Artificial Intelligence (AI) is breaking the hype meter in the contact center industry. While it’s only in the early adopter stages, it appears to be grabbing the attention of contact center technology sellers and buyers.
AI processes and interprets large volumes of data (and I mean large!) to drive action through decisions and predictions—basically trying to mimic human thought processes on inputs and outputs, but on a scale and speed of data consumption and use that we can’t match. AI is dynamic: It learns, gets smarter and suggests improvements.
Here’s where we see opportunities for AI near term:
Self-service “bots” promise greater success rates and user experiences compared to traditional IVR or chat. These tools offer a smart and conversational user interface along with an app that provides answers, guides users through transactions, and knows when to get a human involved and how to find the right one based on the data available. You may hear these solutions called Intelligent Virtual Assistants (IVAs). While some may be single channel (e.g., a ChatBot), a preferred long-term approach is to have an omnichannel platform that can serve customers via a variety of channels—voice, web chat, text chat, etc.
Moving over to routing, an AI-enabled engine can use all the information available about who is calling (or emailing, or texting, or chatting, or…) and why, as well as other available context data, to get the customer to the right agent. The app can use the self-service activity (or additional context-driven conversational questions), as well as behaviors, intent, and tone to hone in on the real customer need and drive the best outcome. This routing is better and smarter than the familiar IVR and CTI because it leverages all that context (data) and isn’t just “rules” driven.
Workforce optimization (WFO) is a field ripe for change with AI. All those contact recordings provide unstructured data that can be processed and assessed. Quality Monitoring could become increasingly automated but perhaps more importantly, drive more targeted action in coaching and developing staff. QM provides a good example of how AI will help understand what was said, as well as considering tone and sentiment, or emotions, taking a leap beyond what we’ve been able to do with speech analytics so far. What was said helps with process compliance (processes and procedures, security, regulations, etc.) while the rest is all about customer experience.
AI-enabled workforce management (WFM) has the potential to create better forecasts, staffing projections, and schedules because of its ability to consider more data, more variables, and more complex patterns and trends than a human. Similarly, it can be more predictive and a faster learner. Apply that “skillset” in real-time and imagine the possibilities to proactively adjust (e.g., tweak break times, ask reps for voluntary time off or overtime, tap reserve staff). Not to slam all those hardworking WFM staff and CC leaders, but replacing manual decisions that often lag events and “chase” service level probably sounds pretty appealing to any center when dynamic, unpredictable events hit.
While analytics should be a part of any AI effort, speech, text, and data analytics are specific WFO suite capabilities that can also improve. We may see breakthroughs as the AI looks at more data, faster, with more context—and therefore, insight—and with better predictive capabilities.
AI-powered desktop tools will guide the agent, provide answers or indicate what to do next through the predictive, “next-best action” smarts that are again consuming massive amounts of both structured and unstructured data. The app could “listen” to a speech or text conversation and drive desktop workflows to assist the agent with speed, accuracy, and first contact resolution. It could tap the right knowledge quickly and proactively, and develop knowledge based on the conversation, desktop activities, and outcomes. The agent could use the same “bot” as a customer, with user profiles defining who can see (or hear) what.
There is much to do with AI and the possibilities intrigue. To launch your center on a good path, focus on the business problem you are trying to solve and match technology to it. If your project starts out as “We want to use AI…” you are on the wrong path. Tie the planning into your digital transformation and other strategic initiatives, such as personalization and pursuing cross-selling and upselling. Keep the omnichannel focus to extend applications across channels and avoid silos.