The Intersection of Performance and Trust
As AI adoption has accelerated within financial service organizations, customer experience (CX) leaders face critical decisions and inflection points when it comes to implementation. The conversation has progressed past the point of whether AI is a tool that can be leveraged to reduce costs and improve efficiency. CX leaders are now tasked with answering the question: How do we operationalize human-centered AI at scale while ensuring customer trust, agent confidence, and accountability, particularly in the customer experience space? With this acceleration comes the risk of losing trust.
Operationalizing human-centered AI at scale not only reduces the risk of automation overload, it also goes beyond just balancing humans and AI. It’s about embedding a human-centered AI perspective into an organization’s governance, workflows, and the performance models that set the standards the organization is held to. CX leaders have a unique responsibility when it comes to addressing this challenge. They must consider how to protect customer trust while improving efficiency, increasing operational performance, and creating repeatable outcomes without deteriorating the experience.
Is Human-Centered AI Optional?
Enormous opportunities for customer-focused industries have come about from AI adoption. However, these opportunities also present a risk that must be mitigated, as organizations implement automation that can unintentionally erode customer and employee trust. This is especially true when there is an overemphasis placed on automation. There should be a focus on embedding AI into the day-to-day operations of employees in ways that support human judgement, not replace it.
AI adoption by employees is the most important key to successful organizational outcomes. To create a higher likelihood of success and adoption, the tools that are being created and deployed should ensure alignment and evolution of the people, processes, governance standards, and accountability that run alongside it. Focusing on the novelty of the technology alone is tempting and creates a race to quickly implement AI solutions that target offsetting costs. However, these cost savings can bring new risks and challenges, often requiring organizations to rehire for new roles, smooth over any fallout, or struggle to support the AI complexity itself. When a holistic approach is taken, organizations can build truly scalable and trust-centered AI solutions that are done with human-centered design, minimizing at least some of the challenges.
Governance Before Gadgets
An early indicator that demonstrates an organization’s readiness for AI is governance. AI maturity is reflected within internal mechanisms that emphasize accountability and clarity. This governance ensures proper deployment, reducing organizational risk, ensuring ethical use and adoption, and promoting safe innovation. Organizations that have established governance structures find value in cross-functional AI taskforces that include representation from Customer Experience, Operations, Analytics, and more.
Additionally, Employee Experience (Human Resources) plays a major role, as AI changes behaviors within an organization long before outcomes of implementation are seen. It is critical that Employee Experience leaders are a part of AI taskforces as they are responsible for policy creation and enforcement, workforce transitions, accountability shifts, and role reskilling. Employee Experience teams ensure that AI decisions are connected to operating realities.
To ensure safer innovation, governance teams such as AI taskforces should be responsible for, but not limited to:
- Defining the organization’s ethical and operational guardrails
- Prioritizing AI agent use cases
- Ensuring human-centered principles are embedded in decision-making
AI taskforces can also help organizations move from isolated use cases to cases that allow for repeatable execution. Standard approaches that mature organizations adopt include internal AI factories that allow them to move focus away from these one-off uses cases to production-ready machines. AI factory operating models provide organizations with standards surrounding design, testing, and deployment as well as clear pipelines for new AI agents. By leveraging AI taskforces and factories, organizations can create clear, sustainable, and repeatable AI operating models that accelerate production and adoption, while mitigating risk.
Discernment, Not Clicks
AI literacy is not about mechanics; it is fundamentally about the judgement of the human using or deploying the tool. Training should be built around judgment, not tools. Traditional training models are insufficient in today’s AI-enabled organizational environments. To create effective training, organizations must move away from the approaches they have always taken when it comes to knowledge transfer. This includes shifting from training that focuses on button clicks and navigating between one system to another and moving towards ensuring the ability to interpret AI outputs, recognizing when to bypass AI recommendations, and understanding the limitations of technology. Knowing when to rely on AI, when to challenge it, and when it should not be used are skills that define true AI literacy within organizations. But these skills are often difficult to apply when outdated training models are being used.
Measuring What Matters
Merely layering workflows with new tools will not create successful human-centered AI. When workflows are redesigned in conjunction with AI solutions, it supports automation that accelerates routine work. Additionally, ensuring role clarity between the human and the AI agent prevents a break down of trust that can occur when people are not sure “who” is in charge. This accountability creates an opportunity to shift how metrics are measured within AI augmented environments.
Traditional metrics built within legacy performance frameworks can create unintentional outcomes that penalize the employee. This includes following AI recommendations designed for traditional human decision making. For example, AI tools may make recommendations that are in place to optimize long term outcomes, personalize resolution, and mitigate risk. However, legacy metrics that have been put in place to reward quick resolution, binary results, or strict adherence to scorecards may conflict with an AI enabled approach to resolution. This misalignment can create a contradiction resulting in employees relying on work arounds for AI recommendations or ignoring the recommendations completely, which reduces adoption and usage.
Reimagined approaches for metrics and accountability include monitoring quality at scale, identifying coaching opportunities, and rewarding AI supported decision making. This shift creates a culture within organizations in which employees are rewarded for thinking and not just executing blindly. This reinforces the behaviors that are needed to create trust and adoption resulting in long term scalability and efficiency outcomes.
Looking Ahead
The success of an organization’s AI strategy starts with the foundation that has been put in place to support the people that work with it. It is not determined by how powerful the technology is, it’s about what has been done to create a strong operating model as its foundation. For meaningful adoption to occur, organizations must ensure proper governance, ethically responsible leadership, and accountability. This supports a culture of trust. Human-centered AI should focus on creating room for enhancing decisions. It’s important to remember that a well thought out change management strategy is just as important as the AI strategy itself. Without proper change management for both customers and employees, even the most well-built AI agent runs the risk of negatively impacting trust, potentially resulting in low adoption, reduced customer satisfaction and customer attrition.
Susan Weaver’s career progression over the past 25+ years from a front-line processor and call center representative to Chief Customer Officer has been a profound and rewarding learning experience. Her innovative approach to contact center operations and relationship management has redefined the importance of the customer experience at Slavic401k. By consistently placing the customer at the heart of every decision, Susan has fostered a culture of empathy, accountability, and excellence, achieving outstanding results for both the business and the customers they serve.
