Part 2 of a 3 Part Series

In Part 1 of our series, we discussed how carriers are shifting from passive "bit-pipes" into active value-creators by monetizing subscriber data. However, revenue generation is only part the battle. In this part we will touch on the need to aggressively attack the "friction" of manual operational costs by leveraging AI to solve the "Big Data" heavy lifting. Let’s now focus on how Telecom Carriers Use AI to Automate Workflows, Improve Circuit Design, and Reconcile Legacy Systems.

For decades, the telecommunications industry has been bogged down by labor-intensive, manual processes—specifically the "stare and compare" activities required to bridge the gap between fragmented legacy systems. Today, AI-driven automation is transforming these static, expensive workflows into adaptive, efficient systems. Telecom networks have modernized rapidly, but many internal operational processes still rely on legacy systems, manual workflows, and disconnected data sources. Engineering, provisioning, and planning teams often spend more time validating data than designing networks or improving service delivery. Artificial intelligence is now being deployed across telecom operations to solve these long-standing challenges—not by replacing legacy systems, but by intelligently connecting and augmenting them.

Automating Manual Workflows Across Telecom Operations

Many telecom workflows still depend on “stare-and-compare” activities, where staff manually review and visually compare records across multiple systems because the underlying data does not align. These activities commonly occur during service order validation, design, provisioning and billing reconciliation, and they introduce both delay and human error into otherwise automated processes.

AI is different from workflow automation. Workflow automation takes an existing workflow and automates it, so a person doesn’t have to do it. That is great, it saves manual errors and ultimately the automated “bot” works around the clock to complete the work, resulting in better accuracy and more productivity. The true value of AI goes much further than simple automation. While AI in 2026 will continue to automate these workflows, it does so by operating as a cross-system intelligence layer. It continuously ingests data from OSS and BSS platforms through APIs, flat-file exchanges, or direct database extracts, then normalizes differences in schemas, naming conventions, and identifiers.

Once normalized, AI models identify mismatches, missing attributes, and anomalous patterns, scoring discrepancies based on likelihood and impact. Over time, the system learns which discrepancies require intervention and which are benign, allowing human attention to be focused only on true exceptions. The ‘thinking and reasoning” bot adds a new dimension to simple automation by eliminating unnecessary steps, resolving discrepancies and suggesting better workflows. The result is a significant reduction in manual validation effort, faster order-to-activation cycles, fewer human-introduced errors, and increased confidence in operational data.

Making Circuit Design More Efficient with AI

Circuit design remains one of the most complex and knowledge-intensive functions in telecom. Designers must account for physical and logical topology, available capacity, historical routing decisions, and undocumented network changes that may exist across multiple systems. This complexity often leads to longer design cycles and higher rates of rework after provisioning.

AI improves circuit design efficiency by analyzing large volumes of historical circuit builds, reroutes, and failures to identify repeatable patterns and proven design approaches. By understanding network topology and capacity constraints and applying engineering rules consistently, AI can recommend optimized primary and diverse routing options while highlighting potential conflicts or constraints before provisioning begins. Design issues delay provisioning and ultimately the installation date of new services, resulting in millions of dollars of delayed revenue each month.

Rather than replacing engineers, AI acts as decision-support tooling that accelerates design while preserving engineering oversight. This leads to shorter design cycles, more consistent outcomes, fewer post-provisioning corrections, and stronger alignment between design intent and actual network behavior.

Reconciling Data Between Legacy Systems

In the telecom industry, data mismatches between siloed systems (like CRM, Billing, and Network Inventory) are a primary driver of revenue leakage and customer dissatisfaction. While traditional reconciliation involves manual audits and rigid, rule-based SQL scripts, AI introduces a layer of "intelligence" that can handle ambiguity and massive scale.

Here are a few examples how AI is used to reconcile mismatches:

Probabilistic Record Matching (Fuzzy Logic)

Telecom data often contains slight variations in service addresses, names, circuit ID’s device IDs, multileg circuit location detail, etc. across multiple systems. Traditional systems fail if a single character is different; AI uses Machine Learning (ML) to identify "likely" matches.

• Entity Resolution: AI models can determine that "St. James Pkwy" in the CRM and "Saint James Parkway" in the Network Inventory refer to the same physical location.
• Probabilistic Scoring: Instead of a simple "yes/no" match, AI assigns a confidence score. If the score is above 95%, it auto-reconciles; if it’s 70-90%, it flags it for human review.

Real-Time Anomaly Detection

Instead of waiting for a monthly billing cycle to find a mismatch, AI monitors data streams in real-time thereby identifying a mismatch prior to bill issuance. For example:

• Data usage vs. Billing Sync: AI compares Call Detail Records (CDRs) and data (IPDR) records from the network with the billing engine. If a high-volume data session appears in the network but isn't reflected in the billing bucket, the AI flags for revenue leakage instantly.
• Customer retention or upsell. If low usage is identified on a customer who is in a high usage package, AI can trigger an alert to the customer retention unit to put the customer in the right package before he discovers he’s paying more in usage fees than they should.

Natural Language Processing (NLP) for Unstructured Data

Many mismatches occur because of "free-text" notes or physical contracts that haven't been digitized correctly.

• Contract-to-System Validation: NLP can "read" digital service contracts and compare the negotiated rates with what is actually programmed into the billing system.
• Employee development: AI facilitates training and development of employees by gathering and managing old M&Ps, and job aids and turning the old outdated material into a new up to date digital library that refreshes itself when new M&P’s are developed.

Intelligent Data Cleansing and Normalization

AI automates the "prep work" that makes reconciliation possible in the first place.

• Schema Mapping: Telecom company mergers meant untold headaches for IT personnel who had to get systems and databases “talking” to each other. Different database structures made this nearly impossible and required major system rewrites. Using sophisticated AI tools, we can now automatically map data from one platform to another. E.g. "Cust_ID" from System A to "Subscriber_Account_Number" in System B.
• Missing Data Imputation: If a record in the Billing system is missing a "Cell Tower ID," AI can use historical patterns to "predict" and fill in the missing link, allowing the reconciliation to proceed.

How Advanced Technologies and Services Inc Helps

Advanced Technologies and Services Inc specializes in deploying AI solutions tailored to real-world telecom environments. ATS helps carriers automate manual validation and reconciliation workflows, improve circuit design speed and accuracy, and resolve long-standing data discrepancies across legacy systems—all without disrupting existing operations.

In Part 3 we will discuss how AI can be useful in telecom regulatory and compliance.