With IT/OT convergence, Industry 4.0, and accelerated digitization, the way the factory floor communicates and deals with IT has changed dramatically over the last decade. And, while we’ve certainly seen a lot of change in the manufacturing sector, it’s obvious that it won’t be slowing down anytime soon. The IIoT industry will be worth $110.6 billion by 2025, which will only accelerate the current digitization patterns.

These developments will place new demands and challenges on the IT that supports industrial infrastructures over the next half-decade. The OT world is becoming more and more dependent on integrating and connecting with previously disconnected networks. IIoT solutions are delivering new data that must be interpreted, saved, and analyzed.

All of this means that IT in manufacturing systems must be versatile and versatile enough to accommodate the new technologies and features introduced.

The following are the developments that will have a huge impact on how industrial IoT solutions will need to be changed in the coming years.

IT and OT Convergence

The convergence of IT and OT is now the most powerful driver of the industrial IT transition. Machines that were previously modularized are directly related, and IoT sensors provide data on all facets of manufacturing. It’s becoming apparent that a siloed industrial IoT solution, as was the case previously, is no longer effective.

There are far more interfaces and touchpoints between various production areas today than before, presenting its own set of challenges. How do technicians and server managers make sure the interfaces are working properly? Is data streaming as it should from the manufacturing floor to the data centers or the cloud? What’s the easiest way to get a clear picture of the whole picture?

These are all questions that industrial IT experts will have to answer in the coming years.


According to several sources, the pandemic has resulted in a significant increase in cyberattacks. According to Forbes writer Daniel Newman, there was a 238 percent rise in bank attacks from January to April 2020 and a 600 percent increase in attacks on cloud servers. This demonstrates how the rapid increase of cyberattacks can accelerate cybersecurity in all industries, even engineering.

Although cybersecurity in industrial settings is nothing recent, digitization in manufacturing has brought its own set of challenges. Security vulnerabilities associated with IIoT solutions, sensors, computers, and applications have been well established. Meanwhile, OT modules must be “opened up” to communicate with modern technologies and computers, posing a new range of security concerns. The problem is exacerbated by the fact that security principles and tools built for IT do not always apply to OT.

With all of this said, connecting the OT environment is feasible, but a solid cybersecurity plan is needed. To meet the current demands of OT protection and durable IIoT components and software, the supporting IT will need to be updated.

The Continuing Importance of Edge Computing

On a factory floor, each computer and sensor gathers, processes, and bundles data for review. However, as the number of IIoT devices in use grows, conventional cloud computing may not keep up. You can increase efficiency, lower costs, and boost overall latency and scale by dealing with data at the system level.

Edge computing can become much more useful to the industry as 5G networks become faster, allowing manufacturing to respond to any wired solution more efficiently and  have more knowledgeable real-time research. Automation and creativity will thrive with edge computing due to an increase in APIs for greater programmability and eliminating performance bottlenecks.

Digital Twins

According to Gartner, 75 percent of IoT-enabled businesses use digital twins or invest in the hardware. A computer image of a physical unit or entity is known as a digital twin. The simulated twin enables R&D teams to collect data from replicating actual structures in real-time scenarios.

Manufacturing is at the forefront of this technology because it helps companies distinguish their goods and services from rivals while still generating additional revenue streams. More producers will use digital twins to simulate processes and streamline manufacturing as more factories implement IIoT solutions, artificial intelligence (AI),  and machine learning (ML).

Managed Cloud Services

To stay flexible and competitive, digital innovation and IoT-centric workplaces necessitate a full cloud infrastructure to power smartphones, apps, and databases. The IIoT solutions also necessitate a reliable infrastructure, which has led to a greater dependence on managed cloud providers in manufacturing.

Manufacturers are transforming their data centers into a virtual cloud that can execute  at the point of need. The focus will be on a hybrid cloud infrastructure that enhances

knowledge security while relying on public cloud providers’ services. Hybrid IIoT solutions will also be available, but the transition away from private clouds and a distributed public cloud architecture will continue.

This controlled cloud system can monitor individual devices and records, providing important and practical information about connected goods in manufacturing environments.

This development has already caught on with AWS, Google Cloud, and Microsoft Azure, and IoT data networks are connecting easily as well.

The Role of AI

For the past few years, AI and machine learning have been on the rise. However, we should expect to see a stronger connection between AI and IIoT in the future. The IoT decision-making process will be guided by AI, which will strengthen it. For size and efficiency, AI in IoT drives computationally intensive analytics to the edge. APIs for custom scripting, as well as load-balancing and delivery features, are all included.

Manufacturers must identify analytics and machine-learning models on the platform for better results and quicker response times at a granular level. What are the options for doing this? Using data from the school. This is the secret to controlling the output data used to set up machine learning models and allow real-time automation and decisions that support a factory.

Furthermore, data and infrastructure management systems will most likely continue to extend their use of AI and machine learning to provide businesses with more streamlined IT operations. These AI-powered systems will be able to do more daily activities, improve procedures, and predict and react to potential threats and challenges.

The Human Element

The concept of Industry 4.0 is based on technological advancements. The new manufacturing plant is powered by AI, edge computing, virtual testing, and IIoT solutions. But without the human aspect, none of this works. Employees can transition to decision-making roles dependent on data support as technology continues to migrate plant staff roles away from tedious, repetitive activities.

Human staff must think differently on the job for IoT deployments to be competitive, necessitating a continuous effort to prepare employees for sometimes high-tech activities. Human intervention is needed to upgrade and track IIoT solutions, necessitating the acquisition of new skills. Employees would also need to reshape their factory floor operations. The effect of data produced by devices on the supply chain must be properly understood by management.

Machines are not replacing people. Instead, a modern synergy of humans and robots is ensuring that manufacturing and operations are simplified. This entails devoting time, resources, and effort to ensuring that it is completed correctly. This entails determining where new work openings will exist and what form of retraining will be needed.

Wrapping Up

In 2021, we should continue to see these trends and challenges (among others) play out. Seeing IIoT solutions continuing to boost product production and distribution and making factories more productive, ensuring better safety and advancing the skillsets of manufacturing workers worldwide.