How AI Customer Experience Enriches Customers And Employees
Artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) are becoming more and more sophisticated. AI has countless applications, but a 2019 trend is its adaptation for the work environments of organizations. AI is instrumental in the daily processes of the workplace and maybe not in the places you would expect – AI customer service.
AI technologies are being used to streamline and shape customer experience (CX). With the utilization of acquiring, analyzing, and responding to customer feelings and feedback, AI is proving phenomenal ROI for organizations that implement the technologies. AI cuts service costs by increasing operating efficiency and employee productivity. Companies are racing to implement AI customer service solutions to improve both customer and employee experiences.
How does AI benefit the service desk and help desk? Through the reduction of mean time to resolution (MTTR) and increased productivity, AI offers solutions that lower cost and increases operational gains. AI generates an incredible level of personalized experiences that would normally be too inefficient if done manually by humans. The implementation of AI customer service employs virtual assistants to rapidly and substantially increase various levels of customer experience.
Although AI shows incredible results, many organizations have trouble finding where to start. Developing and designing a goal for customer experience allows companies to have an attainable vision for success. The next step would be to create a team of qualified workers led by a designated CX leader. Succeeding in both these steps would help, but the key factor in the adoption of AI is having a deep understanding of the customer journey as well as a deep understanding of the AI.
Additionally, organizations are still hesitant to adopt AI customer service solutions for their environments. Roadblocks and barriers are not fun to deal with, and spending time overcoming them can feel like a waste. Asking yourself key questions will help get you through some potential concerns about implementing AI customer service solutions. If the AI solution produces an error, is there a chain of accountability that leads to a resolution? Do we have enough valid, underlying data for the AI to work with as a baseline? Is there a way to reliably measure results?
Regardless, you may want to keep in mind three notable barriers that can slow down your implementation of AI customer service solutions for your organization:
- Many intelligent automation solutions are professional-services-driven, and many issues can arise because of this.
- You will need extensive upfront training to create algorithms and models (which may require manual labeling data).
- Hiring data science talent can be difficult.
Tracking your progress is another aspect that you should keep in mind. To accurately measure the success of your AI customer service solution, make sure to find the right metrics and key performance indicators (KPI). You can begin by looking at projects that have easier opportunities for tracking and measuring metrics; this might help you with future projects.
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