Your AI is only as reliable as the data your network delivers
A new position paper from Transforma Insights, sponsored by FLOLIVE®, traces how coverage gaps, latency and handovers end up inside your AI's inputs. And what connectivity has to look like once AI depends on it.
An AI model can only work with the data that reaches it. In IoT, that data has to cross a cellular network first.
When a sensor or vehicle drops off the network, the AI usually keeps running on incomplete or delayed data. Its outputs can look plausible and still be wrong. And the people using it tend to blame the model.
Connecting AI: The critical role of connectivity in AI data quality, by Transforma Insights, follows that chain from network to business outcome across 6 themes. It ends with an AI-native connectivity platform built on 7 data elements, including network intelligence and compute availability.
Transforma Insights named the intersection of AI and IoT one of its key IoT Transition Topics for 2026. Flolive is the first global network purpose-built for physical AI, with control over how and where device data travels.
What You'll Learn
- The 3 types of AI that run on IoT data (customer, real-time operational and security AI), and why one device can feed all 3 with different data needs
- How 5 connectivity factors, from availability to routing, turn into missed events and false alerts
- What an AI-ready network includes: QoS guarantees, multi-network orchestration, intelligent routing, edge compute and APIs that expose network intelligence
- How closed-loop remediation catches a connectivity problem before it becomes a data quality problem
- A worked example: an autonomous robot fleet across several countries, with AI workloads split between device, edge and cloud