline and finance can make smarter decisions associated with staffing,production,capital expenditures,and other areas.In addition,AI adopters stat

76、e that total sales forecasting error and inventory cost forecasting error have decreased due to AI.25th percentileMedian75th percentile*AI impact:Computed at respondent level.AI contribution as a percentage of respondents current value.Source:IBV Performance Data and Benchmarking Program.2021.Higher

77、 with AILower with AI0FIGURE 14AI impact on typical overall forecast errorHalf of AI adopters credit the technology with a decrease of 20%or more0Typical overall forecast error(median)=2.5%Estimated AI impact*on metric-50%-20%24Case study Global pharmaceutical company:Improving forecasting14A global

78、 pharmaceutical company grappled with low forecast accuracy at a drug level using traditional methods,such as using historical data to estimate future metrics.Moreover,slow reaction to competitive shifts resulted in a subpar return on investment and long turnaround times for analyzing data and deter

79、mining actions.To address this,the company developed a cognitive simulator solution that drives end-to-end dynamic,integrated,and streamlined financial planning.The solution includes a machine learning-based forecast;an external perspective for true market potential through a live scenario-planning

80、engine;and an accurate account of the impact of client and competitor actions,policy changes,market dynamics,new regulations,and more.As a result,drug-level forecast accuracy increased to 97%from approximately 85%.And by optimizing investments and driving significant efficiencies through on-demand p

81、lanning,the enterprise recorded an estimated$115 million in incremental profit over two years.2425AI adopters state that total sales forecasting error and inventory cost forecasting error have decreased due to AI.2601Develop a transformation map.Prioritize a clear,well-defined transformation strateg

82、y for the finance function.This allows the CFO to establish goals while identifying new and evolving technology opportunities.Look beyond one-dimensional,cost-driven intelligent automation.Finance can then create a transfor-mation map to articulate distinct steps and investment requirements to deliv

83、er AI.Support this map with business cases and a benefits tracking approach to deliver return on investment,cost reduction,risk mitigation,and insights.Include prerequisites with an emphasis on fundamentals.Before implementation of AI,invest in the people skills,processes,data,and culture needed to

84、take full advantage of the technology.Put in place a deliberate,thoughtful approach to AI talent acquisition and development.Establish process and systems commonality.Use tools for process mining to help identify the most efficient and effective path and rogue variants.Place data at the center of th

85、e transformation.Standardize financial and nonfinancial data definition and establish a data governance framework.In addition,create central repositories to aggregate financial,operational,and externally curated data.The rewards of implementing AI for finance can be immense.Yet only one-fifth of res

86、pondents are operating or optimizing AI for record to report,procure to pay,order to cash,or financial planning and analysis.To realize clear financial value from AI,finance needs to integrate AI into its transformation strategyas well as measure performance more systematically.Action guideAIs quant

87、ified impact on the finance function2702Leverage the“garage”concept and align with the broader organization.AI initiatives in the finance areas impact the entire organizationso enlist leaders from finance,the business,technology,and operations to co-create and co-execute.These centralized innovation

88、 teams help accelerate envisioning the future and building out and scaling up the adoption of AI as it touches on sales and supply chain(order to cash)and lines of business(financial planning and analysis).Drive an agile innovation incubator for creating automated and AI-enabled finance capabilities

89、.Select and measure key process metrics,creating a baseline that enables the tracking of ongoing performance to quantify the impact of AI.03Implement with an eye on speed and outcomes.Create an implementation plan for the AI-driven finance transformation,including business objectives,milestones,and

90、costs.Include pilots and staged investments to rapidly achieve success and demonstrate the value of the solutions.This can also build underlying AI readiness capabilities.When pilots are successful,finance can then move to production.Measure KPIs as defined in the business case and compare with the

91、baseline to quantify benefits realization.Communicate regularly with stakeholdersespecially because critical data is required from them.For example,data from suppliers in an AI procure-to-pay implementation will need to be cleaned and serve as input into new systems.Ramp up execution by formal weekl

92、y and monthly reviews with business stakeholders to understand roadblocks,critical path,and value realization.28Caitlin H brings 17 years of industry experience to her current role as Partner,Data,and Finance Transformation in IBM Consulting.She is responsible for the data practice,partnering with d

93、ata and finance leaders to implement data strategy,ERP systems,governance,change management,data fabric,and data and AI use cases.Caitlin has built IBMs global client community of more than 1,300 Chief Data Officers and Chief AI Officers,delivering the industrys longest running data and AI summit se

94、ries.About the authorsMonica P is a Partner and leads the Global Finance Transformation practice within IBM Consulting.She has 20 years of experience split between consulting and industry roles.Monica has built a reputation for driving hypergrowth through optimizing finance operating models through

95、human centric transformations integrated with ERP modernization,intelligent operations,digital,technology and data for C-suite clients.She is a people champion and continues to drive diversity,equity and inclusion awareness and engagement across IBM Consulting.2829Spencer L Lin is the Global CFO Lea

96、d for the IBM Institute for Business Value.He is responsible for market insights,thought leadership development,competitive intelligence,and primary research on the CFO agenda and trends.He is a co-author of the last eight IBM Global CFO Studies.Spencer has over 25 years of experience in financial m

97、anagement and strategy consulting.29Annette LaP is the CFO Lead for the IBM Institute for Business Value Performance Data and Benchmark-ing program.She manages financial management benchmarking and regularly conducts benchmark studies on finance-related topics.Annette has over 30 years of experience

98、 in financial management and consulting.30Study approach and methodologyIn partnership with APQC,we surveyed 1,000 senior finance personnel(roles included CFO,controller,and finance and accounting director).Respondents are from a variety of industries and reported on their finance organization perfo

99、rmance and AI adoption in record to report,order to cash,procure to pay,and financial planning and analysis.The respondents had overall responsibility for finance and accounting and could answer questions related to strategies,budgets,FTEs,and practices for the entire organizations finance function.

100、The survey was conducted in 2021 to quantify the impact of AI on finance operational level metrics.Participants provided information on current performance in key operational metrics and AI adopters also provided estimates of how the current metrics results were impacted by AI.Of the 576 AI adopters

101、(defined as those piloting,implementing,operating,optimizing AI in any of the four finance areas),we asked:What is your value for the KPI today(including influence from AI,since these are AI adopters)?What do you estimate that your KPI value would be,were it not for AI?We calculated the influence of

102、 AI at the respondent level in two ways(see Figure 15):AI contribution to KPI score:the raw difference made by AI to the KPI score AI impact as a percentage of the respondents current value.KPI score(with AI)13%Estimation of KPI score without AI12%AI contribution to KPI score1%AI impact(AI contribut

103、ion as percentage of KPI score)1%/13%=8%KPI score(with AI)45Estimate of KPI score without AI50AI contribution to KPI score-5AI impact(AI contribution as percentage of KPI score)-5/45=-11%Examples of AI increasing KPI scoreExample of AI decreasing KPI scoreFIGURE 15Calculation of the influence of AI

104、on a KPI31The scope of the survey was global,including 25 countries across the Americas,Europe,Asia/Pacific,the Middle East,and Africa.The surveyed enterprises represented 16 industries and included a range of enterprise sizes(see Figure 16).Data cited in this study is self-reported by study respond

105、ents.FIGURE 16Survey demographics Automotive5%Life sciences12%Banking10%Media and entertainment5%Chemicals and petroleum refining6%Other manufacturing5%Electronics10%Retail5%Fast-moving consumer goods5%Services11%Government5%Telecommunications carriers3%Healthcare providers5%Transportation4%Insuranc

106、e5%Utilities5%Industry distributionAfrica and Middle East5%Asia Pacific40%Central and South America8%Europe29%US and Canada18%Regional distributionParent organization revenueLess than$100 million5%$100 million to$500 million28%$500 million to$1 billion21%$1 billion to$5 billion20%$5 billion to$10 bi

107、llion6%More than$10 billion21%32IBM Institute for Business ValueFor two decades,the IBM Institute for Business Value has served as the thought leadership think tank for IBM.What inspires us is producing research-backed,technology-informed strategic insights that help leaders make smarter business de

108、cisions.From our unique position at the intersection of business,technology,and society,we survey,interview,and engage with thousands of executives,consumers,and experts each year,synthesizing their perspectives into credible,inspiring,and actionable insights.To stay connected and informed,sign up t

109、o receive IBVs email newsletter at can also follow IBMIBV on Twitter or find us on LinkedIn at ibm.co/ibv-linkedin.Related reportsHow to create business value with AI C-suite:CFO Strategic I generation enterprise performance right partner for a changing worldAt IBM,we collaborate with our clients,br

110、inging together business insight,advanced research,and technology to give them a distinct advantage in todays rapidly changing environment.About Benchmark InsightsBenchmark Insights feature insights for executives on important business and related technology topics.They are based on analysis of perf

111、ormance data and other benchmarking measures.For more information,contact the IBM Institute for Business Value at .33 Copyright IBM Corporation 2022IBM Corporation New Orchard Road Armonk,NY 10504Produced in the United States of America|November 2022IBM,the IBM logo, and Watson are trademarks of Int

112、ernational Business Machines Corp.,registered in many jurisdictions worldwide.Other product and service names might be trademarks of IBM or other companies.A current list of IBM trademarks is available on the web at“Copyright and trademark information”at: document is current as of the initial date o

113、f publication and may be changed by IBM at any time.Not all offerings are available in every country in which IBM operates.THE INFORMATION IN THIS DOCUMENT IS PROVIDED“AS IS”WITHOUT ANY WARRANTY,EXPRESS OR IMPLIED,INCLUDING WITHOUT ANY WARRANTIES OF MERCHANTABILITY,FITNESS FOR A PARTICULAR PURPOSE A

114、ND ANY WARRANTY OR CONDITION OF NON-INFRINGEMENT.IBM products are warranted according to the terms and conditions of the agreements under which they are provided.This report is intended for general guidance only.It is not intended to be a substitute for detailed research or the exercise of professio

115、nal judgment.IBM shall not be responsible for any loss whatsoever sustained by any organization or person who relies on this publication.The data used in this report may be derived from third-party sources and IBM does not independently verify,validate or audit such data.The results from the use of

116、such data are provided on an“as is”basis and IBM makes no representations or warranties,express or implied.JDEPLEMJUSEN-01Notes and sources 1“The CFO Study:Strategic IntelligenceCFOs as architects of action and champions of change.”IBM Institute for Business Value C-suite Series.January 2022.https:/

117、 IBM Institute for Business Value Performance Data and Benchmarking Program.Unpublished data.2022.3 Based on internal IBM client information.4“AI-Based Cash Application Software.”HighRadius Corporation.Accessed July 25,2022.https:/ Based on internal IBM client information.6“Total uncollectable balan

118、ces as a percentage of revenue.”APQC.Accessed October 28,2022.https:/www.apqc.org/what-we-do/benchmarking/open-standards-benchmarking/measures/total-uncollectable-balances7“Days Sales Outstanding.”APQC.October 12,2022.https:/www.apqc.org/resource-library/resource/days-sales-outstanding8 Manole,Lucy.

119、“5 AI Technologies Powering Intelligent FP&A Solutions.”IT Chronicles.February 8,2022.https:/ Ibid.10 Primerano,Bill.“The future of Financial Planning and Analysis with Digital Transformation.”IBM blog.April 5,2021.https:/ and leaner with intelligent analytics.”IBM case study.Accessed July 27,2022.h

120、ttps:/ Marmer,David.“IBM brings affordable planning AI to the SMB.”IBM blog.May 20,2020.https:/ Halloran,Grant.“What FP&A teams need to know about AI/ML.”Diginomica.February 8,2021.https:/ Marmer,David.“IBM Planning Analytics delivers continuous integration with Watson.”IBM blog.June 3,2021.https:/ Based on internal IBM client information.