When I first started working with the Internet of Things (IoT) nearly 10 years ago I used to lead presentations with a “the world is changing, and it’s changing fast” mantra. Now, with the rise of new advanced technologies driven by artificial intelligence (AI) I simply start with “nothing is going to be like yesterday!”.
In this increasingly connected world, it is only by looking back that you can comprehend how quickly things have changed. In 1984, when I left secondary school and the original Apple MacIntosh computer went on sale, there were only 3,000 devices connected to the internet.
In 2008, the number of connected devices surpassed the number of people on the planet – at nearly seven billion. That has more than doubled again in the last eight years and is conservatively forecast to triple again in the next three to four years to around fifty billion connected devices, or what can be called “smart objects”.
The outcome will be innovation fuelled by connectivity. Everything you have seen in technology innovation in the last eight years, which is a lot if you look back, will effectively be multiplied by three in impact and all happen in the next 3 to 4 years.
This IoT is well under way, from when I first started working in the nascent industry, to now, and it has evolved from science projects into an industry which Gartner says will reach US$1.5 billion in sales this year.
Alongside this IoT surge we are also seeing new technologies emerge, blockchain being one of the most hyped. This distributed ledger technology will empower the world to authenticate data in a trusted format like never before, and will grow at such a rate that Cisco predicts 10% of global GDP, or around US$9 trillion, will be stored on blockchains by 2027.
Collecting and authenticating data is only part of the puzzle though, the ability to process this data for the general good and positive economic outcomes will require analytics and the ability to visualise the outcomes of the analytics, on a scale we cannot begin to even imagine.
The analysis of this data will see a sharp rise in AI applications, where we are dealing with so much data that machines will be needed to perform tasks that traditionally required human intelligence.
In the book Prediction Machines the authors, all eminent economists, give a very straightforward definition of what it is we really will get from AI – effectively cheap prediction. This ability to dramatically improve prediction will have a significant impact on our everyday lives, reducing uncertainty, and creating new economic opportunities at the same time.
As an industry, AI is growing exponentially with AI related start-ups increasing nearly 14-fold since 2000, with 61 per cent of businesses surveyed with a technology plan saying they are adopting AI, according to a survey by Narrative Science.
Some examples of AI implementation include Robotic Process Automation (‘RPA’), simply automating repetitive manual tasks by machines to increase productivity. JP Morgan Chase invested in this technology and recently introduced a Contract Intelligence (COiN) platform. What previously involved a manual review of 12,000 annual commercial credit agreements requiring 360,000 man hours, can now be done in seconds by machine, according to an initial implementation of COiN.
AI has also seen the rise of ‘Chatbots’, which convert human language into machine language and vice versa. Chatbots can answer customer questions, provide technical support internally and externally, gauge customer mood (by analysing tone of voice) and even act as virtual counsellors. For example, the government of Singapore has been working with Microsoft to create Chatbots for select citizen services, intended to function as digital representatives.
The rise of AI in prediction is also moving quickly into industry, and companies like Presenso, an Israeli start-up, are working with companies such as Total-Eren to deliver predictive maintenance solutions for power generation assets, looking at mean time to failure and saving large dollar amounts for their customers with preventative maintenance solutions.
Once all this data has been collected, authenticated and analysed, we are left with the problem of how to visualise it, and this is really where it will all come together. Augmented and virtual reality solutions are moving from the ‘first person shooter’ gaming world, rapidly into the commercial world. Deloitte predict this market will grow from US$9 billion in 2017 to US$160 billion by 2021, a CAGR of 113%, and one to watch for budding angel investors and fund managers alike.
One example of a successful AR solution is from the furniture giant Ikea. Their “Ikea Place” application, built in collaboration with Apple, allows customers to virtually place furniture inside their home. The app also allows users to capture and share photos and videos of virtually-placed furniture with family and friends. It’s not hard to see how other companies will move quickly into this area.
Things are also moving fast in the traditional VR space. KFC have been an early adopter of the technology, developing a fully immersive VR experience for staff to train them in chicken preparation – giving them a 5-step process to follow if they want to reach an “escape room”.
And in 2017, there were almost 19,000 KFC stores in 118 countries using AI to detect areas where greater efficiencies can be achieved within the business – not only for training purposes.
Over the next 10 years, IoT, blockchain, AI and VR/AR technologies are going to be the core applications that drive business transformation. While sitting back and watching is going to be great fun, getting involved as a developer, investor, supplier or customer will literally change your life.
Matthew Smith, is the Managing Director of Digital Insights (HK) Limited and the prior Global Head of Market Development, Internet of Things, at Cisco Systems, where he worked in a variety of senior roles for 18 years.