Data Department helps telecom operators and networking companies use machine learning (ML), simulation and AI (artificial intelligence) to see delivery risk early and find value in operational data. Through a US partner, we worked with a global networking company: a Monte Carlo simulation that showed a large migration programme would miss its targets, and service value and anomaly detection for several business units.
Where does AI pay off in telecom and networking?
Telecom and networking businesses run large programmes and large installed bases. Both produce detailed records that are rarely used for forward-looking decisions. The questions are often simple: will we hit the target, and where is the value or the risk?
| Decision | What ML, AI or simulation does | Typical data |
|---|---|---|
| Will a roll-out or migration programme hit its targets | Monte Carlo simulation of how current work converts into completed sites, week by week | Project-management records, step history, resources |
| Where to add resources, and when | Scenario runs that compare staffing and schedule options before committing | Resource plans, step durations, fall-out rates |
| Where service value sits in the installed base | Service value analytics across service requests, returns and installed equipment | Service-request, returns and installed-base data |
| Which returns or service patterns look unusual | Anomaly detection that flags cases for engineering or commercial review | Returns, failure codes, service history |
| Which customers are likely to leave | Churn and propensity models that rank customers for retention offers | Usage, billing, complaints, contract data |
| How engineers find answers in technical documents | A RAG (retrieval-augmented generation) assistant that cites its sources | Configuration guides, procedures, release notes |
Relevant case studies
- Programme delivery simulation: a global networking company, through a US partner, in 2023. The company had a migration contract with a large telecom carrier to replace legacy circuit-emulation equipment with newer, more efficient hardware. It earned revenue only when a site's migration was completed, and the contract had time-based targets. We took the programme's records from its project-management software, broke the migration process into steps, estimated the conditional probability distributions for moving from each step to the next (including fall-outs and step durations), and simulated how the current workload would convert into completed migrations week by week. The finding: with current resources, the programme would miss its site-migration targets for the coming year by a wide margin. That forecast turned out to be correct.
- Service value and anomaly detection, in the same partnership: analysis of service-request, returns and installed-base data, for several business units.
Why a simple simulation can be enough
The simulation method was deliberately simple. Its value was making the gap between plan and capacity visible early, while there was still time to change resources or commitments. Many programme risks do not need a complex model; they need someone to turn existing records into a forecast that management can act on. Our optimisation and simulation service describes the approach.
What are typical first projects in telecom and networking?
- A delivery forecast for one programme, built from records you already keep in project-management tools.
- Anomaly detection on returns or service requests for one product family.
- A technical document assistant for field or support engineers. See our generative AI assistants service.
Each starts with a two-week Discovery: ranked use cases, a data check and a go/no-go decision. The free 30-minute AI opportunity review is a quick way to test fit.
Data and integration notes
- Project-management records with dated step changes are enough for a programme simulation; no new data collection is needed.
- Service, returns and installed-base data usually sit in separate systems; joining them is often the first piece of work.
- Infrastructure: we deliver on AWS (Amazon Web Services), Google Cloud, Azure or on-premises.
Frequently asked questions
What is a Monte Carlo simulation?
A method that runs a process many times with random variation, here step durations and fall-outs, to show the range of likely outcomes instead of one plan date.
Can you work through our existing consulting or technology partner?
Yes. Our telecom and networking work was carried out through a US partner.
Do you need clean data to simulate a programme?
No. Step histories from project-management software are usually enough; the simulation shows where the data is too thin to trust.