CX organizations are grappling with an unprecedented imperative to harness AI's transformative potential. They’re under pressure not merely to adopt AI, but to assess its longer-term, game-changing potential for customer service. This, coupled with the effort required to pilot AI applications, makes project selection a challenging undertaking. The market is awash with AI capabilities, many of which are marketed as features of existing platforms. This approach can narrow the appreciation of AI's truly transformative potential in reshaping CX operations.
In part one of this series, we outlined a structured 8-step process for prioritizing AI applications to evaluate. Now, we confront the next critical challenge: navigating the vast and often overwhelming array of AI use cases in the CX space.
Part two presents a comprehensive inventory of AI applications, strategically categorized by key jobs to be done (JTBD). This framework serves two essential purposes:
- It aligns AI use cases directly with your organization's strategic priorities, enabling more focused selection.
- It provides a holistic view of AI's potential, transcending the boundaries of existing applications.
Here are the ten CX JTBD:
Job #1: Let the Customer Help Themselves
As part of digital-first strategies, offering self-help options to customers is paramount, allowing them to find answers to their questions or complete transactions using self-service. These options empower customers to resolve issues independently, reducing the need for direct assistance from customer service representatives. They leverage a combination of AI technologies:
- AI speech has reached unprecedented precision levels, making voice self-service sound like real humans.
- AI provides powerful options for authentication, combining biometrics and pattern recognition techniques.
- Natural language processing, either using traditional NLP or the capabilities of Large Language Models (LLMs).
- AI can be used to uncover possible intent candidates for automation and generate numerous utterances.
- Generative AI (GenAI) significantly enhances access to FAQs and knowledge bases, providing canned or generative answers tailored to customer inquiries.
Job #2: Manage Knowledge
Knowledge management, a long-neglected aspect of customer service, involves tedious workflows for compiling responses to customers' questions and incorporating feedback from both customers and agents. Traditional keyword-based search, even when enhanced by semantic search, often yields too many results. GenAI can transform knowledge management in several ways:
- GenAI can turbocharge semantic indexing to only pull relevant articles using grounding techniques such as Retrieval Augmented Generation (RAG).
- GenAI can assist with all stages of the knowledge lifecycle, including creation, repurposing, reformatting for specific channels, translation, and copywriting - tasks that were previously human-intensive and time-consuming.
Job #3: Select the Optimal Resource
Once an interaction needs to be handled by an agent, the optimal resource must be chosen based on the customer's selections in self-service, the skills of available agents, and the importance of the call. AI can optimize interaction routing in two ways:
- By analyzing customer speech or text, AI can surface intents beyond the customer's initial selections, providing a more comprehensive understanding of their needs.
- AI can match interactions with the best-suited human agents based on various factors such as skills, experience, and past performance. A longer-term promise of AI is to optimize routing without relying on a complex array of interlinked rules, which are difficult to manage and modify.
Job #4: Enable Agents
Associates handling the most complex and high-impact customer interactions require support to deliver exceptional service. Enterprises are also looking to improve productivity. AI-assisted agent support is among the most popular use cases, as it puts a human in the loop, providing guardrails and validating AI recommendations. AI can be leveraged to:
- Surface intents, emotions, and/or sentiments of live conversations, enabling agents to better understand and respond to customer needs.
- Suggest responses pulled from knowledge bases, ensuring accurate and consistent information is provided to customers.
- Provide cues and recommendations related to behavior, compliance, or next-best actions, guiding agents to deliver optimal service.
- Summarize interaction segments as they are passed to another channel or agent, eliminating dead air and providing context for seamless transitions.
- Translate conversations in real-time, facilitating communication with customers who speak different languages.
- Remove background noise and reduce accents, improving the clarity of the conversation for both the agent and the customer.
Job #5: Update Systems and Fulfill Customer Inquiries
After an interaction is complete, numerous systems need to be updated, and specific workflows may be required to fulfill the customer's request, such as initiating a return and replacing a defective product. AI can augment traditional automation in several ways:
- Generative AI (GenAI) can be used to map integrations to enterprise systems, reducing the effort required to develop and maintain these integrations.
- Autonomous agents provide another option for workflow automation. These AI-powered agents can navigate complex processes, gather necessary information, and execute tasks independently, reducing the need for human intervention.
- AI can eventually automate after-call work and dispositions, eliminating the need for agents to manually update systems and categorize interactions.
Job #6: Understand the What, Why, and How of Customer Inquiries
Despite the abundance of measurements, many contact centers struggle to understand why customers are calling, what they are trying to accomplish, and how they navigate through the various channel options. AI is well-suited to analyze journeys and uncover patterns, providing invaluable insights:
- AI can uncover intents and utterance candidates for automation by analyzing recurring requests.
- AI can analyze actual conversations and metadata to uncover the underlying reasons behind customer contacts. AI can also surface overall customer sentiment, and estimate key metrics such as Net Promoter Score (NPS) or Customer Satisfaction (CSat) with unprecedented accuracy.
- AI can stitch together interactions related to the same journey, providing unique insights into omnichannel bottlenecks and frictions.
Job #7: Help Supervisors
Supervisors often face challenges in managing their teams effectively, as they juggle multiple tasks and struggle to prioritize their efforts. Moreover, they frequently lack the time to provide adequate coaching and support for their team members' development. AI can assist supervisors in several ways:
- AI can spot difficult conversations in real-time that require immediate attention, allowing supervisors to intervene and provide support when needed.
- AI can streamline the Quality Management (QM) process by listening to all conversations, pre-scoring them, and flagging specific segments for supervisors to review.
- GenAI can provide a natural language interface for supervisors to make changes to complex systems.
Job #8: Plan Resources
The ever-changing landscape of customer interactions, with a blend of self-service and assisted channels, has pushed traditional statistical methods for forecasting traffic and planning resources to their limits. Additionally, scheduling must better accommodate employee preferences to maintain a satisfied and motivated workforce. These challenges present optimization problems that are well-suited for AI solutions:
- Machine learning (ML) algorithms can predict traffic and resource needs with unprecedented accuracy. AI can also create accurate forecasts that accommodate the diversity of interactions and channels in today's contact centers.
- AI can generate schedules that balance business needs and employee preferences.
- AI can make real-time adjustments throughout the day to cope with changing conditions.
Job #9: Hire, Onboard, and Train Employees
Contact centers face the constant challenge of high turnover rates, often ranging from 20-30%, making hiring and onboarding a continuous process. Additionally, customer service representatives require ongoing training to handle increasingly complex inquiries and coaching to deliver exceptional experiences.
- AI can be applied to identify the traits of best-performing agents and create personalized development plans.
- AI can provide role-play training based on real conversations.
Job #10: Personalize Experiences
The personalization of contact center experiences has been the quest for the holy grail. Organizations have struggled to connect interactions as part of the same customer journey and to unify customer contexts spread across various applications. AI promises to be a game changer:
- AI can resolve and bridge interactions and identities related to the same customer journey.
- AI can analyze large datasets, combining profiles, histories, and preferences to personalize experiences in a whole new way.
This comprehensive inventory of AI applications, categorized by key jobs to be done, illustrates AI's potential across all facets of customer experience (CX). The challenge for leaders is clear: make sense of the plethora of options in front of them, visualize the longer-term art of the possible, and create a roadmap that balances the need to make experimentations that deliver short-term results and progress toward a more strategic impact delivering on the transformative promise of AI for CX. By leveraging these frameworks, CX leaders can chart their path forward and position their organizations at the forefront of the AI-driven CX revolution.