For mobility managers, zero usage appears to offer an easy savings opportunity. A line shows no activity, so the natural response is to disconnect it. The difficult question is timing. Acting after one month captures more savings but creates a higher risk that the user still needs the service. Waiting too long reduces that risk, but much of the available savings disappears while the organization continues paying for inactive lines. A recent sample analysis of 12 months of

Mobility expense management is often approached as a reporting exercise. Review the monthly bill. Identify large cost increases. Find unused lines. Check roaming. Analyze plans. Produce a savings report. Each of these activities is useful. But each answers a different question. The problem arises when one report is expected to provide the complete picture. A month-over-month cost report can tell you what changed, but it may completely miss waste that has remained unchan

Most Technology Expense Management providers already have a valuable sales asset sitting inside their business: their own data. Across multiple clients, carriers and billing periods, TEM platforms accumulate information on spend, service counts, average cost per service, carrier mix and cost trends. Individually, each client dataset is useful. Combined and standardized, those datasets can become something much more powerful—a benchmarking engine that helps sales teams identif

Unbilled optimization is one of the most effective ways to manage wireless costs before an invoice is generated. By analyzing current-cycle usage and adjusting plans before the billing period closes, organizations can reduce unnecessary plan costs and avoid many predictable overage charges. But there is an important limitation: Unbilled optimization can reduce overage risk, but it cannot guarantee zero overage. One reason is backbilling — usage or charges that are not visible

For years, mobility cost optimization has depended heavily on experience, data quality, and knowing which questions to ask. A carrier proposal may look attractive on the surface, but the real answer is usually hidden in the details: usage patterns, plan mix, overage exposure, device behavior, contract terms, and the customer’s actual business needs. Recently, I have been using AI to support mobility analysis and contract negotiation work, and the impact is becoming very real

In telecom expense management, the traditional path from idea to solution is often too slow. A business team identifies a need. Requirements are documented. The request goes into a queue. The development team prioritizes it against everything else already in flight. Weeks or months later, someone finally starts building. By that point, the original business need may have shifted, the data may need to be handled differently, or the client may now be asking for something m

In mobility expense management, normalized cost and usage data is often presented as a major step forward. It creates consistency across carriers, simplifies reporting, and gives analytics team a cleaner dataset to work with. On the surface, that sounds like exactly what organizations need. If all carrier data can be structured into one common format, then surely it should be easier to optimize wireless plans, identify waste, and improve mobility policies. The problem is t

For years, telecom expense management has depended heavily on manual review. Teams spend hours analyzing invoices, usage, and charge descriptions to understand why expenses when up or down from one period to the next. In many organizations, this work is repeated every month by skilled analysts who must interpret complex billing data and then explain the main drivers of change. This is exactly the kind of process where AI can create real value. Not because it is flashy. Not

For many Telecom Expense Management providers, regex has long been one of the hidden engines behind invoice processing. Carrier invoices are rarely clean. Billing descriptions vary by carrier, contract, feature, plan, and region. The same type of charge may appear under slightly different wording from one month to the next, while entirely new descriptions can appear when contracts are renegotiated or services change. To create structure from that complexity, TEM teams ofte

In most wireless contract negotiations, the discussion focuses on price sheets, discounts, usage averages, and historical billing trends. That approach is common, but is is also limited. The real opportunity is not just reviewing rates. It is using optimization methods such as integer programming and similar mathematical techniques to evaluate how different contract structures actually perform across an entire population of lines, plans, and usage patterns. When used corre

In mobility expense management, some of the most important control points are easy to overlook because they often live inside routine operational work. Suspension, reinstatement, and cancellation tracking is one of those areas. On the surface, it may seem like a simple administrative process. A client sends an email, a carrier confirms an action, and someone updates a spreadsheet. In reality, this workflow often becomes a key bridge between carrier inventory, billing valid

In Part 1 Mobility Rate Plan Optimization Is Risk Management (Not Just Cost Cutting) I argued that mobility rate plan optimization is not just a cost-cutting exercise. It is a risk management problem. In this post, I'll walk through a practical framework for doing that. If your unbilled report is imperfect, pooled plan optimization is a forecasting problem. The goal is to decide how much pooled capacity buffer to buy (via higher pooled tiers or unlimited) to reduce overage

Most mobility optimization strategies look simple: Keep lines on the cheapest plans Watch unbilled usage during the month Upgrade high-usage lines to avoid overage Reset at the next cycle and repeat U.S.-based carriers typically allow plan changes to be backdated to the beginning of the billing cycle as long as the change is an upgrade, so this workflow can be very effective. But there's a problem that turns "optimization" into "risk." The hidden dependency: unbilled usage ac

For most of its history, Telecom Expense Management (TEM) has served a very specific type of customer: the traditional enterprise that buys WAN access, Internet connectivity, mobile services, and MPLS circuits to connect offices and users. TEM platforms were designed to answer financial questions. What do we have? Where is it? Who bills us? How much are we paying? Are we paying correctly? Can we optimize it? The model worked well for two decades. But a new category of

In a traditional mobile account, it's easy to identify a line: you just use the phone number . For smartphones, that works fine as a unique service ID in your inventory and billing systems. With IoT lines , it's not that simple. IoT services often don't behave like phones. Many don't use voice at all. Some don't even truly "use" the phone number that the carrier assigns. Yet the same account may contain multiple identifiers: ICCID MSISDN IMSI If you pick the wrong one as

Why Monthly Mobility Invoice Audits Still Matter (Even After Issues Are "Fixed") There's a common assumption in mobility expense management: once an issue is fixed with a carrier, it should stay fixed. In practice, that's not how it works. Large mobility carriers including AT&T, Verizon, and other operate highly complex billing systems with multiple platforms, provisioning paths, contracts, and automation layers. A change in any one of those layers can unintentionally rein

Federal Universal Service Fund (USF) recovery charges can appear on an invoice even when you don't see any separate "voice usage" line items that month. That's because USF isn't triggered by minutes. It's tied to the carrier's assessable end-user telecommunications revenue (primary interstate/international revenue) and how the carrier chooses to recover that cost. Importantly, the FCC does not require providers to pass their USF contribution through to customers, and it als

Contract negotiations and operational strategy go hand in hand. The rates you negotiate don't just determine your monthly invoice - they shape the day-to-day work required to manage usage, prevent surprises, and control risk. A "good deal" on paper can become a costly operational burden if the rate structure pushes risk onto the customer. To show what I mean, here's an IoT example comparing two real-world pricing designs: AT&T vs. T-Mobile. The Key Point: Rate Design Drives

For years, Bring Your Own Device (BYOD) has been sold as a simple equation: let employees use their own phones and laptops, save money on corporate devices, and everyone's happy. Fast forward to end of 2025, and the picture is a lot messier. The global BYOD market is now worth well over $100 billion and growing fast, driven by hybrid work and the expectation that "work can happen anywhere." Most organizations allow at least some form of BYOD, and many employees use personal

A Policy Checklist for Enterprises For years, international roaming has been painfully simple and painfully expensive. An employee lands in London, their phone lights up with a "Welcome abroad!" text, and suddenly you're paying $10-$15 a day for the privilege of email, maps, and Teams calls. Travel eSIMs break that model. With a few taps, a traveler can download a local or regional data plan, often at a fraction of classic roaming rates. That's great for the employee's exp