The smarter home powered by AI will emerge gradually over the next decade and decades beyond. Many more companies are touting AI in messaging. Still, it can be argued that what AI practitioners would define as true artificial intelligence is some form of big data analytics. Specific use cases for AI in the connected home cross all verticals and will ultimately be what helps drive the adoption of new devices and services in the home.
Currently, 37% of US households have a smart home device, and 36% have a home security system. In addition, 56% of US households have smart TVs, and smart speakers/displays reached 53% adoption in the US. In the connected health market, 64% of US broadband households have used a telehealth service in the past 12 months; 37% own a wearable, and 25% have a connected medical device.
The incremental application of artificial intelligence to a variety of connected product use cases is expected to become a significant factor in driving adoption as intelligent outcomes increase product value. There are so many HUGE implications for the leading applications of AI. The value to consumers and providers in vertical markets, including professional and self-monitored security, smart home automation, digital health, energy and water management, and home networking, is immense. In addition, the applications within entertainment and tech support are also wide-reaching.
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Artificial intelligence applications are deployed by the most innovative firms in their product categories. Both legacy players and startup market leaders with leading-edge IP, understand the immense potential of AI. These applications can improve the user experience, personalize features, predict and automate preferable actions, protect and manage the home network, and reduce false alerts and unnecessary notifications.
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Companies can begin capturing value through AI with applications that have a clear and quantifiable ROI. When simple use cases are matched with business objectives, AI implementation is no longer an IT or marketing task with vague outcomes. For example, the investment in AI will be validated when AI can clearly optimize the customer experience to reduce churn or drive operational efficiencies that reduce costs.
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AI will drive business value through enabling enhanced capabilities over non-AI powered solutions. Just as connected products will supplant non-connected models, AI is creating a new value tier that will allow brands to be more competitive at the same price point or demand higher prices for hardware and services. AI can reduce operational expenses for service and support and create new revenue opportunities derived from enhanced knowledge of the customer and deeper engagement with them.
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Adequate funding for investment in AI often requires companies to think more broadly about its value than for one particular business segment. A cross-functional analysis of how AI insights can inform multiple tasks of the company will reveal its full value. This more comprehensive way of thinking also breaks down the data silos that often hamper AI implementation efforts. Executive alignment is needed to transcend the functional needs of marketing, sales, IT, maintenance, and service.
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A robust value chain of AI tools is developing, shortening development time, and reducing deployment costs of AI-powered solutions. The training and refining algorithms for specific use cases are still the “secret sauce” of most companies. Scaling expertise and getting access to large task-specific data sets remain big challenges.
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While AI generates powerful insights and predicts courses of action, companies need to consider how they engage with consumers around that information. Where AI is being used to optimize human behavior and processes, behavioral scientists and UX designers become just as important as data scientists. Machine-human interaction can be a delicate process, especially as consumers are in the early stages of these interactions.
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Interactions with consumers around AI-driven features are best treated gently and incrementally. Recommendations can lead to nudges which can lead to full automation after the user has affirmed certain courses of action or preferences. Consumer control and confidence in data sharing are critical. The intelligence of a product or system functions best when it is an extension of the owner’s intelligence, offering more convenience, safety, savings, and peace of mind.
An inherent build or buy decision underlines all of these technology infrastructure choices. As with all technology investments, options should be vetted by considering short-term and long-term needs, careful consideration of how AI investment is tied to return on investment, and where companies can best invest internal resources to create differentiated value.
Parks Associates has been researching the technology markets for almost four decades. Our extensive library of proprietary consumer and industry data has been collected through our primary and secondary research work. We survey 10,000 internet households each quarter in the US to track the adoption of all tech and services for consumer and small to medium businesses. For more information about access to our research data, insights, and services, please visit www.parksassociates.com or contact me or any of our team directly.
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