The rapid advancements in artificial intelligence have made their potential use in customer success (CS) teams increasingly apparent and necessary. Despite this recognition of their importance, there remains a disparity between ambition and real-world execution. As revealed by EverAfter’s Digital Customer Success Benchmark, 72% of CS teams currently acknowledge AI as crucial, yet only 32% have managed to successfully deploy a live use case. Even more sobering is that a mere 3% have extensively implemented AI solutions. The industry appears to be stuck in what can be described as “pilot paralysis,” where 31% are in the exploratory phase of AI solutions, and another 31% are piloting such projects without advancing into full-scale production. These statistics illustrate a clear picture: while the potential and desire for AI utilization exist, significant hurdles, including data quality concerns and operational challenges, hinder progress and efficiency.
The Challenges of Scaling AI in Customer Success
Amid the ambitious pursuit of AI integration within CS teams, data quality has emerged as the primary challenge, as backed by a survey of CS professionals predominantly in leadership roles across North America and EMEA. This issue surpasses even budgetary constraints and skill availability. Furthermore, a significant proportion of essentially all CS teams, approximately 27%, find their efforts consumed by onboarding—a time-intensive process detracting from more strategic activities. Compounded by these challenges, CSM bandwidth limitations surface as a critical impediment. Early-stage customer success managers often juggle 28 manual interactions per week compared to just four automated ones. This significant disparity between manual and automated interactions highlights the inefficiencies plaguing CS teams. The industry hence finds itself at a bottleneck, where enthusiasm for AI does not translate into measurable results or enhanced efficiency, and manual processes still overburden personnel.
Bridging the Ambition-Execution Gap
As companies work to incorporate AI more effectively, there’s a noticeable shift towards enhancing practical workflows, offering glimpses into the successful use of AI. Organizations advancing in this area are focusing on concrete improvements like AI-generated quarterly business review (QBR) summaries and predictive indicators for customer churn. These small-scale implementations provide valuable insights into utilizing AI for refining productivity and decision-making. However, personalization poses a significant challenge, with only 52% of customer service (CS) teams feeling confident about their capabilities. Even more concerning, merely 33% of teams manage to offer personalized customer experiences to more than half of their clients. This underscores the urgent need to close the gap between AI ambitions and practical execution. Ensuring high-quality data is crucial in overcoming these obstacles, pointing towards an essential path for CS teams aiming for AI-driven growth and success. Addressing foundational data needs can pave the way for developing future AI strategies, facilitating a smoother transition from initial projects to fully integrated solutions.