If you walk into any tech conference these days, the buzz around artificial intelligence (AI) is impossible to ignore. At Mobile World Congress in Barcelona this past March, for example, the power of AI was on display wherever you went. Take Deutsche Telekom’s launch of its AI Call Assistant, which enables real-time translation and call summarisation, and will eventually be able to action requests. So, if it’s clear during a conversation that a reservation is necessary, the AI assistant can make the booking. Or iFlytek’s introduction of a pair of AI-powered smart glasses, which are designed to enhance cross-language communication by providing real-time subtitle translation displayed on the lens and an integrated speaker that plays the translated audio.
But when one looks beyond the high-tech and flashy consumer applications and focuses more on the operational and support layer, AI innovation is less apparent. This is particularly true in the OSS/BSS (Operations Support Systems / Business Support Systems) space.
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To be clear, this isn’t to say that there hasn’t been any innovation in the OSS/BSS space or that AI isn’t rewriting the rules. But a lot being positioned as innovation is, in my opinion, just incremental improvements marketed with fancier language.
Take Chatbots as an example of this. Some of the earliest iterations of this technology date back to the 1960s, when chatbots used pattern matching to read words that users entered into a computer and then pair these to a list of possible scripted responses. Obviously, modern large language models make chatbots more capable but the function is much the same. The same is true for churn prediction capabilities. Being able to analyse customer behaviour data and flag subscribers at risk of leaving has been around for years. What has changed is the volume of data available and the sophistication of the models we have available to assess who is most likely to jump ship.
So, how can telcos and other players in this space leverage AI to deliver real, measurable change? One area where AI has potential to drive genuine innovation is through context-aware automation, which allows systems to understand the broader operational or customer context before making decisions. For example, instead of automatically escalating every network fault in the same way, an AI-driven OSS platform could assess the severity of the outage, identify affected enterprise customers, determine potential SLA breaches, evaluate network congestion levels, and prioritise remediation based on real-time business impact. A more obvious application of this can be used to improve customer service environments. Here, AI can tailor responses and workflows based on a specific issue and on a customer’s history and usage patterns.
Another significant opportunity lies in using AI to optimise processes more intelligently through better use of operational and customer data. Telecom operators sit on enormous volumes of network, billing, customer and service data, but much of it remains siloed and underutilised. AI has the potential to bring this data together to improve decision-making across both OSS and BSS functions. For example, AI could combine network performance data with customer churn trends to identify where infrastructure upgrades might be needed.
These use cases move beyond simple automation and towards truly intelligent operations, where AI ups efficiency and improves customer experience. If AI is used simply to replicate existing processes faster, we miss its potential. The real opportunity lies in rethinking problems from the ground up and applying AI in creative, context-aware and strategic ways to solve real-world challenges.
