Currently, humans are more connected than they’ve ever been in human history. There are five billion phones in the world. The internet user spends over two hours per day on social media. And by 2020, there will be four devices for each human.
Considering this rapid adoption, businesses are keen to jump on board. In a study from IBM’s Institute for Business Value, two-thirds of C-suite executives said they planned to incorporate IoT into operating models. Clearly business leaders see utilization of their own data as a competitive advantage.
Connected devices will be our main source of data moving forward, exponentially collecting more and more with each coming year. But here’s the problem with all of this data – we drown in it if we don’t have technology to make sense of it.
Artificial intelligence (AI) is our solution to this problem. We need it to assist us in making sense of massive data amounts. Ultimately, it can help us better manage, monitor, predict, recommend, and optimize our businesses.
It’s not possible with just IoT and humans. But when we add AI to the mix, opportunities start flourishing. Imagine what could be produced with a significant reduction in maintenance costs, both in quality and quantity. How will your product output change if you choose to reduce repair times, MRO inventories, and even capital investment by implementing this?
All sectors are adopting AI-powered IoT in some way. The electronics industry will likely have 411 million wearable devices by 2020. The defense industry uses IoT to update F-35 fighter jets.
So, what’s holding us back?
The first thing that stops most leaders is trust. As humans, we’re inherently distrustful of such a massive technology when we don’t understand how it works. Here you see another time in human history where we felt similarly: the first automobile.
People were so disturbed by this new invention coming down the street, one inventor suggested putting false horse heads at the front of the vehicle just to help people understand what this machine was intending to do.
Fastforward to today, where this concept seems to have returned. Jaguar Land Rover tested eyes that signal perception to pedestrians, and Ford experimented with light signals to communicate what the car is doing.
Fear mustn’t hold us back. We must educate everyone in our organization about what AI-driven IoT is and what it can do. You should feel confident that you understand the AI you receive: how it was programmed with bias, how it operates, how it evolves. Just like you rely on a nutrition label to tell you what’s in your food, you should be evaluating the contents of your AI.
The second inhibitor is a lack of confidence in the belief AI-driven IoT can work with humans. To believe so requires a shift in how we educate and train (or retrain). IBM’s CEO Ginni Rommety puts it well when she said, “100% of jobs are going to change.” We must take action to retrain workers to be prepared for this. In fact, when we refer to AI at IBM, we mean it in the sense of “augmented intelligence.”
Yet all of what I’ve mentioned is internal. There’s an external element that I find to be a third inhibitor: collaboration. The idea of how much we keep versus share from partners and even competitors must change, because the advantages of non-traditional partnerships are revolutionary.
With blockchain, on a secure ledger companies are sharing immutable records to reduce time and paperwork. But it’s more than that. It makes food safer, since we can track just how fresh produce is, knowing when it was shipped from the farm. And we have greater confidence that our metals and stones are authentic.
What’s holding leaders back from maximizing IoT is refusal to rethink tradition. It’s time to embrace new ways of working and new ownership models. We must treat data as assets we trade and monetize.
But it’s up to you. I’ve shown you that the revolution is happening, and that there are leaders and laggards. So now I challenge you to reflect on what you will do differently to make the most of AI-powered IoT.
Learn more about IoT in manufacturing, automotive, or electronics.
Sources:
1 Local SEO Ranking Services. The mobile revolution. http://localseoranking.net/mobile-marketing-2/the-mobile-revolution
https://www.ibm.com/services/insights/c-suite-study/iot
2 Entrepreneur. How do your social media habits compare to the average person’s? (2017) https://www.entrepreneur.com/slideshow/306136
3 Business Insider. There will be 24 billion IoT devices installed on Earth by 2020. (2017) https://www.businessinsider.com/there-will-be-34-billion-iot-devices-installed-on-earth-by-2020-2016-5
4 IBM. Intelligent Connections: Reinvent the enterprise with Intelligent IoT. (2018) https://www.ibm.com/services/insights/c-suite-study/iot
5 IDC. Data Age 2025: The Evolution of Data to Life-Critical. (2017) https://www.seagate.com/www-content/our-story/trends/files/Seagate-WP-DataAge2025-March-2017.pdf
6 Forbes. Wearable tech market to be worth $34 billion by 2020. (2016) https://www.forbes.com/sites/paullamkin/2016/02/17/wearable-tech-market-to-be-worth-34-billion-by-2020
7 TIME. The 50 worst cars of all time. (2017) http://time.com/4723114/50-worst-cars-of-all-time/
8 Mashable. Jaguar Land Rover autonomous pods have eyes to 'talk' to pedestrians. https://mashable.com/article/jaguar-land-rover-self-driving-car-eyes
9 YouTube. U.K. PM May, IBM's Rometty on Government-Business Partnerships. (2018)
