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Project portfolio managementfor AI projectsDeveloping a framework to manage thechallenges with AI portfoliosMaster’s thesis in Management and Economics of InnovationLUCAS ANDRÉNJONAS MEDDEBDEPARTMENT OF TECHNOLOGY MANAGEMENT AND ECONOMICSDIVISION OF ENTREPRENEURSHIP AND STRATEGYCHALMERS UNIVERSITY OF TECHNOLOGYGothenburg, Sweden 2021www.chalmers.seReport No. E2021:049

REPORT NO. E2021:049Project portfolio managementfor AI projectsDeveloping a framework to manage thechallenges with AI portfoliosLUCAS ANDRÉNJONAS MEDDEBDepartment of Technology Management and EconomicsDivision of Entrepreneurship and StrategyCHALMERS UNIVERSITY OF TECHNOLOGYGothenburg, Sweden 2021

Project portfolio management for AI projectsDeveloping a framework to manage the challenges with AI portfoliosLUCAS ANDRÉNJONAS MEDDEB LUCAS ANDRÉN, 2021. JONAS MEDDEB, 2021.Report no. E2021:049Department of Technology Management and EconomicsChalmers University of TechnologySE-412 96 GöteborgSwedenTelephone 46 (0)31-772 1000Gothenburg, Sweden 2021

Project portfolio management for AI projectsDeveloping a framework to manage the challenges with AI portfoliosLUCAS ANDRÉNJONAS MEDDEBDepartment of Technology Management and EconomicsChalmers University of TechnologyAbstractArtificial intelligence (AI) has rapidly developed during the past decades, opening the doors to newopportunities for many organizations. The automotive industry is no exception, and AI is regularlyseen as a leading technology for many disruptive trends. Thus, successfully incorporating AI as acapability in the organization is essential for future competitive advantage and growth. However,many organizations still experience challenges reaping the fruits that scaled AI initiatives present.Some of the challenges for reaching enterprise-wide implementations of AI include the difficultyof selecting which initiatives to scale, and communicating the business value of these. Similarproblems have previously been tackled within the research field of project portfolio management(PPM). Indeed, previous literature has a history of adapting PPM practices to suit the needs of newproject types. However, the portfolio management literature on AI projects is seemingly nonexistent up until this point. In collaboration with researchers and a newly established department atVolvo Cars, responsible for applying and diffusing AI in the organization, this study sets out todevelop a new framework for the PPM practices of AI projects. To fulfill this aim, two researchquestions are investigated in this thesis. The first question delineates the main characteristics of AIprojects from a portfolio perspective. The second research question explores how PPM practicesneed to be customized to support these characteristics. Regarding the first question, the studyidentifies significant characteristics relating to the portfolio evaluation criteria Reward and cost,Risks, and Synergies. In addition, AI projects exhibit characteristics such as heavy dependencies ondata, experimentation-driven development, and high levels of unpredictability. Building on thesefindings, the second research question establishes that PPM practices for AI projects need to becustomized accordingly. The findings point to the necessity of structured and ordered PPMpractices, where projects are continuously evaluated as information is gathered. Therefore,techniques including scorecards and integrated exit criteria are proposed to reduce the projects andachieve a strategically aligned AI portfolio. In conclusion, to handle the unpredictable nature of AIprojects, project selection tools from previous literature need to be customized. Furthermore,particular emphasis needs to be placed on the structure and order of the PPM to support informationacquisition in a resource-efficient manner, even if AI projects often take an experimentation-drivenapproach to development. Ultimately, to successfully implement PPM practices for AI portfolios,adopting a perspective of project reduction rather than relying on previous notions of projectselection seems essential.Keywords: project portfolio management, project selection, artificial intelligence, AI, PPM

Contents1. INTRODUCTION .11.1 BACKGROUND .11.2 PURPOSE AND RESEARCH QUESTIONS .21.3 CONTEXT AND LIMITATIONS.21.4 REPORT STRUCTURE .32. LITERATURE REVIEW.42.1 INTRODUCTION TO PROJECT PORTFOLIO MANAGEMENT .42.2 TECHNIQUES FOR PROJECT PORTFOLIO MANAGEMENT .52.2.1 Prioritizing projects.62.2.2 Achieving a balanced portfolio .72.2.3 Achieving a strategically aligned portfolio .82.2.4 Accounting for project interdependencies .102.3 PROJECT EVALUATION CRITERIA .102.3.1 Reward and cost .112.3.2 Risks .112.3.3 Synergies.122.3.4 Strategy .132.4 THE STAGE-GATE MODEL .132.5 ARTIFICIAL INTELLIGENCE IN ORGANIZATIONAL CONTEXTS .143. METHOD.163.1 RESEARCH STRATEGY AND DESIGN .163.2 RESEARCH PROCESS .163.2.1 Data collection . Sampling . Interviews . 183.2.2 Data processing and analysis .193.3 RESEARCH QUALITY .203.4 ETHICAL CONSIDERATIONS .224. EMPIRICAL FINDINGS .234.1 CHARACTERISTICS OF AI PROJECTS .234.1.1 Reward and cost .244.1.2 Risks .254.1.3 Synergies.264.1.4 AI-specific characteristics .274.2 CHARACTERISTICS OF THE PPM FOR AI PROJECTS.294.2.1 PPM processes .304.2.1.1 Structured PPM process . 304.2.1.2 Ordered PPM process . 314.2.1.3 Continuous project reduction . 324.2.2 PPM goals and characteristics .334.2.2.1 Realization of synergies . 334.2.2.2 Ensure strategic alignment . 344.2.2.3 Organizational environment. 365. CONSTRUCTION OF THE PPM FRAMEWORK.385.1 STRUCTURE AND ORDER OF THE PPM FRAMEWORK .385.1.1 Structure that facilitates information acquisition.385.1.2 Resource-efficient and customer-centric order .395.1.3 First conceptual contribution to the framework .405.2 SUPPORT CONTINUOUS PROJECT REDUCTION AND PORTFOLIO RE -EVALUATIONS.405.2.1 Exit criteria and prioritization protocols .405.2.2 Periodical re-evaluations of the portfolio .41i

5.2.3 Second conceptual contribution to the framework .425.3 ENSURE LINK BETWEEN AI STRATEGY AND PORTFOLIO .455.3.1 Strategic buckets and integrated objective fulfillment .455.3.2 Third conceptual contribution to the framework.465.4 TRANSPARENCY TO REDUCE POLITICIZATION OF AI.485.4.1 Increase transparency .495.5 SUMMARY OF THE CONCEPTUAL FRAMEWORK .496. DISCUSSION .506.1 CHARACTERISTICS OF AI PROJECTS FROM A PORTFOLIO PERSPECTIVE .506.2 APPROPRIATE PPM PRACTICES FOR AI PROJECTS .516.3 CONTRIBUTIONS AND CONTEXTUAL REMARKS.527. CONCLUSION.54REFERENCES .56APPENDICES .64ii

1. IntroductionAdvances within artificial intelligence (AI) during the past decades have presented immenseopportunities for organizations in the automotive industry. Not only does it enable new growthopportunities by transforming consumption of mobility, but it also presents opportunities forautomating large parts of the manufacturing process (Breunig et al., 2017). Similarly, Kässeret al. (2018) recognizes AI as an essential technology for facilitating four major disruptivetrends faced by the industry, including connectivity, electrification, autonomous driving, andshared mobility. Despite the opportunities for creating a competitive advantage, feworganizations in the automotive industry manage to reap the fruits AI presents. CapgeminiResearch Institute found in a survey with 500 automotive executives that only 10% of thecompanies in the industry successfully implement AI at scale (Capgemini Research Institute,2019). However, five times as many organizations are experimenting with proofs of concepts,pilot projects, or have chosen to implement AI only in isolated business units (CapgeminiResearch Institute, 2019). Although no silver bullet exists for reaching enterprise-wide AIdiffusion, some identified problems provide a direction to where solutions could yield greatimpact. As cited by the executives, some of the most prominent hurdles include organizationalchallenges such as difficulties in selecting which AI initiatives to scale, and the challenges indemonstrating future financial returns during the pilot stage (Capgemini Research Institute,2019).In an effort to reach enterprise-wide diffusion of AI initiatives, the Volvo Car Corporation hasrecently organized a department responsible for diffusing AI throughout the organization. Inthe centralized team, similar problems as the ones outlined by Capgemini Research Institutehave been encountered. Not only is the selection of which initiatives to pursue perceived asdifficult, but the fact that AI projects in general are recognized as unpredictable render manypreviously established techniques unreliable. Additionally, the novelty of AI makes projectclassification and estimations uncertain. One possible solution for these problems, identifiedby the team, is the development of portfolio practices to suit the characteristics of AI projects.Indeed, solutions to similar problems associated with project selection have previously beenresearched within the field of project portfolio management (PPM) (Cooper et al., 2000).Hence, the search for refined PPM practices for AI could most probably be found by furtherinvestigating these issues from the perspective of AI.1.1 BackgroundProject portfolio management finds its roots in the seminal works of Markowitz (1952), whoformulated the groundwork for modern portfolio theory. Although Markowitz’s work focusedon selecting financial asset portfolios based on risk and return (Markowitz, 1952), thegeneralizability of Markowitz’s portfolio theories has long been recognized as applicable forother domains of managerial practices (Gibson & Nolan, 1974). Building on the portfolioperspective, McFarlan (1981) adopted the ideas to portfolios of information systems projects1

in response to high project failure rates. While McFarlan (1981) still primarily emphasized therisk element on both the project- and portfolio-level, subsequent research has included a widerarray of factors such as strategic fit and project synergies (Cho & Shaw, 2013; Cooper et al.,2001).Up until today, research on PPM has been customized to fit many different portfolios. Rangingfrom portfolios for technology development projects (Cooper, 2006), radical innovationprojects (Paulson et al., 2007), to IT projects (Neumeier et al., 2018), the field covers multipleproject types and industries. Consequently, the literature on PPM for IT and technologyportfolios is dense, with different techniques, methods, and practices proposed for bestmanaging such project portfolios. However, as outlined above, the Volvo Car Corporation hasexperienced challenges with the high levels of uncertainties AI projects present. Furthermore,the tools used for some other project types typically rely on substantial and precise input data,something that is not always easily attained in AI projects. Thus, there exist a practical needfor the development of new practices and tools, and to extend the research field of PPM tobetter suit AI projects. Indeed, specialized research literature on portfolio managementpractices for AI projects is still sparse, or even non-existent. Therefore, this thesis sets out tosolve the problems the Volvo Car Corporation has experienced, and to contribute to the fieldby introducing a new customized framework for AI projects.1.2 Purpose and research questionsThe purpose of this thesis is to propose a framework of how PPM practices can be customizedto fit the characteristics of AI projects. The framework intends to function as a managerial tooland therefore, the aim is to take a practical perspective during the construction of theframework. In order to do so systematically, two research questions have been formulated.Firstly, the study aims to outline the characteristics of AI projects from a PPM perspective,using commonly employed evaluation criteria from literature. Consequently, the followingresearch question has been formulated:(1) What are the main characteristics of AI projects when investigated from a projectportfolio management perspective?Secondly, once the key characteristics of AI projects have been identified, the study aims toinvestigate how these affect PPM practices. Additionally, the study aims to manifest theknowledge gained from the first research question into a framework. Thus, the followingresearch question was formulated to enable these ambitions:(2) How can project portfolio management practices be customized to fit the characteristicsof AI projects?1.3 Context and limitationsThis study has been carried out in collaboration with the Volvo Car Corporation, hereafterreferred to as Volvo Cars. More specifically, the study was conducted at the AdvancedAnalytics & AI department (AA&AI). This department was, at the time of the study, recently2

established as a centralized business unit responsible for driving AI initiatives and diffusing AIin the organization. However, as the name of the department implies, AA&AI also engagesin other data science and advanced analytics initiatives. Although these projects could be moreprecisely described with other terms than “AI”, they are still similar enough to be labelled asAI projects. Indeed, the term AI is ambiguous and encompasses multiple branches ofscience. Nevertheless, the thesis will from now on refer to all initiatives undertaken by theAA&AI department as “AI projects”. This language is consistent with the everyday languageused at Volvo Cars and was adopted to minimize confusion throughout this thesis. However,such a definitional decision merits explanation. In this study, the distinguishing factor used todefine an AI project is that it carries out a predictive task of some type as its core activity, usingstatistical models and data. These models used for making predictions are all referred to as “AImodels” in the upcoming chapters of the thesis, unless otherwise specified. Otherproject types than AI projects will collectively be referred to as non-AI projects, if not furtherdescriptions are made. Furthermore, the AA&AI department does not contribute to researchand development of new AI models, but uses already existing ones modified to suit the needsof the organization. Thus, the focus of the department’s undertakings is to diffuse and applyAI, rather than to conduct research for new algorithms.Consequently, some of the contextual factors limit the study from the beginning. While therecertainly exist other limitations, the authors recognize two primary ones. Firstly, the study’sscope is limited by the AA&AI department’s projects. Although no project proposals explicitlyare presented in the thesis, all analyzed projects and interviews within Volvo Cars are relatedto the automotive industry. Thus, the generalizability across industries of the study remainsuncertain. Secondly, Volvo Cars is a complex organization with multiple factors affecting thePPM. While the authors primarily have relied on respondents from AA&AI, it is undoubtedlythe case that other organizational factors will influence the PPM which not fully have beenaccounted for.1.4 Report structureThe upcoming parts of the thesis are divided into the following six chapters: (1) Literaturereview, (2) Method, (3) Empirical findings, (4) Construction of the PPM framework, (5)Discussion, and lastly (6) Conclusion. The first chapter outlines the thesis's theoreticalframework and provides an overview of previous literature within PPM. Additionally, a briefintroduction to AI is included to equip readers with necessary terminology. Thereafter, thefollowing chapter explains the research method used throughout the study. The third chapteroutlines the study's empirical findings. Once these results have been presented, the next chaptersynthesizes the findings into the framework for the PPM of AI projects. Lastly, the remainingtwo chapters contain the discussion of the results in relation to previous literature, and theconclusion of the thesis.3

2. Literature reviewThis chapter presents a literature review to support the answering of this study’s researchquestions. Initially, the chapter introduces the field of project portfolio management and thegoals associated with it. This overview includes frequently used techniques and methods torealize the goals of PPM. Additionally, it includes a summary of a set of criteria crucial to usewhen evaluating projects from the PPM perspective. Complementary to the PPM perspective,a brief overview of the Stage-Gate model is included. Lastly, the chapter includes anintroduction to AI in the context of large organizations.2.1 Introduction to project portfolio managementProject portfolio management comprises a large set of activities and techniques to manage anorganization’s current and future projects (Cooper et al., 2002; Miller, 2002). Unlike the projectmanagement’s (PM) focus on doing things right to ensure success on the project level, PPM isconcerned with doing the right things by selecting an optimal set of projects (Cooper et al.,2000). Thus, PPM takes a centralized, high-level view over all projects, and evaluates thembased on a larger set of factors than possible on the project level (Kendall & Rollins, 2003).This centralized perspective on projects enables a better view of synergies and makes itpossible for managers to conduct risk and financial analyses of multiple projects together(Reyck et al., 2005). Consequently, PPM is not concerned with choosing projects that appearoptimal in isolation, but instead with finding the best set that combinedly constitute an optimalportfolio (Cooper et al., 2002; Reyck et al., 2005).To construct an optimal portfolio, the literature on PPM outlines several goals that the portfoliomanagement team should aim to fulfill. One suggestion of a set of goals is described by Cooperet al. (2002) who emphasize that PPM should aim to maximize business value, ensure abalanced portfolio, align projects with strategy, and select the appropriate number of projects.Similarly, Miller (2002) emphasizes the importance of selecting projects to maximize thevalue, align the mix of projects with the strategic intent, and to continuously revise the portfolioto guarantee an appropriate mix of projects. The goals proposed by Cooper et al. (2002) areoften accepted as essential to achieve with the portfolio management practices by scholars(Coulon et al., 2009; Killen et al., 2008).To fulfill these goals, the PPM includes numerous activities and decisions necessary toundertake. Multiple activities have been proposed in previous literature (Cooper et al., 2002;Reyck et al., 2005), which Kaiser et al. (2015) summarize into three groups: identification andgathering of project proposals, project prioritization and selection, and continuous managementof a project’s ongoing fit with the portfolio. The identification and gathering of projectproposals is often associated with the acquisition of input data about the project’s businessimplications and technical feasibility (Archer & Ghasemzadeh, 1999; Cooper, 1990). Scholarsoften describe these activities as sequential and distinct from the prioritization and selectionstep (Archer & Ghasemzadeh, 1999).4

The prioritization and selection of projects is associated with how projects are ranked betweeneach other to ultimately form the optimal portfolio. Thus, the prioritization of projects is aprerequisite for the project selection as only a subset of all potential projects can be executeddue to resource scarcity (Oh et al., 2012). Therefore, the project selection is an extension of theprioritization and is concerned with choosing the highest prioritized projects to maximize thevalue of the portfolio. However, the projects that the portfolio consists of are continuouslychanging throughout the projects’ life cycles. Therefore, some scholars emphasize the ongoingmanagement of the portfolio, with periodic reviews as one suggested managerial practice touse (Cooper et al., 1997b; Jeffery & Leliveld, 2004). The ongoing management of the portfolioaims to ensure the strategic alignment of the portfolio and to guarantee an appropriate projectmix (Oh et al., 2012), which often is done through periodic reviews. These reviews aresuggested to be performed with annual, semi-annual, or quarterly intervals by some researchers(Cooper et al., 1997b), but are often performed more frequently in high-performingmanagement teams (Jeffery & Leliveld, 2004).During the past decades, various suggestions on how the PPM activities best are conductedhave been proposed. Suggestions include a vast array of methods and techniques thatsupposedly fulfill the main goals of the PPM, and assists portfolio managers in carrying outthe previously outlined activities. Three major schools of approaches to PPM have emerged,according to Oh et al. (2012). The first category includes methods that use objective functionsand constraints to optimize the portfolio on one or multiple factors. Thus, these approaches arebest characterized as taking a mathematical optimization approach to PPM. Typical techniqueswithin this category include linear, dynamic, or integer programming, which in theory arepromising, however, seldomly employed or studied in practical contexts (Cooper et al., 1997a).The second category comprises prioritization-centric methods (Oh et al., 2012). The reasoningbehind these methods is to prioritize and select projects based on numerous predefinedcharacteristics that best maximizes the value of the portfolio (Blichfeldt & Eskerod, 2008).Techniques associated with the methods in this category can be divided into financial andcomparative models. Financial methods prioritize projects based on a project’s financialoutlooks, while comparative models are more inclusive and based on characteristics that extendthe financial analysis (Cooper et al., 2001). Lastly, the third category of PPM methods is bestcharacterized as taking a strategic management approach to PPM (Oh et al., 2012). Thesemethods complement the prioritization approach by emphasizing portfolio balance andinterlinkage with the portfolio strategy (Oh et al., 2012). To reach the goals of the PPM withthe strategic management approach, portfolio managers tend to employ techniques such asstrategic buckets, portfolio maps, and various visualization tools (Wang & Hwang, 2007).2.2 Techniques for project portfolio managementEven if there exists an academic consensus of the primary focus of the PPM practices, opinionsdiverge regarding which of the approaches to take to best fulfill the objectives. One of thereasons behind this divergence is that the context plays a vital role for the performance of thedifferent methods (Blichfeldt & Eskerod, 2008; Christiansen

formulated the groundwork for modern portfolio theory. Although Markowitz's work focused on selecting financial asset portfolios based on risk and return (Markowitz, 1952), the generalizability of Markowitz's portfolio theories has long been recognized as applicable for other domains of managerial practices (Gibson & Nolan, 1974).

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