The next chapter in our MVNO/telecom AI series.
We began by mapping the most valuable AI applications for MVNOs and how to turn them into real business impact. We then showed why MVNEs and MVNAs sit at the center of the data opportunity and how better data flows unlock better decisions. Following, we explored the foundations, concluding that clean and connected data matters more than complex tools. We introduced a simple way to rate organizational readiness, so teams don’t start running before they are ready to walk. Further, we described how to turn intelligence into new revenues rather than only efficiency, and we set the broader context by arguing that AI is the next big industry shift, comparable to the disruption brought years ago by 3G. This new chapter moves from why to how: what MVNO leaders can do early in 2026 to turn pilots into production and real financial outcomes.
Timing matters! As previously commented, a recent MIT study found that 95% of enterprise GenAI pilots fail to deliver ROI. The issue isn’t model accuracy in the lab; it’s the lack of learning loops, weak ties to real workflows, and poor measurement in the field. Other industry work points to the same path forward: start with the ambitioned business results, redesign the operational workflows around them, and instrument the whole chain so that impact can be proven beyond a demo. In short, for MVNOs with lean teams and tight budgets, the winning move is not “more models.” It is better integration, clearer goals, and steady iteration.
The scenario MVNO leaders face
Most MVNOs have now tried at least one AI use case, often a chatbot, a churn model, or a simple pricing adjustment. These efforts help teams learn, but many stall when they meet day-to-day realities: incomplete data from host operators, messy joints between BSS, OSS, etc., and differences between offline training data and the real-time signals available when a decision must be made.
MVNOs usually face three obstacles when moving from a promising proof of concept into everyday operations. First, data arrives from multiple places (host operator, billing, CRM, service logs), and in different shapes. This slows down delivery and increases the chance of mistakes when teams try to stitch it together under tight timelines. Second, the signals used to train a model offline do not always match the signals available when a realtime decision is needed. That mismatch undermines confidence and produces uneven results once the system goes live. Third, different groups repeatedly rebuild the same metrics (churn features here, fraud features there), while using slightly different definitions. That duplication wastes effort and makes it harder to compare outcomes across use cases. These problems are solvable; the solution usually starts with shared feature definitions and simple checks that verify the data used in training still matching what the production system is receiving.
We have written about these gaps before, especially the cost of fragmented data and the danger of building silos. The pattern is familiar: a small proof of concept looks good, but once it is connected to the real workflow, campaign tools, customer care desktops, or service assurance queues, accuracy decays, quality drifts, latency rises, and confidence drops. The result is a long list of pilots with little to show on the actual P&L. The MIT report captured that reality across industries and we observed the same pattern inside our MVNO subdomain.
Principles to keep projects on track
Turning principles into action benefits from simple discipline. A project should be started by clearly stating the expected outcome and the few metrics that will define and measure its success. Whether the goal is to reduce churn in a prepaid segment, or increase subscriber acquisition without sacrificing operational margin, or prevent fraud losses, or shorten subscriber inquiry resolution time, the team should agree on the metrics that will define success, the boundaries for its operation, and the time window that will be used to judge results. This sounds obvious, but it is the main reason pilots drift: without a clear result in mind, teams tend to optimize for technical scores instead of targeting measurable business results.
Next, the model should be built inside the workflow that makes the outcome happen. A churn score that is not connected to a campaign tool cannot reduce churn; a quality alert that is not connected to the ticket queue cannot lower inquiry resolution time. Industry guidance emphasizes that integration and governance need to be present from the first day to avoid the gap between pilot and production.
Finally, the solution should be treated as a living process. Each decision produces an outcome (did the customer stay or churned? was the ticket closed? was the flagged case truly a fraud?). Those outcomes should be collected and used to improve the performance of the AI engines in a continuous manner. This steady learning loop is the “missing link” behind most failed pilots described in the MIT work: systems that do not learn remain stalled at pilot level.
One Feature Store for Multiple Solutions (“Write Once, Use Everywhere”)
There is one architectural move that removes a lot of friction for MVNOs. Instead of each team rebuilding the same information from raw CDRs or billing exports, standardize the feature definitions once and make them available for both training and real-time decisions. When a churn model and a fraud model use the same rolling windows, the same subscriber keys, and the same data freshness rules, two good things happen. First, the classic “it worked in the lab but not in production” problem is minimized, because the data manipulation logic is the same in both places. Second, processing costs are significantly reduced, as there is no reprocessing of the same raw data in different use cases. This “write once, use everywhere” pattern is how largescale platforms keep models consistent and maintain speed as use cases grow.
Finally, adding basic monitoring for data drift and regularly checking that training (off-line), and serving (real-time) inputs do still match, continuous recalibration prevents model accuracy “decays” over time, that frequently frustrates business owners after initial production.
Use Cases for MVNOs to prioritize in 2026
Churn prediction and retain actions work well when the scope is narrow and the features are simple enough to keep them fresh: rolling usage, top-up recurrence, recent customer care contacts, and/or device changes. Project could start with a small segment where action can be taken quickly, for example prepaid heavy‑data users who have stopped recharging, pushing attractive new offers or service checks when the AI model determines that subscriber is at risk to churn. It will be important to measure not just the retention scores, but also the ARPU after the retentions, to ensure that margin is not impacted by the churn prevention actions.
Pricing and offers. Dynamic pricing does not have to be complex. Many MVNOs get good results by adjusting bundles for a few key segments based on seasonality, elasticity by usage group, and simple quality signals. Optimization may start with already trusted data and short experiments can be run. The aim should not be to build a perfect optimizer, but to prove that better timing and better targeting can lift margin without hurting subscriber satisfaction.
Advanced segmentation benefits strongly from shared definitions. Subscriber segments can be defined across the whole MVNO operation for different teams to reuse, going well beyond the traditional metal classification (bronze, silver, gold, platinum). Examples can be: movers and travelers, new-to-network, bargain hunters, family sharers, etc. Business rules should be in a feature-specific layer, so marketing, customer care, and product teams all see the same groups with uniform definitions and attributes.
Anomaly detection focusing on service assurance is often driven by practical traffic KPIs such as latency, call drop rates, and session failures, which help to identify potential sources of service quality degradations before actual customer complaints rise. These patterns frequently shift with promotions, roaming traffic and device updates, so simple drift checks in the data platform should be implemented to alert the team when the model’s view of the world no longer matches reality.
Fraud detection. Telecom fraud continuously evolves, with new tactics that combine spoofed identities, SIM swaps, and automated scams. A compact set of metrics such as provisioning changes, unusual call or SMS bursts, and location and device mismatches, help in identifying a large share of risky behaviors with few false alarms. Recent industry reviews highlight how attackers are now using AI to scale scams, reinforcing the value of real‑time data and fast learning cycles on the operator side.
Closing thoughts
The broader industry context is clear: next years’ MVNO winners will be those building intelligence into daily operations. MIT findings underline the same point from another angle: the small share of pilots that produce strong returns are those that learn from outcomes, fit the workflow, and are evaluated against business goals. MVNOs are well placed to act on this strategy roadmap, moving faster than larger operators, adopting shared features sooner, and proving value in weeks by targetting focused narrow processes at a time. The key take-away to move from ideas to results is: one business outcome defined, one AI model integrated into the workflow, one learning loop implemented, and one feature store used across multiple solutions.
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