Transparent, ethical AI implementation creates a new foundation for customer confidence in an increasingly automated landscape. Without proper guardrails, businesses risk not just failed interactions but permanent damage to hard-earned brand loyalty and customer trust.AI in Customer Service: Navigating the Pitfalls
When AI delivers customer experience correctly, it feels almost magical. The consequences can be jarring and immediate when it fails, though. It might be a recommendation that misses the mark, a response that feels mechanical, an inexplicable decision, or even a generic – or worse, incorrect – name in a supposedly personalised interaction. This is the tightrope businesses walk in 2025 as they navigate implementing AI-enabled customer experiences.
The question isn't whether to embrace AI in customer journeys, but how to steer clear of the serious missteps that permanently damage customer trust. Cognizant’s New Work, New World research shows that generative AI could deliver up to $1.043 trillion in annual growth to the US economy alone by 2032.
Yet achieving this economic potential depends on avoiding a fundamental paradox: the technology that promises unprecedented personalisation is the same technology that can make customers feel profoundly violated when implemented poorly.
Cognizant research shows this discomfort is measurable. When surveying customer acceptability with AI engagement, the Comfort Quotient drops dramatically from 47 during the discovery phases to just 27 when making actual purchases. This stark decline highlights a critical truth: technical capability without emotional intelligence leads to disaster.
A transparency imperative
Many technologies have disrupted the workforce over time, but none has provoked as much fear and misgivings as generative AI. From concerns about the "black box" nature of AI decision-making to anxieties about bias and error, the technology has a trust deficit to overcome.
The complexity of AI systems can make their decisions genuinely puzzling to explain or predict, creating genuine risks for businesses that implement them without adequate guardrails. A system that makes decisions affecting customers without transparent reasoning can quickly undermine years of hard-earned brand trust.
Building trust in AI-powered experiences requires a demonstrable commitment to mitigating any detrimental effects of the technology on people, society, and the business itself. Organisations must be transparent about how their AI systems are developed and deployed, the values they're designed to uphold, and the measures to ensure they do so.
This transparency must show in concrete terms how AI decisions are made and how outcomes serve the broader goals of the business and the well-being of customers. When an AI agent recommends a product, suggests a next action, or personalises content, the reasoning should be accessible and understandable, not hidden behind algorithmic obscurity.
“As innovation moves forward, the industry needs security standards for building and deploying AI responsibly. That’s why at Google we introduced the Secure AI Framework (SAIF), a conceptual framework to help secure AI systems. We also offer a resource hub, SAIF.Google, to provide security professionals with useful assets such as the Risk Self-Assessment Report.” Google Cloud
Beware the creepiness factor
Perhaps the most devastating phenomenon in AI-enabled customer experience is what might be called "the creepiness factor". Manifestations of this might be AI that answers questions not yet asked or anticipates needs before they're expressed, which can create profound discomfort despite being technically impressive.
A telling example from healthcare illustrates this dynamic. When a healthcare provider used AI to generate personalised medical assessments following consultations, patients reported high satisfaction, until they learned the service was AI-driven. The revelation created immediate unease and even revulsion, despite no change in the quality of information provided.
The threshold between helpful anticipation and unsettling prediction remains a delicate balance. Given that our research indicates that 90% of jobs could experience some degree of disruption from generative AI, including those responsible for designing customer experiences, maybe it's understandable that there is mistrust. Certainly, this transformation will require a new sensitivity to the psychological boundaries of technology-mediated relationships.
The key insight for experience designers is to recognise that humans don't just want optimal efficiency – they want to feel in control. This explains why 73% of customers express discomfort with AI making financial decisions on their behalf without explicit approval, despite being comfortable with AI offering financial advice.
Building confidence through design
There are many ways to establish confidence in AI-powered systems and avoid significant pitfalls. For instance, trust metrics can be incorporated directly into applications, providing visibility into the data the model used to make its decisions. Systems can also produce confidence ratings alongside their decisions, giving users insight into the reliability of the output.
As the design of these systems matures, their inner workings will become less mysterious, increasing overall confidence in AI-generated content and decisions. We already see this evolution in intelligent agents that can explain their reasoning in natural language, making complex probabilistic processes accessible to non-technical users.
Organisations must also build safeguards that minimise the risk of AI bias, error, and ethically problematic decisions. Using multiple AI agents, surrogate predictive machine-learning models, explainable decision models, and human oversight creates a system of checks and balances that ensures more responsible outcomes.
“For an organisation to be successful, it is important to define the organisation’s guiding AI principles to articulate foundational requirements and expectations, as well as use cases that are explicitly out of scope. The guiding principles should be flexible and not overly prescriptive, capturing commitments from which the organisation won’t deviate; for instance, a focus on safeguarding customer privacy or ensuring a human is involved in reviewing AI-generated decision-making for certain use cases.” Google Cloud
Human in the loop
Our research has revealed a crucial insight into governance: human oversight remains essential in implementing AI. This recognition provides a critical framework for experience design.
While AI can dramatically enhance customer experiences, the most successful implementations maintain a clear path to human assistance when needed. Customers need to know that behind the technology is someone who can understand nuance, exercise judgment, and respond with empathy when the situation requires it.
Our research shows that "exposure scores" for customer service representatives are projected to grow from 11% today to surpass 63% by 2032. This doesn't mean these roles will disappear but rather that they'll evolve to focus on the complex, emotionally intelligent interactions that AI cannot handle, becoming even more valuable in the process.
Success in AI implementation requires organisational alignment around three fundamental principles that shouldn't be treated as separate domains but rather interconnected dimensions of a cohesive approach. First, organisations must design for transparency by making AI capabilities visible but not intrusive, ensuring users understand when interacting with AI and how their data informs experiences, without overwhelming them with technical details.
Second, AI systems must prioritise human values, reinforcing rather than replacing the human touch. Values like empathy, fairness, and respect have to be encoded into every interaction, extending to accessibility by creating experiences that adapt to individual abilities and preferences without manual adjustment.
Finally, the most potent AI experiences don't exist in isolation but form part of an intelligent ecosystem. By connecting experiences with their underlying data, technology and operations, organisations orchestrate contextual, responsive journeys that adapt to individual needs while maintaining coherence across touchpoints.
“In order to be successful, it is important to involve stakeholders from various disciplines to provide their subject matter expertise to evaluate AI initiatives. This can vary across organisations, but will typically include representatives from teams such as IT Infrastructure, Information Security, Application Security, Risk, Compliance, Privacy, Legal, Data Science, Data Governance, and Third Party Risk Management teams.” Google Cloud
Setting realistic expectations and finding the right partner
The reality of AI-powered customer experience is more complex than many organisations initially anticipate. While companies like Netflix have achieved impressive results with their recommendation systems – creating seamless, intuitive user experiences – even Amazon, with access to one of the most extensive customer data repository on the planet, still frequently misses the mark with product recommendations.
This disparity illuminates a concerning truth: even with vast resources, AI-powered CX remains challenging to perfect. Organisations should set realistic expectations and recognise the need for specialised expertise in this rapidly evolving field.
This is precisely where Cognizant Moment excels. Unlike traditional approaches that fragment customer experience across departments, Moment integrates research, strategy, design, product, and marketing capabilities under one organisation. We break down the silos that typically separate these functions, enabling unparalleled collaboration in developing AI-enabled experiences.
What makes Moment particularly powerful is its approach to orchestrating intelligent ecosystems. Within life sciences, for instance, we've developed tools that aggregate and visualise complex data for internal stakeholders, making previously impenetrable information actionable. Similarly, our work in financial services has enabled insurers to process vast data streams more effectively by implementing AI-powered interfaces that simplify complex operations.
Our internal AI tools and platforms demonstrate this approach in action. Initially developed for our own marketing efforts and now used with clients with household names, this platform streamlines everything from audience targeting to content creation and performance analysis. The system helps identify target audiences, assists with copywriting, generates creative assets through AI, and provides robust analytics – all through a unified interface that makes marketing operations dramatically more efficient.
Through Moment, we're pioneering experiences that are truly unique to each user. By taking individual prompts and preferences, our systems can generate customised content and visuals on the fly, explicitly structured to engage each person. This represents the future of digital interaction – websites and applications that dynamically rearrange themselves to meet individual needs, pushing aside irrelevant clutter and presenting precisely what's needed at that moment.
“AI should become not only powerful, but also ethical and transparent, as this will enhance brand differentiation, improve customer engagement and increase customer loyalty and trust.” Google Cloud
Converging powerful AI capabilities with thoughtful, human-centred design represents a new frontier for customer experience. By building trust through transparency, control, and value, organisations can avoid the epic failures that have doomed early AI implementations and instead create experiences that don't just work well, but feel right.
Ready to build AI-powered experiences that earn and maintain customer trust? Contact us to discover how Cognizant Moment can help your organisation create intelligent, ethical customer journeys for the AI age.
To learn more about Google Cloud, click here.
