Telecom operators are increasingly being told that AI can run their BSS. That assumption is not just optimistic – it is dangerous.
Imagine if an AI agent working autonomously inside your BSS misconfigures a discount tier for enterprise customers. Not by much. A few percentage points off the threshold logic. The agent had correctly interpreted the commercial intent; it just applied its own probabilistic judgement to the parameters.
By the time the error surfaces in reconciliation, three months of invoices are wrong. The financial exposure runs to seven figures. The regulator wants an explanation. The audit trail leads to a model that cannot explain exactly why it made the decision it did.
This is not a far-fetched scenario. It is the logical consequence of a seductive but fundamentally flawed idea: that AI can be trusted to operate at the deterministic core of telecom revenue management.
It cannot. And understanding why matters more than most current discussions about “agentic BSS” acknowledge.
Probability is a feature. Until it isn’t.
Large language models are probabilistic by design. They do not compute outcomes; they predict them, drawing on statistical patterns to generate the most likely response. That is precisely what makes them powerful for content generation, anomaly detection, customer interaction and advisory workflows.
It is also what makes them architecturally unsuitable as the execution layer for charging and billing.
Revenue management systems are not advisory systems. They are financial truth engines. Billing is not about prediction. It is about certainty. The same charging data record and the same pricing rules must produce the same rated outcome every time. Not the most statistically likely outcome, not a contextually reasonable approximation, but an identical result, every time. If a billing outcome cannot be reproduced exactly, it cannot be defended.
Regulators worldwide have imposed significant penalties on CSPs for billing inaccuracies. There is no tolerance band. In telecoms billing, “almost correct” is simply wrong.
The real risk isn’t replacement. It’s contamination.
Much of the current debate about AI in telecoms has focused on whether AI will replace BSS platforms. It will not, and most serious voices in the industry have moved past that question. The more dangerous and less-discussed risk is subtler: what happens when probabilistic AI agents begin orchestrating workflows that interface with deterministic revenue systems, without sufficient governance to separate the two?
The scale of adoption makes this urgent. According to an Omdia survey commissioned by Ericsson, 46% of CSPs expect generative AI to impact their BSS/OSS operations within the next two years. Amdocs has already launched what it describes as an agentic operating system for telecoms, designed to coordinate AI agent activity across BSS and OSS processes. These approaches assume AI can safely orchestrate complex operational decisions. That belief deserves closer scrutiny, particularly when those decisions touch revenue. The architecture is arriving, whether governance frameworks are ready or not.
As agentic capabilities mature, AI agents are increasingly initiating transactions across multiple enterprise systems in real-time. An agent might dynamically adjust an offer, modify a discount threshold or alter customer entitlements. Each action is individually plausible, but they can quickly compound before any human reviews the outcome. The charging engine then executes faithfully against parameters it was given by a system that was, at its core, making educated guesses.
That is the architectural risk. Not AI replacing billing. AI contaminating it: probabilistic decisions bleeding into deterministic processes through the seams of agentic integration.
Determinism is not a legacy. It is leverage.
There is a tendency in technology discourse to frame determinism as a legacy characteristic and AI as a modern one. In revenue management, this is not just wrong; it is the inversion of the truth. Determinism is what allows every transaction to be traced, verified and defended under audit.
For vendors such as Cerillion, this deterministic core is not a legacy constraint, but a deliberate architectural choice.
CSPs have spent decades building end-to-end integrity into their operations: ensuring what is sold can be provisioned, what is provisioned can be billed, and what is billed can be reconciled and recognised. That discipline is not a constraint on innovation. It is the foundation on which customer trust, regulatory standing and financial reporting are built.
The regulatory environment is making this more explicit, not less. The EU AI Act, now in phased enforcement since 2025, classifies AI systems involved in financial decision-making as high-risk, subject to strict transparency, auditability and human oversight obligations. Penalties for non-compliance reach up to 3% of global annual turnover. CSPs deploying agentic workflows that touch revenue-critical processes without strict governance are not just taking a technical risk. They are taking a regulatory and financial one.
As automation scales, the value of structured product models, governed policy enforcement and fully auditable workflows rises in direct proportion to the speed of the automation around them. The CSPs that will scale intelligent automation most effectively are those that recognise determinism not as a legacy constraint to be overcome, but as a competitive differentiator to be preserved.
AI belongs at the edge, not the engine
None of this is an argument against AI in the revenue stack. It is an argument for deploying it where it adds genuine value and being disciplined about where it does not.
AI can identify billing anomalies at a scale no human team can match. It can explain complex invoices in plain language, cutting customer contacts at the point of confusion. It can surface revenue optimisation opportunities from usage patterns that would otherwise remain buried. It can accelerate offer configuration, dispute resolution and operational triage.
These are high-leverage applications. They work precisely because AI sits alongside the deterministic core rather than inside it.
The future of BSS is not AI replacing billing. It is AI accelerating everything around a governed, rules-based core: one that remains predictable, reproducible, transparent and auditable regardless of how intelligent the systems surrounding it become.
In a world where transactions are increasingly initiated by agents operating faster than human review, that governed core is not a legacy liability. It is what makes scale trustworthy. The operators who understand that the certainty of every outcome is what underpins customer trust, regulatory standing and financial integrity are the ones best positioned to capture the genuine advantages that AI offers, without inheriting its risks.
In the age of agentic AI, the real question is not how much you can automate. It is how much of what you automate you can stand behind.
Dominic Smith, Marketing Director, Cerillion plc
For over 30 years Dominic has been at the leading edge of the BSS/OSS industry, involved in the development, delivery and support of innovative billing and revenue management solutions, as well as contributing to the evolution of industry standards in key groups including the TM Forum and GSMA. In his role as Marketing Director at Cerillion, Dominic leads the company’s market strategy and product direction, as well as marketing communications and product marketing activities.
