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With more than a trillion connected devices expected to be in use by 2020, the Internet of Things (IoT) has become a critical focus area for many businesses, yet the rate of IoT adoption among companies lags behind the pace of industry development.

According to recent research,

(1) Security,

(2) Integration with IT and operational technology systems, and

(3) Uncertain return on investment

remain the biggest barriers to adoption.

The Most Significant Barriers to IoT Adoption:

1. IoT Security

Since 2016, the major barriers to IoT adoption have remained the same but all are heavily outweighed by security. With the ability to literally connect anything, companies are expanding their risk very rapidly. With more devices connected, there are more potential threats.

True network security requires a pro-active, holistic,  end-to-end approach.

Security Threats

Experts predict that by 2025, there will be as many as 75 billion connected IoT devices. Much of the embedded firmware running on these devices is insecure and highly vulnerable, leaving an indeterminate number of critical systems and data around the world at risk. The ability to manage the performance and safety of these new systems and their interaction with the network is essential for business adoption in order to ensure optimal operation while minimizing the risk to the company.

On the security front, many companies fear that compromised connected devices could fuel massive online attacks or provide criminals with access to critical business systems and data.

Faulty or hacked robotic systems on factory floors or operating rooms could pose a threat to human safety and security. According to Bain and Company, sensors disrupted by streaming inaccurate data could hurt a company’s ability to make significant operational decisions.

Data Security & Data Privacy

One of the main driving forces increasing pressure on companies is the new regulations such as the EU General Data Protection Regulation, which impose strict data protection requirements and security breach penalties on companies, including data breaches. The State of California has passed a similar data protection regulation and New York State is poised to do so as well.

2. IT / OT Integration

  • Ease of Integration with Existing IT and OT Systems

IoT vendors have made it difficult for customers to integrate their IoT solutions into business processes, OT / IT, and may underestimate the concerns of their customers. Developers must know and understand the industry of their customers and the typical challenges of implementation in order to be able to offer more comprehensive end – to – end, secure solutions.

To overcome the interoperability challenge, the IoT implementation process must ensure that the selected IoT platform is compatible with the devices to be connected in the environment. It must also successfully support the communication protocols to be implemented.

New enterprise IoT devices are often characterized as operational technology (OT) and unlike traditional laptops and smartphones, they are typically directly linked to the goals of a specific business line.

  • Operational technology (OT) is hardware and software that detects or causes a change through the direct monitoring and/or control of physical devices, processes and events in the enterprise.
  • IT/OT integration is the end state sought by organizations (most commonly, asset-intensive organizations) where instead of a separation of IT and OT as technology areas with different areas of authority and responsibility, there is integrated process and information flow.

Increasingly, these OT systems rely on IT infrastructure and services, thus increasing the overlap of skills to manage the two and justifying the need for greater collaboration between IT and OT to maximize business efficiency. Cultural and organizational barriers and a legacy of conflicting goals between IT and OT groups can add to the challenges.

To understand the precise role integration plays in IoT, you need to understand the need for integration to manage real-world IoT data. In order to keep your IoT data under control, API Management also comes into play for workflow modernization.

3. Uncertain Returns on Investment

As sensors and analytics help companies determine when maintenance or replacement is needed, predictive maintenance emerges as one of the first attractive IoT use cases. However, this has decreased since customers found the ROI takes longer than expected. The initial investment needed to implement an IoT ecosystem is one of the biggest challenges slowing IoT adoption often due to costs exceeding the expected returns to date.

According to a recent Forbes Insights survey of 700 executives, 60% of enterprises are, with the help of their IoT initiatives, expanding or transforming with new lines of business, while 36% are considering potential new business directions. In addition, 63% are already delivering new or updated services directly to customers thanks to their IoT capabilities.

With such high expectations of significant business outcomes, business leaders are eager to invest in IoT, but not before they see a credible cost analysis of the return on investment in technology.

Uncertain ROI goes hand in hand with the previous two adoption challenges mentioned. Due to ongoing security and implementation challenges, large scale deployments and ROI may take longer than expected to materialize.

Ian Hughes, an IoT analyst at 451, explained that companies experimenting with IoT incrementally are likely to be more comfortable with the ROI issue, whereas those who invest in it will be more concerned about the returns to solve big problems, or to ‘ digitally transform. ‘ He said,

“I think when a company looks at it as a new thing that they have to install and implement and roll out, then it becomes a different discussion to when it’s an incremental rollout of IoT. To say, ‘here’s a new thing, it’s going to cost some money’, they then challenge why they want to do that. Whereas most of the IoT stuff that we see is this incremental approach, so things are gradually coming in.

IoT also often bounces around industry verticals, which often confuses people. Its power is being able to pull things from more than one place and decide what to do, not just zone in on a thing and that’s a tricky thing for people to navigate. That’s why a lot of it comes through via data centers, via buildings and via industrial processes.

We’ve got lots of buzzwords – we’ve got cloud and we’ve got big data – well, IoT feeds big data. And big data doesn’t care where the data comes from. So, when you apply analytics and machine learning to that, that mass of data is when you start to get some benefit. You notice that one particular part of your business that would appear identical to another is actually performing better.

You realize that some of the peripheral things that you weren’t looking at before you can tune to make other parts of the business better. It’s that wide disparity of data is what helps IoT – but that’s why it’s difficult to say, right I’m going to install an IoT system. It might be something outside your normal kind of business that can tell you that stuff.”


IoT has become a cornerstone of digital transformation of many organizations, enabling them to optimize existing operations and excel in creating exciting new business models and pursuing them.

Cloud service providers as well as individual analytics and infrastructure software vendors, are lowering barriers to IoT adoption by offering simple, scalable implementations.

Despite the practical hurdles, Bain expects the market to grow to reach $520 billion by 2021. That’s up in 2017 from $235 billion. Gartner Research estimates that by 2020, the number of internet – connected devices will reach 20.4 billion, up from 8.4 billion in 2017.

Enterprises are still optimistic about the business value and potential of IoT to deliver a positive ROI; however, many plan to implement extensively or at a slower rate than originally considered.  Despite the barriers, many IoT customers continue to plan with an eye toward wider adoption expectations. They’re focusing on learning what customers really want and need as a key predictor of eventual success.

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