The ability of digital assistants to use and understand everyday language and help us in our homes, our cars, at work and on the go opens up huge possibilities for businesses. Better still, by performing real-world tasks such as manning call centers they can boost customer experience and lower costs.
But it’s hard to ignore their limitations, namely that the AI model powering voice-enabled digital assistants needs context to perform. In other words, the likes of Google Assistant and Amazon Alexa may let you build your personalized smart home, order food from Just Eat or stream Netflix to your TV, but they still can’t carry out general conversations or perform random tasks. They can only understand, do and say what they’ve been trained for – albeit exhaustively – in closed domains. And that’s just across the devices, apps and services they can connect with.
With GPT-3, BART, and now Meta’s Project CAIRaoke, the technology is improving. Meta, for example, claims that Project CAIRaoke will be capable of having much better contextual conversations, as it combines the four AI speech models used in digital assistants today. It also says that its new neural model will reduce the work required by developers to add a new domain.
But when using these technologies to build your own voice-enabled digital assistant, there’s no doubt that quality assurance (QA) is crucial in the journey to train the AI.
Voice assistants and speech recognition technologies may be the future. But businesses should ensure that their digital assistants know much more than just how to talk. To realize the full benefits of the technology, organizations need to understand:
As consumer trust and privacy is everything, this last point is the trickiest.
A comprehensive QA strategy for any AI-enabled, voice-powered digital assistant should focus on more than the technical aspects, and cover:
Imagine asking a digital assistant about the premium due date for your car insurance. To get the right answer, the AI needs to understand your personal details, the number of cars you own, which car insurance is nearing renewal, the process for renewal, and also, the details of any discounts on your premium and more.
In reality, this information is nothing more than knowledge of business processes. But it’s a crucial component to consider before deploying digital assistants. Without training and testing the AI for the correct responses based on business processes, the digital assistant won’t live up to customer expectations.
To execute voice commands such as those used to order food, play music or stream videos to your TV, a digital assistant will need to communicate with nearby devices and relay information to other apps or services. Take controlling internet-connected appliances such as smart-home light switches, doorbells, cameras, thermostats and plugs.
This is orchestrated via a technology ecosystem of software and hardware that allows communication and relaying of information to perform a task. Businesses should validate digital assistants for this interoperability and the ability to communicate with sensors or devices.
Imagine that a woman asks a digital assistant to read the news headlines. It’s crucial that the AI doesn’t use her gender, ethnicity or religion to decide which news items to read out and from which sources.
AI ethics are about the moral principles and techniques intended to inform the development and responsible use of AI technology. Hence, the EU’s proposed AI Act. This is designed to address the risks generated by specific uses of AI and ensure that Europeans can trust what AI has to offer.
The financial and reputational damage caused by unconscious biases in the development of AI are well documented. But you should also tread carefully to avoid encoding AI biases that may prove detrimental to individuals, your business or wider society.
This is where consumers’ greatest fears lie. Take a customer asking your digital assistant about the availability of funds in their personal bank account, for example. Your digital assistant shouldn’t share the answer with a third party.
Likewise, customers need to know that any voice data and knowledge that companies hold on them to deliver more meaningful interactions in the future is stored securely and separately. And because this information is stored in the cloud, stringent security parameters should also be put in place to avoid theft and/or loss through cyber-attacks.
