AI, Cloud, and Distributed
Operations Raise the Bar

The urgency around transparency is increasing as AI and automation reshape both network services and procurement itself.

On the network side, analysts expect steady growth in telco cloud and automation investments. Omdia, for example, forecasts double-digit growth in telco cloud markets as operators pursue higher levels of operational automation and AI-assisted management. It's evident that automation and self-service portals are becoming essential to give enterprise customers real-time visibility and control over 5G and edge services.

On the enterprise side, procurement functions are undergoing their own transformation. McKinsey’s research on AI-driven procurement finds that organizations deploying advanced analytics and generative AI can unlock savings on the order of 10 to 20 percent and improve procurement efficiency by 25 to 40 percent. However, these benefits depend on one critical prerequisite: high-quality, unified data about what an organization is actually buying, from whom, under what terms, and how it is being used.

Telecom remains one of the most challenging categories in this respect. Spend is distributed across many suppliers; line items are complex; services are long-lived; and the mapping between invoices, circuits, locations, and workloads is often incomplete. For AI to deliver meaningful insights in this context—whether for anomaly detection, optimization, or forecasting—enterprises first need a much stronger foundation of transparency.

Elements of a Transparency-First Operating Model

Across the market, a set of best practices is emerging that together form the outline of a more transparent, resilient operating model for enterprise connectivity. While each organization will implement these differently, several common elements stand out.

Unified, Validated Inventory

The starting point is a single, authoritative view of every circuit, service, and connectivity-related asset—across all providers and geographies. This typically includes:

• Normalized service identifiers and attributes
• Mappings to locations, accounts, contracts, and invoices
• Status, bandwidth, and technology details
• Associations to applications or business units where possible

In practice, building this baseline often requires combining provider records, internal CMDB entries, TEM data, and on-the-ground validation. But once established, it becomes the backbone of all subsequent lifecycle and optimization work.

Lifecycle-Aware Processes

Rather than treating quoting, ordering, provisioning, renewals, and disconnects as isolated events, leading organizations are adopting lifecycle-aware processes that track services from initial need through end-of-life. This aligns with the intent behind standards such as MEF LSO, which emphasize coordinated management and control across the entire service lifecycle and across multiple domains.

In a transparency-first model, every lifecycle event—new order, bandwidth change, migration, disconnect—is recorded against the unified inventory, so that contractual, financial, and operational views remain in sync.

Data-Driven Commercial Models

Commercial structures are also evolving. In some cases, enterprises use traditional fee-based consulting and tooling. In others, they participate in provider-funded models where advisory or lifecycle services are compensated via standard industry commissions rather than direct enterprise fees.

Regardless of the specific approach, the trend is toward models that:

• Preserve the direct commercial relationship between customer and provider
• Make pricing and terms fully visible to the enterprise
• Align incentives around accuracy, performance, and long-term optimization

The common thread is that economic friction should not be a barrier to better visibility.