You're struggling to streamline customer support interactions. How can process automation help?

With Gen AI, implementing automated chatbots and virtual assistants can provide immediate responses to common queries, reducing wait times. Use automation to route customer inquiries to the appropriate support agents based on their skills and availability, ensuring faster and more accurate resolutions. Automate repetitive tasks, such as ticket creation and data entry, freeing up support staff to focus on more complex issues. Implement automated follow-up systems to keep customers informed about the status of their inquiries and ensure timely resolution. Utilize data analytics to gain insights into common customer issues and improve support processes continuously. Automation can help find and present info on demand for your agents/employees.

It’s a common tale, especially in fast-paced environments: Streamlining customer support interactions can seem like a daunting task. However, process automation can be a game changer with Agentic AI Gen AI it is at different level. By automating repetitive tasks such as ticket sorting, data entry, and initial customer inquiries, your team can focus more on complex issues that require human attention. Implementing chatbots with GenAI can provide instant responses to common questions, enhancing customer satisfaction. Moreover, automation tools can analyze customer interaction data to identify trends and improve service strategies. This shift not only increases efficiency but also boosts the overall quality of customer support.

AI-Driven Ticket Routing: Utilize AI to categorize tickets based on keywords and issues, improving response times and agent efficiency. Chatbot Assistance: Deploy AI chatbots for routine queries, allowing human agents to focus on complex issues. Self-Service Resources: Develop a comprehensive knowledge base and FAQs for quick customer resolutions, reducing support ticket volumes. Automated Email Responses: Implement automated emails for common queries or updates, ensuring prompt customer service. Data-driven Insights: Use automation to gather and analyze customer interaction data. Workflow Streamlining: Integrate automation for tasks like data entry and emails Continuous Improvement: Utilize automated surveys for real-time feedback.

Let's think about the tasks that developers have to detail requirements or do process mining to understand the scope of their initiative. These two solutions for me can be done using screen recordings and converted to text and get recommendations using Copilot

Whenever you need to prioritize, list 4 benefits. Risk, volume (FTE), time and using copilot it can help you look in detail without external factors which is the most strategic project when you focus and look for new resources for your back log

You could provide the user the option to select which category/route best describes their query or have them write a free-hand query and then analyze it with an LLM (such as GPT or Claude) to categorize it. The best way to decide is to understand your customer profile, but also to do some A/B testing and see where you get the most engagement and happier clients.

An automated knowledge base—because nothing says 'top-tier customer service' like directing users to solve their own issues. Perfect for the DIY enthusiast, it ensures that customers spend quality time sifting through articles and FAQs, feeling that warm glow of independence.

Not only directing the user to the relevant articles; with the latest advances in GenAI, the AI models can now come up with the most relevant articles, analyze them, and then provide a natural response to the user without them having to click on extra links or do more research. This can provide the most human-like automated interaction.

Los chatbots conversacionales pueden cumplir esta función, especialmente en modelos generativos reforzados con métodos como RAG. De ese modo, la persona no solo se autogestiona, sino que además lo hace consultando en lenguaje natural.

Automating feedback—so we can finally reassure our customers that their voice matters, even if it’s just analyzed by a robot. Send out those surveys like clockwork and collect those precious stats to tweak your processes. Oh, and let’s not forget, reminding your team that they can always do better.

Todo proceso se encuentra abierto a iteración, ya que las necesidades de los usuarios pueden variar constantemente. El comportamiento de los usuarios nos pueden dar luces sobre cómo entienden la aplicación, o cómo desearían que funcione.

With a lot of the tools, you can also set up SLA (Service Level Agreement) times for your staff members. These indicate how fast a query should be first responded to and/or resolved. If the SLA is not met, a supervisor can be set up to get an alert to make sure nothing slips through the cracks.

When we think about RPA with the project management structure, this creates a lot of risk, quality and analysis controls. I believe that RPA teams need to be agile and lean to deliver solutions in waves and over time. Take care of this situation when you are studying