Tony Morris, Presales CX Leader – Solutions Architects at Miratech shares his insights on AI implementation strategies in contact centers
Over the past decade, the public’s perception of AI implementation strategies has undergone a remarkable transformation, particularly within the realm of customer experience (CX).
In 2024, when individuals mention “AI,” they are typically referring to Generative AI powered by Large Language Models (LLMs). These sophisticated models, such as ChatGPT, have become household names, driving a wave of innovation in contact centers worldwide.
Enterprises are increasingly leveraging AI to revolutionize CX, recognizing its immense potential to enhance efficiency, boost customer satisfaction, and drive revenue growth.
In today’s fast-paced business landscape, staying ahead of the competition means harnessing the power of AI to deliver personalized, seamless, and frictionless experiences across every customer touchpoint.
Key Use Cases of AI
There are several popular key use cases for AI. From automated chatbots to sentiment analysis, AI technologies are reshaping the way contact centers interact with customers. I want to focus on the two most popular use cases pushed by most contact center vendors:
Agent Assist with Knowledge – while Agent Assist was introduced years ago for Knowledge Management repositories, LLMs allow for the human-like synthesis of all knowledge into a more thoughtful response. The benefit to the business can be an increase in First Call Resolution with a collateral lift in Customer Satisfaction and Net Promoter Score.
Summarization – which is no longer new, but this feature is presented in multiple ways:
- Post-interaction summarization can shave wrap-up time by presenting notes of voice or text-based interactions to the agent immediately and allow them to move quickly to the next waiting interaction.
- Meeting summarization for virtual notetaking and analysis (via speech analytics) leading to more productive meetings and follow-up. There are even standalone solutions that offer this advanced service regardless of UCaaS, CCaaS, or Meeting platform if they can participate in the meeting as a passive observer.
AI Implementation Challenges
There is always a lot of buzz around AI products and features, but not much focus on implementation details. It makes sense though because who wants to spoil the excitement over pesky little details? As we have all learned through experience, projects that introduce new functionality into a contact center may fail regardless of how cool the product features are.
This happens for several reasons, most commonly:
- Resistance to Change: The agent population wants to maintain the status quo. They already know all the workarounds for the legacy solution and are apprehensive about learning new technologies. One way to overcome this is for organizations to provide training and support to agents.
- Information Overload: The influx of AI-generated insights and recommendations can sometimes overwhelm agents, leading to confusion and inefficiency. I found that by centralizing and providing contextual guidance, businesses can enhance agent productivity and customer satisfaction.
- Exclusion: To ensure successful AI adoption, it is important to include representation from each stakeholder group during the buying process.
Importance of Change Management
Contact center leaders have seen this movie before and they understand the critical importance of end-user adoption and the change management process in driving the success of AI implementation projects. We all know that everyone is happy when an implementation goes well. Without careful planning and execution, the journey to implementation may seem daunting.
Simply getting the project off the ground from a technical perspective is the first step. A project kickoff is easy, but then you must attend to those details nobody wanted to discuss during the sales cycle. You got the shiny new toy, but it didn’t come with complete instructions. Here are some steps that you need to take while implementing AI:
- Addressing implementation challenges: While the excitement of a new project is palpable, the real work begins once the implementation is underway.
- Navigating system and project complexity: AI implementation raises a bunch of questions from synthesizing knowledge across silos to automating business intelligence through speech and text analytics.
- Maintaining focus: Despite AI’s potential, let’s not lose sight of the project scope and regain focus to drive tangible business results.
- Continuous improvement: Like any other journey, AI implementation requires organizations to remain agile and respond to evolving needs. Ask for feedback from stakeholders and leverage data analytics so you can enhance the performance of your AI solutions.
Selecting the Right Partner for AI Implementation Success
That said, it is possible to bring that new solution into the fold of your overall IT ecosystem so that you can realize the business benefits of Generative AI. It won’t necessarily be completed by the professional services team from the software vendor.
They are the experts in their domain and sometimes can assist with integrations directly connected to their platform. However, any custom development or hands-on configuration of third-party solutions will be your responsibility. For that, you must decide if your in-house expertise is sufficient or if you need to partner with an outside firm.
Cost and competing priorities will oftentimes lead to a business decision to put these large transformations into the hands of capable partners. The chosen partner must have full-stack developers on staff, have experience with complex multivendor implementations and, most importantly, they must have a consulting practice that will ask the right questions. The focus should be on all stakeholders and ensuring their needs are being met in service of the ultimate business goal.
Conclusion
As you embark on your journey to enhance customer experiences through AI, finding the right partner is key to your success. Look for an organization with a track record of tailored solutions and deep expertise in AI implementation.
With the right guidance, you can navigate the complexities of your AI implementation strategies confidently.
If you’re seeking more insights or support, don’t hesitate to reach out to Miratech.
Bio Tony Morris
With 27 years in Presales across carriers, equipment manufacturing, service assurance, and cloud solutions, Tony specializes in AWS, Cisco, Five9, Genesys, Google, and NICE. His focus areas include Healthcare, Financial Services, Retail, and Transportation, offering tailored solutions and expert support to diverse clientele.
