The intelligent connectivity offered through the Internet of Things and Edge Computing opens up vast opportunities for businesses. Edge analytics, machine learning, computer vision and other emerging trends will lead to new product development, enhanced relations with customers, and faster time to market.
 
However, conceptualizing these innovations and turning them into reality are separate issues. The business value of new ideas or processes, as they have always been, need to be tested and validated. Most organizations cannot commit enough time and resources to experiment with new approaches. Employees may generate 100 new ideas a day, but the organization may only have enough resources to pursue one of those ideas.
 
With the IoT and intelligence at the edge, the challenge becomes even more daunting. IoT and Edge Computing are constructs made real through complicated combinations of hardware, software and network stacks, many of which may be out of the reach and purview of the organization.

A study released by Cisco finds that, despite the forward momentum for IoT, 60 percent of IoT initiatives stall at the proof of concept stage and only 26 percent of companies have had an IoT initiative that they considered a complete success.
 
Without experimentation, speed of innovation is limited to a slow crawl. Yet innovation is critical to companies seeking to develop new services and bring existing services, processes and platforms to new customers, partners and employees. Until recently, testing and trying out new ideas and processes used to incur more costs than it was worth. But digital technologies – such as online A/B testing, rapid prototyping, and computer simulation – enable experiments to be conducted quickly and cost-effectively.
 
To succeed long term in the digital economy, companies need to “evolve their strategies by experimenting with small offerings and learning what their customers value,” writes Jeanne Ross in MIT Sloan Management Review. “Eventually, big companies will become successful digital companies because they know how to scale successful experiments.”
 
A comprehensive effort to conduct regular and frequent experiments should be part of any digitally driven company. “By combining the power of software with the scientific rigor of controlled experiments, your company can create a learning lab,” write Ron Kohavi of Microsoft and Stefan Thomke of Harvard University in Harvard Business Review. “The returns you reap – in cost savings, new revenue, and improved user experience – can be huge. If you want to gain a competitive advantage, your firm should build an experimentation capability and master the science of conducting online tests.”
 
There are many innovations that can be delivered via digital experimentation with IoT technology. New deployments may be of high-level strategic value, such as introducing a value-added monitoring service for products installed at customer sites. Or, they may provide operational advantage, such as enabling enterprise administrators to link sensors measuring and monitoring temperatures, pressure and vibrations on production machinery to control systems.
 
And digital experimentation is inexpensive compared to the potential alternatives – failed implementations of costly technology or organizational inertia that typically throws sludge into the innovation process. Often, big organizations fall prey to paralysis by analysis and spend more time, energy and money debating an idea than it would take to actually try it out.
 
With digital experimentation, trying out new ideas or concepts becomes relatively quick and easy. Enterprises can even try multiple methods for solving the same problem to discover the ideal solution. It's important to remember that not every idea – even ones that sound good on the surface – is a money maker. Not every idea is going to improve your operation. Not every idea saves lives. But if you can lower the effort and cost, you're more likely to discover some truly relevant IoT implementation that will move your business forward.

Digital experiments provide the following advantages:

• They bring IT and operational technology together. Digital experiments align technology resources and apply them to business opportunities and problems.
• They strengthen management's confidence for investing in IoT initiatives. The results of digital experiments help demonstrate the value delivered from various IoT efforts.
• Unsuccessful experiments are just as valuable. Potential waste of organizational resources is avoided.
• They bring together complicated scenarios. There are many moving parts to an IoT network, supporting multiple standards, multiple technologies, multiple data types and multiple vendors. Most testing and experimentation environments are designed to support a single-vendor environment.
• They help companies get more value out of technology investments. Devices and online services may be sitting, underutilized.

 
“[Some] organizations have discovered that an ‘experiment with everything’ approach has surprisingly large payoffs,” said Kohavi and Thomke in Harvard Business Review. “At a time when the web is vital to almost all businesses, rigorous online experiments should be standard operating procedure. If a company develops the software infrastructure and organizational skills to conduct them, it will be able to asses not only ideas for websites but also potential business models, strategies, products, services, and marketing campaigns – all relatively inexpensively. Controlled experiments can transform decision making into a scientific, evidence-driven process – rather than an intuitive reaction.”

Learn more here: https://www.adlinktech.com/en/iot-digital-experiments.aspx

It isn’t just the software making AI-powered edge devices possible. From a hardware standpoint, today's devices are increasingly capable of supporting the power and capacity requirements of AI algorithms. Sensors themselves are supporting significant memory and processing capacities within very tiny footprints, which is mitigating, if not eliminating, the need for transmitting data to central servers.
 
“Technical or energy constraints make it impossible to stream all that data to the cloud where the AI resides,” according to Rudi Cartuyvels, lead for Imec’s Smart Electronics & Applications R&D. “In addition, there are also use cases where patterns should be recognized instantaneously, such as with radars that need to detect people or vehicles in the path of a self-driving vehicle. There, the time delay of a round-trip to the cloud is simply prohibitive.”
 
Examples of applications benefitting from AI-charged edge computing include medical devices, manufacturing systems and vehicles. Medical devices, in particular, have an acute need for at-the-edge intelligence – critical data in the operating room, for example, has to be processed in a timely fashion in order to provide the right information for physicians or doctors to act upon.
 
Another potential area is machine vision, through cameras and visual analytics. For example, a camera can be positioned in a distribution facility to monitor and manage – in an instant – goods that are moving between trucks and pallets. With the combination of inexpensive and high-capacity hardware supporting intelligent software, organizations have only begun to explore the possibilities for achieving highly efficient operations and superior customer satisfaction.
 
The advantages of AI-enhanced decision-making at the edge include the following:

• Edge-based AI is highly responsive, and closer to real time than the typical centralized IoT model deployed to date. Insights are immediately delivered and processed, most likely within the same hardware or devices.
• Edge-based AI ensures greater security. Sending data back and forth with Internet-connected devices subjects data to tampering and exposure even without anyone being aware. Processing at the edge minimizes this risk, with an additional plus: Edge-based AI-powered devices can include enhanced security features. 
• Edge-based AI is highly flexible. Smart devices support the development of industry-specific or location-specific requirements, from building energy management to medical monitoring.
• Edge-based AI doesn't require a PhD to operate. Since they can be self-contained, AI-based edge devices don't require data scientists or AI developers to maintain. Required insights are either automatically delivered where they are needed, or visible on the spot through highly graphical interfaces or dashboards.
• Edge-based AI provides for superior customer experiences. By enabling responsiveness through location-aware services, or rerouting travel plans in the event of delays, AI helps companies build trust and rapport with their customers.

 
As we move forward into the highly connected digital economy, inevitably, intelligence will move to the edge. The powerful combination of AI and the IoT opens up new vistas for organizations to truly sense and respond to events and opportunities around them.