Conversational AI Investment on Your Radar for 2022?

Gartner recently published its annual list of strategic technology trends that will shape the year ahead for businesses around the world. It’s no surprise that included among the 2022 list are cloud-native platforms, artificial intelligence (AI) engineering, and total experiences. At the intersection of these we can find conversational AI solutions.

Conversational AI is the technology that allows us to use speech and natural language to transform the way we work, connect and interact with each other and the world around us. In the workplace, conversational AI often looks like a chatbot or virtual agent that automates some aspects of customer service, employee engagement, and so on.

Increasingly, conversational AI solutions are helping more and more retailers create personalized customer experiences, attract and retain more shoppers, and empower employees by supercharging their efficiency and productivity. It’s an area that’s grown in importance; and more, it’s gaining attention.

In fact, in a recent survey of CIOs and technology executives, 48 percent said they’re planning to deploy an AI-based platform in the next 12 months. And, when combined with “responsible AI” and “MLOps,” these investments represent a significant strategic focus in the near term.

Supply chain, logistics, agent/labor retention, and employee recruitment challenges — now also combined with pandemic-driven, dynamic consumer behaviors — mean retailers must rethink customer engagement strategies. Conversational AI solutions like messaging, chatbots and virtual assistants are ideally suited to meeting high customer expectations, delivering positive shopping experiences, and even helping prevent fraud — but only when they’re done right.

Poorly designed retail chatbots frustrate customers, unnecessarily escalate queries to a live agent, and otherwise create underwhelming experiences for shoppers and agents alike. These projects can underperform for a variety of reasons: everything from failing to fully understand customer needs and business objectives to choosing the wrong conversational AI platform.

The truth is that, despite the “chat” in their name, most retail chatbots aren’t good conversationalists, which is why the underpinning of your solution — the conversational AI itself — really matters. Choosing the right platform and aligning the project to organizational priorities, channel strategy and, perhaps above all, your customers’ needs is essential.

Likewise, creating an effective chatbot requires a team effort; developers, designers, data and speech scientists, testers, and business stakeholders all must collaborate quickly and effectively. Cloud-based platforms have made this type of collaboration possible, especially when the team is laser-focused on creating a chatbot that solves for a specific problem.

Let’s say, for example, that your brand wants to launch a chatbot that will help alleviate some of the contact center’s call volume by answering customer questions about order status. Knowing this objective will shape key aspects of the chatbot’s design, what information it needs access to, how advanced the conversational AI platform needs to be, and the depth and breadth of the chatbot’s knowledge. Additionally, having a clear objective from the start of the project will help you recognize success when it arrives so you can attribute return on investment and then make continued, targeted investments going forward.

As retailers examine their priorities for 2022 and consider which AI technologies represent good investments in the coming year, conversational AI demands a closer look. Done right, conversational AI solutions can not only help to meet growing customer expectations for self-service, but they can also improve sales, agent efficiency and satisfaction, and reduce operational costs across the brand.

Seb Reeve is the director of intelligent engagement business development at Nuance Communications, an American multinational computer software technology corporation.