We may not notice it, but both artificial intelligence and the Internet of Things are driving massive innovation in consumer and business environments. The smartphones in our pockets, for instance, are equipped with some 9 to 13 sensors and have been running machine learning and deep learning via on-device firmware and in the cloud for years. But to ascertain the broader impacts of these trends on consumers, businesses, and ecosystems, we must consider how they converge and the implications therein. What follows are three examples.

1. A more ‘human’ IoT

Although connected devices and even robots have been around for years, they have struggled to transcend the typing, clicking, touching interface of the mobile and laptop world. Their utility has remained largely tethered to another device or power source. They have struggled to achieve mainstream adoption. While an estimated 15 billion IoT devices are forecast to be in consumers’ hands by 2020, manufacturers have been scratching their heads about how to inspire such a significant growth trajectory.

Thanks to advancements in machine and deep learning, as well as natural language processing and understanding, reliable voice recognition is redefining how we interact with machines. The ability to simply talk to an IoT device or environment means the human-machine interface starts to look a lot more like a human-human interface. From a technological adoption standpoint, this doesn’t just improve the interface — speaking over typing or tapping; heads-up, not down — it unlocks entirely new markets. Consider how this enables elderly folks to use home care apps, helps disabled folks to enjoy internet services, or welcomes less tech-savvy users.

Not only are humans innately wired to learn and produce language with relatively little effort, voice conveys umpteen unseen elements of communication — emotion, tone, cultural nuance, etc. — we use to gauge interactions. This is the inherent personalization we employ in social interaction, alongside our long-held tendency to imbue objects with human characteristics. We trust and identify with things when we anthropomorphize them. Add to these interactions adjacent advancements in processing speed and devices can respond dynamically, incorporating context from diverse data sets, customer preferences,  and real-time contextual signals.

This unlocks a new era of personalization wherein our devices become trusted partners, concierges, media curators — maybe even friends?

2. New modes of enterprise scalability

Although artificial intelligence introduces a new universe of risks to enterprises — automated decision-making, privacy, compliance adherence — it also represents a range of new opportunities to scale and expand efforts many organizations have been building as part of their existing digital transformation efforts. Opportunities span every single business function, but consider the following examples when viewed alongside IoT:

  • Customer service and support: There’s a lot of buzz about chatbots, but when virtual assistants span devices, it allows companies to scale support. Agents are best for simple common queries and can triage to human agents for more complex or sensitive support issues. They can also create continuity across channels (read: not having to repeat the same information). Brands can deliver service experiences to the most appropriate interface, using services like DialogFlow to easily integrate natural language understanding into their products. Imagine, for instance, if you could simply ask your router to configure Wi-Fi or run a security diagnostic, or ask your connected car to schedule your next oil change.
  • Sales: Equipping salespeople with mobile devices and wearables in order to acquire and log information in the field has become commonplace. But advancements in computer vision and deep learning plus IoT are equipping sales with new capabilities. Virtual and augmented reality are growing in adoption as both a B2B/technical sales and consumer sales tool. In industrial capacities, VR offers the ability to view intricacies of machinery, parts, or tooling before a purchase order is signed. In consumer, companies like Uniqlo are experimenting with virtual dressing rooms, allowing customers to “try on” clothing without ever disrobing.

3. Ecosystem intelligence

The fundamental currency between IoT and artificial intelligence is data. The recent resurgence of AI is driven by the colossal amounts of data that billions of existing IoT devices have been generating for decades. Conversely, AI’s role in the IoT market is to accelerate and deepen analytics, parsing signal from noise to enable new services. We’re already seeing devices transform:

  • From “smart” — which until only recently meant “connects to the internet and has an accompanying mobile app” …
  • To “intelligent” — characterized by the ability for devices to learn from their interactions with users, service providers, and other devices, as well as from the interactions with all devices in the network, product line, or fleet.

One notable example of a company leveraging IoT and AI for “ecosystem intelligence” is Tesla, whose fleet of self-driving vehicles learn from each other. The experience of one Tesla is transmitted to and “learned” by all other cars in the network.

Ecosystem-level learning across organizations has also become possible. For instance, Tesla also shares data with the U.S. Transportation Department and rival automakers. Open data, open development frameworks, and shared standards are all critical for the interoperability and scale of AI and IoT — and the societal benefit of both.

Jessica Groopman is an industry analyst and founding partner at Kaleido Insights, a company that advises corporate leaders on how to transform the kaleidoscope of technological disruption into clear, actionable strategies.