Klarna Support Reversal Shows Full Automation Risks Brand Equity

Ai4

Automating frontline customer interactions destroys perceived service authenticity and degrades brand equity, proving AI must offload administrative tasks rather than replace human workers.

AI-assisted summary of Panel 8 - 1: Customer Love in the Age of AI: Frontline Service Relationships and Brand Equity* — original source. Check the original for full context.

Source published: 2026-08-18

Detailed notes

## Background Published on YouTube by the Ai4 channel on August 18, 2026, this presentation features Carolina Rodriguez, a PhD candidate at Claremont Graduate University and founder of Custamor. The session addresses how organizations design human-AI customer service systems and examines the impact of frontline automation on employee identity, service authenticity, and long-term brand equity.

## TL;DR Carolina Rodriguez argues that while generative AI delivers major operational efficiencies, over-automating customer interactions can severely erode brand trust and customer loyalty. Contrasting Klarna's abrupt reversal on full automation with Trader Joe's high-equity, human-first frontline model, she demonstrates that AI is most powerful when supporting rather than replacing human judgment. Rodriguez presents a doctoral research model and contact center field experiment testing how human-AI service configurations affect frontline role identity, perceived service authenticity, and Keller's Customer-Based Brand Equity. Ultimately, she advocates using AI to eliminate administrative friction so frontline staff can focus on building genuine, high-empathy connections.

## Atmosphere & style The session delivers an engaging blend of academic rigor and actionable corporate strategy, presented with clarity, warmth, and steady authority. Rodriguez anchors abstract conceptual frameworks in relatable consumer experiences, moving fluidly from the universal agony of automated phone trees to nuanced corporate case studies like Zappos and Klarna. Watching the presentation feels like attending a focused masterclass that challenges tech-first hype in favor of a thoughtful, human-centered vision for the future of service.

## Key takeaways - [00:04](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=4s) Poor customer service experiences put an estimated $3.7 trillion in global sales at risk annually, making service design a critical driver of business outcomes. - [00:04](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=4s) The strategic imperative for organizations is not whether to adopt AI, but how to deliberately design AI systems around the human interactions that define the brand. - [03:12](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=192s) Klarna's initial heavy AI automation drastically cut resolution times, but led to service quality decline and an eventual strategy reversal to rehire human agents for complex empathy and expertise. - [03:12](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=192s) Trader Joe's demonstrates that prioritizing frontline human interactions builds superbrand fandom, word-of-mouth advocacy, and over $23 billion in annual sales without traditional digital support channels. - [05:55](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=355s) The research model hypothesizes that human-AI service design influences frontline employee identity, which enhances perceived service authenticity, ultimately driving customer-based brand equity. - [07:04](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=424s) Keller's Brand Equity framework indicates that direct frontline interactions shape consumer judgments and feelings, which are essential for achieving top-tier brand resonance. - [07:04](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=424s) Complementary service design pairs AI's computational efficiency and data processing with human strengths in empathy, emotional attunement, and nuanced judgment. - [07:04](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=424s) When frontline workers feel empowered and equipped rather than treated as robotic script-readers, they invest greater care and authenticity into customer interactions. - [10:07](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=607s) A proposed multi-method field experiment compares Human-Only, AI-Assisted, and AI-Primary contact center conditions using matched dyadic surveys from both agents and customers. - [10:07](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=607s) Evaluating AI service integration requires moving beyond narrow operational efficiency metrics like handle time and deflection rate to assess trust, authenticity, and customer lifetime value. - [12:19](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=739s) Zappos' philosophy of tracking Personal Emotional Connections illustrates how AI can be leveraged to offload administrative tasks and free human agents to build brand-defining relationships.

## Detailed walkthrough ### [00:04](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=4s) Bridging Academia and Practice: The Strategic Impact of Customer Service

Carolina Rodriguez introduces herself as a second-year PhD candidate at Claremont Graduate University, transitioning from the practitioner world into academia with the goal of bridging the gap between industry and scholarly research. Her work centers on an urgent question facing modern enterprises: how organizations can design human-AI service systems that cultivate customer relationships and build long-term brand equity. To frame the discussion, she asks the audience to reflect on their worst customer service experiences—whether stranded by an airline, navigating banking issues, or dealing with an insurance company. She highlights the universal agony of getting trapped in endless interactive voice response (IVR) phone trees pressing zero, or watching a chatbot loop repeatedly without offering a resolution. This widespread frustration leaves consumers feeling annoyed and ignored, often driving them to abandon the chat, hang up the phone, and vow never to return.

By contrast, Rodriguez prompts the audience to consider their best customer service encounters—moments where a representative patiently stayed on the line until an issue was resolved or simply took the time to listen. In these standout experiences, customers rarely remember the underlying technology stack; they remember the human being. As Rodriguez emphasizes, technology exists to support relationships, but people are the ones who create them. When service fails, the financial fallout is severe: an estimated $3.7 trillion in global sales is put at risk every year due to poor customer experiences. Consequently, as organizations rush to deploy generative AI across frontline support, the fundamental strategic question is no longer whether to adopt AI, but how to deliberately design AI around the human interactions that define the brand.

[03:12](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=192s) Automation vs. Human Touch: Contrasting Klarna and Trader Joe's

To illustrate the stakes of customer service design, Rodriguez contrasts two high-profile, divergent corporate approaches: Klarna and Trader Joe's. In 2024, fintech giant Klarna generated significant headlines by aggressively migrating its customer support operations almost entirely to AI. Initial operational metrics appeared stunning: the AI handled 2.3 million conversations, slashed ticket resolution times from eleven minutes to under two minutes, and performed the equivalent workload of 700 full-time employees. However, by 2025, Klarna abruptly reversed course and began rehiring human service agents. Klarna’s CEO conceded that an excessive focus on efficiency and cost reduction had degraded overall service quality into an unsustainable state. As Klarna’s VP reflected, in an increasingly automated world, "nothing is more valuable than a truly great human interaction," prompting the company to double down on empathy, expertise, and real human conversations.

```mermaid flowchart TD accTitle: Klarna Automation Backlash vs Trader Joe's Human-Centric Model subgraph Klarna["Klarna: Efficiency-First Trap"] K1["Heavy AI Automation"] --> K2["2.3M Chats Handled & Faster Resolutions"] K2 --> K3["Lower Quality & Loss of Empathy"] K3 --> K4["Strategy Reversal: Rehiring Human Agents"] end subgraph TraderJoes["Trader Joe's: Human-First Brand Equity"] T1["Frontline Staff as Brand Core"] --> T2["Authentic Human Interactions"] T2 --> T3["Superbrand Fandom & $23B+ Revenue"] end ```

On the opposite end of the spectrum, Trader Joe's demonstrates how authentic human interaction directly generates brand equity. Built on the foundational philosophy that in-store crew members are what truly differentiates the brand, the grocery chain drives over $23 billion in annual sales without traditional digital customer service channels. Frontline crew interactions foster intense customer loyalty, viral word-of-mouth advocacy, and a dedicated "superbrand fandom"—exemplified by consumers enthusiastically lining up just to purchase store-branded canvas tote bags. When AI is deployed to offload routine administrative tasks rather than replace staff, frontline employees gain the cognitive and emotional bandwidth needed to exercise judgment, express genuine empathy, and deliver meaningful moments.

[05:55](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=355s) Core Research Questions and Hypotheses

Rodriguez structures her academic inquiry around how AI service architecture impacts the frontline workforce and customer perceptions of brand value, rather than viewing frontline workers merely as operational costs to be minimized. Her study explores two central research questions:

1. How does human-AI service design shape the relationship between the customer and the frontline employee? 2. How does frontline employee identity influence service authenticity, and how does that authenticity contribute to customer-based brand equity?

From these foundational questions, Rodriguez derives three primary hypotheses to test empirically in live organizational environments:

- **Hypothesis 1:** The right mix of human-AI service design positively influences how frontline employees perceive and define their professional roles. - **Hypothesis 2:** A stronger, more empowered frontline employee identity positively influences customer perceptions of service authenticity. - **Hypothesis 3:** Greater perceived service authenticity positively influences customer-based brand equity.

```mermaid flowchart LR accTitle: Sequential Research Model H1["Human-AI Service Design"] -->|"H1"| H2["Frontline Employee Identity"] H2 -->|"H2"| H3["Service Authenticity"] H3 -->|"H3"| H4["Customer-Based Brand Equity"] ```

[07:04](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=424s) Theoretical Framework and Literature Review

The investigation is grounded across four distinct bodies of academic literature, synthesizing disciplines that are typically studied in isolation:

```mermaid flowchart TD accTitle: Theoretical Foundations of the Study subgraph Keller["1. Keller's Brand Equity Pyramid"] direction TB K_Res["Brand Resonance: Loyalty, Attachment, Community"] K_Judg["Judgments & Feelings: Direct Service Interactions"] K_Perf["Performance & Imagery"] K_Sal["Brand Salience"] K_Sal --> K_Perf --> K_Judg --> K_Res end

subgraph CoreLit["2–4. Supporting Theoretical Literature"] L2["Human-AI Service Design (Huang & Rust)"] L3["Social & Role Identity Theory (Service Environment Context)"] L4["Service Authenticity (Yagil & Medler-Liraz)"] end ```

- **Customer-Based Brand Equity (CBBE):** Drawing from Kevin Lane Keller’s CBBE pyramid—which builds upward from brand salience, through performance and imagery, to consumer judgments and feelings, and peaks at brand resonance—Rodriguez's study zeroes in on the judgments and feelings layer. Direct frontline encounters heavily dictate how consumers evaluate a brand and whether they develop the emotional attachment and community connection that define top-tier brand resonance. - **Human-AI Service Design:** Grounded in research by Ming-Hui Huang and Roland T. Rust, highlighting that AI and humans possess distinct, complementary service capabilities. AI excels at high-volume computation, data processing, and routine workflows, whereas human employees are indispensable for nuance, emotional attunement, and empathy. - **Frontline Employee Identity:** Anchored in social identity and role theories, showing that an employee’s operational environment dictates how they perceive the meaning and value of their work. When workers feel equipped and empowered to genuinely help customers rather than act as robotic script-readers, they invest greater care into each interaction. - **Service Authenticity:** Built on work by Dana Yagil and Hadas Medler-Liraz examining customer perceptions of emotional labor. Consumers possess a sharp radar for artificial interactions and quickly discern whether a frontline representative is authentically listening and tailoring help to their unique situation.

[10:07](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=607s) Proposed Field Experiment and Methodology

To rigorously test the theoretical model, Rodriguez details a proposed multi-method field experiment situated within live customer support contact centers. The experimental framework compares three distinct service conditions:

1. **Human-Only:** Traditional human agent handling the interaction without AI intervention. 2. **AI-Assisted (Human-in-the-loop):** Human agents augmented with real-time AI tools, knowledge retrieval, and automated administrative drafting. 3. **AI-Primary:** AI agent directly handling the primary customer tier, with human escalation available.

Following each customer interaction, the methodology deploys matched, dyadic post-service surveys administered to both the employee and the customer to analyze both perspectives of the exchange simultaneously.

| Participant Group | Target Measurement Variables | |:--- |:--- | | **Frontline Employees** | Role clarity, employee identity, perceived meaningfulness of work, and empowerment. | | **Customers** | Perceived authenticity, trust, satisfaction, brand value associations, Net Promoter Score (NPS), and repurchase intentions. | | **Organizational & Behavioral Data** | Customer Lifetime Value (CLV) where accessible, paired with AI-driven sentiment analysis to evaluate conversational emotional tone. |

By synthesizing these disparate operational and relational metrics into a sequential model, the research moves beyond narrow efficiency metrics (such as handle time and deflection rate) to evaluate the true holistic impact of AI integration on human workers, consumer trust, and enduring brand value.

[12:19](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=739s) Lessons from Zappos and Next Steps for Industry Collaboration

Rodriguez invokes the late Tony Hsieh, visionary founder and CEO of Zappos, who famously considered the telephone to be one of the greatest branding instruments ever created and viewed customer service as a company’s primary marketing investment. Zappos scaled to a $1.2 billion enterprise by prioritizing genuine human connections, tracking what they termed "Personal Emotional Connections" (PECs). Whenever a representative formed a meaningful bond on a call, they celebrated by marking a shared chalkboard in the contact center. Rodriguez argues that AI presents the ultimate opportunity to revive this ethos at scale: by automating mundane ticketing tasks, AI can liberate frontline professionals to focus on empathy, complex problem-solving, and relationship-building moments that generate lasting brand equity.

Looking ahead, Rodriguez outlines the roadmap for the remainder of her doctoral dissertation at Claremont Graduate University: - **Industry Partner Recruitment:** Seeking contact center partners willing to field-test the experimental framework across an initial sample size of approximately 30 customer service agents. - **Cross-Channel & Multi-Industry Scope:** Expanding the study across diverse industry verticals and communication channels, including voice, email, and live messaging. - **Neuroscience & Physiological Measures:** Exploring grant funding and research sponsorships to integrate physiological indicators of trust and authenticity into customer interaction studies. - **Collaborative Co-Design:** Inviting business leaders navigating AI transformations to collaborate directly, ensuring the final research produces actionable frameworks for sustainable human-AI service design.

[15:06](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=906s) Concluding Remarks and Transition to the Next Panelist

Wrapping up her presentation, Carolina Rodriguez notes that she has additional test results on hand to share, but checks in with the session moderators to see how to handle the remaining time. She asks whether she has time to open up the floor to audience questions or if she should instead hand over the microphone: "the test results to deliver, but if I may open up to any questions with my time, or do I switch over to the next panelist?"

Upon receiving confirmation from the organizers to transition directly to the next panelist rather than taking Q&A, she cordially thanks the audience and concludes her session, passing the floor along to the next speaker.

## Notable quotes > [00:04](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=4s) Technology exists to support relationships, but people are the ones who create them. — Carolina Rodriguez

> [00:04](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=4s) The fundamental strategic question is no longer whether to adopt AI, but how to deliberately design AI around the human interactions that define the brand. — Carolina Rodriguez

> [03:12](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=192s) nothing is more valuable than a truly great human interaction — Klarna VP

> [05:55](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=355s) How does human-AI service design shape the relationship between the customer and the frontline employee? — Carolina Rodriguez

> [05:55](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=355s) How does frontline employee identity influence service authenticity, and how does that authenticity contribute to customer-based brand equity? — Carolina Rodriguez

> [05:55](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=355s) The right mix of human-AI service design positively influences how frontline employees perceive and define their professional roles. — Carolina Rodriguez

> [05:55](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=355s) A stronger, more empowered frontline employee identity positively influences customer perceptions of service authenticity. — Carolina Rodriguez

> [05:55](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=355s) Greater perceived service authenticity positively influences customer-based brand equity. — Carolina Rodriguez

> [12:19](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=739s) Personal Emotional Connections — Tony Hsieh

> [15:06](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=906s) the test results to deliver, but if I may open up to any questions with my time, or do I switch over to the next panelist? — Carolina Rodriguez

## Mentioned Ai4, Carolina Rodriguez, Claremont Graduate University, Custamor, Dana Yagil, Hadas Medler-Liraz, Keller's Brand Equity Pyramid, Kevin Lane Keller, Klarna, Ming-Hui Huang, Roland T. Rust, Tony Hsieh, Trader Joe's, Zappos

## Video description Carolina Rodriguez, PhD Candidate | Founder, Custamor, Claremont Graduate University

What happens when organizations automate the very interactions that shape how customers experience a brand? As AI becomes a larger part of customer service, leaders face a fundamental design decision: which moments should be handled by technology, and which still require people? This talk explores how Human-AI service design influences frontline employee identity, service authenticity, and ultimately customer-based brand equity. Attendees will leave with a new perspective on the strategic role of frontline employees in AI-enabled service and practical questions to consider before redesigning customer-facing work. Join this session to rethink where your organization draws the line between automation and the human moments that build lasting brand equity.

[Source](https://www.youtube.com/watch?v=1ewjHdg5Y2g)

中文简报

Klarna客服策略转向凸显全面自动化的品牌资产风险一线客户互动自动化会破坏感知的服务真实性并贬损品牌资产，证明人工智能应当分担行政任务而非取代人类员工。 Carolina Rodriguez 认为，虽然生成式人工智能带来了显著的运营效率，但过度自动化客户互动会严重侵蚀品牌信任和客户忠诚度。通过对比 Klarna 在全面自动化上的骤然逆转与 Trader Joe's 以人为本、高品牌资产的一线服务模式，她证明了人工智能在辅助而非取代人类判断时能发挥最大价值。Rodriguez 提出了一项博士研究模型及联络中心实地实验，用以检验人机协同服务配置如何影响一线员工角色认同、感知的服务真实性以及 Keller 的基于客户的品牌资产。最终，她主张利用人工智能消除行政琐事摩擦，使一线员工能够专注于建立真诚且高度共情的人际联结。糟糕的客户服务体验每年可能导致全球约3.7万亿美元的销售额面临风险，企业面临的核心战略课题并非是否采用AI，而是如何围绕定义品牌的人际互动来设计AI系统。 (0:04) Klarna曾激进转向AI客服以缩短工单处理时间，但因服务质量下降和同理心缺失而重新雇佣人类座席；而Trader Joe's则通过重视一线员工互动，在没有传统数字化客服的情况下斩获超230亿美元年收入并打造出超级品牌拥趸。 (3:12) 该研究构建了一套序列模型并提出假设：人机协同服务设计会重塑一线员工认同感，进而提升顾客感知到的服务真实性，最终增强基于顾客的品牌资产（CBBE）。 (5:55) 研究拟在联络中心开展实地实验，对比仅人工、AI辅助及AI主导三种场景，通过员工与顾客的双向问卷及情绪分析，评估AI对人机协同工作与品牌长期价值的综合影响。 (10:07)

Reading summary

Carolina Rodriguez argues that organizations should design human-AI service systems around frontline workers to protect brand equity, rather than pursuing automation purely for operational efficiency. Efficiency Traps Versus Human-Driven Brand Value (3:12) Rodriguez notes that poor customer experiences put an estimated $3.7 trillion in global sales at risk annually, making service design a strategic priority rather than a cost-cutting exercise. She contrasts Klarna's recent pivot with Trader Joe's service model to illustrate the risks of over-automation. In 2024, Klarna automated support to handle 2.3 million conversations and cut resolution times from eleven minutes to under two, doing the work of 700 employees; by 2025, it began rehiring human agents after executive leadership acknowledged that excessive efficiency compromised service quality. In contrast, Trader Joe's generates over $23 billion in annual sales without traditional digital support channels, relying instead on in-store crew members to drive customer loyalty and brand advocacy. Rodriguez suggests that using AI to absorb repetitive administrative tasks frees frontline workers to provide empathy and judgment, which are the core drivers of memorable customer relationships. Theoretical Framework Linking Identity to Brand Equity (5:55) Rodriguez's doctoral research at Claremont Graduate University synthesizes four academic frameworks: Kevin Lane Keller's Customer-Based Brand Equity (CBBE) pyramid, human-AI service design by Huang and Rust, social and role identity theories, and service authenticity literature by Yagil and Medler-Liraz. Focusing on the 'judgments and feelings' tier of Keller's pyramid, the model examines how service interactions influence consumer resonance. The conceptual model advances three sequential hypotheses: first, that human-AI service design shapes how frontline employees perceive their professional roles; second, that an empowered employee identity enhances customer-perceived service authenticity; and third, that higher perceived authenticity directly strengthens customer-based brand equity. Field Experiment Design and Next Steps (10:07) To test these relationships, Rodriguez outlines a multi-method field experiment in live contact centers comparing three conditions: human-only support, AI-assisted agents with human oversight, and primary AI handling with human escalation. The study utilizes matched, dyadic post-service surveys administered to both agents and customers. Agents are evaluated on role clarity, empowerment, and perceived work meaningfulness, while customers are measured on perceived authenticity, trust, Net Promoter Score, and repurchase intentions, combined with customer lifetime value and conversational sentiment data. Drawing inspiration from Zappos founder Tony Hsieh's focus on 'Personal Emotional Connections,' Rodriguez plans to test this framework initially with approximately 30 customer service agents across voice, email, and messaging channels. Future research extensions include pursuing grant support for physiological measures of trust and collaborating with industry partners on sustainable human-AI deployment. The presentation concluded without audience questions due to session time constraints, leaving the proposed experimental framework and its preliminary results open for ongoing field testing and industry partner recruitment.

精炼阅读摘要

克莱蒙特研究生大学（Claremont Graduate University）博士生 Carolina Rodriguez 指出，全球每年因糟糕的客户服务面临约 3.7 万亿美元的销售损失风险，企业在生成式人工智能时代的关键战略并非单纯追求自动化，而应设计能够赋能一线员工、传递真实感并构建长期品牌资产的人机协同服务体系。效率陷阱与人际连接的商业价值对比 (3:12) 当企业客户服务陷入交互式语音应答（IVR）按零死循环或聊天机器人反复报错时，消费者往往会产生挫败感并流失客户。Rodriguez 引用行业估算指出，低劣的客户体验每年使全球约 3.7 万亿美元的销售额面临风险。然而卓越的服务体验中，客户记住的通常不是背后的技术架构，而是耐心倾听并解决问题的员工，这表明技术应服务于关系建设，而非切断人际接触。金融科技企业 Klarna 与杂货零售商 Trader Joe's 展现了截然相反的战略路径。Klarna 在 2024 年将客户支持大规模迁移至 AI，虽然处理了 230 万次对话、将工单解决时间从 11 分钟压缩至 2 分钟以内，并承担了相当于 700 名全职员工的工作量，但因服务质量下滑和同理心缺失，公司在 2025 年推翻全自动化决策并重新雇佣人工客服，其管理层承认过度追求效率损害了服务本质。相比之下，Trader Joe's 缺乏传统数字化客服渠道，依靠店内员工的真实互动实现了超过 230 亿美元的年销售额，并培育出排队购买品牌帆布袋的高忠诚度粉丝群体。这印证了将 AI 用于消除行政负担、让人工释放认知与情感宽带的重要性。人机协同与品牌资产的理论逻辑与假设 (5:55) Rodriguez 的学术研究围绕一线员工身份认同与品牌价值感知展开，提出了三项核心假设：合理的人机协同服务设计能积极影响一线员工对自己职业角色的定义；更强的一线员工职业认同能正向促进客户感知到的服务真实性；更高的服务真实性感知则会直接强化基于客户的品牌资产（Customer-Based Brand Equity, CBBE）。该模型旨在跳出将一线服务人员视作纯粹运营成本削减对象的传统视角。该研究整合了四个维度的学术文献：一是 Kevin Lane Keller 的品牌资产金字塔，重点关注一线互动所塑造的消费者判断与感受，进而攀升至品牌共鸣；二是 Huang 与 Rust 关于人机服务设计的研究，强调 AI 擅长高通量数据计算和常规流程，而人类在同理心与细微情感感知上不可替代；三是社会认同与角色理论，解释员工在获得赋能时能投入更多关怀而非照本宣科；四是 Yagil 与 Medler-Liraz 关于服务真实性与情绪劳动的探讨，指出消费者能够敏锐分辨员工是真诚倾听还是流于形式。联络中心现场实验设计与评估指标体系 (10:07) 为了在真实企业环境中检验上述理论，Rodriguez 提出了在联络中心开展多方法现场实验的方案。实验设定了三种对比条件：仅由人工坐席处理的传统模式（Human-Only）、配备实时知识检索与自动起草等 AI 工具的人工协作模式（AI-Assisted / Human-in-the-loop），以及以 AI 处理为主、保留人工升级通道的模式（AI-Primary）。该实验采用双向匹配问卷（dyadic post-service surveys），在服务结束后同时收集员工与客户双方的数据。员工端重点衡量角色清晰度、员工身份认同、工作意义感与被赋能感；客户端则测量感知真实性、信任度、满意度、品牌价值联想、净推荐值（NPS）与复购意愿。此外，研究还计划接入客户终身价值（CLV）等企业数据，并结合 AI 情绪分析技术来评估对话基调，打破单纯依赖单均处理时长和分流率等狭隘效率指标的局限。重塑服务定位与跨行业产学合作路线 (12:19) Rodriguez 引用了 Zappos 创始人 Tony Hsieh 的经营理念，指出 Hsieh 曾将电话视作最重要的品牌建设工具，并把客服定位为企业的核心营销投资。Zappos 曾通过在联络中心设立黑板记录“个人情感连接”（PECs），建立起价值 12 亿美元的企业规模。Rodriguez 认为，现代 AI 的价值在于承担琐碎的工单处理任务，从而规模化重塑这种注重深度连接与复杂问题解决的服务文化。在其博士论文后续规划中，Rodriguez 提出了具体的行业合作蓝图：首先招募首批约 30 名客服坐席规模的联络中心合作伙伴开展现场测试；其次将研究范围从电话渠道拓展至电子邮件、即时通讯等多渠道及不同垂直行业；同时寻求科研资助，尝试引入神经科学与生理测量指标来量化评估互动中的信任与真实感，最终与行业管理者共同开发可持续的人机服务设计框架。该报告主张企业应停止将一线客服仅视作成本中心，转而通过人机协同设计释放员工的情感连接潜力；其局限在于目前的实证框架仍处于现场实验方案与约 30 名坐席的招募阶段，生理指标等跨学科构想亦需依赖进一步的资助与行业合作验证。

Detailed notes — zh

## Background 该内容由频道 Ai4 于 2026 年 8 月 18 日发布在 YouTube 平台。演讲由克莱蒙特研究生大学博士候选人兼 Custamor 创始人 Carolina Rodriguez 主讲，主题聚焦于人机协同服务设计如何影响一线员工角色认同、服务真实性，进而帮助企业在人工智能时代界定自动化与人性化交互的边界，构建持久的基于客户的品牌资产。 ## TL;DR 演讲指出全球每年因劣质客户体验面临高达 3.7 万亿美元的销售损失风险，并通过 Klarna 激进自动化后的策略逆转与 Trader Joe's 的人性化服务对比，强调客服的核心在于人际关系而非单纯追求降本增效。Rodriguez 提出了“人机协同设计—员工身份认同—服务真实性—品牌资产”的因果链条理论模型，旨在阐明 AI 解放重复劳动后如何赋能员工创造发自内心的服务体验。为验证该假设，她设计了涵盖纯人工、AI 辅助与 AI 主导的现场对照实验及双向问卷，并结合 Zappos 建立“个人情感联结”的经典案例，向业界领袖发出了开放真实客服场景共同开展实证研究的合作号召。 ## Atmosphere & style 整场演讲兼具学术研究的严谨逻辑与商业实践的敏锐洞察，氛围亲切且充满启发性。Rodriguez 开篇以大众共鸣强烈的糟糕客服痛点切入，迅速唤起现场听众的情感共鸣，随后流畅地过渡到扎实的理论模型与前沿实验方案构建。演讲风格沉稳自信、充满对一线服务人员价值的人文关怀，展现出产学研深度融合的热情与务实探索态度。 ## Key takeaways - [00:04](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=4s) 技术的价值在于支持人际关系，但唯有人性化的一对一互动才能真正建立并维系客户关系。 - [02:10](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=130s) 全球每年因糟糕的客户服务体验面临的潜在销售额损失高达 3.7 万亿美元，客服质量已关乎企业核心生存。 - [02:10](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=130s) 生成式 AI 时代的核心商业战略不再是“是否使用 AI”，而是如何围绕塑造品牌体验的人际互动来设计 AI。 - [02:10](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=130s) Klarna 在激进推进 AI 替代人工客服后因服务质量下降逆转策略，重新加倍投资于同理心与真人专业服务。 - [02:10](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=130s) Trader Joe's 凭借员工与顾客间真实的人际互动构建了强大的品牌资产与狂热的忠诚粉丝文化。 - [05:05](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=305s) 一线员工不应被视为需要削减的成本中心，而是客户体验品牌的核心载体；AI 负责日常琐事，人类负责情感倾听。 - [05:05](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=305s) 研究提出递进因果链条假设：人机协同服务设计提升员工身份认同，进而增强服务真实性，最终提升基于客户的品牌资产（CBBE）。 - [07:04](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=424s) Keller 品牌资产模型中的最高阶段是品牌共鸣，深度的人性化服务互动是建立情感依恋与忠诚度的关键推动力。 - [07:04](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=424s) 客户具备敏锐的分辨力，唯有倾听、灵活响应和真情投入所构成的服务真实性，才能赢得客户信任。 - [10:01](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=601s) 研究采用多组对照现场实验（纯人工、AI 辅助、AI 主导），结合双向匹配问卷与多模态情感分析量化服务体验。 - [11:26](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=686s) 借鉴 Zappos 打造“个人情感联结”（PECs）的经验，电话客服可转化为企业最具长期价值的营销与品牌建设工具。 - [13:28](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=808s) 研究呼吁企业界与学术界合作，在多渠道客服场景中通过生理学及神经科学指标探索人机协同的最优范式。 ## Detailed walkthrough ### [00:04](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=4s) 个人背景与客户服务中的“人”与“技术” 演讲者 Carolina Rodriguez 介绍了自己的背景。她是克莱蒙特研究生大学（Claremont Graduate University）的二年级博士研究生，目前正从实务界向学术界转型，致力于在两者之间搭建桥梁。她的研究核心聚焦于当前许多企业都在思考的问题：组织应如何设计“人机协同”（Human-AI）服务系统，以强化客户关系并构建基于客户的品牌资产（Customer-Based Brand Equity, CBBE）。在深入学术探讨之前，Rodriguez 引导现场听众回顾自己经历过的“最糟糕的客户服务体验”——可能是某家航空公司的航班延误处理、银行的不作为，或是保险公司的推诿。她生动地描绘了客户被困在无休止的电话按键菜单中拼命按“0”试图转人工，或是在死循环的聊天机器人中问题得不到解决的沮丧场景，现场引起了强烈的举手共鸣。当面对这种被忽视和烦躁的处境时，客户往往会愤怒挂断、关闭对话，并誓言再也不光顾该品牌。与此相对，当回想“最棒的客户服务体验”时，人们记住的往往不是技术系统，而是某个耐心倾听、一直在线直到解决问题的人。技术的作用是支持关系，但唯有人才能创造关系。 --- ### [02:10](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=130s) 3.7万亿美元的代价与两大商业案例：Klarna 的反思与 Trader Joe's 的坚持在全球范围内，糟糕的客户服务每天都在发生数以百万次，其带来的商业代价极其高昂。据估算，全球每年因糟糕的客户体验而面临损失的销售额高达 3.7 万亿美元。这已不仅是客服部门的问题，而是关乎企业生存的重大商业挑战。在生成式人工智能（Gen AI）迅速融入客户服务体系的当下，核心战略问题不再是“是否使用 AI”，而是“如何使用 AI”——即如何围绕塑造品牌体验的人际互动来设计 AI。 Rodriguez 引用了两个极具对比性的商业案例来说明人际互动对品牌的重要性： - **Klarna 的激进自动化与策略逆转**：2024年，金融科技公司 Klarna 因将客户服务几乎全部转向 AI 自动化而登上头条。初期数据看似令人惊叹：AI 处理了 230 万次对话，将问题解决时间从 11 分钟大幅压缩至 2 分钟以内，相当于承担了 700 名全职员工的工作量。然而到了 2025 年，Klarna 逆转了这一战略，重新雇佣人工客服代表。其 CEO 直言公司过度关注效率与成本控制，导致服务质量下降且不可持续；副总裁则明确指出：“在自动化世界中，没有什么比一次真正优质的人际互动更有价值。这就是为什么我们要加倍投资于服务的人性化一面——同理心、专业技能与真实的对话。” - **Trader Joe's 的人性化品牌资产**：年销售额超过 230 亿美元的零售连锁品牌 Trader Joe's 则展现了截然不同的路径。该品牌坚信门店内员工与顾客的真实互动才是品牌脱颖而出的核心动力，这种人际联结催生了极高的品牌忠诚度、口碑传播乃至狂热的粉丝文化（如顾客通宵排队购买其标志性托特包的现象）。 --- ### [05:05](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=305s) 核心研究问题与三大假设许多企业仍将一线客服岗位视为应当削减或被替代的成本中心，但本研究将一线员工视作客户体验品牌的核心载体。当 AI 接管了所有重复性、例行性的日常任务后，员工便能腾出更多时间和精力去倾听、展现同理心、运用专业判断，并与客户建立真诚的互动。基于此，研究提出了两个核心问题与三项主要假设： 1. **核心问题一**：人机协同服务设计如何塑造客户与一线员工之间的关系？ 2. **核心问题二**：一线员工的身份认同（Identity）如何影响服务真实性（Service Authenticity）？这种真实性又如何促进基于客户的品牌资产？ ```mermaid flowchart LR accTitle: 研究假设逻辑模型 subgraph S1["服务设计"] A["人机协同服务设计 (Human-AI Design)"] end subgraph S2["员工端感知"] B["一线员工身份认同 (Employee Identity)"] end subgraph S3["交互体验"] C["服务真实性 (Service Authenticity)"] end subgraph S4["品牌结果"] D["基于客户的品牌资产 (CBBE)"] end A -->|"H1 (+)"| B B -->|"H2 (+)"| C C -->|"H3 (+)"| D ``` - **假设一（H1）**：合理的人机协同服务设计对一线员工对其自身角色的认知（身份认同）具有正向影响。 - **假设二（H2）**：更强的一线员工身份认同对服务真实性具有正向影响。 - **假设三（H3）**：更高的服务真实性对基于客户的品牌资产（CBBE）具有正向影响。 --- ### [07:04](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=424s) 文献基础与理论框架：从 Keller 品牌资产模型到服务真实性该研究的三项假设植根于四个关键维度的学术文献与理论体系： 1. **基于客户的品牌资产（CBBE）**：采用 Keller 的经典品牌资产金字塔模型（显著度 $\rightarrow$ 性能与形象 $\rightarrow$ 判断与感受 $\rightarrow$ 品牌共鸣）。本研究重点关注直接的服务互动如何塑造客户的“判断与感受”，进而上升至金字塔顶端的“品牌共鸣”（Resonance）——即忠诚度、情感依恋、深度互动与社区归属感的结合点。 2. **人机协同服务设计（Human-AI Service Design）**：参考学者 Huang 与 Rust 的研究，AI 与人类在服务工作中各有优势。AI 擅长处理常规化任务与大规模数据，而人类员工在需要同理心和复杂情感支持的情境中不可替代。 3. **一线员工身份认同（Frontline Employee Identity）**：基于社会认同与角色理论，参考 Echeverri 等学者的研究。服务环境直接塑造员工对其角色的认知；当员工感受到自身工作具有意义、确实在帮助客户解决问题时，他们会在服务互动中投入真正的关怀与专注。 4. **服务真实性（Service Authenticity）**：基于 Yagil 和 Medler-Liraz 等学者的研究。客户具备敏锐的洞察力，能够分辨出服务互动是流于形式的机械应付还是发自内心的关怀。倾听、针对具体情况的灵活回应与真实的情感投入构成了服务真实性的核心。 --- ### [10:01](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=601s) 研究方法：多组对照现场实验与情感分析为了在真实商业环境中验证理论模型，Rodriguez 提出了严谨的现场实验（Field Experiment）研究方案： - **实验设计与分组**：在真实的客户服务中心环境中设置三种对照组别： 1. 纯人工服务组（Human-only） 2. 人工+AI 辅助组（AI-assisted） 3. AI 主导组（AI-primary） - **双向匹配问卷调查（Dyadic Surveys）**：在每次服务交互结束后，同时向一线员工与客户发放简短问卷，通过数据匹配从对话双向视角进行实证分析： - **员工端测量指标**：角色清晰度（Role Clarity）、身份认同（Identity）、工作感知（Perceptions of Work）。 - **客户侧测量指标**：真实性感知（Authenticity）、信任度（Trust）、满意度（Satisfaction）、品牌价值认知、品牌联想、净推荐值（NPS）、重购意愿（Repurchase Intentions），并在企业数据支持下纳入客户生命周期价值（CLV）。 - **多模态分析技术**：引入基于 AI 的情感分析（Sentiment Analysis），对服务交互文本及语调进行情绪色彩捕捉，全方位衡量人机协同设计对各维度的影响。 --- ### [11:26](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=686s) 理论贡献与 Zappos 案例：打造“个人情感联结” 这项研究的学术与实践贡献主要体现在两方面：第一，它打破了过去将人机服务设计、员工身份认同、服务真实性与品牌资产割裂研究的现状，将它们整合进一个递进式的因果链条模型中；第二，它将管理者的视角从单纯追求运营效率与成本节约，转向探索 AI 设计对员工角色价值与客户体验的深层意义，解答企业如何平衡人机配比以创造长期品牌价值。 Rodriguez 引用了 Zappos 传奇创始人兼前 CEO 谢家华（Tony Hsieh）的经典理念。Tony Hsieh 曾将电话客服视为“最佳的品牌建设工具之一”，并将客户服务看作终极的营销投资。Zappos 凭借创造令人难忘且真实的互动，铸就了 12 亿美元的商业帝国。在 Zappos 内部，这种互动被称为“个人情感联结”（Personal Emotional Connections, PECs）。每当客服人员与客户建立起一次深度的 PEC，他们就会跑到办公室的黑板前记上一笔。这一简单却极具象征意义的动作，极大强化了员工对服务价值的自豪感与认同感。AI 的真正机遇正是在于将员工从琐事中解放出来，赋予他们充裕的时间去倾听、解决问题并创造这种高价值的 PEC。 --- ### [13:28](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=808s) 未来研究规划与行业合作号召作为二年级博士生，Rodriguez 强调目前该研究设计已成型，接下来的关键任务是在博士二年级阶段落地推进现场测试。为此，她向在场的业界领袖发出了合作邀请： - **样本需求**：寻找拥有约 30 名座席代表的呼叫中心或客服团队作为样本进行实验部署。 - **多渠道与多行业测试**：希望将研究拓展至不同的服务渠道（电话呼叫、电子邮件、在线 Chat 文本客服）及多种行业场景。 - **前沿方法探索**：计划引入神经科学（Neuroscience）中的生理学测量指标（Physiological measures），更客观地量化客户在交互过程中的信任度与真实感体验。 - **科研资助与共建**：积极寻求研究赞助与基金支持，与正在经历 AI 转型痛点的先锋企业共同探索人机协同的最优范式，将 AI 真正转化为增强人际关系与沉淀品牌资产的引擎。 ### [15:06](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=906s) 演讲结语与问答过渡演讲者 Carolina Rodriguez 在展示完其关于人工智能与一线服务关系的研究论点后，向现场观众提出了问答邀请。她指出，虽然目前非常期待并准备好分享具体的测试结果（"the test results to deliver"），但考虑到剩余的时间安排，她询问是现在接受现场观众的提问，还是直接将讲台移交给下一位与会嘉宾（"do I switch over to the next panelist?"）。主持人对此随即做出回应，明确指出了接下来的会议流程，表达了对演讲者的谢意（"The next panelist. Thank you very much."），并引导会议顺利过渡到下一位专家面板嘉宾的分享环节。 - **演讲者**：Carolina Rodriguez（克莱蒙特研究生大学博士候选人、Custamor 创始人） - **核心议题**：人机协同服务设计（Human-AI Service Design）、一线员工身份认同与品牌权益（Brand Equity） - **环节状态**：结束个人主题演讲，顺利交接至面板讨论的下一位演讲嘉宾 ## Notable quotes > [00:04](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=4s) 技术的作用是支持关系，但唯有人才能创造关系。 — Carolina Rodriguez > [02:10](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=130s) 在全球范围内，糟糕的客户服务每天都在发生数以百万次，其带来的商业代价极其高昂。据估算，全球每年因糟糕的客户体验而面临损失的销售额高达 3.7 万亿美元。 — Carolina Rodriguez > [02:10](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=130s) 核心战略问题不再是“是否使用 AI”，而是“如何使用 AI”——即如何围绕塑造品牌体验的人际互动来设计 AI。 — Carolina Rodriguez > [02:10](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=130s) 在自动化世界中，没有什么比一次真正优质的人际互动更有价值。这就是为什么我们要加倍投资于服务的人性化一面——同理心、专业技能与真实的对话。 — Klarna 副总裁 > [05:05](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=305s) 当 AI 接管了所有重复性、例行性的日常任务后，员工便能腾出更多时间和精力去倾听、展现同理心、运用专业判断，并与客户建立真诚的互动。 — Carolina Rodriguez > [07:04](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=424s) 客户具备敏锐的洞察力，能够分辨出服务互动是流于形式的机械应付还是发自内心的关怀。 — Carolina Rodriguez > [11:26](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=686s) 电话客服是最佳的品牌建设工具之一，客户服务是终极的营销投资。 — 谢家华（Tony Hsieh） > [11:26](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=686s) AI 的真正机遇正是在于将员工从琐事中解放出来，赋予他们充裕的时间去倾听、解决问题并创造这种高价值的 PEC。 — Carolina Rodriguez > [15:06](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=906s) the test results to deliver — Carolina Rodriguez > [15:06](https://www.youtube.com/watch?v=1ewjHdg5Y2g&t=906s) The next panelist. Thank you very much. — 主持人 ## Mentioned Carolina Rodriguez, Custamor, 克莱蒙特研究生大学 (Claremont Graduate University), Klarna, Trader Joe's, Zappos, 谢家华 (Tony Hsieh), Keller, Huang, Rust, Echeverri, Yagil, Medler-Liraz, Keller 品牌资产模型, 基于客户的品牌资产 (CBBE), 个人情感联结 (PECs) ## Video description Carolina Rodriguez, PhD Candidate | Founder, Custamor, Claremont Graduate University What happens when organizations automate the very interactions that shape how customers experience a brand? As AI becomes a larger part of customer service, leaders face a fundamental design decision: which moments should be handled by technology, and which still require people? This talk explores how Human-AI service design influences frontline employee identity, service authenticity, and ultimately customer-based brand equity. Attendees will leave with a new perspective on the strategic role of frontline employees in AI-enabled service and practical questions to consider before redesigning customer-facing work. Join this session to rethink where your organization draws the line between automation and the human moments that build lasting brand equity. [Source](https://www.youtube.com/watch?v=1ewjHdg5Y2g)

Source quotes

[0:04] Carolina Rodriguez: 技术的作用是支持关系，但唯有人才能创造关系。 [2:10] Carolina Rodriguez: 在全球范围内，糟糕的客户服务每天都在发生数以百万次，其带来的商业代价极其高昂。据估算，全球每年因糟糕的客户体验而面临损失的销售额高达 3.7 万亿美元。 [2:10] Carolina Rodriguez: 核心战略问题不再是“是否使用 AI”，而是“如何使用 AI”——即如何围绕塑造品牌体验的人际互动来设计 AI。 [2:10] Klarna 副总裁: 在自动化世界中，没有什么比一次真正优质的人际互动更有价值。这就是为什么我们要加倍投资于服务的人性化一面——同理心、专业技能与真实的对话。 [5:05] Carolina Rodriguez: 当 AI 接管了所有重复性、例行性的日常任务后，员工便能腾出更多时间和精力去倾听、展现同理心、运用专业判断，并与客户建立真诚的互动。 [7:04] Carolina Rodriguez: 客户具备敏锐的洞察力，能够分辨出服务互动是流于形式的机械应付还是发自内心的关怀。 [11:26] 谢家华（Tony Hsieh）: 电话客服是最佳的品牌建设工具之一，客户服务是终极的营销投资。 [11:26] Carolina Rodriguez: AI 的真正机遇正是在于将员工从琐事中解放出来，赋予他们充裕的时间去倾听、解决问题并创造这种高价值的 PEC。 [15:06] Carolina Rodriguez: the test results to deliver [15:06] 主持人: The next panelist. Thank you very much.

Source quotes

[0:04] Carolina Rodriguez: Technology exists to support relationships, but people are the ones who create them. [0:04] Carolina Rodriguez: The fundamental strategic question is no longer whether to adopt AI, but how to deliberately design AI around the human interactions that define the brand. [3:12] Klarna VP: nothing is more valuable than a truly great human interaction [5:55] Carolina Rodriguez: How does human-AI service design shape the relationship between the customer and the frontline employee? [5:55] Carolina Rodriguez: How does frontline employee identity influence service authenticity, and how does that authenticity contribute to customer-based brand equity? [5:55] Carolina Rodriguez: The right mix of human-AI service design positively influences how frontline employees perceive and define their professional roles. [5:55] Carolina Rodriguez: A stronger, more empowered frontline employee identity positively influences customer perceptions of service authenticity. [5:55] Carolina Rodriguez: Greater perceived service authenticity positively influences customer-based brand equity. [12:19] Tony Hsieh: Personal Emotional Connections [15:06] Carolina Rodriguez: the test results to deliver, but if I may open up to any questions with my time, or do I switch over to the next panelist?

Original transcript

[0:04] I'm Golina Rodriguez. I'm a PhD candidate at Claremont University. I'm in my second year there, [0:11] as I mentioned. Um, I'm transitioning from practitioner world into academia and hoping [0:17] to bridge the two. Um, my research focuses on a question that I think a [0:23] lot of businesses are asking right now, and that's how do organizations design human AI [0:30] service systems that strengthen customer relationships and build brand equity. [0:36] Um, but before we dig into research, I'd like you to think about something. [0:42] I'd like you to take a minute and think about the worst customer service experience you've ever had. [0:51] Okay? Maybe it was your airline coming out here. Maybe your bank, [snorts] your insurance company. [1:01] Were you stuck in an endless phone tree pressing zero trying to get out? [1:08] Were you in a chat that looped and never solved your problem? Show of hands. How many of you? [1:18] Yes. It's a lot. It's a problem. Now, think about how that experience made you feel. Annoyed, [1:26] ignored, and what did you do? Did you hang up the phone, disconnect the chat, swear never to return [1:35] to that company again? That frustrating experience can have a real business cost. [1:43] Now, think about the best customer service you've ever had. [1:47] Maybe someone stayed on the phone with you until they solved the problem. Maybe they just listened. [1:54] I bet most of you don't remember the technology. You remember the person [2:00] because technology supports the relationships. It's people that create them. [2:10] $3.7 trillion. The experience that you just thought about happens millions of times a day. [2:18] Collectively, there's enormous price to pay. It's estimated that $3.7 trillion in global sales is at [2:26] risk every year due to poor customer experiences. That's not just a customer service problem. [2:32] That's a business problem. With organizations quickly integrating Gen AI into customer service, [2:41] the strategic question is not whether to use AI, it's how to use AI. It's how to design AI around [2:49] human interactions that shape the brand experience. My research looks at how the [2:55] interactions My research looks at how the design affects frontline employees, service authenticity, [3:02] and ultimately customer-based brand equity. The next examples show why this can be so important. [3:12] In 2024, CLA made headlines by moving customer service almost entirely to AI. [3:21] The early numbers looked impressive. AI handled 2.3 million conversations, [3:27] cut resolution time from 11 minutes to under two, and could do the work of 700 employees. [3:36] But by 2025, CLA reversed its strategy and was rehiring human agents. [3:42] The CEO stated, "We focused too much on efficiency and cost. The result was lower quality [3:49] and that's not sustainable." The VP captured the lesson very clearly. In a world of automation, [3:56] nothing is more valuable than a truly great human interaction. That's why we're doubling [4:02] down by investing in the human side of service, empathy, expertise, and real conversations. [4:10] Clara knows what can happen when efficiency becomes the main priority. Trader Joe's [4:17] gives us a very different picture. It shows how human interaction can become brand equity. [4:28] It's its brand experience is deeply human. The company was built on the belief that people in [4:34] its stores are truly what set the brand apart with more than $23 billion in annual sales. [4:44] Those human interactions help build loyalty, word of mouth, and superbrand fandom. And then [4:52] there's the tote bag craze. People lining up for a grocery store tote bag. That is brand connection. [5:05] Trader Joe's demonstrates how frontline employees can shape what customers feel [5:10] and value about a brand. That leads to the focus of this study. The way companies design [5:17] AI and service affects frontline employees and ultimately customerbased brand equity. [5:27] Many organizations still see frontline roles as cost to reduce or replace. [5:34] This study looks at employees as part of how the brand is experienced. It's the people. [5:40] When AI handles all of that routine work, employees have more time to listen. [5:46] They have more time to show empathy, use judgment, and create genuine interactions. [5:55] That brings me to the two questions at the center of my study. [6:00] First, how does human AI service design shape the customer frontline employee relationship? [6:06] And second, how does frontline employee identity influence service authenticity? And how does that [6:14] authenticity contribute to customer-based brand equity? Together, these questions examine human [6:21] AI service design as an organizational decision with important implications for brand equity. [6:32] These questions lead to three hypotheses. [6:36] These three hypotheses that I want to test are first the right mix of human [6:41] AI service design positively influences how frontline employees see their roles. [6:48] Second, a stronger frontline employee identity positively influences service authenticity. [6:56] And third, greater service authenticity positively influences customerbased brand equity. [7:04] literature review. These three hypotheses are grounded in four ideas from research. I'll [7:11] start with the outcome that I care about most, which is customerbased brand equity. On the left [7:17] is Keller's customer-based brand equity model. It starts with salience. Do customers know the brand? [7:27] Then comes performance and imagery. What does the brand deliver and what does it represent? [7:35] Next are judgments and feelings. How do customers evaluate the brand and how does it make them feel? [7:43] My study focuses here on how direct service interactions shape judgments and feelings. [7:51] And at the very top is resonance, the deepest relationship a customer can have with a brand. [7:59] That's where loyalty, emotional attachment, engagement, and community come together. [8:06] The second idea is human AI service design. Leading scholars Hang and Rust explain that [8:14] AI and people bring different strengths to service work. AI is useful for routine tasks [8:21] and data processing. People are especially important when situations require empathy. [8:30] The third idea is frontline employee identity. [8:35] And I'm going to pick up on my slide here because as literature review goes for you PhD folks, it [8:41] goes and goes. So I'm going to jump back over to my notes and make sure that um I am able to cover [8:49] very important information from the literature review. So the third idea is frontline employee [8:55] identity is grounded in something called social theory. Um scholars at Chavari and Akinson show [9:04] how the service environment shapes how employees see their role. When they feel their work [9:10] has meaning and that they are helping the customer. They are more likely to bring real care [9:16] and attention to the interaction. The final idea is service authenticity. [9:22] Scholars Yagil and Medler Lenos focus on what makes service feel genuine to the customer. [9:30] Research on service authenticity shows that customers can usually tell when an interaction [9:36] feels genuine. They notice when someone listens, responds to their situation, and truly seems [9:42] to care. Together, these ideas explain what I'm exploring, and that's how AI shapes the experience [9:51] that affects customer interactions, and those interactions build customerbased brand equity. [10:01] These ideas give me the foundation. The next step is to test them in a real service setting. [10:07] These are my methodologies. I'm proposing a field experiment in a live customer service environment. [10:14] I'll compare three conditions. Human only, AI assisted, and AI primary. After each interaction, [10:22] the employee and customer will complete a short survey. Matching their responses lets [10:27] me look at both sides of the conversation. And employee measures will include role clarity, [10:35] identity, and perceptions of work. While the customer measures will include authenticity, [10:42] trust, satisfaction, brand value, brand associations, MPS, and repurchase intentions [10:52] where the organizational data are available. I'll also examine customer lifetime value. [11:00] That's not all for methodology. There's more. [11:06] I'll use AI enabled sentiment analysis to capture the emotional tone of the interactions. And [11:12] together, these measures will show how different human AI service designs affect employees, [11:18] customers, and ultimately customer-based brand equity. So, what does this study add? [11:26] Well, the first it brings together ideas that are often studied separately. [11:31] So studied separately are human AI service design. Studied separately is frontline employee identity, [11:38] service authenticity and brand equity. And this brings it all together in a sequential model. [11:45] Second, it shifts the focus beyond efficiency to what AI design means for employees and customers [11:51] and not solely organizations. So what does it mean to employees in their roles and to customers as [11:59] consumers? It explores a practical question many customers are facing right now. What is the right [12:06] mix of human AI to build customer relationships? What is that right mix that creates brand value? [12:19] Tony Sheay, the late founder and CEO of Zappos, called the the telephone one of [12:26] the best branding tools. He believed customer service was the ultimate marketing investment. [12:33] And because of this, Zapos built its $1.2 billion success by creating memorable, authentic [12:42] interactions with their customers. At Zapos, they called those interactions personal emotional [12:49] connections or PEC's. And call center employees every time they would have one would run to a [12:55] chalkboard and mark it. Simple but very meaningful and very visible for the call center employees. [13:04] That's the opportunity I see with AI. When done right, [13:08] AI can give employees more time to listen, solve problems, and create customer experiences with [13:15] uh and build brand value. Those m moments build trust, loyalty, and brand equity. [13:28] So, where does the research go from here? As I mentioned, I'm uh for many of you, [13:34] I am a secondyear PhD student. So, methodology is proposed. It hasn't been tested. I've designed it [13:41] in a way that I hope to implement it the second year of my PhD. So, the next step is to find [13:48] the right industry partners and test this work. And going back to Jill's point, this is what this [13:53] is about. So, if you're interested, please let me know. I'm looking for a sample set of [13:58] about 30 folks in a call center. Um, and I'm in conversation with some exciting companies right [14:06] now, but always happy to have that conversation with you to see what we can do together and test [14:11] this work across different service environments, meaning different industries, different channels, [14:18] um, call, email, chat. I'm also interested in adding physiolog physiological measures [14:25] of trust and authenticity. So there's a little bit of there's some curiosity of neuroscience [14:32] um involved in my study and exploring grant or sponsored research support. [14:39] I'd like to build this research with organizations that are working through these issues right now. [14:45] I'd love I'd love to test that. Um because the real opportunity [14:49] is to design AI in a way that strengthens people relationship and builds brand equity. [14:59] And with that I I end early as I mentioned this is in information my work so I don't have um [15:06] the test results to deliver but if I may open up to any questions with my time or [15:14] do I switch over to the next panelist? The next panelist. Thank you very much.