There’s no doubt about it: artificial intelligence has become a buzzword in mainstream tech, with every industry talking about how they can leverage AI to do things like serve food, review video footage, drive cars, automate business processes and more.
Investors are pouring money into AI-based SaaS and hardware startups, and large companies are investing in finding ways to implement AI to better improve on operational, supply chain and other components of their business. Meanwhile, as IoT continues to expand, the two components go hand-in-hand; a study by Gartner found that “by 2022, more than 80% of enterprise IoT projects will have an AI component.”
But how will AI be used in IoT? Which industries see the most promise for the implementation of AI and how will that impact overall adoption and understanding of this technology for mainstream consumers? And since we’ve already shown a willingness to connect the “things” in our homes, cars and office, will we be as enthusiastic when it comes to artificial intelligence?
Adding the smart in smart home
We’ve seen the conversation shift especially in the smart home, where one-dimensional AI in the form of natural language processing (NLP) is being used in voice assistants like Alexa and Google Assistant. With the growth of smart speakers, those AI-based platforms can leverage voice data gleaned from users to perform tasks, learn and streamline the user experience. While voice-assistants use NLP to process commands, their abilities are limited to user input – asking a voice assistant to turn on the lights for you is still an additional step that a user must take.
What if, instead of asking a voice assistant to perform a smart home related task for you, your home knew what rooms needed the smart lights on based on time of day, time of year, current weather condition, and who’s home; or, what temperature to set each zone on your smart thermostats and what access points needed to be locked or opened depending on the time of day?
That is the next evolution of AI technology in the home, with context and data extracted and leveraged from primarily sensors found within smart devices in the home. This transition will have a huge impact on the way that the smart home is bought, sold and enjoyed.
There are some examples of context-aware smart homes being deployed today – like Vivint Sky, a system that keeps track of macro behaviors in the home to accurately manage and control the smart home devices within. But as we watch the next generation of smart home devices come into the market, we see a need for key technical issues regarding factors like range, security and device interoperability to be addressed, in order to offer a more solidified roadmap for this new era of IoT device applications.
As executive director of the Z-Wave Alliance, a member consortium of more than 700 global member companies supporting the Z-Wave wireless IoT protocol, I’ve been lucky to witness this growth first-hand. With sensors at the heart of the context aware IoT network, there is a need for wireless stnadards that offer longer battery life, increased range, improved processing power and strong device and network security built in.
The next generation Z-Wave technology, known as Z-Wave 700 platform, offers these capabilities, with the benefit of being interoperable with a large portion of popular smart home devices on the market.
Utilities leverage big data
Outside of the home, AI technology shows big promise in industries that rely heavily on the collection of data to analyse and create solutions such as utilities and energy providers. Utilities across the globe are implementing AI on both the back end with demand response and grid management and at the front end with customer usage and demand in order to analyse and leverage large sets of data.
Instead of just capturing this information as in the past, artificial intelligence allows the public utility and/or the energy provider to identify demand spikes and usage patterns and make predictions based on factors like customer behaviors or changing weather patterns and climate models to help make more transformative business decisions.
Smart technology will also play a role in helping utilities with energy management. When the utilities can extend data collection from sensors on the grid to ones in the home through implementation of connected thermostats, meters, lighting and other applications, they are able to provide strategies that can improve budgets or operational needs.
Telecoms use smart to gain edge
The telecom industry is booming, with growth expected to continue – a study from Technavio predicted that “the global telecom IoT market will post an impressive CAGR of more than 42% by 2020,” and a report from IDC says that “63.5% of telecoms are investing in AI systems to improve their infrastructure.”
With the market becoming increasingly more competitive, telecoms and communication services providers are experiencing heightened expectations from customers to provide better experiences and new services and capabilities. AI can step in to assist right at the customer service level; with the help of AI-based chatbots to get customers started while lowering operational or call centre costs. It can also be used in a similar way as in the utilities industry, to analyse and offer data-drive insight into showing telecom operators exactly how their customers are using their services, navigate and troubleshoot issues that arise more quickly, or also help set up devices in the home remotely, without on-site support required.
And as the smart home continues to become more popular with mainstream consumers, telecom operators are looking for ways to leverage consumer-facing AI-based technology, such as smart assistants, to help sell services as well. For example, Dish Network’s partnership with Amazon Alexa to add new top abilities to its Hopper range of set-top boxes gives its customers the ability to set recordings, launch apps and navigate menus all via voice control.
Devices that are intelligent enough to act independently at the edge of a smart environment are still in their early market stages. However, as these new sensing devices and applications reach critical market mass, they are expected to radically impact a broad variety of industries.
Developers in industries like smart home that are seeking to design new devices for AI environments can already choose from a variety of pre-existing industry verticals that are ripe for extensions through AI. At the same time, next-generation sensor opportunities will extend beyond today’s well-known products and device classes. In such an open market, a chosen technology platform must be flexible enough so that new products for existing applications can be rapidly brought to market, and new types of products can be developed on top of a mature, proven framework.
There is no predicting where the next such breakthrough will come from, and from which business or research channel. What is easier to predict is that smart environments will continue to become smarter, and artificial intelligence will play a big role in that future.