Learn
AI in telecom and tower operations
Articles on AI in network operations, from CAPEX planning to autonomous operations.
Published by industry bodies
Articles by MLNetworks experts.
Technical Deep Dive
Modern Data Structures Enabling High-Impact AI-Driven Telecom Operations
Jawad Maaloum, CEO / Published on TM Forum / 8 min read
How modern data architecture enables AI-powered network intelligence. Explore the technical foundations that make autonomous network optimization possible, from multi-vendor data integration to real-time processing at scale.
- Data fabric architecture for telecom
- Multi-vendor data integration strategies
- Real-time AI/ML processing at scale
- Deployment architectures for production
Research & Proof of Concept
Intelligent Telco Operations Through AI-Driven User-Centric Network Insights
MLNetworks Team / Published on TM Forum / 10 min read
Research and proof of concept demonstrating how AI delivers user-centric network intelligence for intelligent, autonomous telecom operations. Learn how we link network quality directly to customer experience.
- User experience optimization with AI
- Network quality prediction models
- Proactive issue resolution strategies
- Customer-centric intelligence frameworks
Latest from our blog
Longer articles from the MLNetworks team.
No articles published yet. Check back soon.
AI in telecom and tower operations
Where it helps an operations team today, and where a person still decides.
Putting the investment budget where it counts
Investment decisions are often made from separate spreadsheets for demand, asset condition and site constraints. Putting those inputs on the same site record lets a team compare options on one basis, and keep the assumptions and the reasons beside the decision.
What changes
- Options compared on the same data
- Assumptions written down with the decision
- Reasons kept once the rollout starts
A decision a finance team can trace back to the data behind it.
Learn moreFinding the energy a site should not be using
Energy is one of the largest running costs of a tower site, and grid, generator, battery and fuel data usually sit in different systems. Reading them together on one time line, against what the site should draw, shows where energy or fuel goes missing and which site needs a visit.
What changes
- Grid, DG, battery and fuel on one time line
- Actual use compared with expected
- Exceptions raised for a person to review
An energy question answered from the readings, not from the monthly bill.
Learn moreAI that prepares the investigation, not the decision
Most of a NOC investigation is gathering data: the alarms, the site, the power and the history. An AI agent can gather it and write a summary that marks what was measured and what was inferred, so the engineer starts from the evidence. A person reviews it and decides what to send.
What changes
- Related alarms grouped into one incident
- Findings shown with the data behind them
- Every decision kept with a person
Less time collecting data, more time deciding what to do about it.
Learn more
Which site gives you the most trouble?
Bring a real case. An MLNetworks expert will walk through it with you.
Talk to an Expert