The graphical user interface and mouse propelled the personal computer into the hands of nontechnical consumers with its user-friendly design. Smartphones and tablets flooded the market with easy-to-navigate apps, controlled not by a keyboard and mouse, but by the swipe of a finger, making intuitive interfaces commonplace. Whether it’s a mouse or a finger, each iteration succeeded by aligning technology to our more human instincts. And what’s more human than having a conversation?
Enter the era of conversational systems. These systems, more commonly referred to as conversational AI (artificial intelligence), combine natural language processing, AI, and machine learning to understand and respond to free-form text or voice—in an engaging and personalized manner. With increased access to cloud computing and sophisticated algorithms, more companies can implement conversational AI solutions that can turn technology adoption into a conversation rather than an exercise in mastering a user interface. Many expect conversational AI to therefore alter the landscape of traditional Web and mobile applications—first by augmenting them and then, at their finest, by displacing them.
The appeal of conversational AI isn’t going unnoticed. Across nearly every industry, conversational systems can be found in our homes, cars, call centers, banks, and hospitals—and the use cases are growing. By one estimate, the global conversational AI market is expected to grow from US$4.2 billion in 2019 to US$15.7 billion by 2024 (with a 30.2 percent compound annual growth rate).1
Two key technology trends seem to be making this growth possible:
In the first of our five-part series on conversational AI (see the sidebar, “A five-part series on conversational AI”), we explore how these systems can enhance customer engagement, workforce operations, and business partner integrations.
Over the next year, we will discuss the implications and use cases of conversational AI. Beginning with our first chapter on the business case for conversational AI, we integrate secondary research and a series of case studies to navigate the following four topics:
Acoustic authentication: Explains how conversational systems can enhance security protocols by integrating voice into the multiauthentication process
The three Ts of conversational systems: Dives into how one can effectively build conversational AI through Training, Tuning, and Testing
Industry use cases: Highlights how virtual assistants appear to be changing the face of customer service in banking, technology, and health care
The liability of conversational systems: Explores how the more we integrate conversational bots into our work and lives, the more we should take steps to understand their liability in terms of insurance, training, auditing, and the ethical implications
Though conversational AI has existed for over a decade, the use cases and applications continue to become more sophisticated and gain traction in a variety of areas. Further, as computing costs continue to decrease while capabilities expand, personalization at scale has likely never been more attainable. three areas where conversational AI can flourish:
While opportunities abound for conversational AI, it’s not a panacea either. Instead, implementations could benefit from considering these three dimensions:
Indeed, these systems are at the cusp of potentially changing the way humans interact with machines.
As we continue on the conversational AI journey, more advanced applications, use cases, and paradigms will likely evolve, resulting in enhanced productivity for businesses and more personalized and accessible experiences for end-users.
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