For years, cellular IoT connectivity has often been evaluated through a familiar set of questions: Is there coverage where the device operates? Is the connection reliable enough? How much data will be transmitted, and what will it cost?
These questions still matter. But as enterprises introduce more AI into IoT applications, they need to consider another question: is the connectivity arrangement appropriate for the AI application that depends on it?
The answer will increasingly depend on what the AI is doing.
AI is not a single workload with a standard set of connectivity needs. Some applications analyse accumulated data to identify patterns, generate forecasts or support business decisions. Others use data to monitor live operations, detect events or inform actions that need to happen quickly. Security-related applications introduce their own requirements around timely information and the ability to identify unusual behaviour. These differences have direct implications for connectivity.
An application analysing equipment performance over time may be able to work with data delivered periodically. An application supporting real-time operational decisions may be much more sensitive to latency, interruptions or missing information. Applications involving video or other data-intensive inputs can also place substantially different demands on available bandwidth.
Consequently, there is no single connectivity specification that is optimal for every AI-enabled IoT deployment. Requirements need to be derived from the application, including the consequences of delayed or incomplete data.
Coverage remains a necessary starting point, but it is not a complete measure of suitability. A device can be connected and still fail to deliver the quality of data an AI application needs.
Latency and jitter influence how quickly and consistently information arrives. Packet loss can leave gaps in a data stream. Interruptions and handovers can affect continuity, while bandwidth constraints may limit the volume of information that can be transmitted.
These factors need to be considered in context. A network characteristic that is relatively unimportant for one application may be critical for another.
Enterprises also need to look beyond the cellular network itself. Data delivery can involve multiple stages, from the connected device through the network and edge infrastructure to cloud platforms and AI services. A problem at any stage can affect the information ultimately available for analysis.
The practical challenge is to connect network performance to application outcomes, rather than treating connectivity as an isolated technical service.
This creates an opportunity for cellular IoT connectivity providers to broaden their role. Rather than focusing exclusively on providing access to networks, they can help enterprises understand how connectivity performance affects their data pipelines.
That could involve greater visibility of service quality, identifying the causes of data delivery problems, prioritising important traffic, or using available network options more intelligently. In more complex deployments, it may also mean coordinating connectivity with edge and cloud processing requirements.
The aim is not simply to provide the fastest possible connection. It is to deliver data in a way that meets the needs of the application, taking account of performance, cost and other operational constraints.
For enterprises, this means connectivity choices should be informed by the AI use case and its data requirements. For providers, it points towards a more application-aware approach to service design and management.
As AI becomes a more integral part of IoT, connectivity is likely to be assessed increasingly on its contribution to data quality and application performance. That places greater emphasis on visibility, appropriate service levels and the ability to adapt connectivity to changing requirements.
Transforma Insights examines this shift in Connecting AI: The critical role of connectivity in AI data quality, a free Position Paper sponsored by FLOLIVE. The report sets out why AI workloads can change connectivity requirements, and how cellular connectivity providers can respond.
Read the report to explore what AI means for the future of cellular IoT connectivity.
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.
