AI-driven IoT: What Businesses Need to Know About the Next Frontier

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In this special guest feature, Abhishek Bishayee, Associate Vice President – Strategy and Solutions at Sutherland, believes that while AI-driven IoT is already making its mark, we are only at the start of this exciting union and realizing the potential extent of its impact. Abhishek has over 15 years of delivery and consulting experience in technology and analytics, and is currently the solutions and go-to-market lead of Sutherland’s Transformation and Innovation practice. Having lived in 3 continents and direct work experience across all five, Abhishek has had global exposure to multiple industries across the AI and Analytics value chain. He is a keen student of the evolving technology landscape and has great passion for education and teaching. He often has guest lectures at various management institutes.

The rapid advancement of artificial intelligence (AI) is dramatically changing almost every industry imaginable. As businesses strive to gather as much data from as many sources as possible, incorporating AI into their business processes allows them to enhance overall performance and gain a competitive edge.

With our highly connected world — predicted to generate a whopping 175 zettabytes of data by 2025 according to Forrester — AI is also making waves as a natural complement to the Internet of Things (IoT). The combination of both technologies enables businesses with a physical presence to reap greater insights from the large volumes of data generated by a slew of IoT applications, sensors and devices.

Yet, while AI-driven IoT is already making its mark, we are only at the start of this exciting union and realizing the potential extent of its impact.

The Promise of AIoT

AIoT was coined as a merger between two very critical technologies of our time — AI and IoT — bringing intelligence to the edge of devices like sensors and cameras. While both technologies are important independently, they are also natural compliments to each other. Harnessing the wealth of IoT data generated offers businesses the ability to garner useful insights to boost operational efficiency, avoid downtime and accelerate performance and improved decision making.

It therefore comes as no surprise that businesses are increasingly adopting AIoT. In fact, Gartner predicts that more than 80% of IoT projects will include an AI component by 2022, up from a mere 10% today. Autonomous cars are probably the most striking example of just how quickly this technology is being integrated into our current ecosystem.

In particular, we are seeing many compelling use cases for AIoT emerge during the COVID-19 pandemic. From healthcare to manufacturing to retail, businesses across a wide variety of industries and sectors are completely transforming the way they leverage the wealth of data they have on hand. Faced with the increasing magnitude and complexity of business decisions, AI injected into IoT can deliver augmented intelligence and improve human decision-making.

As an example, the adoption of AIoT in the healthcare sector has proven its weight in gold by allowing local governments and healthcare departments to effectively track people’s movements in order to conduct contact tracing and determine potential clusters, as well as to pre-screen visitors at hospitals to reduce bottlenecks. This is greatly beneficial to the sector in managing the demand for healthcare services during such a critical period of time.

However, despite the myriad of benefits and advantages that AIoT presents, many businesses continue to be held back by complexities such as security, infrastructure or implementation challenges, which they must first address in order to harness AIoT’s full potential.

Keys to Success with AIoT

According to a recent report, nearly three-fourths of businesses worldwide that applied AI to their IoT initiatives reported the value exceeded their expectations. So, as businesses continue to explore the potential of AIoT, what steps can they take to ensure success?

  • Choosing the right technology. For many organizations, particularly those with larger operations, IoT requires components that are able to withstand varying and challenging conditions encountered at the edge of the network. These locations can be anything from onboard vehicles to airplanes to factories, requiring a flexible and adaptive approach to fabrication of components.
  • Ensuring fast and reliable network connectivity. As AIoT use cases require uninterrupted connectivity, organizations must implement proper cabling and systems that guarantee zero data loss, even in the event of limited connectivity.
  • Managing security threats. Each IoT device holds a wealth of data, making them susceptible to security threats. With the spate of cyberattacks in recent years, organizations must be fully aware of the potential threats and prioritize protecting their data over maintaining typical networks. This requires employing new security tools and conducting regular checks across all IT systems.
  • Integrating data for optimal efficiency. AI and IoT devices collect and transform massive volumes of data every single day. In order for AI systems to effectively analyze all the data and make accurate predictions in real-time, robust data integration capabilities are of utmost importance.
  • Taking a holistic approach. AIoT transformation requires planning for the entire lifecycle — including deployment, operational management, sustainment and support — to reap optimal performance and generate the greatest ROI. Reporting and analytics tools are critical to provide a consolidated view of performance data and maximize efficiencies and productivity.

Balancing Skills with Technology

AIoT brings a wide range of benefits including enhanced operational performance, heightened risk management, improved customer experience, initiation of new and innovative products and services, and reduced costs associated with downtime.

However, beyond the technical considerations above, success with implementing AIoT also heavily relies on the right skills and expertise. This means organizations need to employ people with the specialized knowledge to effectively manage the network and run the AI algorithms. Without a fully equipped team — backed by a clearly defined agenda and accountability at the highest levels — AIoT implementations run the risk of failure.

Organizations must consider the long-term scalability and impact of AIoT initiatives, taking into account all aspects of business, technology and people. As they ramp up their deployments, AIoT will undoubtedly continue to spread across industries and deliver exciting new use cases.

Yet, while AI and IoT are proven to be natural allies, balancing the human aspect of its impact and acceptability is equally critical to realize the full promise of AIoT.

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