In the rapidly evolving landscape of technological innovation, executives are increasingly turning to customer service and support leaders with a pressing question: "What's your plan for using generative artificial intelligence?" Since the launch of ChatGPT in November 2022, Gartner has fielded numerous inquiries from leaders grappling with high expectations and the complexities of deploying generative AI. Here we guide customer service leaders on how to formulate a strategic plan that not only meets executive expectations but also aligns with business outcomes, mitigates risks, and delivers tangible benefits.
Understanding the Landscape and Setting the Stage
The allure of genAI lies in its potential to revolutionize customer service through automation. However, the enthusiasm is often tempered by uncertainty regarding implementation timelines and outcomes. A well-crafted strategy begins with establishing context and credibility. This involves familiarizing oneself with key genAI concepts, such as large language models and prompt engineering, and understanding the enterprise use cases and associated risks.
To set the stage for a productive discussion, leaders should frame the conversation in terms of departmental and organizational goals. Emphasizing the opportunity to enhance efficiency by boosting agent productivity can serve as a compelling narrative. It's crucial to acknowledge the nascent nature of genAI technology and to approach it with a measured perspective. This involves understanding the spectrum of use cases, from simple and low-cost applications to complex and expensive ones, and recognizing the importance of starting with a minimum viable product (MVP) or trial to establish a feasible roadmap.
Focusing on Internal Use Cases and Building Expertise
While some executives might advocate for immediate customer-facing applications, a prudent approach involves initially concentrating on internal use cases. This strategy allows organizations to build internal expertise, such as prompt engineering, while controlling costs by leveraging existing vendor investments. By prioritizing improvements in representative productivity, organizations can mitigate risks associated with genAI, such as accuracy issues and hallucinations. The productivity gains achieved can then be reinvested into other initiatives, such as enhancing digital channel strategies or exploring new genAI use cases.
Gartner research suggests that significant near-term use cases for genAI will focus on augmenting rather than displacing employees. This aligns with the recommendation to avoid direct customer-facing applications of ChatGPT and genAI due to concerns about privacy and data security. Instead, the emphasis should be on leveraging genAI to improve service agent productivity, thereby enhancing margins and enabling reinvestment into value-enhancing activities.
Rationalizing the Strategy and Demonstrating Impact
A robust strategy is underpinned by data and research. Leaders should gather evidence, such as time studies on internal-facing use cases, to support their arguments. For example, analyzing the time agents spend summarizing case notes or assessing the quality of email communications can provide valuable insights. External studies can also be leveraged to reinforce the case for genAI. By demonstrating potential time and cost savings, leaders can build a compelling case for investment in genAI.
In addition to data-driven arguments, an emotional appeal can be made by illustrating the consequences of inaction. Highlighting the impact on employees and customers if genAI is not adopted can underscore the urgency of the initiative. For instance, the inability to produce timely knowledge articles due to time constraints and skill gaps can hinder a self-service strategy. Similarly, high levels of employee disengagement and attrition can be addressed by offloading tedious tasks to genAI, thereby increasing job satisfaction and reducing turnover.
Envisioning the Future State and Presenting a Roadmap
With the groundwork laid, leaders can paint a picture of a future state where genAI seamlessly integrates into the customer service workflow. This vision includes agents having quick, secure access to genAI-powered tools that offload repetitive tasks, enhance communication, and facilitate knowledge sharing. The time saved can be reinvested in value-added activities, such as identifying upsell opportunities or educating customers on self-service options.
To turn this vision into reality, a strategic roadmap is essential. Leaders should first explore capabilities offered by current vendors, as developing custom solutions can be cost-prohibitive. The roadmap should progress from experimentation with large language models to deploying agent-assist tools and, eventually, customer-facing applications. By focusing initially on reducing after-call work, organizations can achieve significant productivity gains while building credibility and experience.
Crafting a strategic plan for genAI in customer service requires a balanced approach that aligns with organizational goals, mitigates risks, and delivers measurable benefits. By focusing on internal use cases, building expertise, and leveraging data-driven insights, customer service leaders can confidently present a roadmap that positions their organizations for success in the era of genAI.
Patrick Quinlan, Daniel O'Sullivan, and Uma Challa are all senior director analysts at Gartner.