Editor’s note: The following is a guest article from Maria Marino, a VP analyst in Gartner’s Product Marketing group.
Generative AI has the potential to be a customer experience game changer that improves customer satisfaction, and many organizations start out with good intentions. But if an organization is not customer-centric, the tendency is to revert back to easy-to-prove cost savings at the cost of longer term, transformational customer experience improvement.
While organizations may be clear on what time or cost savings they hope to achieve from implementing generative AI, they remain unclear on customer outcomes. As generative AI automates more tasks, processes and customer-facing interactions, widespread application of the technology could actually lead to an erosion of customer empathy in the absence of customer-centric goals.
This narrow focus on automation leads to a slippery slope of simply chasing after more short-term savings whether in the form of time, people or money. But such efficiencies may not move the needle when it comes to meaningful and sustainable differentiation.
Revenue and retention leaders can use the following actions to pinpoint the value generative AI brings to customer experience programs.
Weighing the costs and benefits of generative AI
The pace of generative AI adoption is markedly different from other technologies.
In just two months following the introduction of ChatGPT in 2022, it had 100 million users, the fastest growth of all web services to date, according to an UBS analysis of Similarweb data. Organizations will face significant risks if they remain in waiting mode.
Ultimately, organizations who wait to utilize generative AI are at risk of competitors creating a gap that cannot be closed, as well as risk their CX as customers’ expectations rise rapidly when they receive highly relevant, personalized messaging and experience faster response times that generative AI makes possible.
However, organizations that have already deployed generative AI may begin incurring significant costs. Generative AI can be an expensive technology, whether they are funding native engineering talent and large language models or using vendor platforms.
For organizations that have already deployed generative AI, they should establish a mechanism to measure cost versus benefit that can run continuously as the technology evolves and costs change. Such a mechanism should:
- Incorporate additional costs beyond the technology: This should include costs for addressing risks, maintaining compliance with regulatory standards, rigorous quality checking to prevent unintentionally bad experiences, and adapting internal processes as more tasks and analytical processes become automated.
- Factor in the costs of upskilling employees and change management: While employees do not need to be prompt engineers, they do need to learn how to craft queries using customer data that will yield accurate insights to maximize the value of the technology tools.
Establishing AI governance to seize CX opportunity, not just mitigate risk
Generative AI can be a boon for CX, but it can also pose risks in the form of inaccurate customer responses or content. Such risks make AI governance vital.
It’s important for leaders to establish a set of AI principles that align with the values of their organization about how to use AI and when not to use it. These principles will apply across many audiences including customers, employees, partners and other stakeholders.
Enterprise-wide AI governance is a cross-functional forum designed to ensure these principles are adhered to, coordinate the organization’s approach to risk mitigation, technology adoption and deployment, and foster competitive differentiation using AI technology.
Similarly, CX governance is typically managed by a cross-functional steering committee to foster the collaboration required to share customer insights, conduct feasibility analysis, coordinate resource deployment and evaluate the impact of CX initiatives.
AI governance should coordinate with CX governance, either formally — by folding CX into AI governance — or informally with shared representation. This linkage should streamline the development of new customer-facing experiences, services and products while avoiding the risk of disjointed customer experiences that result from a lack of cross-functional collaboration.
Organizations must design AI governance not just to contain risk and ensure coordinated technology deployment, but also to target competitive differentiation. This coordinated AI-CX governance strategy enables CX innovation by providing a predictable path for assessment and launch.
Organizations should start with low-risk pilot programs to test generative AI. By balancing near-term productivity gains and long-term CX innovation and establish enterprise AI governance, CX leaders can foster a collaborative mechanism that enables the development and launch of innovative customer experiences.
