Artificial Intelligence has long been a buzzword, but it has also now become ubiquitous. Telcos have made and are seeking to make significant investments in AI – so the expectation is set: AI must generate returns in-line with other investments. With that focus in mind, where is the value and how can we unlock this in 2026?
AI isn’t just a tool. It’s a fundamental element of transformation that our industry must embrace. To do so, we must collaborate and share lessons, so we can, collectively, make progress and turn vision into practical reality. In the spirit of this, in this article, we’d like to share some key learnings from our own AI journey that can drive success in the year to come.
We all know that AI and ML will become central to telecoms networks. There’s very little dissent on that now – but there is still plenty of room for debate regarding the pace of change.
That’s because the real questions are:
Can we demonstrate tangible, repeatable value from the capabilities it brings, and
When will this value be captured sufficiently to make a difference?
This uncertainty is real – despite recent gains. Foundation models have vastly expanded their parameter scales, their multimodal competencies and context windows – by orders of magnitude. This rate of improvement is without precedent in the history of software systems – and enhancements across multiple dimensions are likely to be seen in 2026.
The results of this can be seen in rapidly advancing industry benchmarks and also the increasingly sophisticated behavior of modern large language models. It’s also clear from the growing number of use cases – AI evidently has a role to play almost everywhere.
At Polystar, we already expect results from operational use cases.
For example, AI tools can help us to accelerate the identification of emerging network and service issues, enabling us to summarize complex network phenomena across multiple domains, and resolve incidents with new levels of speed and reliability.
Equally, many operators have invested in AI to support customer-facing applications and service touchpoints. These investments will scale from trial cases to reach widespread adoption. For example, innovations — such as the ability to improve NPS through accurate sentiment analysis at true scale, AI enhanced digital interactions and customer journeys, and more intuitive and responsive sales and support experiences — are already showing how AI can deliver value quickly.
All of this means that operators are gaining clarity over how AI-enablement can benefit key processes and operations.
The next step is crucial: converting promising PoCs into deployments that deliver positive operational impact.