5 common ways AI and IoT startups are addressing market needs

2 November 2018 / , / By Jan Helbing

Last month, the Orange Fab team had a chance to connect a select group of industry partners and venture capitalists with some of Silicon Valley’s top early-stage startups working at the intersection of AI and IoT. Orange Fab’s goal is to connect startups to corporations for strategic partnership and investment opportunities. As such, we listened to more than 15 startup presentations spanning energy, healthcare, automotive, and industrial sectors.

The convergence of these diverse startups revealed the common ways they were addressing market needs – here are the five commonalities they shared:

From reactive fixes to predictive maintenance

Whether it’s industrial equipment, home appliances or cars – advances in IoT and AI technology are transitioning from reactive to predictive maintenance. By comparing historical data sets with real-time data streaming from sensors, companies are increasingly predicting asset failures before they occur. Next up? Integrating these anomaly detections into the supply chain so that a broken part is ordered (and delivered) before a human technician could have even known that something was wrong. All of a sudden, it’s not hard to imagine a world with no plane delays or broken down vehicles.

Interoperability is the new norm

It’s no secret that to truly realize the value of IoT, the industry needs interoperability. There are so many platforms, sensors, controllers – and they all need to talk with one another in order to drive adoption. In fact, most startups now tout that they’re hardware or cloud agnostic as one of their key selling points. Interoperability also allows customers to choose from various vendor solutions, which further accelerates innovation, market competitiveness, and growth.

An AI solution is only as good as its user experience

A company could have the best tech available, but if it’s hard to use and non-intuitive, it just won’t succeed. For as complex an AI platform is, the implementation needs to be seamless – because, after all, AI is supposed to make people’s jobs easier. It’s not just the UX that requires ease-of-use, but also any hardware setup as well, like installing the IoT sensors that collect the data.

Startups are gaining traction with large organizations, and fast

It wasn’t long ago that the chances of an early-stage startup partnering with a large corporation were essentially zero. It was just too complex and time consuming to create any real value for both organizations – but now, it’s commonplace for corporates to have a chief innovation officer and teams specifically dedicated to sourcing startups and emerging technology. Through this shift in mentality, not only is traction with startups growing, but it’s significantly reducing the time it takes to execute (a POC, partnership) as well. This is great news for startups, as validating technology and building trust in market is critical to securing capital investment.

Power and range are the key differentiators

Vast amounts of IoT sensors need to collect small amounts of data, and often in remote environments where traditional communication technologies – cellular, satellite, and Wi-Fi – are too power hungry and expensive. Large enterprises and small startups are now bringing long-lasting, cost-effective solutions to market by using new low-power wide-area network (LPWAN) communications technologies.

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