Chapter 1: Introduction
1.1 Background and Context
Forming more efficient creative processes has been the magnum opus of designers in-practice, particularly followingthelaunchofgenerative artificialintelligence(AI) Copilotsforcommercial use-thecorpussurrounding design for processes and AI-driven innovation has been taken by storm. As design consultancies, one of the key innovative agencies, are in the locus of creative problem solving and value creation, it is vital to explore how they can be optimised to produce shared value for businesses, clients and consultants
AI has potential to reshape the fourth revolution, with transformative capabilities comparable to the idiosyncratic internet boom Its ability to carry out cognitive tasks (Brynjolfsson and McAfee, 2014) such as natural language processing (NLP), has been formally and informally adopted to aid efficient task completion, exemplified by the now infamous rise of ChatGPT (Wu et al., 2023).
Though AI is often framed as a radical innovation itself; it is crucial to acknowledge the importance of implementing it within small-scale improvements in processes, product iterations, and progression in services, otherwise known as incremental innovation Eventually, AI’s paradigm-shifting innovations will diminish and smaller improvements within the design practice will be a necessity (Tekic and Füller, 2023).
The exemplary multidisciplinary global design consultancy, IDEO, focuses not only on crafting strategies for breakthrough innovations for business growth, but also engages in incremental innovation, for instance enhancing Procter & Gamble’s digital tool development through Datascope. As IDEO is a ‘paragon of innovation’ (Kelly and Littman, 2001), its exploration offers a wealth of information to discover and analyse.
1.2Aims and Objectives
This dissertation focuses on the paradigm shift in technology, workforce, and processes within various system contexts in design consultancies that are changing due to prominentAI-driven innovation. It seeks to identify best practices, challenges and limitations, and butterfly effect of AI as a system intervention on traditional processes and value creation.
To accomplish this, the dissertation centres around the research question: ‘How can design consultancies, specifically IDEO, optimise AI implementation into their incremental innovation strategies to enhance value creation?’
The initial research phase will focus purely on the application of systems thinking and complexity principles to the existing model of two of IDEO’s projects; FEMSA’s digital ecosystem for OXXO stores and Beyond the Bag initiative (BTB), [4.2], serving as the basis for the comparative case study. Through this exploration, practical recommendations are formulated to encompass elucidation of case-specific, complex dynamic interrelationships, and outline a new theory to optimise value creation post-AI implementation.
1.3 Justification and Scope
Throughout the taught postgraduate curriculum, modules such as Innovation Management and Design Ecologies piqued curiosity regarding potential research interests. Amalgamating innovation theory with the prevailing discourse surroundingpractical applicationsof emergingAI tools within an‘ecology’buildfoundations associated with this research.
The rapidly evolving landscape of design practices is driven by dual forces of continuous improvement and ‘unparalleled’technological advancements inAI (Kasula, 2016). Refinement of design methodologies for project processes is paramount to delve into, due to evolving consumer expectations and market saturation, compelling cyclical innovation strategies in organisations through trends and a ‘resource-based view’ to leverage its competitive capabilities and bargaining power (Krakowski, Luger and Raisch, 2022).
This research will synergise systems thinking and innovation theory [2.4] through exploration of IDEO supported by the plethora of project contexts (Henke, 2021). Its renowned position as an innovative firm serves as a
benchmark to exploreprojects with andwithoutAIimplemented within itsprocesses, providing arange of insights into how it can be optimised
Chapter 2: Literature Review
This literature review establishes a theoretical framework after review and segmentation of topics at the locus of this dissertation. The review of existing corpus surrounding core foundational principles, key theories, history, dogma, and terminology associated with AI, innovation strategies, and systems thinking principles lay the groundwork for a holistic understanding of theoretical underpinnings. This comprehensive approach illuminates distinct intersecting elements of these domains and raises pertinent questions about their in-practice applications. The literature review will navigate these distinctions and intersections to enhance the discourse on AI and innovation within the context of design consultancies.
2.1 The Emergence of AI in Design Practices
This section will explore howAI is integrated into design practices, by exploring implementation ofAI in creative processes.AI has been front-page news consistently for the past few years and caused the stir attributed to largescale radical innovations. Sheikh, Prins and Schrijvers (2023) defineAI as ‘a technology that enables machines to imitate various complex human skills’, comprised by a series of complex algorithms, comparable to the human brain.
John McCarthy coined the term ‘artificial intelligence’ in 1956, pivotally marking the beginning of a new altered recapitulation in computational thought. Foundational ideas of AI were shaped by many synergistic ideas, one being articulated in Vannevar Bush’s seminal essay As We May Think proposing a system designed to enhance human cognition (Bush, 1945). Building upon these concepts, Alan Turing (2009) posited the intriguing notion that machines may one day be able to simulate human understanding - ideas since then catalysed and imbued progressionof the field. Concerns regarding foresight into implications for technology today are validated through present conversations surrounding specificity and ‘curation’(Popova, 2012).
The analogy ofAI being likened to an onion aptly visualises these nested subsets of its capabilities [Figure 1].AI, machine learning (ML), deep learning (DL), and generative AI, centripetally become more complex and sophisticated (ScrumAlliance, 2024).
Many of the field’s breakthroughs have occurred in MLand large language models (LLMs), a subset of generative AI, which consume extensive text data to grasp subtleties of context and semantics to produce responses resembling human beings. This emergence could influence operational strategy changes following addressal of known issues through regulation of data management and privacy for ‘ethical usage of data’ (Tekic and Füller, 2023), combatting inertia amongst senior management, outlined compliance requirements (Hradecky et al., 2022), and neutralising ‘algorithmic bias’(Simon, Wong and Rieder, 2020). The standard of acceptance is evidenced by extensive informal adoption of user-friendly interfaces such as ‘ChatGPT, Bing Chat’ and Google Gemini (Teubneretal.,2023),provingonceAIprogressionhasplateaued andbeenthoroughlyregulated,thereisasublime scope of opportunity for research and development (R&D) and practical use.
Though brief history and key terminology have been discussed, the exploration of AI’s origins from a social perspective remains an open area for scholarly review. Here, the largely contested theory of technological determinism can be explored - social reasoning for AI’s development holds significant importance within the systemofinnovation.Chandler(1995)detailsargumentsforandagainstthetheory -the‘technologicaldeterminist view’ claims developments are ‘technology-led’ as the prime social driver supported by technocratic notions of ‘inevitability’. Proponents of this view, including Marx and Smith (1994), state the mere possibility of technological development compels its realisation as an operational necessity
Critics argue for opposing beliefs emphasising that social transformations, are complex and involve numerous interacting factors which results in ‘difficulty isolating causes [from] effects’(Chandler, 1995). To take a personal stance on thematter, this compelling yet contentious perspective acknowledges theprofound influence technology has on societal evolution – the researcher maintains a critical and nuanced lens towards pure determinism. Karl Marx believed technology impacts mental models and overall attitudes while acknowledging that many other factors are at play (Tripathi and Rai, 2022,p. 267), aview is resonated with; hence this dissertation aims to address the system surroundingAI holistically.
To build upon the theoretical discourse regarding technology’s societal impact, it is paramount to understand reverse salience - a concept identifying lagging components within a technological or non-technical sub-system, impeding its ability to reach development goals and overall function, seminally introduced by Thomas Hughes
Figure 1: AI Capabilities and Complexity Model
(1983). This indicates that technology can expose shortcomings within systems, subsequently becoming innovation opportunities within socio-technical systems. In consultancies, it can manifest in obvious or nuanced ways such as orthodox practices or inefficientprocesses impacting workculture, exemplified byNokia’s infamous decline due to the research and development departments divided on working on Symbian and MeeGo operating systems which ‘exacerbated delays’in an ‘application-centric’space requiring swift adaptations (Doz, 2017).
AI’s abilities could become a reverse salient if its rapid advancement outpaces industry’s’ flexibility and competence to adapt, rendering traditional consultancy methods obsolete due to AI’s automation and decisionmaking. AI, initially intended as a solution, may cause bigger challenges for firms (Hughes, 1987, p. 33). Consultancies must harmonise roles and processes to anticipate disparities between elements of their system to adapt to the new technological paradigm.
Current knowledge underscoresAI’s practical benefits yet highlights critical considerations such as ethical usage and algorithmic bias. However, gaps remain in understanding real-world applications within complex sociotechnical systems. This dissertation must address whole systems to navigate intricate interplay of these factors whilst recognising Hughes’ (1987) assertation of interventions intended as solutions presenting new challenges This sets the stage for a deeper investigation into both traditional and AI-driven processes, impact, and value in consultancies
2.2 Intersection ofAI and Innovation in Design Consultancies
Technological innovation has been at the forefront of competitive success (Schumpeter, 1983) with firms relying on new technologies, evidenced by Johnson & Johnson products within ‘the last five years account[ing] for over 30 percent of sales’(Schilling, 2013, p. 1). In design consultancies, innovation can be described as the process of creating valuable solutions aligning with client needs and organisational goals, often by utilising frameworks such as design thinking to steer experimentation and reframing problems to uncover opportunities (Rösch,Tiberius and Kraus, 2023).
Traditionally, consultancies relied heavily on manual processes and human creativity including cumbersome tasks such as ‘stor[ing] information on paper’ and utilisation of ‘scale[s] and measuring kit[s]’ accompanied with the risk of human error and time consumption (OpsMatters, 2023).
Today, AI tools are becoming indispensable to design consultancies to enhance their workflows, leveraging its manifold abilities for a potential radical shift in design processes of solution-making ranging ‘from Netflix recommendations to autonomous cars’ (Marcus and Rosenzweig, 2020) According to the UX Design Institute (2024), the ‘top 5AI-powered tools for user research’, MiroAssist, Dovetail, Maze, Notably, and the Figma plugin QoQo, can predict user behaviour and preferences, and automate various facets of the research process.
Reliance onAI will only grow, through more frequent usage ofAI Copilots, ‘conversational, generativeAI-based assistant[s]’, and custom-built copilots (Bendersky, 2024), illustrated by IDEO and Ethiqly’s rapid prototyping through OpenAI’s LLM (IDEO and Fizel, 2024). After advancements, heavier unsupervised reliance on AI Agents, ‘entities that perceive and act upon their environment’ (Kapoor et al., 2024), and autopoietic or ‘autonomous agents’can be established (Franklin and Graesser, 1996).
Prototype testing for the AI Agents, ‘DuetDraw’, an AI interface for drawing was studied. Findings depict AI received low ratings in ‘perceived predictability, comprehensibility, and controllability of the drawing tasks’. Through provision of thorough instructions for prompt engineering, negative results can be mitigated (Oh et al., 2018, p. 2), acknowledging success metrics ofAI implementation vary context-dependently, though scope within creative environments is clear considering continuous improvement to realise full potential.
Gartner’s ‘hype cycle’ is an interesting measure to reference when examining future steps in intersections of AI and innovation [Figure 2]. According to Gartner’s (2024) recent publication, AI could derive maximum value if thought leaders focus future system on compoundedAI techniques as generativeAI ‘has [already] passed the Peak of Inflated Expectations’ - the importance of open innovation and absorptive capacity is recapitulated (Gartner, 2016) and potential emergence of human-machine teams and roles There will be a need to master incremental innovation of novel ideas instilled into workflows rather than focusing purely on optimising existing solutions,
known as the ‘incremental innovation trap’, hence AI playing a significant role in striking a balance between incremental and radical innovations in consultancies (Tekic and Füller, 2023, p. 8).
In examining this intersection, we know technological innovation drives competitive success, despite traditional reliance on manual processes. However, as AI’s predictability remains low and long-term impacts are unclear, additional research should aim to exploreAI’s predictability and incremental strategies to optimise full potential.
2.3AI and Value Creation
In a business context, value to tangible and intangible assets derived from stakeholders (customers, employees and shareholders) from products, services, and experiences, may include financial gains such as ROI, customer satisfaction, social standing, or strategic marketing positioning (Kopecká, 2017). According to Gurnani (2020), companies following Environmental Social Governance principles must approach value through investments into long-term sustainability and competitiveness.
Metrics encompass consideration of financial strategies, customer perception, internal processes, and learning and growth, through tools such as Kaplan and Norton’s (1992) Balanced Scorecard [Figure 3]
Figure 2: Hype Cycle for AI (Pasqal, 2024)
Since the commercialisation ofAI, seemingly the most significant role it has played is the reduction of grunt work and automation ofrepetitive tasks The study undertaken byMollick (2023) indicates consultants utilisingAI were able to complete their tasks 25.1% faster and with 40% improved quality in comparison to those who did not incorporate AI workflows. Findings highlight substantial benefits of AI in enhancing strategic assets such as competitive advantage, speed, quality, and overall efficiency of consultancy services. Revenue generation and cost reduction occur as a butterfly effect - the McKinsey Analytics (2020) survey states that half the sample companies adoptedAI into a minimum of one business function resulting in lowered costs.
It can be argued, consultants experience major impacts fromthe integration ofAI into design processes. Hu (2022) states, as AI continues to evolve, it compliments human creativity, though outcomes remain a product of human ingenuity. In the face of this fourth technological revolution, creativity is perceived as uniquely human, however, value created through AI integration is undeniable. As well as monetary benefits, AI supplements and augments human creativity by identifying patterns, generating novel designs and offering insights driving innovation.These aspects are aptly illustrated by the case of Tata Consultancy Services engaged with ‘cloud-based platforms and solutions’ leveraging AI to deliver ‘hyper-personali[s]ed’ customer experiences, resulting in an increase of ‘customer engagement and retention rates’(Panditrao, 2023).
AI also brings amplevalueto clients through improved service qualityandpersonalisationby automating complex cognitive functions such as prediction and data evaluation. Chandra and Rahman (2023) state that ML’s ability helps consultants understand their clients’ users better. AI opens opportunities for ‘customer co-creation’ by empowering consumers with these advanced cognitive resources, facilitating deeper engagement, and augmenting human potential, thereby fostering collaborative partnerships between clients and consultants.
The immense value AI brings to businesses, clients and consultants comes with its potential challenges. Hashfi and Raharjo (2023) extensively detail challenges and impacts of AI implementation, including data quality and availability forAI algorithm training, ensuring compatibility, generational and organisational resistance, the skills gap amongst employees with traditional workflows, and unpredictability.Although investments in data cleaning, data augmentation and sharing, and prioritisation of systems leveraging interoperability features can mitigate these, particularly through a holistic change management plan.
There are aspects in research and in-practice that we still do not fully understand including long-term impacts of AI on human roles. This research should focus on how to optimise teams withinAI-driven processes and develop strategies for incremental change management to address gaps by exploring projects enhancing co-created value.
Figure 3: Balanced Scorecard (Kaplan, 2009, p. 4 fig 1)
2.4 Theoretical Framework
The purpose of this section is to select a framework for provision of a guided structure and depth to research findings grounded in a theoretical underpinning. This dissertation employs systems thinking with innovation theory to exploreAI’s role in optimising innovation within design consultancies to create value.
Systems thinking is a holistic approach examining key interactions and interdependencies within a system. Systems dynamics and systems thinking have undergone significant evolution since its introduction by Jay Forrester in Industrial Dynamics (Forrester, 1997). The proposal that social systems should be modelled as flows and accumulations linked via feedback loops interweaving with chaos theory and the ‘butterfly effect’ (Lorenz, 1972) Since the inception of this paradigm, there have been many criticisms arguing that this technocratic worldview simply cannot be applied to natural systems (Lane, 2007), though overlooked due to the subjective nature of modelling interactions and processes.
Barry Richmond (1993), instrumental in promoting terminology associated with the systems approach, supports the widely accepted doctrine that many issues faced, particularly in social subsystems, are challenging to address in isolation (Meadows, 2008), as solutions in one area of a system can have unintended consequences other ‘components’ (Bertalanffy, 2003). Ackoff (1997) infamously referring to systems as ‘messes’, advocates for reductionism and the ‘cause-effect’ methodology to simplify the chaos of heavily interconnected dynamic systems.
According to Jackson (2003), core principles that link systems thinking to management in-practice are: holism, interconnections and interdependence between parts, structure determining behaviours, multiperspectivity, problem-solving, strategic planning, organisational change, and learning and adaptation. These principles can be linked through this dissertation to comprehend complex ecosystems within design consultancies, hence examination of technology, people, and processes – ‘interdependency demands systems thinking’ (‘2015 Conference on Systems Engineering Research’, 2015, p. 670). This can be achieved by visualising feedback structures through diagrams such as causal loop diagrams, stakeholder landscapes, and systemigrams [Figure 7]
Exploration of organisations as fluid systems (Senge, 1997) and potential consequences of systems interventions enables development of more effective insights and solutions, counteracting the ‘growing intractability’of tightly wound subsystems that surround consultancies (Richmond, 1993, p. 113).AI is becoming a prominent component as ‘technological systems expand, [with potential] reverse salient develop[ment]’(Hughes, 1987, p. 33).Analysis of symbiotic feedback loops and ‘multiple-loop non-linear systems’prepare people with managerial roles within consultancies to be ‘more aware of the dangers of unintended consequences’, and the importance ‘of treating symptoms rather than causes’.
Systems thinkers and thought leaders seek research for the betterment of ‘clients, decision-makers and problem owners’ (Jackson, 2003, pp. 60-61), aligning this theoretical underpinning with the dissertation’s focus. The researcher’s views regarding ‘the relationship between theory and research’ are imperative (Bell, Bryman and Harley, 2022), encompassing holistic understanding of consultancy ecologies and are referenced throughout the visualisation of data collection.
The supplementary theory focused on during this dissertation will be innovation theory as it ties in the dynamics of innovation within consultancies andAI, its systems intervention. The four key types of innovation; sustaining, disruptive, incremental, and radical are of high importance. Schumpeter (2021) introduced concepts of ‘creative destruction’arguingthat innovation ofnew products, processes, and markets is the key driver of economic growth. Striking a balance between incremental improvements and radical technologies and processes is vital to maintain competitive advantage while retaining user experience at the forefront of practices (Tekic and Füller, 2023). This discourse has recently been illustrated bytrusted tech-reviewer, Marques Brownie, issuingpoorreviews to Human AI pin and Rabbitr1 pocket companion for lacking basic features such as a timer (Brownie, 2024). This speaks to the speedy product-oriented launch approach of many companies frequently compromising user experience to become the ‘first movers’(Schilling, 2013, p. 89).
Systems thinking provides a comprehensive view of the design ecosystem while innovation theory focuses on the machinations of change management and continuous improvement. Together, they offer a nuanced understanding ofAI-driven innovation systems.
2.5 Research Gap
Mariani et al. (2023) write that most current research is predominantly exploratory due to the unpredictable nature of AI’s development, and innovation scholars lack comprehensive understanding of existing research and most relevant new gaps, hindering identification for future enquiry in this domain. Therefore, there is a growing need for systems thinking and holistic viewpoints in this relatively new field where human creativity, complexity principles, and innovation thinking synergise within the context of practical applications, particularly outside the healthcaresectorwheremostresearch onAIcurrentlystands.Thecomplementaryrelationshipbetween innovation theory and systems thinking enables the visualisation and exploration of the cascading effects, or butterfly effect, ofAI technology insertion, particularlyAI’s composite techniques (Gartner, 2024).
AI is often framed as a radical innovation due to its transformative potential. However, navigating the innovation continuum and ‘hype-readiness continuum’(Ricketts, 2017) is vital. Currently, products are being launched with arguably, a disregard, for user experience purely to reap first mover’s advantages.As global investment increases, a flood of poorly executed developments inAI will be launched (Agrawal, Gans and Goldfarb, 2019). The initial hype around AI has entered the ‘trough of disillusionment’ (Gartner, 2024); after its plateau, incremental innovation will ensure sustained growth and adaption. Therefore, a problem-solving approach is crucial. Incremental innovation strategies need to be developed further in the context of AI; failure to continuously improve risks overlooking the emergence of a reverse salient. Due to the importance of it in long-term success, the research within this dissertation will be centred around incremental innovation.
Other key gaps that this dissertation that must address; practical knowledge regarding human roles within both traditional andAI-driven processes and explore open innovation and co-creation in a project setting.Additionally, value creation is often centred solely around business metrics and quantitative KPIs, but this study aims to focus on qualitative measures such as customer retention and long-term sustainable benefits.
Chapter 3: Methodology
The methodology was meticulously constructed by employing the ‘research onion’ model [Figure 4] which delineates a guiding framework for decision-making, ensuring a thorough and methodical approach (Melnikovas, 2018).
Figure 4: The Research Onion (Saunders, Lewis and Thornhill, 2011)
3.1 Research Philosophy
This study’s research philosophy requires a consideration of real-world applications. Thus, pragmatism, as articulated by Charles Sanders Peirce, is adopted, as it places emphasis on ‘practical bearing’ (Campbell, 2011). Contrary to the positivist belief which advocates for strict objectivity and application of universal laws, pragmatism enables integration of diverse methods to address actual issues
Therefore, it is apt for derivation of categories and themes for practical recommendations that arise from the consideration of dynamics within consultancy settings.
3.2 ResearchApproach
3.2.1
Data Collection
The exploratory nature of this dissertation is anchored in secondary desk research, utilising qualitative data collection spanning across a three-month period. Given the time constraints, the research adopts a cross-sectional timeline. Data collection strategies incorporated are through case study research with secondary heterogeneous data consisting of document analysis and ‘artifacts’ (‘Proceedings of the ServDes2018 Conference’, 2018) comprising of journal articles, news articles, blog posts, and reports collated.
Comparative case study research actively involves ‘a detailed investigation’ and engagement with existing literature and creation of new insights within the context to ‘illuminate the theoretical issues being studied’ (Hartley, 2004). The topic of interest in this dissertation is value creation in consultancies, in this case, IDEO, due to its involvement in designing breakthrough offerings. This applied research aims to explore and determine system components within two of IDEO’s projects: FEMSA’s digital ecosystem for OXXO stores and the Beyond the Bag initiative, attributing systems thinking principles.
In laying the groundwork for this proposal, it is essential to acknowledge researcher positionality (Crouch and Pearce, 2012) especially when navigating analysis regarding real-world case studies. As the investigator’s background is based in the academia of the design and innovation discipline, the case study is explored via the lens of strategic transformation, user experience (UX) alongside human-centred design (HCD) approaches, and value addition to the clients, customers, and consultants. Thus, serving as a means of solution-making rooted in design thinking within this research process, as opposed to sole evaluation of economic and business metrics of IDEO. Though it should be noted that the investigator has limited tacit knowledge of consultancy settings.
3.2.2. Reasoning and Data Analysis
Inductive reasoning, an iterative ground-up approach (Thomas, 2003), is employed in tandem with the Grounded Theory Method (GTM) pertaining to ‘theory building’ (Khan, 2014) ‘developed inductively from data’ (Moghaddam, 2006).
Though origins of grounded theory are largely divided into two strands: Glaserian being ‘open, selective and theoretical coding, at incremental levels of abstraction’, and the stricter Straussian ‘[o]pen coding, axial coding, selective coding’(Urquhart, 2016)
This dissertation utilises the revised and more flexible GTM proposed by Stauss and Corbin (1998), due to its systematic approach to analysing the sources, and enablement of relationship exploration between emerging concepts. Figure 5 details the process followed: open coding, axial coding, application of categories derived through literature regarding the case studies, and lastly selective coding to derive new theory from the core category, with insights offered into thought-processes through analytical memos (Lee et al., 2019).
In the early stages of source collation, the topics of interest for each iteration are appointed to maintain purposeful theoretical sampling that will be funnelled iteratively through the process of systematic categorisation [Figure 6]. Aldiabat and Le Navenec (2018) state ‘the number of data collection methods used depends on the complexity and novelty of the research’, hence, for each iteration the range of sources to collect is defined; open coding (1015 sources), axial coding (20-25 sources), and each case studyapplication (5-10 sources).There will be an attempt to find as many relevant sources as possible till theoretical saturation emerges.
6: Theoretical Sampling Strategy
Although procedures of GTM are not usually associated with use of visual data (Konecki, 2011), they will be incorporated within systems thinking based tools as ‘building blocks’to generate insights (Moghaddam, 2006).
Asystems map serves as a model for representing the dynamic behaviour of a system, framing evidence from four types ofinformation:participatory, qualitative, quantitative, and inthis case, existing evidence (Barbrook-Johnson and Penn, 2022b). Applications of systems thinking principles consist of feedback loops, complexity principles
Figure 5: Linear Phases Model for Grounded Theory
Figure
(Forrester, 1997), and analysis of multifaceted processes of the ecology of IDEO within the context of FEMSA and the BTB initiative. Therefore, the systems mapping tools employed in this dissertation are; causal loop diagrams to visualise hierarchical nodes, including balancing and reinforcing loops; systemigrams used to ‘eliminate and converge perspectives’in a narrative format (Sauser, 2020); and the iceberg model to underpin the project complexities in regards to mental models applied to the determinacy of a potential reverse salient [Figure 7].
3.3 Ethical Considerations
Secondary data is derived from existing primary data; therefore, ethical considerations include maintaining transparency in data sourcing, ‘respecting intellectual property’and avoiding plagiarism by citing all information derived via secondary sources (Resnik, 2015). Transparency and integrity are maintained throughout the analysis of case studies set globally, without social and confirmation bias’s skewing analysis further mitigated by reflexivity (Olmos-Vega et al., 2022). Charlesworth (2012, p. 86) states that there are ‘grey areas’ converging between ethics and legality, therefore, all information is predominantly publicly available.
Chapter 4: Case Study
This chapter conducts a detailed analysis employing grounded theory for the two chosen case studies at IDEO: FEMSA’s digital ecosystem for OXXO Stores and the Beyond the Bag initiative covering data collection, the GTM coding visualised diagrammatically, and identification of patterns that have emerged through analytical memos.
4.1 Overview of IDEO
IDEO, founded in 1991, stands as a pertinent global design consultancy, lauded for its adoptions of a HCD approach to innovation and creative problem-solving (Zhang and Dong, 2008). Its establishment through the merger of three design firms, David Kelley Design, Moggridge Associates, and ID Two, has burgeoned into a formidableentity. Ithasbecomeathoughtleaderwithin creativespacesthroughplatformextensionssuch as IDEO U and OpenIDEO offering educational value,and spaces topractice open innovation,encouragingpositive change through design thinking. Some notable projects contributing to its global recognition are Apple’s first mouse
Figure 7: Critical Appraisal of Systems Maps
design in 1980, though with controversial ergonomics, and Help PillPack, an IDEO start-up acquired byAmazon in 2018 offering a home-delivery system addressing user pain points.
IDEO developed the nascent process and now widely adulated five-step methodology underscoring a method for design development and innovation strategies following the steps: understanding markets, storytelling and observation of users, brainstorming and synthesis, evaluation and refining prototypes, field testing and implementation for commercialisation (Blakeney et al., 2009; Morrison, 2018) although, this varies across different sources. This was documented on an episode of ABC Nightline in 1999 through the redesign of a shopping cart and is now known as the ‘Deep Dive’process (Kelley, 2001).
The Deep Dive process is concurrent with design thinking principles; epitomising socio-technical systems, as delineated by (Ackoff, 1994),amalgamating ‘mechanical’and ‘social’system components to cultivate an adaptive milieu within its second place. Central to IDEO’s triumph are its incremental innovation strategies, particularly through its deep-dive method that fosters efficacious ‘solution-oriented’ (Barsalou, 2017) problem-framing by immersing its consulting teams into iterative phases of empathising, defining, ideating, prototyping, and testing to devise UX design (Dam, 2016).
In recent years, AI has been seamlessly integrated into IDEO’s creative processes, successfully leveraging AI to enhance creativity through GPT-4 for ideation and drafts of marketing copy; ‘OpenAI api [GPT3-davinci-003]’ for constructive criticism; ‘DALL-E or Midjourney’for idea inspiration; experimental insight analysis chatbots; Tome.app to produce AI-generated slide decks; and Takashi Wickes, an Interaction Designer at IDEO uses Gen AI outputs within Miro, Reduct for interview transcription; Figjam, and Notion to ‘streamline the synthesis process’(Kunovsky and DeRuntz, 2023). IDEO has also contributed to ongoing conversations surroundingAI in itsR&D,asAIPassionCoach,ahypotheticalappprototyped tocollateperspectivesfromGenZ,withsomestating that Gen AI shortcuts may reduce their capacity to develop their unique identity.
Through regular engagement withAI tools and thought leadership, IDEO perpetuates its legacy for incrementally implementingAI informing design and innovation of products, services, and experiences for its clients.
4.2 Rationale for Comparing the Two Projects
The aim of this case study is to comparatively analyse two of IDEO’s projects, FEMSA’s digital ecosystem for OXXO stores and Beyond the Bag initiative.
The rationale for selecting these case studies is rooted in pre-determined criteria. Both case studies are:
1. In the same or similar industries
2. Follow Deep Dive processes and human-centred approaches
3. Within the last five years
The key pre-determined difference between the case studies is:
1. One usingAI for its creative processes
2. One with absence ofAI for its creative processes
These control variables allow for insight generation within distinctive systems maps, both creating value for differing communities. Varied system components with distinct challenges encourage determination of a reverse salient and elucidation of how IDEO can optimiseAI-driven innovations for value creation
4.3 FEMSA’s Digital Ecosystem for OXXO Stores
4.3.1
Project Background and Objectives
IDEO’s project with FEMSA, initiated in 2022, sought to create an omnichannel digital ecosystem to enhance customer value at OXXO stores, the convenience store chain located in LatinAmerica (IDEO, 2022). FEMSA, a prominent retail and beverage company, operating over 22,000 stores and serving 13 million customers daily, play a large role in the retail and financial transaction services. The active users of the digital wallet, Spin by OXXO,
reached 6.9 million by 2023 (FEMSA, 2023a, p. 6), demonstrating substantial reliance of the core customer base on these systems
The project’s objectives were to achieve a comprehensive digital transformation and improve customer engagement through FEMSA’s strategic incremental changes (FEMSA, 2023b), crucial for sustaining business value following the celebration of 45 years in the Mexican market in 2023. The ‘FEMSA forward strategy’ is associated with business verticals including Proximity and Health, Coca-Cola FEMSA, and Digital@FEMSA, key stakeholders in the path to creating an integrated customer experience. For instance, the development of a new loyalty program, Spin Premia which resulted in a 31% increase of OXXO Mexico sales associated with the program (FEMSA, 2023a, p. 15).
4.3.2 Process, Insights, Value
The project commenced withthe initialphase ofdeepdiveprocesses; to understand,observe andstory-tell –IDEO consultants met with customers and potential users in their homes, as interacting with communities for product development provides more nuanced social context regarding how users ‘think and feel about their experience’in context of the project goals (Stevens, 2023). By ‘leverag[ing] user communities as a source of open innovation’ (Shah and Nagle, 2019), the development cycles are shortened ‘33%-50%’with subsequentcosts reduced (Naylor, 2021).
For development of the digital ecosystem, AI tools were utilised to analyse customer data, synthesising themes and trends to derive insights for personalised user experience design (UXD) within the app’s features.
Following iterations of Spin Premia, FEMSA launched a version of the app, targeting a single city in Mexico to test the service, typical of incremental innovations. Following the official launch, the platform offers a ‘safe and user-friendly platform’that many Mexican consumers utilise, hence realising initial aims of the project to increase customer loyalty and retention.
According to the 2023Annual Report of FEMSA(2023a), the value stemming from this project due to IDEO and Digital@FEMSA’s involvement boosted productivity, ‘economic and social value’ alongside acceleration of OXXO sales to ‘high double-digit[s]’ and exceeding ‘1000 net new OXXO store threshold’ whilst expanding locations.
4.3.3 Challenges and Solutions
AI has the potential to revolutionise user research processes by analysing extensive datasets collated from customer feedback, behavioural patterns, comprehension of hidden end requirements, and interaction with chatbots (Peruchini, Modena and Teixeira, 2024). However, this may come with further challenges regarding ethical concerns.
The most prominent challenge associated with designing a ‘super app’ for a vast customer base was the ‘operational complexity’of bridging the global gap between diverse teams, customers, and communities (IDEO, 2022). Though AI can develop rich user personas, the presence of humans largely avoids current teething issues regarding AI engines. This notion is supported by Fan et al. (2022) stating that perspectives should shift away fromtotalautomationandrecognisingthatAIshouldbeusedtosupport, notreplace,decisionsandtasksofdomain workers.
4.3.4 Grounded Theory
For further analysis of the case study regarding the collaboration between IDEO and FEMSA, employment of GTM analyses categorises systematically. By coding sources and weaving in systems maps core categories are derived, visualised diagrammatically to comply with systems thinking mapping styles, [extensive process detailed inAppendices]
Open coding commences GTM [Appendix A], involving coding of data excerpts In the context of FEMSA’s digital ecosystem, the identification of initial code uncovers fundamental elements within the retail sector, serving as a preliminary basis for coding.
Figure 9 visualises and interprets the interconnections between open codes, depicting reinforcing loops, that could potentially become ‘Fixes that Fail’ or a ‘Shifting Effect Loop’ (Galanakis, 2006), speaking to the concept of intended solutions becoming challenges.
Figure 8: Open Coding
Axial coding [Appendix B] reassembles data by establishing further connections between categories from additional sources alongside ones from IterationA[Figure 10].
Figure 9: Interpreting Open Codes via a Causal Loop Diagram
Selective coding [Appendix C, D], refines categories with applications of FEMSA’s project ecosystem. The core concept derived is holistic digital transformation for competitive advantage [Figure 11].
Figure 10: Axial Coding
Figure 11: Selective Coding - FEMSA's Digital Ecosystem for OXXO Stores
Furthering understanding of complex narrative interactions, Figure 12 encapsulates the essence of FEMSA’s project, providing a deeper in-context understanding of underlying dynamics between creative processes and AI Insights from the overall case study and selective coding are depicted within the structure of the systems map
12: Systemigram based on FEMSA Case Study and Core Category
To understand better the dynamics of the larger system at play, Figure 13 highlights specific negative feedback loops potentially causing inefficiencies.
Interpreting Figure 14 demonstrates issues such as operational inefficiencies and slow decision-making due to poorly integrated AI processes and the misconception that AI can solve all complex operational problems within global consultancies, collectively impacting the progress of the otherwise positively received project.
Figure
Figure 13: Most Significant Negative Feedback Loops in FEMSA's Project System
4.4 Beyond the Bag Initiative
4.4.1 Project Background and Objectives
The 2020 BTB initiative and challenge, launched by IDEO in partnership with Closed Loop Partners, a lead firm in circular-economy innovation, aimed to identify and test solutions proposed by ‘entrepreneurs and investors to pitch’novel concepts and ideas for the redesign of retail bags and solutions for ‘single-use plastic-bag waste’. The Consortium was convened for this reinvention of retail bags in conjunction with largeAmerican chains including Walmart, Target, and CVS (Wilson, 2020).
This systemic challenge garnered a lot of attention with over ‘450 submissions’and ‘nine finalists entering a sixmonth accelerator’(IDEO, 2020) resulting prototypes launched within approximately seven months [Figure 15]. This design for policy and shift in infrastructure and mental models regarding norms for shopping habits took place approximately over seven months [4.4.2], with incentive for innovators being a portion of the $1 million funding and participation in a CircularAccelerator program with further support for scaling and budding piloting opportunities.
Figure 14: Iceberg Model –
4.4.2 Process, Insights, Value
The initiative garnered a collaborative approach, consistent with the paradigm of open innovation without employing AI in its processes. During the project’s inception in 2020, AI was not popularised for practical usage and was in its nascent stages of innovation.
The success of the project highlighted the potential of open innovation and absorptive capacity, defined by Todorova and Durisin (2007) as ability to recognise and assimilate the value of new knowledge and apply it commercially. By crowdsourcing ideas, particularly with such larger input, quantity breeds quality It enables teams to tap into diverse pools of creative ideas and solutions addressing needs of communities due to diverse range of thought for UX and accessibility.
The initiative provided substantial value to various global stakeholders involved. For the clients, a biproduct of the eco-friendly solution was the reduction of environmental footprint, complying with protocols such as UN Sustainable Development Goals to reduce global carbon dioxide emissions by 45 per cent by 2030 from 2010 levels (UNSD, 2021). According to Deloitte’s (2023) study, consumers have become more environmentally conscious, so companies that demonstrate genuine commitment to sustainable solutions become more trusted.
Consumers benefit from sustainable and convenient alternatives to single-use plastic bags that align with modern consumer preferences, as the challenge participants are not solely ideators but they are also clients’ future consumers (Karpukhina et al., 2023, p. 42). Social and environmental value manifests through the promotion of green thinking within the retail sector, furthering movements of systemic attitudes towards a more sustainable future.
For IDEO, this project reinforced reputation as drivers for successful sustainable problem-solving and demonstrated an expertise and willingness to engage within communities with a shared goal for positive change.
Outcomes of the challenge range from reuse models, technological strategies, and innovations in material compositions. Some winners are ChicoBag’s addressal of the relatable pain point of forgetting your reusable bag with a borrow-on-site scheme; Returnity’s reusable shipping and delivery bags alongside an e-commerce and delivery system facilitating their use; SmartC created by 99Bridges, powered by the Internet of Things (IoT) for incentivisation through rewards; and Domtar’s stretchy and durable material for the bags made of 100% cellulose - further details of all winners are detailed in OpenIDEO’s (2020a) announcement.
4.4.3 Challenges and Solutions
Though crowdsourcing as a method for innovation reduces R&D costs, shares innovation risk, and potentially decreases the time taken to bring products to the market (Cricelli, Grimaldi and Vermicelli, 2021), it poses significant challenges. There can be a lack of quality control (Niu et al., 2018), intellectual property issues, and strategic misalignment of ideas from the innovator submissions such as overlooking market needs. These factors can result in wasted resources and funding, however mitigation through legal documents and clear reiterated submission criteria can avoid this.
Figure 15: Challenge Journey for Beyond the Bag (OpenIDEO, 2020b)
Scalability and practicality of the solutions may be criticisable, mitigated through expert interviews, community feedback, and a thorough vetting process. Provision of mentorship and funding also grounds the applications’ ideas in real-world insights. The absence of AI through this intense process causes longer timelines, length initiation processes and need for more funding, however, as improvements were incremental through IDEO’s systematic methodology rather than a sudden shift, it is more manageable in the absence ofAI.
4.4.4 Grounded Theory
As the theoretical sampling for both the open and axial coding are shared between case studies [Figure 8, 10], the next phase of data collection is selective coding [Figure 16] within the context of the BTB case study [Appendix E, F]. The core category derived is collaborative innovation and strategic transformation for sustainable retail solutions, emphasising how IDEO leveraged diverse perspectives and expertise to drive systemic change for environmental issues, rather than isolated efforts.
Figure 17 illustrates the interconnections between IDEO’s deep dive process and their non-AI driven methodologies mapped via key stakeholders involved, discovered through researching the project background, narratively highlighting unique variables and nodes
Figure 16: Selective Coding - Beyond the Bag Initiative
Figure 18 depicts that there are always opportunities and gaps for inefficiencies, for instance in understanding users and rapid prototyping phases, two key phases of IDEO’s deep dive process – manual data collection and
Figure 17: Systemigram based on Beyond the Bag Case Study and Core Category
need for customer feedback to be constant rather than designated to one phase could address the issues regarding mental models [Figure 19]
Figure 18: Most Significant Negative Feedback Loops in Beyond the Bag Project
Chapter 5: Findings
This section offers insights into how challenges in IDEO’s projects for FEMSA’s and Beyond the Bag could be mitigated to optimise value creation in future projects and whether there is potential reverse salience. In this dissertation, research focuses predominantly on implementation of AI-driven processes compared to the absence ofAI within IDEO’s deep dive methods. The varied contextual focus of the studies, one rooted in retail efficiency and the other in sustainability, impacted the core categories generated. The comparative analysis of the two case studiesbarescritical insightsintotheimpactthatAIhasonincrementalinnovationstrategies,thetangiblebenefits, limitations, and best practices to understand howAI processes can be optimised for value creation.
5.1 ComparativeAnalysis
Through the evaluation of IDEO’s FEMSA and BTB projects, there will be an exploration of how different contexts influence processes of incremental innovation and value created for stakeholders involved through commonalities in creative processes and challenges faced, highlighting differences betweenAI-enhanced methods and traditional approaches, in accordance with IDEO’s deep dive methodology.
Due to differing contexts of both case studies, outcomes differ significantly. For FEMSA, the focus on retail and integrated digital ecosystems within a highly competitive market and overall landscape, led to a project centred within technologically based solutions for the conception of Spin Premia with exclusive product offerings and loyalty programs for customer retention. Primary value created was within an increase in sales and operational efficiency of OXXO stores, resulting in provision of maintaining a competitive edge in the Mexican market and possibility for Spin by OXXO to purchase a competitor this year (Madry, 2024). In contrast, BTB addressed systemic environmental challenges necessitating a diverse range of unique and sustainable innovative solutions. This second project arguably holds a greater level of importance for IDEO as it aligns with the proliferating need for larger chains to align with global environmental values and UN Sustainable Development Goals. It demonstrates IDEO’s commitment to sustainability, not only bettering the consultancy’s reputation, but also the way IDEO perceives itself as a ‘promotor’of green thinking (Hannemann, 2019).
Primary similarities between the two projects lie in their heavy stakeholder engagement, to ensure practical solutions that strategically align with intended objectives (Stickdorn and Jakob, 2011). For FEMSA’s project, AI
Figure 19: Iceberg Model - BTB
tools were leveraged to streamline communication anddata sharingamongst globalstakeholders including OXXO Store managers, Spin by OXXO, and Digital@FEMSA. Likewise, for BTB, IDEO had to interact with many innovators collectively to foster co-created value, often with face-to-face interactions for a slower, but more engaged, creative process.
Another overarching similarity is the HCD approach and focus on UX within the deep dive process. However, there are differences in user research to prioritise customer end needs to create a ‘desirable, feasible and salable’ service (Shane, 2008). FEMSA engaged in-person with customers, but utilised AI tools to analyse its data, resulting in the acceleration of lengthy processes through automation – overall there was a fusion of human and AI processes. Conversely, for BTB, IDEO followed traditional deep dive processes for the entirety of the project and fostered HCD withoutAI, particularly through open innovation for co-creating environmental value, resulting in prolonged project progression.
Bothprojectsiterativelydeveloped,refined ideas,andprototyped throughcustomerfeedback cycles.Ononehand, FEMSA facilitated AI-driven rapid iteration by quickly analysing data and leveraging predictive modelling techniques (though in its early stages), being less resource-intensive due to reduced manual efforts though with necessities for more investments in computational infrastructure. Whereas BTB gathered feedback manually for physical prototype creation, leading to slower but more thorough and intuitive development cycles, requiring significant human time and effort for IDEO consultants, Closed Loop Partners, and Consortium Partners.
Through their respective similarities and differences, they face the common challenge of managing balancing and reinforcing feedback loops depicted in [Figure 9, 12, 17]. FEMSA faced known challenges associated with incipientAIimplementation –misinterpretationoralgorithmicbiasesindatacollection [Figure13] causingfurther concerns in ethicalAI adoption. Integration and complexity ofAI requires technical expertise causing a skills gap and reluctance from senior management to insert adaptations into decades-old deep dive processes (Kolbjørnsrud, Amico and Thomas, 2017). By comparison, BTB’s most significant negative feedback loop is delays in manually prototyping and gathering data.
Resource constraints pose as another common challenge, stemming from FEMSA’s need for significant computational resources for AI and BTB’s labour-intensive manual processes. Additionally, IDEO’s global presencepotentiallycausesdifficultiesinstakeholdercoordinationasFEMSAneeded toalignvariousdepartments for future goals for the digital ecosystem, and BTB faced communication bottlenecks among geographically dispersed stakeholders.
Overall, comparison of core categories leads to the conclusion that FEMSA was supported by AI-driven innovation leveraged for competitive advantage. Alternatively, BTB’s core category emphasised human collaboration and best practices for sustainable solution-making without relying on advanced AI-technology and software plug-ins to supplement traditional deep dive processes.
5.2 Synthesis of Findings
It is revealed that AI influenced incremental innovation strategies and deep dive processes at IDEO, particularly at FEMSA.AI tools facilitated rapid data analysis to accelerate iterative development processes such as feedback collection and prototyping. FEMSA’s project focused on holistic digital transformation for competitive advantage, by leveragingAI to enhance operational efficiency and data-driven decision-making. This contrasted with BTB, where the absence of AI led to a slower creative process, though with deep stakeholder engagement, particularly with consumer communities themselves. It emphasised collaborative open innovation and strategic transformation for sustainable retail solutions, relying on HCD approaches.
Through the determination of their challenges and negative feedback loops; resource constraints, algorithmic bias, and delays in manual prototyping, the emergence of reverse salience is uncovered – practical recommendations are stated [5.2] to mitigate this and optimiseAI implementation. In the FEMSAproject, inefficiencies manifest in the reinforcing loop for ethical adoption [Figure 9], as well as customer data bias leading to a ‘shifting effect loop’ (Galanakis, 2006) of solutions that fail to address user pain points impacting customer trust. For BTB, delays in manual prototyping have a butterfly effect on R&D processes, limiting the consultancy’s ability to adapt to new market trends, and potentially lose first-movers advantage
A significant personal insight into AI in projects is that once invested in, foundations for advanced technology have formed, usable for multiple projects in the future. Once a strong basis for processes in relation to AI are established, it enhances organisational capabilities, further informing company growth and profitability by increasing value delivery.
Chapter 6: Discussion
6.1. Interpretation of Findings
The comparative case study analysis responded to the research question via exploration of IDEO’s deep dive processes in relation to AI. Value was generated through AI usage for data analysis in user research, leading to operational efficiency and competitive advantage through increased customer loyalty and increase in OXXO stores. However, the challenge of algorithmic bias highlights the need to address concerns regarding ethical adoption of AI. With the composite technique of combining AI with human creativity and direct customer engagement in Mexico, IDEO leveraged technological advancements in FEMSA’s digital ecosystem to drive the key metric for value, competitive success.
In the BTB project, IDEO did not useAI for its creative processes but instead relied on open innovation and HCD. This led to value creation through community and multistakeholder co-creation towards a shared goal, driving sustainable solutions. However, the slower manual iteration due to traditional brainstorming and prototyping methods limited creative output, highlighting that technological innovation is crucial to optimising creative processes.Therefore, to mitigate these challenges, IDEO needs to incrementally integrate human-AI collaboration within the AI-automated processes. These findings illustrate how the new theory derived through the amalgamation of comparative case study analysis and GTM is the most significant derivation throughout this dissertation.
6.2. Practical Recommendations for IDEO
To address the challenges in these case studies, practical recommendations for IDEO, and other consultancies with similar project contexts, should be provided to optimiseAI implementation and enhance value creation.
Firstly, to achieve the first core category, holistic digital transformation and gain competitive advantage, IDEO shouldfocusonconstantiterationsofAI-driven customerinsights.Byimplementingreal-timecustomerbehaviour metrics incrementally, IDEO can help develop more personalised and targeted development strategies for its clients. This facilitates more tailored offerings in collaboration with Digital@FEMSA, resonating with customers of the Mexican stores (and extended chains) through actionable insights. This serves as a preventative strategy for AI-driven algorithmic bias skewing customer data analysis, and it assures that proposed innovative solutions address true user pain points.
Strategic implementation of AI’s ability to predictively model can be leveraged to anticipate customer demands and provide product recommendations. It can also be used for predictive maintenance for accurate forecasting of equipment failures, supply chain management, resource allocation, inventory management, and pricing data. These come under the umbrella of ‘Decision Intelligence’that provides a ‘commercial application of AI to drive profit and growth’ (Taylor, 2019). This brings value to the retail store chains through the limitation of fallout, elevation of ROI, and empowerment within work culture (Deloitte, 2023b). These strategies for AI implementation not only further FEMSA’s initial goal for value creation, the increase of customer engagement and retention, but also drives up sales and reputation of OXXO’s brand reputation to remain leaders in market, speaking to FEMSA’s core category.
For the second core category, collaborative innovation and strategic transformationfor sustainable retail solutions, IDEO can integrateAI (as a systems intervention) to address its key challenges surrounding traditional processes within the deep dive method. AI is not only valuable for digital processes, but can support physical prototyping (e.g., for single-use plastic bags), by producing digital twins that are virtual models to simulate how physical properties act under various conditions – if this is carried out prior to physical prototypes, significant time and
cost is reduced (Neumann, 2023). These models can also be visualised via virtual reality (VR) or augmented reality (AR) to market solutions to retailers, potentially increasing investments in the solutions.
AI can also automate meeting summaries, sentiment analysis, and language translation (NLP) in real-time for global stakeholders, to enhance overseas collaboration and ideation (e.g., Otter.ai). For instance, as OXXO Stores are located predominantly in Mexico, communication channels need to be clear between multi-lingual stakeholders of IDEO and FEMSA. Specifically in the context of BTB’s project, there are potential bottlenecks among geographically dispersed innovators and retailers, which would not be an obstacle if this recommendation were integrated.
AI implementation in the context of project such as BTB can also be in the innovators’solutions themselves. For example, for SmartC’s rewards system, AI can be used for efficient personalisation of incentives, to manage tracking and lifecycle of bags and tags, provision of chatbot experiences to support customer experience (Adam, Wessel and Benlian, 2020), and data-driven insights for clients into peak shopping times to inform marketing strategies according to customer engagement. As SmartC already proposed a tech-based solution, similar to Joulebug, the integration obstacles for AI implementation are relatively low, though it necessitates further investment and technical expertise, provided by IDEO. Recommendations of AI usage fill gaps that would otherwise remain unsolved, demonstrating a potential to optimise AI implementation at any point in a project lifecycle.
The key factor that must be retained throughoutAI being a system intervention within old and new projects is the need for diversity in human-AI collaboration, otherwise known as human-machine teams (Fan et al., 2022). It is also important to acknowledgeAcemoglu and Restrepo’s (2019, p. 210) assertion that ‘[e]xcessive automation not only creates direct inefficiencies, but may also hold productivity growth down by wastefully using resources and displacing labo[u]r’. This is particularly important to note in the context of design consultancies as balancing human creativity with automation retains an innovative edge whilst ensuring radical and heavy technological advancements, such asAI copilot plug-ins and agents (e.g.,Adobe Sensei) are complimenting human talent rather than stifling innovation.
These projects highlight the importance of context-specific approaches to produce successful innovation, demonstrating that both AI-enhanced and flexible traditional methods can be optimised synergistically to create significant value when strategic goals are aligned. Despite their differing contexts, both projects underscore the transformative value that implementing advanced AI technologies and tools can bring to consultancies. To optimiseAI implementation, there should be a synergy between strategies used for both case studies.
6.3. Existing Literature and Theoretical Implications
Throughout the research, there were constant recapitulations of aforementioned concepts in the literature review [Chapter 2]. The first being Hughes’ (1987) concept of reverse salience, where intended solutions may pose challenges, evident in FEMSA’s project. Despite AI’s potential, algorithmic bias in user research poses ethical concerns, evident in Figure 9 and 12. It also reinforces the literature on technological innovation driving competitive success (Schumpeter, 1983).AI’s role in increasing customer loyalty, and sales at OXXO, exemplify how technological advancements can bring competitive value.
Gartner’s (2024) insights into howAI can derive maximum value through compositeAI techniques as well as the emphasis on open innovation (Gartner, 2016), are key points furthered within the new theory derived within this research [5.3]. The new theory also furthers insights by (Fan et al., 2022) and Brynjolfsson and McAfee (2014) of the emphasis on human ingenuity and machine processing power’s collaboration. Generally, the theory within this research builds upon existing arguments that technological advancements must be supplemented with human power, otherwise it may potentially become a detriment to the system as a whole.
Some core principles that link systems thinking and practical applications, according to Jackson (2003), that were integrated within the thought process of this research were holism, interdependence within parts, structure determining behaviour, and learning and adaptation [2.4]. The exploration of IDEO’s projects concurred with this lens – integration ofAI into a holistic system can drive organisational and structural changes of processes between interdependent parts. Consequently, application of systems thinking principles served as a highly fruitful means for exploratory applied research such as this.
6.4. New Theory Derived Via Grounded Theory and Comparative Case StudyAnalysis
The last phase following the application of a comparative case study to the process of GTM is the emergence of new theory and concepts ‘grounded in data from the field’ (Khan, 2014). The core categories merge into a new theory statement:
‘Human-AI Collaboration for Incremental Value Creation’;
This theory proposes that the integration of AI-driven incremental innovation, complemented by continuous human learningandintermediation,formsarobustframeworkthatenhancesvaluecreatedindesignconsultancies, such as competitive advantage or sustainable solution-making. Considering that AI models have not reached maturity, the synergy between human technical expertise and transformativeAI capabilities is vital for optimising outcomes.This approach leverages the strengths ofAI in processing large datasets, ability to identify patterns, and predictive analytics, with humans’unique creative expression and contextual understanding.
This notion suggest that human-AI collaborative teams integrated in incremental innovation strategies create a dynamic and adaptive framework to create value, whether that is through co-creation via open innovation, or between managerial stakeholders.AI implementation necessitates human intervention and proofing to ensure that insights generated based on UXD and HCD are contextually relevant.
‘Adaptive learning’ (Senge, 1997) and iterative feedback loops are imperative to achieve successful levels of incremental innovation for actionable and manageable changes, ensuring that AI solutions are adaptable and scalable. Adaptive processes entail ‘real-time data rather than a predetermined series of steps’ (Daugherty and Wilson, 2018). This means that AI engines require regular updates and comprehensive training provision to data models, or DL to ensure better data quality and quantity, mitigate chances of bias skewing results, and avoiding hallucinations, all integrated with the various types of AI For humans, this means intensive training and change management to adhere to the latestAI advancements and best practices. This flexibility is essential for long-term value and consultancy capability creation.
This theory builds upon existing concepts regarding the shift in more human-AI roles but introduces a new paradigm through the emphasisof the interdependence between humans’technical expertiseandAI in incremental innovation strategies.
Practical implications of this theory on businesses entail higher profitability, improved market positioning, and long-term sustainable solution-making with customer feedback integrated throughout development and postlaunch of products and services. The theory also has practical implications to the broader AI ecosystem by highlighting the significance of human-AI synergy promoting the iterative development of mature and reliableAI technology. Design consultancies can adopt the perspective of this theory to offer more comprehensive and efficient solutions to their clients.
The time horizon itself poses limitations – best practices for GTM include conduction over a longer period or till theoretical saturation to engage with large quantities of literature and sources, both of which require significant time commitment. However, during the three-month period, research tasks were spread out, consisting of general literature review; data collection for grounded theory; creation, interpretation and analysis of systems maps within context of case studies and in-practice; and new theory. Due to the time invested spread thinthrough various tasks, time constraints were relatively tight.
Chapter 7: Conclusion
7.1. Practical Implications
As the focus of the AI-driven innovation is framed around incremental strategies, it is important to note that multiple sources commenting on in-practice implementation state that a thoughtful methodological adoption, as opposed to rushed full-scale integration, will result in higher success metrics and bring more value to businesses, clients, and consultants. This serves as a preventative strategy to avoid consultant pain points such as burnout and irrevocable reputational damage (Macaluso, 2023). Once again, the idea that there needs to be balance between
incremental and radical shifts in technology’s implementation in design arises [2.2], otherwise risking falling into the ‘innovation trap’(Tekic and Füller, 2023, p. 8).
By combining human creativity with AI’s transformative power, consultancies can operate smoothly and with greater ease. Essentially, the risky ‘set-it-and-forget-it’ mentality, must be avoided. This includes human-AI collaboration for user research, virtually prototyping, automating processes and communication channels between global stakeholders, predictive modelling and analysis, and personalisation of products, services, and experiences [detailed in 5.2, 5.3]. This will foster more effective customer co-creation processes, furthering the capabilities of IDEO evidenced in the BTB project. Once significant investments have been made in improving and optimising AI-driven processes and human-AI teams and procedures, consultancies will be on the path to build an internal framework for best practices, increasing and sustaining organisational capability.
7.2. Challenges and Limitations
Despite the valuable exploration within this dissertation, the research has several limitations that must be acknowledged to provide background for reliability of the findings.
Finding IDEO’s case studies that precisely fit the criteria for this research provided challenging. The criteria, or control variables, set for the comparative case study projects were specifically outlined [4.2], hence both projects being in retail, following deep dive methods, and initiated in the last five years. The differentiation between one project using AI, and one without, still delivered valuable insights into the impact of AI on value creation within the retail sector.
FEMSA and BTB do not have detailed case studies and there are many gaps in information and internal insights of inner workings, aside from IDEO’s Work page. Moreover, this dissertation employs a purely desk-based approach to the case study – no primary empirical evidence was collected through the form of interviews or focus groups consisting of consultants from IDEO with specialism in AI and deep dive processes. This was due to the difficulty finding a large and representative sample of participants that fit the very specific criteria (Thomas, 2017, p. 47). Therefore, some conclusions drawn regarding project-specific processes risk beingmerely speculative and generalised.
7.3. Future Research Direction
As analytical memo writing provides an outlet for immediate idea notation and are ‘designed to tap the initial freshness of analyst’s theoretical notions’ (Glaser and Strauss, 1999, p. 107), they serve as an insight into ideas for potential theories and patterns, questions arising, and links to research focus. The memos regarding axial coding predict relatively accurately the final theory, stating it may ‘explore how AI can facilitate better collaboration between cross-functional departments and human-machine teams’[Figure 10].
The memos also express potential future pathways for research and some answered questions.
How can design consultancies structure its human-AI collaboration models to optimise value creation in?
How can AI be leveraged to identify and connect with global innovators for open innovation to create social and environmental value?
To further this dissertation research in particular, the following steps would be to explore case studies across differing industries to generalise findings for practical application, or to help identify industry-specific challenges and opportunities (other than retail). Future studies may also weave into ideas of AI for co-creation or change management, considered holistically.
7.4. Final Remarks
This research underscores the critical role of human-AI collaboration in optimising incremental innovation strategies within design consultancies to enhance value creation. By integrating AI purposefully and ethically, consultancies like IDEO can avoid reverse salience and drivecompetitive success and foster sustainable solutions.
The insights gained from the FEMSAand BTB case studies highlight potential challenges ofAI within traditional creative processes such as the deep dive method, paving the way for future research and practical applications that balance technological advancements with unique human creativity.
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Appendices
AppendixA
IterationA: Open Coding Sources Regarding: Retail Sector and Customer Experience Open
▪ Ethics and regulation regardingAI ▪ Perceived safety and customer friendliness
Data Utilisation and Personalisation
Customer Experience Enhancement
▪ Dynamic personalised experience
▪ AI crafting personalised customer experiences
▪ AI elevating customer engagement and business growth
▪ AI-powered conversational shopping assistants
▪ AI-driven productivity enhancement and cost saving
▪ Capturing and analysing personalised customer data
▪ Customising and optimising customer journey withAI
▪ Dynamic personalised experience
▪ AI crafting personalised customer experiences
▪ AI elevating customer engagement and business growth
JA1: ‘A successful adoption of artificial intelligence in retail by enhancing customer experience with the help of artificial intelligence must be based on ethical principles but also on the degree to which clients consider the used technology is safe and also customer friendly’(Tiutiu and Dabija, 2023).
BA1: ‘Artificial intelligence (AI) enables retail businesses to go beyond their limitations and craft a dynamic, personalized experience that elevates customer engagement and business growth’(Joshi, 2024).
BA2: ‘Consumers want a personalized experience and it's estimated retailers using AI-powered conversational shopping assistants to drive a personalized shopping experience could boost revenue by 5% to 10%, according to Bain & Company research that also revealed Gen AI-powered productivity enhancement and cost savings could boost retailers' already thin margins’(Mottl, 2024).
JA2: ‘We are now at the point where competitive advantage will derive from the ability to capture, analyze, and utilize personalized customer data at scale and from the use of AI to understand, shape, customize, and optimize the customer journey’ (Edelman andAbraham, 2022).
BA1: ‘Artificial intelligence (AI) enables retail businesses to go beyond their limitations and craft a dynamic, personalized experience that elevates customer engagement and business growth’(Joshi, 2024).
NA1: ‘AI is reducing that friction by giving “consumers the information they need to make better, faster decisions,” it adds. “When technology is integrated to improve
AI Implementation Challenges and Gaps
▪ AI providing information for better, faster decision
▪ Integration of AI to improve customer experience
▪ GenerativeAI enhancing customer engagement
▪ AI’s role in the shopper journey online and in-store
▪ Confidence and gaps inAI implementation
▪ Gaps and blind spots inAI implementation
▪ Sense-making in design, problem identification, algorithmic problem-solving
their experiences not added as an afterthought brands and retailers can finally deliver the convenience and flexibility consumers have come to expect,” according to the report’(Langford, 2024).
NA2: ‘AI and machine learning are not ‘new’ for retailers - but the acceleration of generative AI is unlocking potential to drive customer engagement in the shopper journey online and in-store’(Baker, 2023).
RA2: ‘In January 2024, HPE commissioned a cross-industry survey covering 14 global markets, with over 2,400 IT leaders participating. The results show that respondents have broad confidence in their company’s AI approach and progress to date. However, a closer look uncovers both worrying gaps – and event complete blind spots – in implementation and insight that could seriously impact future business success’(HPE GreenLake, 2024).
JA3: ‘as creative problem-solving is significantly conducted by algorithms, human design increasingly becomes an activity of sensemaking, that is, understanding which problems should or could be addressed’ (Verganti, Vendraminelli and Iansiti, 2020).
Operational Efficiency and Productivity
▪ AI-powered conversational shopping assistants
▪ AI-driven productivity enhancement and cost saving
▪ AI Streamlining Operations
▪ AI optimizing processes and reducing costs
▪ Improving operational efficiency and enhancing customer experience
▪ AI streamlining operations and decisionmaking
▪ Immediate efficiency gains fromAI
Appendix B
Iteration
B:Axial Coding
BA2:‘Consumers want a personalized experience and it's estimated retailers using AI-powered conversational shopping assistants to drive a personalized shopping experience could boost revenue by 5% to 10%, according to Bain & Company research that also revealed Gen AI-powered productivity enhancement and cost savings could boost retailers' already thin margins’(Mottl, 2024).
BA3: ‘Artificial intelligence has proven to be a valuable tool to help retailers build more profitable operations. Using AI technology and data-driven analytics, retailers can optimize processes, reduce costs, improve operational efficiency, and enhance customer experience’(Medwin, 2023).
RA1: ‘GenAI can help streamline operations, allowing leaders to make faster, betterinformed decisions across retailers’internal value chains. The technology also offers immediate, no-regret efficiency gains, as well as applications that could redefine decision making in retail’(Sukharevsky et al., 2024).
Sources Regarding: IDEO, AI in Consulting, Incremental Innovation Categories
Collaboration and Organisational Structure
Focus andArgument
Collaborative helping for success:
▪ Collaboration from colleagues supporting each other
Flat organisation structure:
▪ IDEO’s success with a flat organisational structure (minimal management levels and dynamic project teams formed and disbanded based on project life cycles)
Flat Organisation Structure:
▪ IDEO’s success with a flat organisational structure (minimal management levels and dynamic project teams formed and disbanded based on project life cycles)
GenerativeAI as a co-worker:
▪ GenerativeAI as a co-worker enhances creative processes and forms constructive relationships with technology
Augmented Intelligence:
▪ Scope for extending and enhancing human capabilities through technology
▪ Shifting from designing artefacts to designing relationships with intelligent systems
▪ Operational Efficiency and Productivity
▪ AI Implementation Challenges and Gaps
JB1: ‘In the highest-performing companies, it is a norm that colleagues support one another’s efforts to do the best work they can. That has always been true for efficiency reasons, but collaborative helping becomes even more vital in an era of knowledge work, when positive business outcomes depend on high creativity in often very complex projects’(Amabile, Fisher and Pillemer, 2014).
JB3: ‘IDEO is an extremely successful example about company to adopt flat organization. Throughout IDEO company, it just has two levels of management and all of their projects are complete by the short time project team. There is no stable project team in IDEO, all the team are formed by different members and disbanded by the life of the project’(Du, 2021).
BB3: ‘For anyone whose day job involves creating things proposals or presentations that require factfinding, writing, imagery, and video it’s changing how we make stuff. Love it or fear it, generativeAI may be our new co-worker.’
‘Interaction Designer Takashi Wickes is using generativeAI to further streamline the synthesis process a method of organizing and interpreting research data in order to identify patterns, themes, and insights that can be used to inform the design of a product or service or experience.’
‘As we would do with any new colleague, we hope to learn how to have constructive relationships with these technologies, understand them better, and collaborate. This phase of theAI revolution feels personal’ (Kunovsky and DeRuntz, 2023).
BB11: ‘“Ultimately, we’re using the term ‘augmented intelligence’to really focus on the fact that we’re extending and enhancing people’s capabilities through technology, as opposed to thinking of technology as a separate thing, or replacing people’s capabilities with technology,” Stringer says.’
‘“We have spent the past hundred, or thousand, years designing artifacts,” Ideo CEO Tim Brown says. “The
Strategic transformation and futuring
Strategic Transformation:
▪ Organisational transformation requires comprehensive planning and integration with the external environment, driven by significant pressures demanding innovation in enterprise strategy
Need for Futuring in Strategy:
▪ Futuring inspires bold visions and strategies for impact change
Problem-Framing in Creativity:
▪ Importance of framing and examining problems to ensure high-quality creative outputs (a problem-focused approach)
▪ Ethical AI Adoption
▪ Operational Efficiency and Productivity
▪ AI Implementation Challenges and Gaps
things we designed were relatively dumb and all of the intelligence in the relationship between us and the artifact came from the human being.Algorithms and technology are taking on their own intelligence. That’s a fundamentally new design problem. We’re designing relationships now as opposed to designing artifacts’ (Budds, 2017).
JB2: ‘The urgent need of transforming organization usually begins when the ecosystem surrounding the enterprise creates significant pressure that demands innovation in enterprise strategy. This transformation requires more than a brainstorming exercise, it requires a thoughtful planning process that integrates with a non-controllable environment’(Lee et al., 2021).
BB2: ‘In design, we are not predicting the future, but rather illuminating a set of possible futures that can inspire a bold vision and action.At IDEO.org, we use the tools of futuring to help our partner organizations conjure ambitious impact strategies, build effective coalitions for systems change, and confront an uncertain future with confidence’(Carey, 2024).
BB8: ‘Almost any creative endeavo[u]r can produce an output–a digital experience, a new brand, a physical product, a reimagined service–but the quality of that output is wholly dependent on how we frame and examine the problem it’s meant to solve’(Higham, 2024).
Incremental Innovation Tying into Competitive Advantage
Innovation impact, speed and culture:
▪ Innovation speed and supportive culture is a mediator in the relationship between innovations and competitive advantage
Incremental innovation’s iterative process:
▪ The goal of incremental innovation is to enhance functionality and operational efficiency
▪ Data Utilisation and Personalisation
▪ Customer Experience Enhancement
▪ AI Implementation Challenges and Gaps
Increased humanmachine teams, while acknowledging risks
GenerativeAI as a co-worker:
▪ GenerativeAI as a co-worker enhances creative processes and forms constructive relationships with technology
High-risk, high reward of AI on human lives:
▪ Endless opportunities and high stakes of AI on human lives
Unknown human purpose inAI era:
▪ Questioning the role and purpose of humans as intelligent machines become more integrated into work and life
Gen Z’s need for creativeAI:
▪ Highlights Gen Z’s desire forAI tools that support creativity, lower entry barriers, and connect communities
AI Enhancing Human Ingenuity:
▪ AI can’t replace human ingenuity, it significantly enhances innovation speed and depth
▪ AI tools fuel human creativity and innovation, enabling faster and deeper problem-solving by synthesising diverse perspectives and unlocking value from massive datasets
AI’s research and iteration:
▪ Ethical AI Adoption
▪ Operational Efficiency and Productivity
▪ AI Implementation Challenges and Gaps
JB4: ‘The empirical results show that incremental and radical innovations have a significant positive effect on competitive advantage. Radical innovation has a greater impact on competitive advantage compared to incremental innovation. Innovation speed mediates the relationship between incremental and radical innovations and competitive advantage. Supportive culture positively moderates the relationship between incremental and radical innovations and innovation speed’(Chen, Xie and Zhou, 2024).
BB18: ‘Incremental Innovation builds upon the foundations already in place, leveraging established knowledge and infrastructure. One of its defining features is a responsive engagement with customer feedback, ensuring that the incremental adjustments directly address unmet customer needs and preferences. The main goals of Incremental Innovation include enhancing functionality, increasing operational efficiency, and elevating the overall quality of products or services. This iterative process reflects a company’s dedication to continuous improvement and adaptability amidst changing market dynamics’(Digital Leadership, 2022).
BB3: ‘For anyone whose day job involves creating things proposals or presentations that require factfinding, writing, imagery, and video it’s changing how we make stuff. Love it or fear it, generativeAI may be our new co-worker.’
‘Interaction Designer Takashi Wickes is using generativeAI to further streamline the synthesis process a method of organizing and interpreting research data in order to identify patterns, themes, and insights that can be used to inform the design of a product or service or experience.’
‘As we would do with any new colleague, we hope to learn how to have constructive relationships with these technologies, understand them better, and collaborate. This phase of theAI revolution feels personal’ (Kunovsky and DeRuntz, 2023).
BB4: ‘When it comes toAI, the opportunities are endless, but the stakes are high our work can have serious implications that affect the lives of real people at unprecedented scale. Designers at IDEO are constantly working on cutting-edge experiments that explore the possibilities and potential pitfalls of a future where humans and machines are ever more entangled’(Perry, 2019).
BB6: ‘Armies of robots already populate the factory floor and digital lawyers write our contracts.As intelligent machines become a fixture in every aspect of our work and lives, they inevitably start challenging our contribution to society, begging the question, do humans still matter? What is our purpose?’ (Malmgren and Martinec, 2018).
AI Tools Enhancing Productivity
▪ AI tools enhance research through efficient reference sourcing and speed up iterative design processes
▪ Human evaluation remains crucial to addressAI’s biases and limitations
Merging Design Thinking with GenerativeAI for Organisational Development
NewAI Tools:
▪ Functions of GenerativeAdversarial Networks (GANs) in creating synthetic images through competing neural networks
AI in risk modelling:
▪ AI and ML can create sophisticated risk models, leading to personalised risk assessments and more relevant products for customers
AI tools to enhance productivity:
▪ AI tools streamline mundane tasks so that consultants can focus on valuable
▪ Operational Efficiency and Productivity
▪ AI Implementation Challenges and Gaps
BB7: ‘When we first started exploring the possibilities of creativeAI products with a group of Gen Zs, they were clear about what they want: tools that scaffold the creative process, lower the barriers to entry, and connect creative communities’(Callahan, Wang and Wickes, 2024).
BB9: ‘It quickly became clear that while GenAI tools can’t replace human ingenuity, they certainly fuel it. When it came to generating solutions to a problem this complex,AI had very little to offer in comparison to human teammates. But it did help us innovate much faster and with more depth, bringing us up to speed on initial stakeholder context and synthesizing and incorporating datasets crucial parts of the process that can take a lot of time.’
‘“The AI enables efficient pattern matching across massive datasets, unlocking value from information that was previously siloed or inaccessible,” explains Jaime Goff, Product Design Lead at Healthworx Studio. “By leveraging these capabilities, we can incorporate a breadth of knowledge beyond what our team could internalize before. It really expands what we can achieve by connecting us to data in new ways.”’
RB1: ‘Human evaluation of the AI output remains crucial due toAI's potential for bias and limitations in holding context’(Healthworx and IDEO, 2023).
BB5: ‘A GAN (GenerativeAdversarial Network) is a type ofAI that pits two neural networks against each other, each one training the other to improve its ability. Unlike other types of AI, GANs don’t just recognize objects they can create them. GANs borrow a feature from biology and pit two competing neural networks against each other. One network, the “generator,” produces synthetic images, while another network, the “discriminator,” tries to guess whether the images are synthetic or real’(Williams, 2018).
BB10: ‘if we take the job of understanding risk, there’s a story to be told about machine learning andAI, and how we’ll be able to create more sophisticated risk models. More sophisticated risk models could lead to a more personalized approach to assessing risk and ultimately the ability to provide more relevant products to customers’(Quinn, 2019).
BB16: ‘Integrating AI tools into consulting practices offers several significant advantages. Firstly, these tools streamline mundane tasks, allowing consultants to focus more on critical thinking and strategy formulation. By automating data analysis and report generation, consultants can save valuable time, boosting their overall productivity’(Insight7, 2024).
Design thinking for complex organisational development:
▪ Design thinking transforms organisational development by integrating human desirability, technological feasibility, and economic viability
Product developments and methodologies:
▪ AI’s potential in addressing complex challenges
GenerativeAI and novel designs:
▪ GenerativeAI can explore and invent novel designs which is extensive added value to the consultancy field
Sociocracy models:
▪ Recommends adopting agile methodologies like Scrum or Kanban for flexible development processes and continuous improvement principles
Human-centredAI assistants:
▪ Success ofAI assistants intuitive, social, trusted, multimodal, and nurturing.
▪ Products require: intuitive entry points, social goals, safety and trust, multimodal interactions, nurturing
▪ Ethical AI Adoption
▪ AI Implementation Challenges and Gaps
▪ Operational Efficiency and Productivity
BB1: ‘Thinking like a designer can transform the way organizations develop products, services, processes, and strategy. This approach, which is known as design thinking, brings together what is desirable from a human point of view with what is technologically feasible and economically viable’(Tim Brown, 2024).
BB13: ‘To succeed, we believe these products will need to be five things intuitive, social, trusted, multimodal, and nurturing’(Kunovsky and Kochoska, 2024).
BB14: ‘GenerativeAI can explore many possible designs of an object to find the right or most suitable match. It not only augments and accelerates design in many fields, it also has the potential to “invent” novel designs or objects that humans may have missed otherwise’(Wiles, 2023).
BB17: ‘Adopt agile methodologies, such as Scrum or Kanban, to facilitate iterative and flexible development processes. These methodologies enable rapid prototyping, quick feedback loops, and continuous improvement cycles’(Jain, 2023).
Appendix C
Iteration C: Case StudyApplication
Sources Regarding: IDEO Case Study FEMSA’s Digital Ecosystem
Axial Categories Final Categorisation
Source Excerpt(s) Strategic Transformation and Futuring; Increased Human-Machine Teams, while Acknowledging Risks
Collaboration and Organizational Structure; Merging Design Thinking with Generative AI for Organisational Development
Digital Transformation and Automation in OXXO Stores:
▪ Implementing digital and automated innovations to enhance customer and collaborator experiences
Data-Driven Digital Ecosystem Adding Customer Value in Interactions:
▪ Creating a digital ecosystem with IDEO to add convenience and value in both physical and digital interactions
▪ Building a digital ecosystem with data analytics to add customer value and maximise revenue, with significant growth in loyalty program users
AI Tools Enhancing Productivity
Incremental Innovation Tying into Competitive Advantage
AI-Driven Optimisation for Operations and Logistics:
▪ UsingAI to optimise inventory, enhance customer experiences, and refine logistics
Scaling and Expansion Potential of OXXO:
▪ OXXO’s growth potential in Mexico and Brazil, aiming for over 30,000 units by the decade’s end
for OXXO Stores
NC1 : ‘This store concept is part of a digitalization and automation program of initiatives that will include innovations such as screens, digital banners, electronic price tags and four self-checkout boxes.’
BC1: ‘FEMSA creates a digital ecosystem to increase customer value at its network of OXXO stores.(...) Digital@FEMSApartnered with IDEO to build that ecosystem, which would not only add convenience, but more value for each peso customers spent in both in-person and digital interactions' (IDEO, 2022).
RC1:‘Building a financial and [digital] ecosystem based on data and analytics that delivers added value for customers while maximizing revenue management’ ‘FEMSA division continued to harness the power of data and technology for our customers and consumers in Mexico in 2023 through our powerful omnichannel digital ecosystem.The number of active users for Spin by OXXO reached 6.9 million as of yearend, and the active users of our Spin Premia loyalty program reached 19.3 million, with more than 31% of OXXO Mexico sales now associated with the program’(FEMSA, 2023).
BC2: ‘FEMSA Comercio, notably through OXXO, has embraced AI to optimize inventory management, enhance customer experiences, and refine supply chain logistics. AI algorithms analyze customer preferences and purchasing patterns to forecast demand accurately, thereby minimizing stockouts and reducing operational costs. Furthermore,AI-powered customer service interfaces streamline interactions, offering personalized recommendations and enhancing overall satisfaction’ (Cash Platform, 2024).
BC3: ‘As such FEMSA traded at a meaningful conglomerate discount, which expanded as group complexity increased, and investors grew frustrated at the nonsensical structure.’
‘Scale engenders considerable purchasing power and high margin commercial income payments from suppliers, with Oxxo being the way to reach the Mexican consumer. Despite Oxxo’s scale advantage, it remains small in the grand scheme of things, accounting for ~2% of the just over 1 million “mom n pop” convenience stores in Mexico. We believe they can grow to >30,000 units by the end of the decade, with potential for almost limitless growth in Brazil, where performance has reached a high level of consistency’(Asset Value Investors, 2024).
Appendix D Selective Coding
Complete Grounded Theory based on IDEO Case Study FEMSA’s Digital Ecosystem for OXXO Stores
Open Coding Axial Coding Case StudyApplication
Digital Transformation and Automation in OXXO Stores
Selective Coding Ethical AI Adoption; Operational Efficiency and Productivity;AI Implementation Challenges and Gaps Strategic Transformation and Futuring; Increased Human-Machine Teams, while Acknowledging Risks
Operational Efficiency and Productivity;AI Implementation Challenges and Gaps; EthicalAI Adoption
Operational Efficiency and Productivity;AI Implementation Challenges and Gaps
Data Utilisation and Personalisation
▪ Customer Experience
Enhancement
▪ AI Implementation Challenges and Gaps
Collaboration and Organizational Structure; Merging Design Thinking with GenerativeAI for Organisational Development
AI Tools Enhancing Productivity
Incremental Innovation Tying into Competitive Advantage
Data-Driven Digital Ecosystem Adding Customer Value in Interactions
AI-Driven Optimisation for Operations and Logistics
Scaling and Expansion Potential of OXXO
Holistic Digital Transformation for CompetitiveAdvantage
