Change is inevitable but humanity is prone to resist it. Queen Elizabeth I refused to grant a patent to the inventor of the knitting machine, fearing it would deprive her subjects of work. Yet it became the first major stage in the mechanization of the textile industry, which later led to the Industrial Revolution.i More recently, disruptors such as robotic automation and Uber have been feared as major drivers of job loss, but the facts show otherwise. In both cases, employment rates and wages remain high or higher than they were before.i,ii
Today’s top targets of resistance remain the same – emerging innovations that promise to transform the world as we know it. Combining the internet of things (IoT) with artificial intelligence (AI) is poised to do just that by untethering computing intelligence from our desktop and infusing it everywhere into our lives – our homes, our cars, our cities and the natural world. And, while current headlines stoke fears around AI Armageddon or job losses, there are just as just as many headlines promising AI will create more jobs and solve all our problems. The truth lies somewhere in between.
Remember that reinvention is a constant
Royal Dutch Shell originally sold seashells and Samsung started as a grocery store selling dried fish. Where would these companies be today if they hadn’t reinvented themselves? The same holds true for technology innovation – while it does disrupt the way things were done in the past, it also opens the door for new opportunities and skills. Consider these examples:i,iii
- Instead of hiring traditional loan officers, a leading fintech platform in China created more than 3,000 data analysis jobs to sharpen AI algorithms for digitized lending.
- GE is training its workers for the jobs of the future through its “brilliant learning” program designed to help employees get ready for the arrival of advanced technologies like robotics and digital manufacturing.
- Microsoft aims to train and certify 15,000 workers on AI skills by 2022.
In fact, as a recent ManpowerGroup report indicates, reinvention is the name of the game when it comes to the global workforce. More employers than ever before (87%) not only plan to increase or maintain headcount as a result of automation, but 84% plan to upskill their workforce to fill talent gaps.iv Digital transformation is empowering companies, and individuals, to grow faster and produce better outcomes. That translates into more and new kinds of jobs, both technical and non-technical. Some of the fastest growing jobs today include the ones obviously related to AI, such as data scientist, machine learning engineer or app developer, but also less obvious ones like business analyst, human/machine interface designer, logistics specialist, or robot mechanic.
Even more telling is the number of new opportunities ignited by the information economy and AI+human collaboration. With interconnected digital platforms as the underlying fabric, virtually anyone can learn and develop new skills, start a company, trade goods and services, crowdsource app testing or solutions to complex problems and even build their own AI. By connecting customers, producers and providers in a one to many fashion, these digital platforms have become essential hubs of collaboration and innovation with their own inherent value.
Learn more: Reinvention in the age of AI
3 steps to success from industry titans
So what does this mean for leaders looking to reinvent their businesses with IoT and AI?
About once a month, I host a discussion panel with technology leaders across the country who have successfully implemented IoT and analytics/AI solutions. The goal of these panels is to extract actionable insights from their experiences through interactive discussions, and our guests include executive leaders from companies such as Oracle, Ericsson, Hitachi, AT&T, GE, Verizon and many others. As these discussions have progressed, it’s become clear that, while no one size fits all, there are some common elements to success. Here are three themes that have come up in every discussion:
- Focus on one challenge to start: While it’s easy to get excited about all the challenges that IoT and AI could solve, it’s a slippery slope. Focusing on one problem or inefficiency relating to a specific process will enable your organization to craft a realistic strategy, allocate the right resources and collect only the data that is needed. Don’t try to boil the ocean. Showing early success by addressing a major organizational challenge will increase executive confidence in and commitment to the initiative. Once you have selected the process to be improved, start by analyzing it from several angles such as inputs/outputs, time value, financial value and other metrics to determine what the next steps are and what data to collect.
- Get the right skills: While many of the skill sets needed for successful IoT/AI projects can be addressed with existing resources after training, it is likely that you will also need to bring in new talent. Almost every executive stressed the need to hire external resources (both permanent hires and consultants) to build new capabilities in the organization. Some key roles include: data scientists and architects, database administrators (DBAs), advanced networking/cloud resources, data security experts, business analysts and customer experience resources. Investing time at the beginning of the project to outline the roles and skills required for your project can prevent costly delays resulting from resource gaps.
- Iterate indefinitely: There’s no such thing as a “fix it and forget it” IoT/AI platform. It’s a commitment to continuous improvement. Finding and refining the value in your data is like discovering the perfect gem. You may start by deploying sensors with a basic framework of descriptive analytics that lead to initial insights. These may need to be refined through various lenses to discover patterns – by intervals of time, geographies, demographics, etc. Sometimes that means developing new algorithms to “test” the different patterns. As insights become clearer, you need to refine those algorithms and begin moving them to closer to the digital edge where the data is being generated and consumed. As you continue to hone your findings with predictive analytics and machine learning algorithms, the path to value becomes clearer.
Reinventing for the future
Humanity has been striving to reshape the world for the better since the beginning of time. The earliest tools mankind invented were shaped out of stone. The latest ones are shaped from technology. Every innovation we create seems to inspire twin currents of fascination and fear – fascination with how it will advance our society and fear that it will render us obsolete. Despite our fears, the reality is that today a greater portion of humanity has access to better jobs, education and healthcare than ever before. Digitalization of the modern world has also accelerated entrepreneurship and the growth of new business models. Interconnected digital ecosystems are key enablers for these innovations and human+machine collaboration. If done right, IoT/AI platforms can help companies accelerate reinvention by getting smarter and yielding better insights over time.
See how in the Digital Edge Playbook for the Internet of Things
[i] World Bank, World Development Report 2019: The Changing Nature of Work, doi:10.1596/978-1-4648-1328-3, 2019. [ii] University of Oxford, Drivers of Disruption? Estimating the Uber Effect, Jan 2017.
[iii] GE, School’s In: GE’s New “Brilliant Learning” Program Will Train Workers For Jobs Of The Future, Mar 2017; TechCrunch, Microsoft aims to train and certify 15,000 workers on AI skills by 2022, May 2019;
If done right, IoT/AI platforms can help companies accelerate reinvention by getting smarter and yielding better insights over time.Chiaren Cushing, Director of Mobile Services & IoT at Equinix.