According to Vanson Bourne, 58% of organizations are currently in the implementation stage of artificial intelligence (AI) with plans for further expansion. It’s during this planned expansion that brands expect to see the greatest benefits of AI such as improved customer and agent experiences and higher annual revenue. What does this planned expansion of AI look like, and why are companies taking so long to get there?
It’s important to note that many are still in the first and most critical step of AI implementation, which is investing in the initial collection and analysis of data. This requires a sort of data sanitation process to ensure complete accuracy, as well as system consolidation to eliminate data silos (consider that the average organization still operates around four CRM systems). This is arguably the greatest delay to planned expansion of AI, yet an understandable one.
Organizations with accurate, “clean” data sets are then able to identify what information should be collected to derive key insights that improve decision-making. This is when brands can take the leap from implementation to planned expansion of AI across the enterprise. Here are three of seemingly endless ways they can do so:
- Automated self-service: The machine learning and natural language processing (NLP) technology behind AI-enabled devices like Alexa and Google Home can be leveraged to build more intuitive self-service automation platforms similar to traditional voice IVR. Machine learning can also be applied around customer conversations to better understand the types of experiences customers are having in a specific communication channel. This can help brands pinpoint the top things customers are saying or top issues being reached out about.
- Agent assistance: In this same way, AI can be applied to make the agent experience more easily navigable with improved workflows. For example, what tools do agents use most for making a sale? Troubleshooting an activity or case? Handling a customer inquiry? AI-based analyses can be used to determine what agents need most and when they need it, improving their overall experience (and subsequently, that of their customers).
- Smart routing based on interaction insights: AI-based analyses can be used to recognize how customers are interacting and what interaction points they are using to anticipate needs and more intelligently engage. For example, if you know this is the fifth time a customer has called into the contact center for a specific issue, you might want to set a rule that escalates that individual somewhere else (say, a certain specialist or your retention-risk team). Machine learning technology on the backend is able to determine the effectiveness of those routing outcomes, enabling you to adapt and improve accordingly.
According to Vanson Bourne, 70% of organizations believe adopting AI is no longer optional but a necessity. If you want to adopt AI more across your organization but don’t know how, schedule a Discovery Workshop with Avaya Professional Services. We’ll help you identify the key areas of your business where AI can have the greatest impact up front and execute plans accordingly.