By Matt Hatton, Founding Partner, Transform Insights
The growing integration of artificial intelligence (AI) into Internet of Things (IoT) applications is changing the requirements placed on mobile connectivity. According to a new position paper from Transforma Insights, sponsored by FLOLIVE, Connecting AI: The critical role of connectivity in AI data quality, connectivity becomes more than a means of transporting information from connected devices to applications. It is increasingly the determinant of the quality of the data that depends on the AI system, and therefore the results that can be delivered.
Connectivity is becoming part of the AI equation
AI performance is usually discussed in terms of model architecture, training data and computing resources. However, even sophisticated models can produce unreliable results when their inputs are incomplete, delayed, corrupted or inconsistent. This is particularly important for the IoT, where AI relies on observations of physical assets, including industrial machinery, vehicles, cameras and sensors. Unlike many web-based data sets, information from these tools is often very specific to a particular asset or deployment, time-sensitive and difficult to replace when lost.
The relationship between connectivity and AI performance is becoming more important as AI moves beyond retrospective analysis to real-time inference, operational decision-making and autonomous systems. Connectivity issues can affect whether observations reach AI systems, whether they arrive in time to be useful, and whether they maintain the continuity and context needed for reliable interpretation.
This creates a broader challenge for mobile IoT providers. The relevant question is not only whether the device can connect to the network. Whether the connectivity service can continuously deliver data with the characteristics required by AI applications.
Different AI applications require different connectivity
The report distinguishes three categories of AI applications with different data quality and connectivity requirements.
AI customers or companies use IoT payload data to identify patterns and support decisions. Examples include predictive maintenance, fleet optimization and industrial or retail data analysis. The application can tolerate temporary connectivity interruptions if the device can store information locally and transmit it later. However, long or systematic gaps can still corrupt historical data sets, especially when devices in a particular location or operating environment are disproportionately affected.
Real-time operational AI has more demanding requirements. Anomaly detection systems monitoring industrial equipment, for example, require relevant observations when events occur. Missing or delayed information can lead to developing errors not being detected or interventions being too late. In these situations, buffering data for subsequent transmissions cannot return the required information value at a given time.
AI security introduce another dimension. Connectivity providers and specialist security companies can analyze device activity, connection patterns, authentication events and traffic characteristics to identify suspicious behaviour. Incomplete visibility can make it more difficult to distinguish genuine security incidents from ordinary connectivity issues.
The same tool can support all three categories, providing application payloads to one model while generating operational and security metadata for others. Each has different requirements, which shows why connectivity should be assessed against specific workloads rather than a single performance criterion.
The consequences of poor connectivity go beyond technical errors. Missing observations can introduce bias, delayed data can impair operational decisions, and incomplete information can produce false alarms or missed events. In industrial applications, this can lead to unnecessary maintenance, equipment downtime or reduced productivity. In safety-sensitive environments, the implications can be even more serious.
There are also risks to trust in AI. If users repeatedly encounter inaccurate predictions or inexplicable decisions, they may conclude that the model is unreliable when the underlying problem of connectivity is broken. The report makes connectivity part of the AI-enabled IoT value proposition, rather than just an infrastructure cost.
From network performance to intelligent data delivery
Traditional connectivity metrics remain important, but the report states that coverage, throughput and uptime alone do not determine whether a network is suitable for AI.
Availability and reliability determine whether information reaches the AI system. Latency and jitter affect whether it arrives on time and in a predictable order. Bandwidth limitations can force applications to reduce sampling frequency, compress data or remove information, while packet loss can introduce gaps. For mobile devices, handovers between cells, networks and technologies can disrupt the continuity of data flow.
The importance of each characteristic depends on the application. Periodic telemetry requires relatively modest performance, while continuous video analysis, robotics and real-time anomaly detection may rely on more predictable data delivery. Low latency isn’t the only consideration: consistent and predictable performance can be just as important.
Connectivity is also a smart routing decision as AI workloads are distributed across devices, edge infrastructure and cloud environments. The most appropriate destination for data may depend on network performance or cost. Compute availability, processing delays, application requirements and data sovereignty policies can all influence the choice.
For example, an AI processing environment close to the device may offer shorter network paths but limited computing capacity. More remote environments can provide better overall response if they have more processing resources. In addition, regulatory or contractual requirements may limit where data can be sent or processed.
As a result, the report supports an end-to-end perspective that includes radio connections, core networks, transport and interconnection, edge or cloud infrastructure, and AI processing resources. Observations in this chain are becoming increasingly important, allowing providers to identify where performance is deteriorating and whether the data flow meets application requirements.
AI can improve the connectivity you depend on
The relationship between AI and connectivity works both ways. While connectivity affects the quality of data fed to AI, AI can also improve the operation of the connectivity infrastructure itself.
Mobile IoT providers have access to a wealth of operational information about network performance, traffic patterns, device behavior and service usage. AI can use this data set to identify emerging errors, detect anomalies, anticipate capacity requirements, optimize traffic and automate problem solving.
This creates an opportunity to move from reactive network management to predictive service assurance. Instead of waiting for devices to disconnect or performance thresholds to be breached, providers can identify patterns associated with increasing latency or packet loss and take preventative action. This may include changing traffic priorities, selecting alternative networks or routes, adjusting resources or initiating operational interventions.
The potential extends beyond individual network components. As AI processing becomes distributed, connectivity platforms can incorporate information about compute availability and workload distribution when deciding where traffic should be directed. The goal is to optimize the combined network-and-compute path rather than separate connectivity.
The report describes this as a reinforcing relationship: connectivity feeds data into AI, AI helps improve connectivity, and better connectivity supports better data and more reliable AI results. For providers managing large and geographically distributed IoT deployments, this can make AI-based optimization and automation a key element of their connectivity proposition.
Evolution to AI-ready and AI-native connectivity
The changes discussed above create opportunities for mobile IoT providers to extend conventional connectivity management to what the report describes. AI-ready connectivity.
Existing services typically focus on device onboarding, SIM lifecycle management, network provisioning, usage monitoring, billing and diagnostics. This remains necessary, but AI-enabled deployment introduces additional requirements in terms of predictable performance, data delivery guarantees, network intelligence and coordination between connectivity and computing.
The report identifies several capabilities that could be part of an AI-ready proposition. This includes a guarantee of quality of service that is in line with the requirements of the application; multi-network orchestration to maintain continuity; intelligent routes that account for computing availability and data sovereignty; integrated edge computing with connectivity; and APIs expose network intelligence to applications.
These capabilities will allow providers to differentiate their services through the quality and reliability of data delivery, rather than competing only on coverage and price. The report does not indicate that the connectivity provider must be an AI platform or application provider. However, the opportunity lies in leveraging network path control, visibility into connectivity conditions and the ability to manage multiple networks to support a wider AI ecosystem.
Looking further, the report describes AI’s original connectivity proposition where AI is embedded throughout services. It can combine device, load, connectivity, network, security, computing and policy information to support autonomous network selection, predictive connectivity management, dynamic allocation of connectivity and computing resources, and closed-loop remediation.
In this model, connectivity services will continuously assess the situation, identify potential problems, select interventions and evaluate the results. It can also use connectivity metadata to help interpret application data, distinguishing genuine changes in the physical environment from changes that appear due to missing observations or degraded network performance.
The main conclusion of the report is that the growth of AI is changing the role of mobile IoT connectivity. The opportunity is not only to provide a faster or more reliable connection, but to manage the conditions under which data is collected, transported and processed. Providers that combine network intelligence with device and compute awareness can develop a more differentiated role in the AI-enabled IoT, helping to ensure that the right data reaches the right AI resources, at the right time and under the right conditions.