New report from the world’s leading IoT analyst firm examines the increasing criticality of optimised connectivity for addressing the requirements stemming from the integration of Artificial Intelligence into IoT.
With the terms ‘Physical AI’ and ‘AIoT’ entering the lexicon of IoT, 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. 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 helps determine whether data arrives in a form that AI systems can interpret, trust and act upon.
A new Transforma Insights Position Paper ‘Connecting AI: The critical role of connectivity in AI data quality’, sponsored by floLIVE, 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. The report identifies that AI-ready connectivity represents more than an extension of existing connectivity management capabilities. While the baseline connectivity functionality needs to have AI-based enhancements, for instance for interaction with the network or anomaly and threat detection, the real transition – as discussed in this report – is to an AI-native connectivity proposition, in which AI is embedded throughout the connectivity service and the provider uses a much broader set of information to manage and optimise the experience delivered to the customer.
Commenting on the findings, author Matt Hatton said: “It’s not an understatement to say that AI is permeating all aspects of the technology ecosystem and IoT is no different. It acts as a driver of adoption for remote sensing as well as requiring changes to IoT architectures to support increasing redaction at the edge. One area that has been less considered so far is the extent to which connectivity providers need to adapt their propositions, particularly in terms of resilience, to address the needs of AI.”
The free Position Paper ‘Connecting AI: The critical role of connectivity in AI data quality’ examines a critical aspect of the intersection of AI and IoT: the impact of connectivity on delivering AI. It considers six key areas:
The report is sponsored by floLIVE.
If you have questions concerning the methodology or the report, don’t hesitate to contact our analysts via enquiries@transformainsights.com.
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.
