AUGMENTED ADVICE Financial INTELLIGENCE, CRAFTED A competitive brand strategy for UBS in the age of augmented wealth advice Fiona van Iersel MSc Marketing — King's College London Module: Brand Management (7QQMO517) Date: Submission date not recorded Independent academic analysis. UBS has not endorsed this report. CENTRAL ARGUMENT UBS can own the premium white space between invisible automation and AI-led advice: technology broadens the field of vision; an accountable adviser turns it into judgement. There by enhancing the brand image as best in wealth management. ASSESSMENT FIT AT A GLANCE Requirement How the report answers it One chosen opportunity Make UBS’s responsible AI capability a visible client benefit. Well-known brand USD 14.1bn brand value; USD 7.005tn invested assets. Evidence base Academic journals, market surveys, industry reports and official company data. Brand-management focus Salience, identity, positioning, experience, distinctive assets and resonance. Competitive strategy A named standard, redesigned journey, proof architecture and measurement system. Executive argument Hypothesis As wealth managers compete to own the most distinctive brand position, the race increasingly centers around delivering the most advanced capabilities without compromising trust. UBS can differentiate by branding its existing AI capabilities as an extension of its trusted, personalised advisory model, transforming cutting-edge technology into a luxury brand asset. 1. Brand introduction UBS is a global financial-services brand headquartered in Zurich, operating across wealth management, personal and corporate banking, asset management and investment banking. Its strategic centre of gravity is Global Wealth Management, where the firm presents itself as the world’s only truly global wealth manager. At the end of 2025, UBS reported USD 7.005 trillion in total invested assets, of which USD 4.753 trillion was within Global Wealth Management, supported by 9,420 advisers (UBS Group AG, 2026). Brand Finance valued UBS at USD 14.1 billion in 2025, equal to 70% of the combined value of Swiss banking brands in its ranking (Brand Finance, 2025). The target audience comprises ultra-high-net-worth individuals, entrepreneurs, executives and families whose financial lives cross jurisdictions, generations and asset classes. Their purchase criteria extend beyond investment performance. EY found that brand and reputation, adviser relationships and digital tools all influence provider choice, even so 45% of wealth clients expect to move between a quarter and half of their assets within three years (EY, 2025). For UBS, relevance must therefore be one with reputation with a well branded value differentiator. UBS launched Branding campaign “Banking is our craft” in 2024 to unite the enlarged group after the Credit Suisse acquisition and strengthen awareness, familiarity and reputation across twelve markets (UBS, 2024). “Craft” carries useful associations of precision, dedication, heritage and tailored work. UBS can now build on this by making its responsible AI capability visible and meaningful as a client benefit, while retaining the judgement, discretion and personal accountability that embody premium advice. UBS is one of several market leaders; within the emerging category of visibly branded, AI-enabled advice, it has room to behave like a challenger. Figure 1. UBS has substantial AI and adviser capability. Source: Author visual using UBS Group AG (2026). 2. Brand audit THE STRATEGIC CASE UBS has built extensive AI capability. Its next brand advantage comes from making that capability easy to understand and visibly accountable to a human adviser and client. UBS already possesses the ingredients of an AI-era luxury wealth brand: global scale, trusted advisers, strong governance and hundreds of live AI applications. The opportunity is to turn that backstage capability into a client-facing promise. This report proposes UBS Augmented Advice, expressed through the line “Technology finds more. Your adviser and you decide what matters.” The strategy extends “Banking is our craft” by making responsible AI tangible across the advice journey, while preserving the human accountability that wealthy clients still demand. Figure 2. UBS capability has not yet become a distinctive client-facing AI brand asset. Source: Author-developed issue tree using the opportunity identified in Intelligence, Crafted with the help of AI and powerpoint to assess whether UBS can convert AI capability into sustainable brand equity, this paper develops a four-part diagnostic framework: visibility, relevance, accountability and repeatable proof. It synthesises brand salience and retrieval (Keller, 1993), service experience (Berry, 2000), journey-wide consistency (Lemon & Verhoef, 2016), and human-centred responsibility in AI-supported decisions (Shneiderman, 2020). 2.1 The market tension Wealth management has entered an awkwardly productive phase. Clients use AI, trust it selectively and still want a human being present when a decision can rearrange a family balance sheets bottom line. HSBC’s 2026 research across almost 10,000 affluent and wealthy investors found that 73% use AI for finance and investment, yet only 12% described AI as the most influential factor in their latest investment decision. Financial professionals and institutions remained the leading source of ideas for 62%, while half preferred a hybrid future combining AI and advisers (HSBC, 2026). Capgemini reports that global high-net-worth wealth reached USD 98.3 trillion in 2025, although only 17% of wealthy clients regard their advisory experience as seamless and personalised. 97% of firms still segment clients mainly by wealth band, a blunt instrument for people whose needs involve businesses, philanthropy, inheritance, tax, health and geopolitics (Capgemini Research Institute, 2026). This creates a timely brand opportunity around augmented intelligence: technology that improves the adviser’s tailored range, preparation and responsiveness without outsourcing judgement. By the end of 2025 UBS had 380 live AI use cases; AI-powered chatbots served 1.2 million clients; its in-house assistant Red and Microsoft 365 Copilot were deployed across the firm with employee AI training; more than 20 million AI-driven client opportunities had been distributed to US advisers (UBS Group AG, 2026). UBS also established the Oxford-UBS Centre for Applied AI and built a governance policy around human autonomy, harm prevention, fairness and transparency (UBS, 2026). The branding question is whether clients can retrieve meaning from it. Public concern about AI reflects worries about privacy, data collection, opaque decision-making and the loss of human control (Puntoni et al., 2021). In wealth management, these concerns are amplified because clients entrust advisers with highly personal financial, family and business information. By not addressing these concerns clients are likely to fill the gap with the wider public narrative that AI is intrusive, unaccountable and replacing human judgement. Shneiderman (2020) argues that trust in AI depends on keeping humans responsible for important decisions, rather than removing them from the process. Doctorow (2026) makes a similar point through his concept of the “centaur”: technology should strengthen human capability, the human head controls the powerful machine body. Rather than avoiding the debate, the brand should confront it directly by making privacy, transparency and human accountability visible parts of its value proposition. UBS can shift the conversation from fear of AI to confidence in responsible augmented advice by defining the boundaries of AI before others assume them. In branding as a Centient module, the industry’s biggest source of uncertainty becomes one of its strongest sources of competitive differentiation. Figure 3. Investors are adopting AI faster. Source: Author chart using HSBC (2026). 2.2 Identity, image and the relevance opportunity Figure 4. UBS Customer-Based Brand Equity Pyramid. Note. Author-created visual based on Keller’s (1993) Customer-Based Brand Equity model. The visual applies Keller’s stages of salience, meaning, response and resonance to UBS and was developed using generative AI with author direction. UBS brand elements and strategic interpretation are based on UBS Group AG corporate materials. Using Keller’s customer-based brand equity model (Keller, 1993) UBS performs strongly at the first stage. It’s three keys, red wordmark, Swiss heritage and global footprint make recognition easy. The emerging weakness is higher in the pyramid. Banking is our craft gives UBS a coherent emotional and functional frame, yet the public AI evidence appears across annual reporting, technology pages, product features and internal tools. Clients can find the pieces, though the brand supplies no memorable retrieval cue that explains how AI changes their relationship with UBS. CFigure 5. CBBE bridge from current UBS meaning to the proposed Augmented Advice position. Source: Author analysis based on Keller (1993, 1999), UBS public communications and the paper's brand audit. Note: This is a diagnostic interpretation, not a consumer survey. Kapferer’s Brand Identity Prism diagnosis (Kapferer, 2012). UBS’s physique is formal and unmistakably serious. Its personality is precise, composed and worldly. It’s culture draws on Swiss risk discipline, discretion and a craft ethos. It’s relationship promise is adviser-led and tailored. The reflection is a globally minded client with complex ambitions; the corresponding self-image is informed, protected and in control. AI fits this identity when it expands the adviser’s vision and improves personal relevance. A technology-first personality would jar with the culture and could make premium advice feel industrial. The opportunity is in alignment: presenting AI through the existing identity. Figure 6. Kapferer's Brand Identity Prism diagnosis of UBS and AI-enabled client journey. Source: Image created by the author with the assistance of ChatGPT (OpenAI) as a conceptual illustration. The intellectual content, analysis and interpretation are the author's own. The visual is based on Kapferer's Brand Identity Prism (Kapferer, 2012) and service branding literature (Berry, 2000; Lemon & Verhoef, 2016). KAPFERER PRISM: IDENTITY FIT PHYSIQUE Three keys, red wordmark, premium global platforms PERSONALITY Precise, composed, worldly CULTURE Swiss discipline, craft, responsibility RELATIONSHIP Adviser-led, tailored, accountable REFLECTION Globally minded client with complex ambitions SELF-IMAGE Informed, protected and in control Figure 7. Kapferer’s Brand Identity Prism applied to UBS. Source: Author analysis based on Kapferer (2012) and UBS public communications. For service brands, the company itself is the brand, and every interaction with the customer shapes brand equity (Berry, 2000). Similarly, Lemon and Verhoef (2016) argue that value is created across the entire customer journey. Therefore, UBS’s AI positioning must be consistently reflected in marketing, adviser preparation, client meetings, recommendations, follow-up and digital services. Otherwise, it risks becoming a short-lived campaign rather than a meaningful and defensible brand differentiator. Figure 8: Trusted augmentation: Formula One as an analogy for UBS’s AI-enabled advisory model. Source: Illustration created by the author using ChatGPT (OpenAI, 2026), based on UBS corporate communications, the Mercedes-AMG PETRONAS Formula One partnership, and the branding framework developed in this paper. Images combined of UBS Formula one car and Hamilton(UBS Global wealth Management 2025). Luxury wealth management is becoming a race for superior capabilities. AI provides better information and deeper analysis. Trust determines whether clients act on it. Formula One offers the same lesson: engineering creates performance, but skilled drivers, engineers and a team win races and trust in investment. UBS already combines advanced AI with trusted advisers. The opportunity is to brand this combination as its distinctive luxury formula one advantage. In wealth management, having the best technology in the hands of the best advisers can create its own sense of luxury: access to tools, information and opportunities that others may not have yet. In investing, seeing an opportunity early can make an enormous difference. The people who recognised the potential of Apple or Bitcoin early saw value before it became obvious to the wider market. Grossman and Stiglitz (1980) established the economic value of acquiring superior information. Clients increasingly expect technology to strengthen this advantage: 49% of consumers now use AI to support savings and investment decisions (EY, 2026), while Capgemini (2026) found growing demand for AI-driven insights in wealth management. UBS can brand its combination of leading AI, global research and expert advisers as access to the best tools available to find and interpret information earlier. For a wealth client, that feels premium: the best technology in the hands of people who know how to use it. The Formula One engine in the hands of someone who knows how to drive it. 2.3 Competitive white space Figure 9. Public proposition benchmark for adviser-enabled Al in wealth management. Source: Author assessment based on UBS (2026), UBS Group AG (2026), Morgan Stanley (2024, 2025) and HSBC Private Bank (2025), with the proposed UBS target shown as a design objective. Note: Scores indicate the strength of publicly available evidence guesstimate, not independently measured customer perceptions. The target row is normative and should not be described as observed performance. Competitors are already naming the hybrid territory. Morgan Stanley’s AI @ Morgan Stanley suite gives advisers a visible branded assistant and a Debrief tool that, with client consent, records meeting notes, identifies actions, drafts follow-up communication and stores a reviewed record. Morgan Stanley reports near-universal adviser-team adoption of its assistant and repeatedly links the technology to more time for human engagement (Morgan Stanley, 2024). HSBC has branded Wealth Intelligence, drawing on more than 10,000 sources to prepare relationship managers for client conversations (HSBC, 2026). Both rivals have converted technology into named adviser propositions, improving mental availability around hybrid advice. UBS holds scale, research depth, adviser reach and governance, but its brand architecture makes the AI story work harder than necessary. Aaker (2012) argues that relevance grows when a brand creates a “must have” subcategory, rather than fighting an endless preference contest. UBS can define responsible augmented advice as that subcategory. The defensible white space combines high AI usefulness with explicit human accountability. Morgan Stanley makes adviser productivity vivid; HSBC makes hybrid decision-making explicit. UBS can own Swiss-grade augmentation: more intelligence in the room, with a named adviser responsible for what leaves it. Figure 10. UBS can make human-accountable augmentation a named competitive space. Source: Author analysis based on UBS (2026), Morgan Stanley (2024) and HSBC (2026). 3. Competitive branding strategy 3.1 Strategic direction: UBS Augmented Advice See Appendix D for a visual exploration of the contemporary meanings and associations of the term augmented, which informed the branding rationale and creative direction proposed for UBS. The recommended strategic platform is UBS Augmented Advice. It is beneath Banking is our craft and translates the masterbrand into an AI-era proposition: Banking is our craft. Intelligence, crafted around you. Technology finds more. You and your adviser decide what matters. The wording protects the brand’s established identity while adding a distinctive promise. “Finds more” signals scale, pattern recognition and broader research. “Decides what matters” returns agency to adviser and client. The line also avoids claims that an algorithm can understand a family, or a legacy. Huang and Rust’s (2021) framework supports this division of labour: AI is strong at automation, analysis and personalisation, while relational value still demands human interpretation. Puntoni et al. (2021) show that consumer responses to AI depend on experiences of data capture, classification and delegation. UBS should therefore brand the boundaries around AI as carefully as its benefits. The strategy has three mutually reinforcing components: a standard that codifies trust, a redesigned advice experience that makes augmentation useful, and a proof system that makes the promise memorable. Together they move UBS from scattered evidence to a recognisable branded capability. The architecture also keeps the UBS masterbrand in charge. Individual AI tools may change names or vendors as technology moves; the durable equity remains with UBS Augmented Advice. This is important to hedge in a category where today’s celebrated model can become tomorrow’s museum piece before the campaign media plan has finished loading. Figure 11. Proposed UBS Augmented Advice brand architecture. Figure 12. Proposed UBS Brand Concept for Augmented Advice. This visual translates the paper’s recommendation for UBS to position AI as “Augmented Advice”, presents technology as an amplifier of advisor expertise, trust and client confidence, while retaining UBS’s established visual identity and premium positioning. Source: Created by the author using AI-generated imagery to visualise the proposed branding strategy, 2026. 3.2 The Augmented Advice Standard First, UBS should publish a concise Augmented Advice Standard, applicable wherever AI materially supports a client interaction or recommendation. Five commitments should anchor it. 1 Human accountability. A named UBS professional remains responsible for client-facing advice, verification and suitability. 2 Informed choice. Clients receive clear notice when AI contributes materially and can choose appropriate levels of AI involvement where regulation and service design allow. 3 Explainable contribution. UBS explains the role AI played in plain language, including the data categories and analytical purpose. 4 Protected information. Client data stays within defined governance, security and retention boundaries, with no casual migration into public tools. 5 Right to challenge. Clients can question an output, request human review and correct information that shaped the recommendation. The Standard turns governance into a brand asset. UBS already states principles of human autonomy, fairness, transparency and harm prevention (UBS, 2026). The strategic move is to translate internal policy into customer language. This creates points of difference within Keller’s meaning stage and strengthens judgements of credibility, consideration and superiority. It also provides the “substance below the surface” required by the Brand Iceberg: staff training, approved systems, audit trails, escalation and measurement must support the visible promise (King’s College London, 2026). Figure13. Brand Iceberg for UBS Augmented Advice. Source: Author visual based on Davidson (1997), King's College London (2026a) and the proposed Augmented Advice Standard. 3.3 Make augmentation visible across the journey Second, UBS should redesign the advice journey around three moments. Before the meeting, an adviser cockpit should combine portfolio data, life events, risk signals, market research and relevant specialist expertise into a short preparation brief. The system can surface questions and anomalies; it should avoid drafting a predetermined answer before the client has spoken. During the meeting, client-approved AI can capture decisions, unanswered questions and agreed actions. The adviser remains visibly in control and can pause the tool. A discreet on-screen marker could show which sources are being used. Good wealth advice often depends on the sentence after the client says, “There is one other thing.” The design must leave room for that. After the meeting, clients receive an Adviser Verified summary through the UBS app or portal. A compact panel states: what AI supported, what the adviser reviewed, which decisions remain open and how the client can challenge or amend the record. This feature moves transparency into reassurance and gives the strategy a recurring distinctive asset. Morgan Stanley’s Debrief proves the operational value of AI-supported follow-up; UBS can differentiate through a stronger client-control layer. Figure 14. Illustrative life-moment prioritisation matrix. Source: Author assessment using the use cases proposed in Intelligence, Crafted. Note: Positions and bubble sizes are strategic judgements, not market-size estimates. The matrix is a pilot-selection tool. The experience should begin with complex, high-value occasions where augmentation earns its keep: succession planning, concentrated-stock risk, cross-border relocation, philanthropy, business sale and multi-generational governance. These moments combine data intensity with emotional consequence. They also match UBS’s global network and integrated business model, making imitation harder than a generic chatbot. BEFORE Adviser cockpit Broader data, life events, risk signals and specialist insight. DURING Client-approved support Capture decisions and actions while the adviser remains in control. AFTER Adviser Verified Show AI contribution, adviser review, open decisions and challenge route. Figure 15. Augmented Advice service blueprint. Source: Author-developed blueprint informed by Bitner et al. (2008), Lemon and Verhoef (2016), Morgan Stanley (2024) and the proposed UBS strategy. Note: The blueprint translates the brand promise into observable moments, backstage systems and controls. 3.4 Build public proof and measure it Third, UBS should build a proof architecture around the platform. The public system should include an annual UBS Trust and Intelligence Index, developed with the Oxford-UBS Centre, tracking wealthy clients’ expectations of AI, privacy, advice and accountability across markets. The research creates news, adviser conversation material and a proprietary cultural lens. Adviser stories should demonstrate how augmentation improves preparation, coordination and judgement, with client privacy protected. Short “Craft Notes” can explain one use case at a time: what the technology noticed, what the adviser added and what changed for the client. The creative idea should feel more workshop than laboratory. UBS’s three keys could organise the content around insight, judgement and responsibility, creating a visual mnemonic that belongs to the brand. Figure 16. UBS’s Three Keys Reframed for Augmented Advice. This conceptual visual reframes UBS’s three-key symbol around insight, judgement and responsibility. It presents artificial intelligence as a tool that expands analytical insight, while human expertise remains responsible for judgement, context and client outcomes. The visual supports the paper’s proposed positioning of “Augmented advice, our promise”, linking the strategy to an established UBS brand asset and creating a memorable organising idea for AI-enabled advice. Source. Created by the author using OpenAI’s ChatGPT image-generation tool, 2026. The concept was informed by UBS’s three-key brand symbol and the analysis developed in this paper. UBS name, logo and three-key symbol are trademarks of UBS Group AG. Existing wealth clients receive the experience through advisers and the UBS app; prospects encounter it through entrepreneur events, family-office forums, thought leadership, sponsorship and the Banking is our craft campaign. Next-generation family members deserve particular attention. They often influence future provider choice, use AI more heavily and may experience the inherited banking relationship as dated. A collaborative, transparent advice model can give them an active role without sidelining the family’s established advisers. Employees need certification before public launch. Relationship managers should practise explaining the Standard in ordinary language, including uncomfortable questions about bias, privacy and error. Brand consistency will depend on whether 9,420 advisers can tell the same story sounding tailored for the client. Figure 17.. Brand value logic for UBS Augmented Advice.. Source: Author-developed logic chain inspired by Keller and Lehmann (2003) and the paper's measurement dashboard. Note: Commercial metrics should be interpreted with appropriate controls because market performance has many causes. Measurement should cover brand and behaviour. Quarterly tracking should assess association with “responsible AI-enabled advice,” perceived personalisation, trust, differentiation and consideration. Experience metrics should include preparation time, adviser adoption, client consent, summary-open rates, corrections, escalation resolution, satisfaction and share of wallet. A phased pilot across the UK, Switzerland, Singapore and the US would test varied regulatory and cultural expectations before wider deployment. Success is a measurable movement from awareness to meaning and resonance. Measurement layer Indicative metrics Brand Association with responsible AI advice; differentiation; consideration; trust. Experience Client consent; Adviser Verified open rate; corrections; escalation resolution. Adviser Adoption; preparation time; training certification; quality of client explanation. Commercial Retention; share of wallet; referrals; uptake in complex-life-moment services. Figure 18. Measurement dashboard for the proposed strategy. Source: Author-developed evaluation framework. Figure 19. Public Branding of UBS Augmented Source: Author generated with AI 4. Evaluation and conclusion Resource / capability V R I O Strategic implication Global adviser network ● ◐ ◐ ● Advantage improves if the experience is standardised. Client and invested-asset data scale ● ● ● ◐ Potential moat, dependent on permission, quality and orchestration. Global research and specialist network ● ◐ ◐ ● Strong support; less rare without distinctive delivery. Swiss trust and craft equity ● ● ● ● Most credible long-term brand differentiator. Oxford-UBS applied-AI relationship ● ◐ ◐ ● Proof and reputational amplifier, not a moat alone. Embedded AI governance and controls ● ◐ ● ◐ Harder to copy when built into workflow and adviser behaviour. Legend: ● strong evidence / potential ◐ partial or execution-dependent * V-Value: Does the resource or capability create value or help address a threat or opportunity? * R-Rarity: Is it uncommon among competitors? * I-Imitability: Is it difficult or costly for competitors to copy? * O-Organisation: Is the company properly organised to capture the value? Figure 20. VRIO assessment of the proposed strategy’s resource base. Source: Author analysis based on Barney (1991), UBS Group AG (2026) and UBS (2026). Note: The assessment identifies potential, not guaranteed sustained advantage. The strategy carries six material risks. First, an AI error in a high-stakes recommendation could damage trust quickly. Mandatory adviser verification, traceable sources and escalation thresholds reduce exposure, although they cannot abolish it. Second, transparency may overwhelm clients. Layered disclosure is essential: a plain-English summary, with technical detail available when requested. Third, personalisation can feel intrusive when data arrives before context. Client consent and purpose limits should govern sensitive life-event signals. Fourth, regulation may develop unevenly across UBS markets, producing an experience that fragments by jurisdiction. A global minimum standard, supported by stricter local modules, would preserve coherence. Figure 21. Illustrative risk heatmap. Source: Author assessment based on the risks identified in Intelligence, Crafted. Note: Likelihood and impact ratings require validation with UBS operational, legal and client data. Fifth, adviser resistance. Co-design, workflow integration, training and performance measures should reward better client outcomes, rather than raw tool usage. Sixth, competitors can imitate campaign language. The moat must come from proprietary data, Oxford-linked research, global specialist access, governance and consistent execution across the journey. These foundations take longer to copy than a slogan. Figure 22. Phased implementation roadmap. Source: Author-developed consultancy roadmap. Note: The timetable is illustrative; final sequencing depends on technology readiness, regulation and internal governance. There is also a strategic limitation. UBS already leads global wealth management, so this report stretches the brief’s preference for a smaller or challenger brand. The choice remains defensible because UBS is highly salient, extensively documented and faces a specific relevance opportunity in an emerging competitive space. The proposed strategy avoids a wholesale repositioning. It develops current equity by linking craft, trust and technology in a form clients can experience. STRATEGIC VALUE UBS has already built much of the engine. The missing asset is a visible steering wheel. UBS Augmented Advice would give clients a simple reason to value AI at the bank: broader intelligence, sharper preparation and faster follow-through, governed by a person whose name they know. That promise is current, commercially useful and difficult to own credibly without UBS’s combination of scale, Swiss discipline and adviser relationships. In an industry racing to sound intelligent, accountability may become the smatrtest brand move of all across markets, generations and moments of uncertainty. References Aaker, D. A. (2012). Win the brand relevance battle and then build competitor barriers. California Management Review, 54(2), 43–57. https://doi.org/10.1525/cmr.2012.54.2.43 Barney, J. B. (1991). Firm resources and sustained competitive advantage. 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Association for Computing Machinery. https://doi.org/10.1145/3351095.3372873 Shneiderman, B. (2020). Bridging the gap between ethics and practice: Guidelines for reliable, safe, and trustworthy human-centered AI systems. ACM Transactions on Interactive Intelligent Systems, 10(4), Article 26. https://doi.org/10.1145/3419764 Shneiderman, B. (2020). Human-centered artificial intelligence: Reliable, safe & trustworthy. International Journal of Human-Computer Interaction, 36(6), 495–504. https://doi.org/10.1080/10447318.2020.1741118 UBS. (2024, January 23). UBS launches new global brand campaign "Banking is our craft". https://www.ubs.com/global/en/media/display-page-ndp/en-20240123-craft.html UBS. (2024, September 21). UBS renews its partnership with the Mercedes-AMG PETRONAS Formula One Team. https://www.ubs.com/global/en/media/display-page-ndp/en-20240921-f1.html UBS. (2026). Innovation and AI at UBS. https://www.ubs.com/global/en/our-firm/what-we-do/technology/innovation-and-ai.html UBS Group AG. (2026). Annual report 2025. https://www.ubs.com/content/dam/assets/cc/investor-relations/annual-report/2025/annual-report-ubs-group-2025.pdf Appendices Appendix A. Evidence and figure source index Evidence Primary source(s) Use in report Brand salience and scale Brand Finance (2025); UBS Group AG (2026) Brand introduction and selection rationale. AI adoption and human trust HSBC (2026); EY (2025) Market tension and hybrid positioning. Personalisation gap Capgemini Research Institute (2026) Opportunity for augmented intelligence. UBS AI capability UBS Group AG (2026); UBS (2026) Proof that strategy rests on existing assets. Competitive comparison Morgan Stanley (2024); HSBC (2026) White-space map and differentiation. Brand frameworks Keller (1993); Kapferer (2012); Aaker (2012); Berry (2000) Audit, positioning and service-brand logic. AI and customer experience Huang and Rust (2021); Puntoni et al. (2021); Lemon and Verhoef (2016) Division of labour, transparency and journey design. FIGURE SOURCES Figure Label Source Figure 1 UBS has substantial AI and adviser capability. Source: Author visual using UBS Group AG (2026). Figure 2 UBS capability has not yet become a distinctive client-facing AI brand asset. Source: Author-developed issue tree using the opportunity identified in Intelligence, Crafted with the help of AI and PowerPoint. Framework synthesises Keller (1993), Berry (2000), Lemon & Verhoef (2016), and Shneiderman (2020). Figure 3 Investors are adopting AI faster. Source: Author chart using HSBC (2026). Figure 4 UBS Customer-Based Brand Equity Pyramid. Source: Author-created visual based on Keller (1993). Developed using generative AI with author direction. UBS brand interpretation based on UBS Group AG corporate materials. Figure 5 CBBE bridge from current UBS meaning to the proposed Augmented Advice position. Source: Author analysis based on Keller (1993; 1999), UBS public communications and this paper’s brand audit. Figure 6 Kapferer’s Brand Identity Prism diagnosis of UBS and AI-enabled client journey. Source: Image created by the author with assistance from ChatGPT (OpenAI). Based on Kapferer (2012), Berry (2000), and Lemon & Verhoef (2016). Figure 7 Kapferer’s Brand Identity Prism applied to UBS. Source: Author analysis based on Kapferer (2012) and UBS public communications. Figure 8 Trusted augmentation: Formula One as an analogy for UBS’s AI-enabled advisory model. Source: Illustration created by the author using ChatGPT (OpenAI, 2026), based on UBS corporate communications, the Mercedes-AMG PETRONAS Formula One partnership, UBS Global Wealth Management (2025), and the branding framework developed in this paper. Figure 9 Public proposition benchmark for adviser-enabled AI in wealth management. Source: Author assessment based on UBS (2026), UBS Group AG (2026), Morgan Stanley (2024; 2025), and HSBC Private Bank (2025). Figure 10 UBS can make human-accountable augmentation a named competitive space. Source: Author analysis based on UBS (2026), Morgan Stanley (2024), and HSBC (2026). Figure 11 Proposed UBS Augmented Advice brand architecture. Source: Author-developed strategy based on the brand audit and cited evidence. Figure 12 Proposed UBS Brand Concept for Augmented Advice. Source: Created by the author using AI-generated imagery to visualise the proposed branding strategy (2026). Figure 13 Brand Iceberg for UBS Augmented Advice. Source: Author visual based on Davidson (1997), King’s College London (2026a), and the proposed Augmented Advice Standard. Figure 14 Illustrative life-moment prioritisation matrix. Source: Author assessment using the use cases proposed in Intelligence, Crafted. Figure 15 Augmented Advice service blueprint. Source: Author-developed blueprint informed by Bitner et al. (2008), Lemon & Verhoef (2016), Morgan Stanley (2024), and the proposed UBS strategy. Figure 16 UBS’s Three Keys reframed as the Augmented Advice Standard. Source: Author-developed conceptual framework based on UBS branding and the proposed strategy. Figure 17 Brand value logic for UBS Augmented Advice. Source: Author synthesis based on Keller (1993), Aaker (2012), Berry (2000), and Lemon & Verhoef (2016). Figure 18 Measurement dashboard for UBS Augmented Advice. Source: Author-developed performance framework based on Keller (1993), Aaker (2012), and UBS strategy. Figure 19 Public Branding of UBS AugmentedI Source: Author generated with OpenAI Figure 20 VRIO assessment of UBS Augmented Advice. Source: Author analysis based on Barney (1991) and UBS evidence presented in this report. Figure 21 Illustrative implementation risk heatmap. Source: Author assessment developed for this report. Figure 22 Phased implementation roadmap. Source: Author-developed implementation roadmap based on the recommended branding strategy. Appendix B. Proposed UBS Augmented Advice Standard CLIENT PROMISE Technology finds more. Your adviser decides what matters. 1. Human accountability. A named UBS professional remains responsible for client-facing advice, verification and suitability. 2. Informed choice. Clients receive clear notice when AI contributes materially and can choose appropriate levels of AI involvement where regulation and service design allow. 3. Explainable contribution. UBS explains the role AI played in plain language, including the data categories and analytical purpose, without pretending every model can reveal its entire internal machinery. 4. Protected information. Client data stays within defined governance, security and retention boundaries, with no casual migration into public tools. 5. Right to challenge. Clients can question an output, request human review and correct information that shaped the recommendation. Operational foundations beneath the promise ✓ Approved tools and source controls ✓ Adviser verification and suitability checks ✓ Client consent and disclosure design ✓ Data security, retention and access controls ✓ Model monitoring, audit trails and escalation ✓ Role-based training and annual certification ✓ Cross-market legal and regulatory modules ✓ Client feedback and correction process Appendix C. Generative AI use declaration KING’S DECLARATION I declare that parts of this submission have contributions from AI software and that this aligns with acceptable use as specified in the assignment brief or guidance and is consistent with good academic practice. The content can still be considered my own words. See below for AI SCREENSHOTS TOOLS USED OpenAI ChatGPT. HOW IT CONTRIBUTED Topic scoping; deciphering what the professor was asking, research planning; source discovery; synthesis of publicly available evidence; structure suggestions; draft wording refinement when my word sounded like gobbledygook, Word count, and recommendations for Word cutting; chart production; AI image development, document formatting; and language refinement. AUTHOR RESPONSIBILITY This author is responsible for the final arguments, selection and interpretation of evidence, verification of every citation, substantive rewriting in my own voice, and compliance with the assessment brief. Appendix D. Appendix D image Visual Mood Board Exploring Contemporary Meanings of “Augmented” This mood board illustrates how the concept of “augmented” has evolved beyond augmented reality to encompass enhanced intelligence, creativity, decision-making, healthcare, productivity and human–AI collaboration. The collage uses recent visual associations, including smart glasses, spatial computing, digital interfaces and human–machine interaction, to show how augmentation is increasingly understood as the expansion of human capability rather than the replacement of human involvement. Note. Created by the author using AI-generated imagery based on visual themes identified in contemporary media and technology culture, 2026. [Archive notes] The original independent-analysis/non-endorsement statement, illustrative-metric qualifications, proposed standards, appendices, figure-source index and generative-AI declaration are retained. Routine administrative cover including student/candidate identifiers, repeated contents entries and the stray contents-page number are omitted. The strategy is a proposed independent academic analysis, not endorsed or implemented by UBS. Figure visuals and AI-chat screenshots are handled separately; no image-embedded chat output is added as authored prose. The source gives 17 August 2026 as a deadline, not an actual submission date. The submission date is therefore left unrecorded.