8 Big IoT Trends To Watch In 2025, According to Analysts And Executives

Executives and analysts talk to CRN about eight big trends they’re seeing in the IoT market this year, ranging from an AI skills gap and the infusion of new AI innovations into products, to a push by customers for interconnectivity among vendors and ongoing security concerns.

With the ongoing AI development boom driving demand for IoT solutions and enabling new capabilities, the industry is facing some growing pains.

These hurdles include a skills gap in the IoT workforce when it comes to integrating new AI innovations into products and services, according to Satyajit Sinha, principal analyst of IoT component, connectivity and security at research firm IoT Analytics.

[Related: How AI Is Driving Demand For IoT Solutions And Enabling New Capabilities]

“Everyone will go through that phase: that if you want to implement AI within your industry, within your company, you need to make your employees skilled [in this area],” he said in a recent interview with CRN.

Sinha is among a handful of analysts and executives who gave their input on what they are seeing for big and noteworthy trends in the IoT market this year.

Among those trends are the impact of President Trump’s evolving tariff strategy and other trade-related actions that have taken place across the world.

Carlos Gonzalez, research manager of industrial IoT and intelligence strategies at research firm IDC, said tariffs have impacted the cost of raw materials, impacting pricing and margins for vendors selling IoT hardware.

“IDC did a sentiment survey and found that 60 percent of enterprises view rising tariffs as a threat to profitability and tech budget stability,” he told CRN.

Another area of concern in the IoT market is cybersecurity, according to Danny Johnson, vice president of IoT and managed connected platforms at Verizon Business.

“Depending on the build of a specific deployment, IoT devices can span multiple locations, involve devices from various manufacturers with different security capabilities and operate in environments where physical security is limited,” Johnson wrote.

“To cover these different potential attack vectors, IoT deployments can require a more complex security setup than the traditional IT environment,” he added.

As for opportunities in the IoT market, Lantronix Chief Strategy Officer Mathi Gurusamy pointed to the need for collaboration to boost the AI-driven industrial IoT ecosystem.

“The future of AI-powered IIoT [industrial IoT] will be defined by collaboration—where hardware, software and connectivity providers join forces to build integrated ecosystems that support hybrid AI models for edge intelligence,” he wrote.

What follows are explanations of these and other big trends in the IoT market this year as explained by Sinha, Gonzalez, Johnson and Gurusamy.

IoT Analytics’ Sinha On The AI Skills Gap Faced By IoT Vendors

As AI is coming into the picture, there is a huge skills gap that we are seeing in the IoT market [when it comes to integrating AI technologies into IoT products and services]. That is a bigger challenge for IoT companies right now, for end-to-end deployments.

It’s all about how you integrate AI into your legacy devices, how you implement and deploy [them] at a much faster cycle. The bigger challenge right now is to create a product and test it and deploy it, [but] the lifespan of that is so big right now for AI use cases [that] the moment people are going into the deployment stage, the technology has changed by that time. So they are saying, ‘We need to revise it.’ So the training of these people is very important.

[There has been a similar challenge in the] convergence [of operational technology and IT] So in IT people are not trained in the OT environment. OT people are not trained in the IT environment. And what is happening is that if those people are not training on both sides but they need to work together, there will be a lot of complications.

So it’s the same thing with AI. We are seeing the same thing that, ‘Hey, you want to work on AI technologies, but your staff is not ready for that. You are hiring third parties to do your AI workloads, but they need to understand your product, which takes a lot of time and investment to just understand your product, end-to-end under NDA.’

Everyone will go through that phase: that if you want to implement AI within your industry, within your company, you need to make your employees skilled [in this area], rather than finding a solution [from] the third party because you want to do it from a long-term perspective, not a short-term perspective.

IDC’s Gonzalez On How Tariffs Are Impacting The IoT Market

It certainly has impacted how a lot of companies are doing business today. So it has increased the cost of raw materials. It’s impacting pricing and vendor margins. IDC did a sentiment survey and found that 60 percent of enterprises view rising tariffs as a threat to profitability and tech budget stability.

It’s delayed equipment acquisition. It has caused supply chain disruption, and companies have taken strategic shifts to ensure there’s not a huge disruption for their customers, whether that’s shifting manufacturing locations and diversifying their supply chain.

It also has led to some innovation in [a] sense. For example, one of the reports that I helped write is [IDC's Global DataSphere IoT Device Installed Base and Data Generated Forecast]. I wouldn’t say that the tariffs are the only reason for this happening, but I do think it impacted it. We’ve seen not a flat line but a reduction in hardware [purchasing]. So companies are not right now projecting to invest in too much hardware in the future, but what we’re doing with data has continued to slope upwards at an aggressive slope.

What’s happening now is that there’s an understanding that hardware is hard to come by, and hardware may continue to be hard to come by down the line. We need to do more with less. And so that’s one of the reasons why I think synthetic data is so important for this conversation—beginning to do more analysis with the information that we have. A lot of that might be coming from unstructured environments like vision systems [and] doing analysis with cameras that are already out there and trying to do a deeper level of analysis from this unstructured data source.

But overall, the tariffs continue to bring a lot of instability into the discussion. But even with that instability, there are some things that customers and vendors just recognize you can’t stop doing. You can’t stop investing in manufacturing, IoT networks, IoT systems, cybersecurity. You can’t stop that level of investment. And I think they’re probably going to be looking at ways to offset that cost; unfortunately, that might impact pricing. But the growth is still going to come. How that cost gets translated down, that’s going to vary, and, at the end of the day, it’s going to impact pricing and vendor margins.

IDC’s Gonzalez On Customers Pushing IoT Vendors To Support Interconnectivity

Interconnectivity [between competitors] is a significant growth [area] within IoT. Customer needs are driving that cooperation between competitors because we can’t wait around for every single vendor to develop their own initiative.

Other third-party players have come in saying, ‘If we can have access to all your data, we can facilitate your data operations or your cloud initiatives or your AI initiatives, but that means the lower hardware vendors need to cooperate [with data sharing].’

So that interconnectivity, whether it’s adopting open standards or open protocols, has really pushed companies, including direct [competitors], to cooperate with each other, which is very interesting to see.

[As for which standards vendors are converging on,] OPC UA [OPC United Architecture] is the standard right now when it comes to open protocols for communication between devices. MQTT [Message Queuing Telemetry Support] is another one. These tend to be the go-to standards, especially in the industrial space. Matter is the open standard that a lot of companies are adopting in the consumer space.

IDC’s Gonzalez On The Rising Use Of Synthetic Data For IoT Applications

One thing that we’re tracking is the rise of synthetic data. Synthetic data—which allows organizations to share information or create simulations based on not the actual data but a copy of that data that protects [intellectual property], that protects sensitive matters— is growing significantly, especially in the terms of like analytics.

If you look at some of the bigger analytical firms, they’re using synthetic data to create their models, analysis and their simulations without having to dive into company-specific, IP-protected information. That’s definitely one of the growing trends around [these] IoT and AI sort of investment areas, these developing architectures, because data is what fuels AI. And we know that there’s a lot of specific data that has to be protected.

It’s that fear within the user base of [whether their] data is protected [and] secure that has driven a lot of the conversation within the IoT world, whether it’s cloud adoption, whether it’s cybersecurity, and now as we look into AI—this analytical level that everybody wants to do. So synthetic data is helping companies answer that question when it comes to being able to do more with your data while protecting it at the same time.

Lantronix’s Gurusamy On Collaboration Fueling The Rise Of The ‘AI-Driven Industrial IoT Ecosystem’

As the industrial Internet of Things evolves, industries face growing pressure to bring real-time intelligence to the edge. No single company can tackle this challenge alone. The future of AI-powered IIoT will be defined by collaboration—where hardware, software and connectivity providers join forces to build integrated ecosystems that support hybrid AI models for edge intelligence.

Building a complete IIoT solution with embedded AI requires contributions from across the technology spectrum. For example, enabling an AI-powered drone or industrial robot involves high-performance cameras and sensors, efficient processors, advanced video compression, reliable connectivity, and cloud platforms for orchestration and analytics. Seamlessly deploying these hybrid AI models depends on ecosystem collaboration that unites all these elements into a single, scalable solution.

This collaborative approach accelerates innovation and shortens time to market for industrial applications—from predictive maintenance and asset tracking to environmental monitoring and automated inspection.

The combined power of AI and IIoT is transforming sectors such as manufacturing, energy, logistics, defense and critical infrastructure. Edge AI devices embedded across factories, power systems and transportation networks are enabling predictive analytics, safer operations and more efficient processes. Collaborative ecosystems ensure that industrial organizations can securely collect, process and act on data wherever it’s generated—in the field, on the factory floor or across global supply chains.

According to Precedence Research, the edge AI market is projected to grow from $25.6 billion in 2025 to $143 billion by 2034, with IIoT applications driving much of that growth. As AI becomes integral to IIoT deployments, collaboration between device makers, software developers and network providers will shape how industries operate, innovate and compete.

Lantronix’s Gurusamy On The Convergence Of AI And Industrial IoT For Real-Time Decision-Making

The industrial Internet of Things is transforming how organizations manage data and make decisions. As factories, energy grids and logistics systems become increasingly connected, businesses need to analyze information in real time. That’s driving a surge in hybrid AI models—where intelligence is shared between edge devices and the cloud to balance speed, cost and performance.

For industrial operations, seconds matter. AI at the edge enables immediate, local decision-making—whether a robot detects an obstacle, a compressor predicts a failure or a drone identifies an anomaly—without waiting for cloud feedback. The result: faster responses, greater uptime and stronger data privacy.

Meanwhile, the cloud handles complex analytics, large-scale data aggregation and ongoing AI model training. Together, this hybrid approach delivers the best of both worlds: real-time intelligence with scalable, long-term insights.

Hybrid AI is accelerating across IIoT use cases, including predictive maintenance to prevent downtime; process optimization in manufacturing and energy systems, remote asset monitoring for pipelines, HVAC and heavy equipment; autonomous operations in drones and robotics. Each relies on secure, efficient compute hardware and reliable connectivity—critical in remote or harsh environments.

With 70 percent of global data expected to reside at the edge within the next decade, industrial IoT will be a major driver of growth in the edge AI market, projected to reach $143 billion by 2034, according to Precedence Research.

Verizon Business’ Johnson On Why Cybersecurity Is IoT’s Biggest Challenge

With the continued growth of IoT devices, [the] threat surface area for bad actors has grown in kind. While 98 [percent] of enterprises surveyed expect real benefits from their IoT deployment within two years, most anticipating returns in less than 12 months, 43 percent of enterprises identified cybersecurity as their biggest hurdle when it comes to deploying IoT.

Depending on the build of a specific deployment, IoT devices can span multiple locations, involve devices from various manufacturers with different security capabilities, and operate in environments where physical security is limited.

To cover these different potential attack vectors, IoT deployments can require a more complex security setup than the traditional IT environment.

That said, companies are already becoming more savvy by implementing zero-trust architectures, private networks with enhanced security to manage these connections, and leveraging AI-powered threat detection to stay ahead of evolving risks. Expanding and evolving technologies like AI and IoT require expanding and evolving approaches to security.

Verizon Business’ Johnson On How AI Is Helping Wrangle IoT Data

AI is changing how enterprises approach connected operations like IoT in ways we couldn’t have predicted even a few years ago. Our recent report found that more than four in five, 84 percent, of enterprises consider AI a key technology for IoT, while 70 percent say it has accelerated their IoT deployments, and there’s a clear reason why.

IoT sensors generate massive amounts of data, creating a tidal wave of uncategorized information that needs to be processed and analyzed before it can be useful. That’s where AI comes in. It takes all that data collected and turns it into insights businesses can act on, quickly and with minimal effort. In manufacturing, this can enable predictive maintenance that catches equipment failures before they cause downtime and supply chain optimization that spots inefficiencies as they develop. It can also support automated decision-making on the factory floor, with incident identification, insight analysis, action planning and execution, and reporting with high automation and nearly in real time.

These changes are also spurring a shift in business mentality around IoT. Organizations that have been historically averse to data management complexities or unclear about ROI are now moving forward because AI accelerates the analytical frameworking and the measurable outcomes they need to justify investment.