Anywhere conversations can be automated, they will be. As Conversational AI improves, the scope of software-driven conversations will expand and its sophistication will increase.
Most enterprises have implemented conversational AI or will soon. Starting off as experiments, this work has been project-based, used a diverse set of technologies and has served a limited number of user roles.
This has resulted in Conversational Sprawl, whereby the enterprise is saddled with random and unplanned growth of Conversational AI, implemented without an overarching strategy. Conversational sprawl introduces both old and new kinds of risk.
A robust Conversational AI engine can cut costs 60-80% below outsourced contact-centers. How much are you spending to recruit, hire and train contact center staff? What about the incremental cost and loss of institutional knowledge when attrition takes place? Don’t overlook healthcare and performance incentive costs. Design Cognigy.AI conversations one time, allow its advanced machine learning to continuously learn and improve and simply update your conversation flows when you want to add new information. No additional training required, no mistakes and a fraction of the cost.
First, because platforms are governable, you can make them smarter. Integrating with internal systems like CRM, Ticketing, HRIS or Inventory Management allows users to complete end-to-end processes through a conversational interface.
You can build and test these integrations once and reuse them in a variety of conversational interfaces and use cases.
Second, platforms provide a variety of user interfaces — from code-based to UI-based — that empower users within and outside of IT. IT can assert control without being a bottleneck.
As a whole, a platform lets you deliver on the promise of conversational automation, allowing you to provide a wide range of capabilities, assert control and governance and, ultimately, surrender all of the day-to-day work to individual business units.