Real Assistance For Virtual Assistants

April 28, 2022 by Fabio Caversan, Vice President of Digital Business and Innovation at Stefanini

An article by Fabio Caversan, originally featured at Forbes here.

In recent years, one of the most exciting and fastest-growing areas of AI innovation has been the field of AI-powered virtual assistants. This includes both well-known consumer devices like Amazon’s Alexa and specialized virtual assistants used by growing numbers of brands and businesses to streamline both verbal and digital customer interactions and save time and money in the process.

Leading omnichannel AI virtual assistant solutions deliver a blend of technical capabilities, flexibility and customization, along with the convenience of swift and seamless integration. The technology powering these solutions has matured rapidly, delivering proven real-world utility with diverse integrations and support for a wide range of web, mobile and social media platform interfaces.

Leading omnichannel AI virtual assistant solutions deliver a blend of technical capabilities, flexibility and customization, along with the convenience of swift and seamless integration."

Fabio Caversan

Vice President of Digital Business and Innovation at Stefanini

From business applications, IoT devices, enterprise solutions and authentication to monitoring, calendar and email, software inventory, web chat and translations and voice services from iconic brands like Microsoft and Google, virtual assistants are a near-ubiquitous presence on the operational landscape for a growing list of industries.

Understanding the promise and potential of AI virtual assistants—and getting a better feel for how to successfully leverage them in your own company—begins with appreciating their capabilities and what is likely to come next for these powerful and flexible new digital tools.

Current Status: Natural Language Understanding Plateau

While aggressive research and intriguing new advances continue to move virtual assistants forward, the steep arc of innovation has leveled off somewhat in the last year—a lifetime in the fast-paced world of technology. Developers continue to find new ways to improve the performance of AI and virtual assistants, but big breakthroughs and transformative leaps forward have been largely absent. What we see instead is the kind of incremental improvements in cost and performance that are valuable, if not exactly game-changing.

Dueling Philosophies

That doesn’t mean impressive work isn’t happening in the virtual assistant space. Some staggeringly large language models are being developed that are expanding from an already billions of parameters to what has recently achieved the almost unimaginable trillions of parametersThose working in that space represent one development school of thought in AI assistants.

These improvements can and do add value by delivering smoother and more seamless engagement, but they can still “break” if questions aren’t asked in specific ways or if the interaction progresses beyond the system’s conceptual boundaries.

A second school of thought contends that no matter how far the envelope gets pushed in that direction, true breakthroughs will be needed to power the next generation of AI virtual assistants and enable them to engage in dynamic conversation that mimics human communication. For industry experts, analysts and observers, the exciting (and yet-to-be-answered) question is what that breakthrough will look like.

Capabilities And Priorities

What characteristics and technical capabilities distinguish the best of today’s cutting-edge AI virtual assistant solutions?

Robust and sophisticated natural language capabilities include:

  • The use of hybrid models that mix neural and semantic networks recognizing millions of concept relationships.
  • A multilingual platform with natural language understanding, including features like synonyms, typing errors, verb tenses and common slang expressions.
  • The ability to comprehend complex contexts, with context memory that retains meaning across subsequent interactions.
  • Self-learning functionality based on user interactions and the capacity to understand user sentiment in ways that facilitate intelligent follow-up and behavioral adaptation.

Flexibility, customization and ease-of-use capabilities include:

  • Support for diverse web, mobile and social media platforms.
  • Technology that can be utilized in virtually limitless business applications and circumstances across industries such as telecom, healthcare, education, hospitality, retail, manufacturing, e-commerce and IT services.
  • Technology that can be deployed, licensed and configured to meet adopter needs in the cloud, on-premise or in hybrid environments.
  • A fully customizable interface that allows full customization for the virtual assistant’s “personality” and, in some cases, appearance.

Speedy and seamless integration capabilities include:

  • Scalable multi-channel architecture with an open and rich API for easy integration with any system.
  • Technology built with low-code and no-code tools for editors or analysts to easily create new contexts and dialogue flows without specialized programming expertise.
  • A speedy and accessible training process that allows users to easily “teach” their virtual assistant and go live in a relatively short timeframe.

Integration 101

Businesses considering integrating an AI assistant into its operations should first try to identify candidates who offer a combination of technical horsepower, customization, speedy integration and proven real-world utility. When it comes to the nuts and bolts of integration, however, the path forward depends largely on the existing tech architecture that is in place.

With a modern cloud-based architecture, for example, the tech side of coding and integration is fairly straightforward. Users should focus resources on conducting high-quality user-centered design sessions, striving to fine-tune the nature of customer interactions with their virtual system in ways that save time and deliver efficient and user-friendly experiences.

If the existing architecture is outdated, then preparing that tech foundation first should be a critical priority before any consideration is given to deploying the virtual system. There are alternative solutions for those who lack the resources or capability to modernize their architecture, such as robotic process automation (RPA), where the virtual system simulates user actions like typing and clicking.

The Way Forward

There is a general sense that years of big hype and sci-fi promises about what AI virtual assistants can do has left some users feeling underwhelmed. The reality, however, is that there’s very real and often significant value in today’s virtual assistant solutions. They improve customer experiences, deliver savings and boost revenue. The key is for users is to be clear-eyed about what is achievable with AI virtual assistants—and for developers to focus more on real-world deliverables in how they present this technology.

At this time, the most meaningful innovation in this space comes from investments not in incremental language and processing improvement, but in the integrations and ecosystem around those systems. Business users don’t need a virtual assistant to interpret Shakespeare and generate independent insights. But adopters, their customers and their professional partners could benefit greatly from new tools and functionality that delivers daily value.

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