AI is increasingly being integrated into IoT applications, creating new demands on the connectivity that links physical devices to AI systems. The quality of AI outputs depends not only on the model itself, but also on whether the data generated by connected devices is complete, timely, consistent and reliable. This report examines the relationship between connectivity and AI data quality, the consequences of poor connectivity, and how AI can in turn be used to improve connectivity. It then considers how connectivity providers can evolve towards AI-ready and ultimately AI-native propositions in which connectivity, network intelligence, device data and AI resources are managed as part of a single data and compute environment.
Transforma Insights has identified the intersection of AI and IoT as one of the key ‘IoT Transition Topics’ (i.e. trends that will have the most impact in the IoT space) for 2026. IoT suppliers are increasingly looking at how to make use of AI for the purposes of optimising their own products and adding additional features, for instance for anomaly detection, fault resolution or connectivity orchestration. However, this is only scratching the surface of the ways in which the growth of AI has implications for IoT. This report examines one critical aspect: the impact of connectivity on delivering AI.
AI performance is constrained not only by the quality of the model, but also by the continuity, timeliness, context and integrity of the data reaching it. Connectivity is therefore not simply a transport layer. It is becoming part of the AI value chain. The right approach to delivering connectivity, as discussed in the sections below, helps determine whether data arrives in a form that AI systems can interpret, trust and act upon.
In this report we examine six key themes:
The AI data quality challenge (Section 3) examines why the quality of data supplied to an AI system is as important as the quality of the underlying model, particularly for IoT applications where data is generated by distributed physical devices. It considers the different dimensions of IoT data quality, including completeness, timeliness, consistency and reliability, and distinguishes between the requirements of customer or enterprise AI, real-time operational AI and security AI. The section establishes the central argument of the report: connectivity characteristics such as coverage, packet loss, latency, congestion and session continuity can directly influence the quality of the data available to AI.
Connectivity as a critical component of the AI data pipeline (Section 4) examines how specific characteristics of connectivity affect the delivery of data to AI systems. It considers availability and reliability, latency and jitter, bandwidth and packet loss, and the additional challenges created by mobility and network handovers. It also explores how connectivity is becoming an intelligent routing decision as AI workloads become distributed across devices, edge and cloud environments, with routing decisions increasingly influenced by factors such as compute availability, cost, application requirements and data sovereignty. The section concludes by considering connectivity in terms of end-to-end data quality rather than conventional network performance metrics alone.
The consequences of poor connectivity for AI outcomes (Section 5) considers what happens when connectivity problems degrade the data available to AI. It examines how missing or incomplete data can affect analysis and introduce bias, and why real-time AI applications are particularly sensitive to delayed or absent information. It also considers the extent to which different AI applications can tolerate connectivity interruptions, including the role of local processing and data buffering. The section then links connectivity problems to business consequences, including missed events, false alerts, delayed decisions and degraded optimisation, arguing that connectivity can ultimately affect confidence in AI and the value generated by an AI-enabled IoT deployment.
Connectivity intelligence: using AI to improve connectivity (Section 6) reverses the relationship examined in the preceding sections, considering how AI can be used to improve connectivity itself. It explores the use of AI for predictive network management, anomaly detection, traffic and resource optimisation, automated troubleshooting and service assurance. It also considers how AI can optimise the wider connectivity experience by taking account of distributed compute resources as well as network conditions. The section concludes by describing a feedback loop in which connectivity provides the data required by AI, while AI is increasingly used to improve the performance of the connectivity infrastructure supporting those applications.
From connectivity management to AI-ready connectivity (Section 7) considers how the connectivity proposition needs to evolve as AI becomes more dependent on predictable and observable data delivery. It examines a range of capabilities that could form part of an AI-ready connectivity service, including quality-of-service guarantees, multi-network orchestration, intelligent routing, workload awareness, edge computing and APIs exposing network intelligence. It also considers the strategic question of whether connectivity providers can move beyond selling connectivity towards providing data delivery assurance and AI readiness, while maintaining a distinct role within the wider AI ecosystem rather than competing directly with AI platform or application providers.
The emerging AI-native connectivity proposition (Section 8) looks further ahead at an AI-native connectivity proposition in which AI is embedded throughout the connectivity service and combines payload, device and connectivity data with network, security, compute and policy information. It explores capabilities including autonomous network selection, predictive connectivity management, dynamic allocation of connectivity and compute, and closed-loop remediation. The section then considers how these capabilities could support managed AI services and position connectivity as an intelligence layer within the IoT architecture, potentially allowing providers to move from managing connections towards managing the conditions under which data reaches the appropriate AI resource.
In addition to the published Position Paper, on the 29th October, Transforma Insights and floLIVE will deliver a Virtual Briefing ‘Connecting AI: Why connectivity matters to AI data quality’ examining the relationship between connectivity and AI data quality, looking at how poor connectivity can affect AI outcomes and how AI can itself be used to optimise connectivity. It will then explore the evolution from conventional connectivity management towards AI-ready and AI-native connectivity, in which network intelligence, device data, connectivity information, compute resources and policy requirements are managed together.
Transforma Insights has identified a series of aspects of the Internet of Things that are going through a period of fundamental transformation. These IoT ‘Transition Topics’ are the subject of Position Paper reports and Virtual Briefings identifying the key aspects of change and how organisations should position themselves to be best placed to realise the opportunities generated.
FLOLIVE® is the first global network purpose-built for physical AI. Its distributed core network combines local cellular connectivity with control over how and where data travels, supporting the flow of information between devices, edge infrastructure and cloud applications. FLOLIVE helps businesses align connectivity with their AI workloads, deployment needs and data requirements. By bringing data closer to where it is processed, FLOLIVE helps businesses reduce latency for faster AI applications and maintain control over data location to support their data sovereignty requirements.
