Conversations That Matter, a podcast from Uniphore podcast

Agent Enablement Before Automation - Matt Dickson - Conversations That Matter - Episode # 65

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Our guest on this week’s episode is an industry leader in providing solutions that solve complex problems in both patient and consumer experience. He’s an expert at identifying key areas of implementation. Please welcome to the show, Chief Operating Officer at Eclipse Telecom, Matt Dickson!

Matt sits down with Host Randy Ksar for an in depth discussion on the challenges of implementing AI in the enterprise. Matt highlights the importance of planning for both the short and long term of your AI, the role different departments play in adoption, and how a bottom up approach to agent enablement can highlight key areas for automation.

Takeaways:

  • While AI may take away a few agent jobs, it will also create more productive employees. By removing mundane tasks, agents are able to be more human and focus on being compassionate and understanding to customer’s complex issues.
  • Agent enablement helps highlight areas for automation. By decreasing friction around certain tasks, you start to get a sense of which tasks are the easiest to automate. Once identified, remove them from workloads, and prepare agents for more complex tasks.
  • The first step of an AI journey is education. Before implementing any solution you need to understand all the ways AI can help your business. With a holistic understanding, you create better compliance and cycles of feedback.
  • A common pitfall for companies implementing AI is that they only focus on short term implementation. It’s crucial to also consider the long term goals and growth of a system, otherwise you run the risk of replacing it in a few months.
  • While many departments in a company are generating buzz around AI, there are a few key drivers of implementation. Contact center leadership, Chief Experience Officers, and help desk departments are the larger drivers of AI.
  • While many jobs in the contact center will stay resilient to AI, there are a few industries that are more vulnerable. Many voice actors and professional narrators are reporting lower amounts of work due to generative voice AI.
  • Natural language processing and natural language understanding both require utterances, but contextualize them in different ways. NLP takes an utterance at face value, while NLU uses contextual information to better classify the utterance.

Quote of the Show:

  • “I’ve always been a believer that agent experience is what creates customer experience.” - Matt Dickson

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