According to PWC, over 30% of consumers will walk away from a brand they love after just one negative experience. Businesses are under more pressure than ever to deliver outstanding customer experiences or they risk costly attrition. One way companies are looking to improve customer satisfaction is through AI. For years, AI has aided customer service, particularly with predictive analysis. Now, generative AI provides innovative tools that support customer service teams with issue resolution and with overall customer experience.
Gen AI promises to revolutionize customer service with personalized customer recommendations, intuitive conversational controls based on natural language processing (NLP), and an optimized presentation of data, such as a summary of a client’s call history. Rather than replacing customer service agents, AI chatbot tools make them much more efficient, allowing agents to focus on higher-level strategy and more complicated customer service requests. Additionally, AI tools can assist agents with those more complex tasks, ensuring they are executed more efficiently and with less human error.
Over the next few years, gen AI is expected to dominate customer support. According to IBM, 85% of executives expect their customers to interact directly with company AIs within two years. However, this shift will present numerous challenges. A Salesforce survey found that 71% of IT leaders were concerned that generative AI would expose their organization to new security risks.
The integration of gen AI into customer support offers a promising future. Customer service will be more efficient, personalized, and provide a better experience for customers. However, this can’t be fully realized until AI’s security and implementation challenges are overcome.
AI use cases in customer service
End-user support
One key to Pomeroy’s strategy in deploying gen AI in customer service is to meet the customer where they are. This concept encapsulates the need to integrate AI solutions into existing workflows rather than relegating them to obscure websites requiring users to log in.
We securely embed AI tools within platforms like Microsoft Teams, Slack, or any corporate collaboration tools our clients currently utilize. This approach facilitates the adoption and acceptance of gen AI tools and enhances user experience.
Agent support
In our approach to enhancing operational efficiency, we recognize the critical role of case summarization for effective knowledge management in customer support. Traditionally, agents are responsible for this time-consuming task.
To address this challenge, we leverage gen AI technology to automate the case summarization process. Utilizing a large language model (LLM) can streamline workloads such as summarizing case notes. Like how tools like Grammarly or ChatGPT function, our AI system within ServiceNow processes relevant text to create concise and meaningful case summaries. These summaries ensure that agents can access essential information when handling recurring call types, significantly improving response times.
Another use for AI in agent support is the concept of the “Knowledge Coach,” which guides agents in real time, enabling them to resolve issues more efficiently. This innovation benefits key performance indicators (KPIs) such as average interaction length, first-call resolution rates, and escalation accuracy. AI can provide timely suggestions based on real-time interaction data.
Backend support (AIOps)
Alongside enhancing standard customer interactions and agent support, a key component of our AI strategy for customer support is AI Operations (AIOps), which entails monitoring and managing infrastructure like data centers, networks, and broadband circuits. We focus on improving the overall effectiveness of our Managed Services delivery by integrating AI-driven guidance for technicians, helping them navigate their responsibilities more effectively.
Traditional monitoring tools are trigger-based, generating alerts when specific metrics exceed predefined thresholds. For instance, a warning is issued if CPU utilization surpasses 80%. However, AIOps focuses on identifying anomalies rather than relying solely on fixed thresholds. For example, if a server has consistently operated at 10% CPU usage for six months and then jumps to 50% over a week, this change may indicate an underlying issue that requires investigation, even though it has not breached the threshold. This capability allows for detecting abnormalities that could impact user experience, even in scenarios where no traditional incident is triggered. Additionally, engineers can use NLP prompts to troubleshoot issues more easily.
We tailor our services to the client’s unique needs. We may monitor their broadband circuits for performance and availability across offices or their data center environment and oversee servers and switches to ensure everything operates smoothly. Our team of engineers is then available to address any issues that arise.
Managed services enhanced with AI help ensure the availability of your collaboration, communication, and customer support tools.
Pomeroy’s long-term AI plans
In the next 24 months, our focus at Pomeroy will shift from gen AI to conversational AI. This technology will facilitate interactions within platforms such as Microsoft Teams, allowing users to engage in conversations or type requests—for example, “I would like to order an HDMI cable.”
As part of our strategic investment in 2025, we will integrate machine learning, gen AI, and other advanced technologies into our existing ServiceNow environment. Additionally, we will expand our offerings to include Professional Services centered around AI.
Why choose Pomeroy for AI customer service solutions?
Integrating AI into your enterprise technology is not easily accomplished and requires substantial time, resources, and expertise. Many IT departments lack the bandwidth to adequately train personnel in these emerging areas. Pomeroy has the experience and know-how to tailor solutions to fit our clients’ environments and will train your staff to utilize these embedded AI tools.
Most importantly, we prioritize our clients’ data integrity and sovereignty by leveraging existing large language models. Our approach ensures that while we utilize advanced models, we do not contribute our clients’ data to them. Data privacy is paramount, and we take this responsibility seriously. Haphazardly deploying a public LLM integration like ChatGPT can inadvertently expose your organization’s and end users’ sensitive data.
Additionally, our industry-focused expertise sets us apart from standard IT service providers. Our deep knowledge of the retail, finance, healthcare, manufacturing, and public sectors allows us to integrate industry-specific insights with our AI solutions, giving us a unique edge in effectively addressing challenges specific to that industry.
Also read: The Value of Using Customer Experience Metrics to Measure Client Satisfaction
Get in touch to learn more about securely integrating gen AI into your customer service workflows.
