A PORSCHE COMPANYSTUDYCHANGERThe New Driving Force of theAutomotive IndustryAuthors Contact personGame CharLeadAugustin FriedelSoftware Defined VehiclesAugustin.Friedel@mhp.comAuthorMarcus WillandMobilityMarcus.Willand@mhp.comLeadMatthias BorchArtificial IntelligenceMatthias.Borch@mhp.comAuthorDr.Nils SchaupensteinerTransformation AdvisoryNils.Schaupensteiner@mhp.comContact PersonStephan BaierArtificial IntelligenceStephan.Baier@mhp.comAuthorPatrick RuhlandTransformation AdvisoryPatrick.Ruhland@mhp.comThe study "Al as Game Changer"and its summary were published by:MHP Gesellschaft fur Management-und IT-Beratung mbHAll rights reserved!No reproduction,microfilming,storage,or processing in electronic media permitted without the consent of thepublisher.The contents of this publication are intended to inform our customers and business partners.Theycorrespond to the state of knowledge of the authors at the time of publication.To resolve any issues,please referto the sources listed in the publication or contact the designated contact persons.Opinion articles reflect theviews of the individual authors.Rounding differences may occur in the graphics.ContentsContents4Table of figures612 Key Findings8Welcome to Change!1001.Revolution and Automotive Market Potential1102.Investment in Companies With an Al Focus1503.Pilot Projects and Implementation1904.Al Models,Levels,and Use Cases234.1 The Game Changer:What Can Be Achieved With Al264.2 Automobile Manufacturers With Low Al Investment94.3 Al Models:Make or Buy?2905.Al Applications Along the Automotive Value Chain315.1 Al Operation in Vehicles and in the Cloud355.2 Al Monetization in Vehicles395.3 Added Value of Al Applications in Companies4006.What the Customer Wants:The User Perspective476.1 Use and Understanding of Al Applications496.2 Advantages and Disadvantages-Generally and in Vehicles496.3 Purchasing Decision,Trust and Willingness to Pay5107.Success Factors and Strategic Approach557.1 Strategy and Goal Planning567.2 Think from the Perspective of the Customer,not the Technology67.3 Organizational Anchoring and Ownership587.4 Local Differences require local Setup597.5 Reducing Complexity597.6 Use and Monetization of Data607.7 Checklist for successful Implementation08.Challenges,Responsibility,and Risks638.1 Costs of Training and Operation648.2 Data and Digitalization as a Basis658.3 Business Models and Cases for B2C and B2B658.4 Ethics and Responsibility678.5 New Risks and Regulatory Challenges6909.Al Applications in the Automotive Industry:7 Recommendations for Action7110.Further Informations75Literature and Sources76Contact International78About MHP79Table of figuresFigure 1:Technology super cycles-artificial intelligence as the next relevant platform shift(Coatue,2024)12Figure 2:Al market size in the automotive sector (Precedence Research,2024)12Figure 3:Total investments in Al companies founded since 2001,in USD billion (Scheuer,2024)16Figure 4:Investment in Al stack layers(Coatue,2024)17Figure 5:Companies with team and budget for Al (Capgemini,2023)21Figure 6:Interconnected Al concepts4Figure 7:Visualization of Al as a pyramid2Figure 8:Classification of Al terms27Figure 9:The performance of Al models compared to human capabilities in the MMLU test (iAsk,2024)28Figure 10:Schematic diagram of the training of Al foundation models for vehicles30Figure 11:Use of Al along the value chain32Figure 12:Significant improvements of functions and features through Al3334Figure 14:Role of on-premise,cloud,and vehicle for Al models35Figure 15:Levels of a software-defined vehide (SDV)(Willand,Friedel,Schaupensteiner,2023)36Figure 16:Different models for ADAS and AD applications and functions37Figure 17:Al's potential at different stages of the value chain (Capgemini,2023)40Figure 18:Use of Al-based solutions by region41Figure 19:Key drivers behind the use of Al in production42GameFigure 20:Decisive issue-fewer users of software due to Al or free software (Coatue,2024)43Figure 21:Possible uses of Al in software development (Wee 2024)44Figure 22:Understanding of Al in cars48Figure 23:Advantages of using Al in cars49Figure 24:The perceived advantages and disadvantages of using Al50Figure 25:Al in cars:purchase motivation or blocker?51Figure 26:Trust in stakeholders with regard to the implementation of Al in vehicles2Figure 27:Willingness to pay for Al functions52Abb.28:Assessment of the future Al competence of car manufacturers by region3Figure 29:Customer and use case first,and then Al applications and models57Figure 30:Dimensions for validating technical feasibility57Figure 31:Training costs for Al models are increasing (Stanford University,2024)64Figure 32:Data availability and quality by region65Figure 33:Customers'willingness to pay is unclear,costs arise for implementation and operation66Figure 34:Classification of Al use case categories and possible business models67Figure 35:Risks associated with the use of Al68Figure 36:Principles and penalties of the EU Al Act70Table 1:The development of Al models divided into different time phases2712 Key FindingsThe widespread use of Al is predicted to be the next relevantplatform shift after cloud transformation-original equipmentmanufacturers(OEMs)need to step up their activities.More than50Onlyof respondents seetime-saving as thebiggest benefit ofAl applications.3.6of respondents in Chinastate that the risks of Aloutweigh the benefits;thisfigure is around 25 percentSkepticism about Alin Europe and the US.applications is greater inthe US than in Europeor China.The most frequently mentioned disadvantages ofAl are fear of loss of control,loss of data protectionand personal privacy,and security risks.8Customers worldwide wantto use Al in cars,but rarelyAl as Game Changer 12 Key Findingspay for it.In China,more than twice asmany customers have alreadyused Al in their cars as inEurope.In China,Al functionsmostly have a positiveinfluence on carToday,Chinese carpurchasingmanufacturers are regarded asdecisions -onlyof respondents2leaders in Al innovation.In fiveyears'time,Japanese OEMs willbe at the forefront,followed byChinese and German OEMs.would not buya vehicle based on Alfunctions.KIAl is not only revolutionizing thein-vehicle customer experiencethe entire value chain isexperiencing disruptive change.Successful implementation ofTraditional car manufacturersAl applications is not possibleare the most trusted when itwithout prior digitalization andcomes to the use of Al,faraccess to specific data sources.ahead of state institutions andnew car manufacturers.9Welcome toChange!Dear readers,Artificial intelligence will be the next platform shift that revolutionizes all industrial sectors.Stakeholders in the automotive value chain have realized that Al is challenging many tradi-tional processes and ways of thinking.The introduction of the PC,the stationary Internetand then the mobile Internet,and Cloud/SaaS previously had a similarly disruptive impact.New business models and profit pools are emerging,while at the same time there are nu-merous challenges to be tackled with regard to technology,partnerships,and ethical issues.In this study,we trace the groundbreaking developments in Al so far and examine the op-portunities and risks within the automotive industry.Accompany us through present andfuture scenarios-with specific recommendations for action for your own strategy when itcomes to implementing Al applications in production and in vehicles.Whether the new technologies meet the expectations of drivers is determined right therein the cockpit.That's why,in Chapter 8,we outline the user perspective based on ourown current data.Our international survey provides information about which products andservices from automotive companies could fulfill Al needs and what the willingness to paylooks like.That makes this study essential reading for decision-makers,ClOs,and applica-tion developers.Investors in Al technologies and Al teams need a consistent,long-term cost-benefit ratio.We therefore examine the direct/indirect monetization of in-car Al and look at new businessmodels based on Al and digitalization.Ultimately,as is so often the case,it becomes clear that the journey into new technologicalterritory is best undertaken with experienced travel guides.Get the know-how you needand always be curious!ENABLING YOU TO SHAPE A BETTER TOMORROWBest regards,Dr.Jan WehingerCluster Lead Software Defined VehiclesMHP Management-und IT-Beratung GmbHLudwigsburg,September 20241001.Revolution andAutomotive MarketPotential11Everyone recognizes that Al is the next platform shiftGoogleAMDcIscofacebooknVIDIAN▣KIAamazonTencentMicrosoft③OpenAIamazonMeta Adobe...GenerativeMobileCloud/DesktopInternetSaasInternet(Web 2.0)(Web 1.0)NetworkingPCMainframe1960-19801980s1990s2000s2010s2015-20202022-.Figure 1:Technology super cycles-artficialintelligence as the next relevant platform shft(Coatue,2024)Al-Based systems for automotive industry to reachUSS 35.7 billion by 203335.726.620.0..in billion US$15.211.79.23.94.75.87.33.220232024202520262027202820292030203120322033Figure 2:Al market size in the automotive sector (Precedence Research,2024)12It is highly likely that the big technology companiesOne fear,however,is that artificial intelligence willsuch as Google,Meta,and Microsoft-which gainedincreasingly replace people and jobs may disappear.in importance with the last platform shifts (super cy-Currently,Al applications are regarded more as a com-cles)-will also dominate the Al age.plement rather than a replacement.Academics suchas Karim Lakhani from Harvard Business School believeAlong the automotive value chain,stakeholders arethat Al will not replace humans.One possible scenariosometimes accused of having responded to the lastis that people who use Al will have a significant advan-platform shifts too late or with an ineffective strategy.tage over workers who do not use it.In our opinion,the relevance of connectivity and doudsolutions was recognized too late and implementationRegarding the question of whether Al will improve thecould have been better.The industry is at the begin-economy,a survey shows a mixed picture.Worldwide,ning of the Al platform shift and there is still the op-34 percent of respondents believe that the use of Alportunity to respond early with a targeted strategy.will improve the economic situation in their country inCompanies like Apple have shown that it is not neces-the next three to five years.This hope is above average"Al Won't Replace HumansBut Humans With Al Will ReplaceHumans Without Al."(HBR,2023)sary to be the first innovator.With a strong Al strategy,in countries such as Thailand,India,and South Africa.a company can also exploit potential as a fast follower.At the lower end of the ranking are countries includ-The market for artificial intelligence in the automotiveing Belgium,Japan,the US,and France(lpsos,2023).industry has shown remarkable growth in recent years.Overall,there are increasing signs that there are farIt is currently estimated to be around USD 3.9 billionmore opportunities than risks.The targeted use of arti-in 2024 and is expected to grow to USD 15 billion byficial intelligence will significantly affect our prosperity2030.Some market analyses anticipate that Al sales inin the coming decades.Al boosts efficency and canthe automotive sector will rise to over USD 35 billioncounter the negative effects of skills shortages,demo-in 2033.Growth from 2024 to 2033 corresponds to agraphic changes,and high location costs.It is now uprate of 28 percent.to the automotive industry to take bold and appropri-ately fast action-and follow a strategically intelligentEstimates in other market reports may be slightly high-approacher or lower,but all show the same trend.This meansthat extensive economic opportunities are being creat-ed along the value chain for manufacturers,suppliers,and service providers.1302.Investment inCompanies Withan Al Focus15