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But that version of the story misses the harder and more interesting question: what does it actually take to serve a world of moving, autonomous, mission-critical devices? Not just to carry their traffic, but to orchestrate their operations, guarantee their service levels, and build commercially viable offerings around them?

That's the question the TM Forum’s Phase III Catalyst — Autonomous and Sustainable Moving IoT Ecosystems at Scale is designed to answer. And it's why Wavelo's involvement in this phase goes far beyond participation. Wavelo brings agentic service assurance, a capability that sits at the heart of what makes Phase III possible.

Phase III marks a turning point for IoMT ecosystems

The earlier phases of this Catalyst did important groundwork. They demonstrated that autonomous moving IoT orchestration was technically achievable and that AI could manage connectivity for moving assets in ways that legacy network management could not.

Phase III tackles the next logical step, commercialization. Technical proof-of-concept and commercial readiness are very different problems. A capability that works in a controlled demonstration needs to be packageable, priceable, and scalable before it becomes a business. Phase III is explicitly designed to close that gap by prototyping monetizable service models such as corridor-based licensing, mission-based pricing, and AI insights-as-a-service, while also expanding the solution beyond drones to cover autonomous vehicles, delivery bots, robotics, and mobile industrial assets across industries including logistics, smart cities, utilities, public safety, and agriculture.

All with the ambition of moving CSPs from selling connectivity to becoming orchestrators of intelligent, sustainable IoT ecosystems.

The assurance problem no one has fully solved

The operational challenges of the Internet of Moving Things place unique demands on modern networks. Whether it is a drone surveying utility lines or an autonomous vehicle, devices are not only using bandwidth, they are performing tasks that must be maintained in real time to avoid failure.

Standard network assurance protocols are ill-equipped for these requirements, as they were designed for static workloads where issues are addressed over minutes or hours. In the context of IoMT, however, responses must occur within milliseconds.

To address this, a new approach to assurance is required—one that can:

• Model the mission of every moving asset as a dynamic service graph.
• Simultaneously correlate network conditions across the RAN, transport, mobile edge compute, and cloud.
• Initiate automated fixes before any disruption impacts the end user or enterprise.

Wavelo’s Agentic Assurance is built to meet these needs by shifting from a reactive to an anticipatory model. It moves beyond managing isolated service and network elements to understanding their collective role in achieving specific operational goals. By enabling autonomous, closed-loop remediation, it ensures the service and network remains continuously aligned with the original intent defined by the enterprise.

From concept to commercial reality

One of Phase III's most significant contributions is its integration of GenAI, AI‑native service assurance, and AIOps intent generation and what-if simulation into the operational workflow.

Intent-based networking has been discussed in the industry for years. The idea—that an enterprise should be able to express what it needs the network to achieve, rather than specifying how—is brilliant. But making it work in practice across a fleet of autonomous devices with varying mission profiles, is a different matter entirely.

Phase III prototypes IoMT the power of AI at the edge.  An enterprise deploying drone inspection corridors doesn't want to configure network slices. It wants to say: these missions require sub-20ms latency, 99.99% uptime, and carbon-optimized routing — and have the platform handle everything else. Wavelo Agentic Assurance provides the service intelligence layer that makes this possible, translating high-level intent into real-time device behaviour, monitoring compliance continuously, detecting anomalies, and enforcing SLAs through closed-loop automation.

The addition of the what-if simulation takes this further, enabling CSPs and their enterprise customers to model how different network conditions, mission profiles, or routing decisions would affect outcomes before committing to them. It's the kind of capability that turns a connectivity contract into a strategic partnership.

Sustainability as a verifiable service

Phase III also showcases one of the more underappreciated dimensions of the IoMT opportunity: sustainability.

Enterprises are increasingly accountable for their own sustainability commitments, and their network partners are part of that picture. The Catalyst incorporates real-time sustainability insights, enabling carbon-aware routing and energy-optimized mission planning, but more importantly, verifiable ESG-aligned reporting tied directly to operational data.

A green SLA backed by real operational telemetry is a meaningfully different product than a sustainability claim in a vendor brochure. For CSPs looking to differentiate on more than price, the ability to offer sustainability-as-a-service with auditable proof is a genuine competitive advantage.

Ready for the real world

What makes Phase III worth watching at DTW isn't any single capability in isolation. It's the combination: network, telemetry and sustainability insights with predictive AI, intent-driven AI,  multi device scalability, and AI-powered insights as a service for 5G value creation with monetization, all unified through TM Forum Open APIs and ODA-aligned components into a single commercially viable architecture.

The Catalyst accelerated Wavelo’s contributions to the new TM Forum Open API;  EDA, and Async API collaboration project. Its work on event‑driven architecture and Async API guidelines and standards is particularly significant because it creates the connective tissue between technical capability and commercial packaging—enabling interoperable, event‑driven operations across digital ecosystems.

Conclusion

The IoMT market isn’t waiting. The enterprises deploying drones, autonomous vehicles, and mobile robots today are making infrastructure decisions now and the CSPs that can offer mission assurance, not just bandwidth or signal coverage, will be the ones that earn long-term opportunities in these verticals.

Phase III of this Catalyst is designed to give those CSPs a concrete, deployable path forward. It proves the technology. It prototypes the commercial models. And it does both within a TM Forum aligned framework that makes adoption achievable rather than aspirational.

The 5G network has long been viewed as infrastructure—pipes for bandwidth. This Catalyst shows something far more powerful: when that infrastructure becomes intelligent, autonomous, and accountable, it transforms into a mission partner for the enterprises building the world of moving things.

Autonomous, AI‑powered IoMT networks at scale turn every mobile device event into new top‑line revenue, while predictive AI at the edge intelligence and sustainable orchestration deliver greener, leaner operations to the bottom line.

Learn more about this Catalyst and Wavelo's participation in DTW Ignite 2026.

They explore why daily-updated data lakes aren’t enough for real-time, autonomous decision-making, the importance of contextual data in building trust between AI and humans, and what’s needed to make agentic AI work in telecom.

The conversation also covers ontology, digital twins, and why “big bang” migrations often fail.

Yet behind the keynote slides and innovation labs, many AI initiatives are struggling to deliver measurable impact. Pilots stall. Models underperform. Insights fail to translate into operational change.

The issue is rarely the sophistication of the algorithms. It’s the data.

Telecom operators don’t suffer from a lack of data. They suffer from fragmented, inconsistent, and poorly contextualized data. OSS and BSS estates have evolved over decades, optimized for transaction processing, not for feeding AI models. Static extracts, mismatched schemas, and manual data stitching create a distorted view of reality. AI doesn’t need more data. It needs accurate, contextual, real-time data that reflects how the business actually operates.

Change Without Control

Telecom is defined by lifecycles.

A subscriber is onboarded. A service is activated. Resources are provisioned. Usage is rated. Bills are issued. Faults are detected and resolved. Changes ripple across systems. These lifecycles span commercial and network domains and touch dozens of systems, often from multiple vendors.

Traditional architectures were never designed to manage this complexity dynamically. They rely on tightly coupled integrations and brittle, synchronous workflows. Introducing a new product, updating pricing, or modifying a network

configuration can take months and carry significant operational risk. Failures are difficult to trace. Recovery is manual. Insight often arrives after customer impact has already occurred.

In that environment, AI is being asked to operate on snapshots of a moving target.

Lifecycle-Aware, Event-Driven Control

What’s required is not another dashboard or another data lake. It’s operational control grounded in lifecycle awareness.

A modern approach treats events as state transitions of real business entities—subscribers, services, orders, and network resources. Not just generic messages or raw telemetry, but meaningful changes in lifecycle state.

When those state transitions are observable and governed in real time, operators gain:

• Ordered, transparent execution across subscriber, service, and order lifecycles
• Deterministic automation with built-in retry, compensation, and rollback
• Clear accountability for what happened, when, and why

This allows automation and AI to operate on reliable, contextual data rather than isolated transactions.

Critically, this doesn’t require a wholesale transformation program. Operators can evolve from brittle, point-to-point integrations toward governed, one-to-many event interactions incrementally. Existing systems continue to operate, but lifecycle visibility and orchestration are layered on top.

That means modernization without rip-and-replace. Control without paralysis. Progress without excessive risk.

Context Is Everything

AI in telecom fails when it cannot understand relationships.

A customer action in the BSS domain, a service modification in order management, and a configuration change in the network may be treated as separate events across different systems. But in reality, they are part of the same lifecycle.

Without a shared semantic foundation—an agreed operational model that aligns commercial intent with network reality—AI models are left inferring context from incomplete signals. That leads to inaccurate predictions, fragile automation, and low trust from operations teams.

A telecom-native semantic layer normalizes lifecycle events across domains. It bridges customer and network data. It creates a consistent operational language that both humans and machines can understand.

For AI, this isn’t a “nice to have.” It’s foundational.

Don’t Throw Away the APIs

Over the past two decades, OSS/BSS environments have been built around synchronous APIs and periodic batch processing. These approaches served their purpose well. They enabled reliable 1:1 application communication and transactional integrity.

But AI-driven operations require something different: large volumes of contextual data, available in real time, and consumable by multiple systems simultaneously.

The good news? Operators don’t need to discard their existing platforms to get there.

Standards bodies like TM Forum have already defined many of the APIs used across OSS and BSS environments. Modern development techniques, including AI-assisted coding, make it increasingly straightforward to transform these API interactions into governed events that can be streamed and shared.

This means legacy systems—regardless of their internal models—can publish and consume lifecycle events in a semantically consistent way. The estate remains intact, but its data becomes usable in motion.

AI That Can Actually Survive

As the industry gathers at FutureNet World in London, the conversation will inevitably center on autonomous networks, operational AI, and agentic systems. These are compelling ambitions.

But autonomy without contextual, real-time data is theatre.

AI initiatives thrive when they are fed accurate, lifecycle-aware, event-driven data that reflects operational reality. They falter when forced to rely on delayed extracts, disconnected silos, and inconsistent semantics.

The path forward is clear: build the data layer that AI can depend on. Govern lifecycles in real time. Normalize semantics across OSS and BSS. Stream context, not just transactions.

Do that, and AI becomes more than a pilot project. It becomes an operational asset.

Learn more about how Wavelo is powering AI with real-time data at wavelo.com.