From natural language processing to predictive analytics and chatbots, Artificial Intelligence (AI) is one of the greatest driving forces behind today’s shift to the digital customer experience (DCX), but what does the research say about the possible return on your AI investment? Here’s what studies teach us about the real-world results enterprises are recognizing when it comes to AI technologies in the contact center.
AI is no longer an “emerging” technology. According to Gartner, global enterprise use of AI has grown by 270% in the last four years alone. AI-based technologies can be purchased as applications helping enterprises apply machine learning for more powerful customer data analysis and agent performance analytics. Other relevant areas for AI include natural language processing, virtual agents/chatbots, predictive analysis, sentiment analysis, real-time speech analytics, skills-based routing and more.
If your organization hasn’t yet implemented AI in some form, there’s a high chance your competitors have—and that should be a concern. About 44% of organizations are using AI in their DCX initiatives, according to Nemertes Research. Organizations that use AI report measurable success across the board, from improved customer satisfaction and sales to reduced contact center agent turnover:
Expectations around AI are high, but what are the actual returns on how AI drives a superior customer experience? Research is helping executives estimate the potential. Just look at contact center analytics, which is one of today’s top applications of AI in DCX projects. A 2019 report from Nemertes Research found that organizations implementing AI-powered interaction analytics saw improvements in:
These numbers are promising, yet the fact remains that measuring ROI on AI can be tricky. Research firm Accenture puts it perfectly: some applications link neatly to projected returns, while others are more complex, indirect, or even a bit unpredictable. The good news is, with the versatility of AI, an investment can benefit many aspects of the contact center. For example, natural language processing and chatbots are the most common applications of AI in DCX projects, which helps explain how Nemertes’ study found a 72% improvement in self-service. But many customers also use AI and intelligent virtual agents for things like natural language call steering, queue callback, appointment setting and reminders, as well as payment collection. These have varying degrees of straightforward traceability but can nonetheless help justify the cost.
ROI assumed, a solid plan must be set for investing in AI. How can organizations confidently do so?
Hands down, there are three things you must do to begin laying the foundation of your AI journey.
Communications are the digital customer experience, and a cloud model is more agile—making backend technology systems better suited for the kind of rapid innovation that’s needed for both AI and DCX. Legacy communications and contact center systems simply lack the processing power needed for leveraging AI to create advanced capabilities around predictive analytics, virtual agents, sentiment analysis, and more. But enterprises shouldn’t move just their contact center to the cloud. Corporate communications should migrate alongside the contact center to create a globally consistent communications delivery platform that serves as a standard foundation for AI innovation. This way, you can leverage AI for both the “front of the house” as well as the “back of the house.” Analysts at Nemertes explain more:
“Approximately 32% of organizations use the same provider for both internal and customer-facing collaboration and communications. Doing so enables consistency across all communications applications and sets the stage for adoption of AI to improve workflows, experiences, and customer engagement.”
Read the full Nemertes report, Transforming the Customer Journey.
You cannot ignore your network; it’s the underlying enabler of your organization’s AI-driven digital transformation. As you increasingly rely on the cloud for critical communications and collaboration, you’ll need rock-solid network reliability, consistent performance and high throughput everywhere in your WAN. You’ll also need enough application intelligence in the network to actively protect and preserve the performance of business-critical applications. And don’t forget that AI data analysis requires processing power at speed and at scale. To ensure the success of your AI deployment(s), you must address network architecture and service quality, making important decisions about which network or partner can be trusted to serve as your cloud delivery platform. Learn more with our latest white paper, which provides seven metrics for evaluating the network beneath your cloud contact center or unified communications solution.
Cloud contact centers and AI alike trigger critical security questions and important decisions. Rather than making security an afterthought, necessary features, responsibilities, and support services should be proactively infused into your digital customer experience strategy. Consider what your AI-powered cloud contact center will need from a security perspective, remembering that you may want a solution that bundles not just reliable network services but also security threat monitoring and response services as well. Many enterprises prefer working with a single partner that can cover a wider breadth of their needs, so they can free more of their IT resources to focus on building a delightful customer experience.
Ready for an all-in-one cloud contact center solution? Masergy combines the best-of-breed technologies from Cisco and pairs them with software-defined network services that deliver globally consistent application performance. Plus, you get omni-channel capabilities, built-in predictive analytics, and open APIs for multiple CRMs—all with unparalleled visibility and control.
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