Why It’s Critical to Identify Use Cases for AI
The most important thing that leaders can do is identify a strategy that aligns to their business outcomes, according to Ken Drazin, head of the intelligent customer experience practice at CDW. “Each business unit will vary,” he explains. “Every department within an organization will have a unique perspective in terms of use cases and outcomes.”
The best way to get started is to consult with trusted partners that can work with organizations to understand pain points and help prioritize, Drazin says. Businesses can then develop strategies and a roadmap that uses the right kind of AI solutions to deliver outcomes that provide the biggest return on investment.
Organizations also do not need to start with the most complex use cases, according to Drazin. In fact, he says, “sometimes, the simplest use cases can also provide the highest ROI for your organization.”
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Chatbots and Call Centers Are Ripe for AI Enhancements
Two relatively easy use cases Drazin identifies use generative or conversational AI as the backbone for virtual chatbots and to enhance the productivity of call center workers.
AI-infused chatbots can help HR and IT departments internally by responding to commonly asked questions in a more natural way. The same technology can also be used on the front end of customer-facing websites to answer questions from customers.
Additionally, AI-powered virtual agents can make contact center workers more productive and improve the customer experience, Drazin says, replacing the traditional interactive voice response system (“press 1 for …”) and either helping customers complete transactions or routing them to the right customer service agent to address their concerns. AI tools can also listen in on a customer’s interaction with a live agent and in real time suggest the best course of action for the agent to propose.
These tools not only help agents, they also “make the customer experience a heck of a lot better because you're providing answers and helping them in the interaction just that much faster,” Drazin says.
On the back end, these tools can also analyze customer interactions to provide predictive analytics and sentiment analysis about products or services. That data can then be used by salespeople to target specific customers with offers they might be more receptive to based on past behavior, Drazin says.
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