Author:SophiaIoT Think Tank Compilation
With the deep integration of the Internet of Things (IoT) and Artificial Intelligence (AI), industries such as manufacturing, energy, and logistics are experiencing an unprecedented wave of intelligence. From edge devices to cloud analytics, from hardware deployment to data-driven decision-making, IoT is reshaping the operational models and competitive landscape of enterprises. However, behind the technological opportunities lie challenges: skill gaps, cybersecurity, supply chain uncertainties, and the impact of global trade policies are testing whether companies can seize the initiative in this digital transformation.
Recently, analysts and executives from IoT Analytics, Verizon Business, IDC, and Lantronix discussed some core insights they observed in the IoT market this year with CRN, and the author has compiled the highlights:
Insight 1: IoT Vendors Face a Significant AI Skill Gap
As generative AI, edge intelligence, and large model inference technologies accelerate their penetration, the IoT industry is undergoing a structural capability reshaping, and the AI skill gap has become one of the core bottlenecks limiting industrial leaps.According to Sinha, Chief Analyst for IoT Components, Connectivity, and Security at IoT Analytics, “We see a huge skill gap in the IoT market, especially in how to integrate AI technologies into IoT products and services.”
First, the imbalance in talent structure and skill spectrum is becoming increasingly evident.For a long time, the core capabilities of IoT vendors have focused on traditional areas such as hardware design, embedded development, wireless communication protocols, and device management. The introduction of AI forces companies to rapidly fill capabilities in algorithm engineering, model training, computing power optimization, data governance, and MLOps. However, the reality is that many engineers lack systematic AI skills, and talents with cross-domain capabilities are scarce and have long training cycles. This results in a clear situation where companies are waiting for technology while pushing for AI empowerment. A similar challenge was encountered during the “integration of OT and IT”—IT personnel are unfamiliar with OT environments, while OT personnel do not understand IT environments. If both sides are required to collaborate without cross-domain training, a multitude of complex issues will arise.
Second, the iteration speed of AI technology far exceeds the product lifecycle of IoT, creating a cycle mismatch.Traditional IoT devices often have lifecycles spanning several years, while AI technology updates at quarterly or even monthly intervals. During the product design, testing, and deployment process, the technological baseline may have changed, leading to the risk of AI solutions becoming outdated before they are put into use. This cycle mismatch not only raises R&D costs but also forces IoT vendors to rethink systematic capabilities such as version planning, hardware-software separation, updatable architectures, and online model upgrades.
Third, external dependencies may provide short-term solutions but are difficult to form long-term competitive advantages.Many companies attempt to accelerate AI project implementation by outsourcing or bringing in third-party teams, but third parties must deeply understand the company’s device logic, protocol stack, data characteristics, and business scenarios, which often requires significant time and communication costs. In cases involving NDAs, this process becomes even more complex. More critically, relying on external forces makes it difficult to build sustainable internal capabilities, which in the long run will weaken the company’s initiative in AI competition. If companies wish to introduce AI internally, they must enable their employees to acquire relevant skills rather than relying on third-party short-term solutions.
Therefore, building an “AI-ready” IoT organizational capability has become a key issue that the industry must face.
Insight 2: Tariffs Have Significantly Changed Corporate Strategies and Supply Chain Strategies
Tariffs have indeed changed the way many companies operate. They have increased raw material costs, affecting product pricing and supplier profits. An IDC market sentiment survey shows that60% of companies believe that rising tariffs are threatening profitability and the stability of technology budgets.
Tariffs have led to delays in equipment procurement, causing supply chain disruptions, and forcing companies to make strategic adjustments to ensure that they do not significantly impact customers, such as relocating manufacturing sites and promoting supply chain diversification.
At the same time, tariffs have also brought about some “innovation effects” in a certain sense. Carlos Gonzalez, Industrial IoT and Smart Strategy Research Manager at IDC, pointed out in the report he co-authored, “I cannot say that tariffs are the only cause of change, but they have indeed had an impact. What we are seeing is not a leveling off of hardware demand, but a downward trend. Companies are currently not planning to invest heavily in hardware in the future, but the application around data continues to grow strongly.”
The current reality is: companies are gradually realizing that hardware supply is already difficult and may continue to be tight in the future, so they must “do more with less hardware.”This is why synthetic data is so critical right now—it allows us to conduct more analysis based on existing information. A large amount of data comes from unstructured environments, such as visual systems, by conducting deeper analysis of data from existing cameras to extract more value from these unstructured sources.
Overall, tariffs continue to bring instability to the market.But even in unstable conditions, both customers and suppliers are well aware that some investments cannot be halted: manufacturing upgrades, IoT network and system construction, cybersecurity, etc. Investments in these areas will not stop. Companies may seek various ways to offset new costs, and these practices will inevitably impact pricing. Growth will continue, but how costs are passed down will differ, ultimately affecting prices and supplier profits.
Insight 3: The Growing Popularity of Synthetic Data in IoT Applications
As IoT and AI deeply integrate, data has become the core asset driving intelligent applications.However, companies often face multiple constraints such as intellectual property protection, sensitive information security, and privacy compliance when utilizing data for analysis, modeling, and simulation. In this context, synthetic data is becoming a key tool for companies to solve this dilemma.
Synthetic data refers to artificially generated data that aims to simulate real-world data. It is generated through statistical methods or using AI technologies (such as deep learning and generative AI). Although artificially generated, synthetic data retains the basic statistical characteristics of the original data it is based on. Therefore, synthetic datasets can supplement or even replace real datasets.
Synthetic data is a highly realistic replication of real data that does not involve original sensitive information, allowing for multidimensional analysis and simulation while protecting intellectual property.Its main applications include:
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Model Training and Algorithm Development:Companies can use synthetic data to generate training sets and build AI models without directly accessing real production or customer data.
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Cross-Enterprise Collaboration:Different vendors or partners can share data for joint analysis or system optimization without disclosing core business data.
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System Simulation and Scenario Testing:Before deploying IoT devices, synthetic data can be used for simulation testing to verify the effectiveness of edge computing, AI inference, and network strategies.
The driving factors behind the development of synthetic data include:
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Data Security and Privacy Concerns:Enterprise users are highly sensitive to the security and confidentiality of their data, which has become an important factor affecting IoT cloud applications, cybersecurity investments, and AI project implementation.
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Cross-System and Cross-Vendor Analysis Needs:As IoT devices become increasingly diverse, data is scattered across different systems. Synthetic data enables cross-platform analysis without the need to exchange sensitive data.
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AI Applications’ Demand for Data Volume and Diversity:The accuracy of AI models relies on large-scale, diverse datasets, but real data is often limited or restricted. Synthetic data can fill this gap, accelerating AI deployment and iteration.
Overall, synthetic data is not only a tool for solving privacy and data security issues but also a key means for enterprises to maximize data value in the IoT and AI era. In the future, as algorithm generation capabilities improve and simulation accuracy increases, synthetic data will play an increasingly central role in the intelligentization of IoT devices, cross-system interconnectivity, and end-to-end AI solutions.
Insight 4: IoT Vendors (Even Competitors) Are Strengthening Interconnectivity Capabilities
In the IoT field,interconnectivity among competitors is becoming an important growth direction. Customer demand is driving cooperation among vendors, as the market cannot wait for each supplier to develop independent solutions.
In the past, IoT systems were primarily based on “single vendor closed ecosystems,” with each vendor building independent systems around their own protocols, devices, and platforms. However,as deployment scales expand and cross-brand device coexistence becomes the norm, customers are gradually unable to accept “island systems”.For example, factories may use PLCs, robots, and sensors from multiple vendors simultaneously; in building scenarios, many systems are constructed by different suppliers; and consumer products involve numerous brands and different protocol families… Customers are actively demanding data interoperability and system compatibility among different vendors to achieve higher operational efficiency and lower system integration costs, fundamentally changing the past competitive model.
At the same time, as cloud vendors, data platform companies, AI service providers, and other third parties increasingly participate in the IoT ecosystem, they propose:“As long as we can access all device data, we can help companies integrate data and achieve stronger cloud or AI capabilities.” This forces underlying hardware vendors to open interfaces, share data formats, and comply with standard protocols, or risk being excluded from the larger data ecosystem.
Therefore, whether adopting open standards or open protocols, this interconnectivity is indeed promoting cooperation among companies (including direct competitors). To achieve cross-vendor and cross-scenario collaboration, the industry is accelerating towards mature open standards, such as: OPC UA, which is currently the standard for open communication protocols between devices, and Matter, which is reshaping the interoperability ecosystem for consumer devices, pushing smart homes from platform fragmentation to unified interconnectivity.
Companies are beginning to realize:They cannot only compete on hardware but must compete on ecosystem capabilities, service capabilities, and integration value—this will bring deeper industry changes: from hardware differentiation to differentiation in software, platforms, and ecosystem collaboration.
Insight 5: The Industrial IoT Is Driving the Rapid Development of Hybrid AI Models
With the continuous development of the Industrial Internet of Things (IIoT), industries are facing increasing pressure to push real-time intelligent capabilities to the edge. No single company can tackle this challenge alone.The future AI-driven IIoT will be centered on collaboration—hardware, software, and network vendors will work together to build an integrated ecosystem to support hybrid AI models for edge intelligence.
Building a complete IIoT solution with embedded AI requires contributions from the entire technology spectrum. For example, to achieve AI-driven drones or industrial robots, high-performance cameras and sensors, efficient processors, advanced video compression technologies, reliable network connections, and cloud platforms for orchestration and analysis are needed.In this environment, hybrid AI models emerge, sharing intelligence between edge devices and the cloud to achieve a balance of speed, cost, and performance.
In industrial operations, every second can impact production efficiency and safety, making the real-time local decision-making capability of edge AI particularly critical.For example, robots can react immediately upon detecting obstacles, compressors can predict potential failures, and drones can identify anomalies without waiting for cloud feedback, which not only enhances response speed and equipment uptime but also provides guarantees for data privacy and security.Meanwhile, the cloud is responsible for more complex analysis, large-scale data aggregation, and continuous AI model training, with both working together to form a hybrid architecture that provides real-time intelligence while supporting long-term insights and scalable analysis.
The application of hybrid AI in IIoT is accelerating, covering everything from predictive maintenance and preventing equipment downtime to optimizing processes in manufacturing and energy systems, remote monitoring of pipelines, HVAC, and heavy equipment, as well as autonomous operations of drones and robots, almost encompassing the entire industrial operation chain. To achieve these applications, companies not only need efficient and secure computing hardware but also reliable network connectivity, especially in remote or harsh environments, where the stability of these infrastructures directly relates to business continuity and intelligence levels.
As data gradually migrates from centralized to edge, it is expected that in the next decade, about 70% of global data will reside at the edge.According to Precedence Research, by 2034, the edge AI market size is expected to reach $143 billion, with the industrial IoT becoming a significant driver of growth in the edge AI market.
Insight 6: Cybersecurity Remains One of the Biggest Challenges Facing IoT
As the number of IoT devices continues to grow, the potential attack surface is also expanding.Although surveys show that 98% of companies expect to gain substantial benefits from IoT deployments within two years, and most expect to see returns in less than 12 months, 43% of companies still view cybersecurity as the biggest challenge facing IoT deployments.
Depending on the specific deployment architecture, IoT devices may cover multiple locations, involve devices from different vendors with varying security capabilities, and operate in physically secure environments. To address these diverse potential attack paths, IoT deployments often require more complex security systems than traditional IT environments.
Danny Johnson, Vice President of IoT and Managed Connectivity at Verizon Business, states: “As a result, companies are becoming more mature and intelligent: by implementing zero-trust architectures, establishing security-enhanced dedicated networks to manage device connections, and utilizing AI-driven threat detection technologies to proactively identify and defend against evolving risks. As technologies like AI and IoT continue to develop, the means and strategies for ensuring security must also continuously expand and evolve to address new threats.”
Insight 7: Artificial Intelligence is Disrupting the Way IoT Data is Processed
Artificial intelligence is changing the way companies manage interconnected operations like IoT in ways that were unimaginable just a few years ago.Recent reports indicate that over four-fifths (84%) of companies consider AI to be a key technology for IoT, and 70% of companies state that AI has accelerated their IoT deployments, with clear reasons behind this trend.
IoT sensors generate massive amounts of data, creating a flood of unclassified information that must be processed and analyzed to unlock value. AI plays a crucial role in this process, transforming the vast amounts of collected data into actionable business insights quickly, efficiently, and with minimal additional human intervention. In manufacturing, this means AI can enable predictive maintenance, providing warnings before equipment failures lead to downtime, while optimizing supply chains by identifying and correcting inefficiencies in real-time. AI can also support automated decision-making on the shop floor, including event recognition, insight analysis, action planning and execution, and highly automated, near-real-time report generation.
These changes are also driving a shift in corporate IoT philosophies. Companies that were once cautious about the complexity of data management or unclear about ROI are now accelerating forward, as AI can expedite the establishment of analytical frameworks and provide quantifiable results, thus providing strong justification for investment decisions.
Source:8 Big IoT Trends To Watch In 2025, According to Analysts And Executives, CRNWhat is Synthetic Data? IBM
