Agentic Business Assurance: Why ROI Will Determine Adoption

Revenue assurance has traditionally relied on controls, reconciliations and specialist investigators to identify revenue leakage, investigate anomalies and resolve discrepancies. These practices remain essential, but as telecom operations become more complex, connecting the dots across systems, processes and data is becoming increasingly challenging.

AI agents offer an opportunity to rethink this approach.

Rather than treating detection, investigation and remediation as separate activities, agents can help connect them into a more continuous lifecycle: identifying anomalies, tracing potential root causes, assembling supporting evidence and recommending corrective action. With appropriate human oversight, this could help revenue assurance teams move beyond identifying exceptions to resolving them more efficiently.

However, the technology itself will not determine how quickly operators adopt agentic business assurance. The business case will.

From technical promise to measurable business value

For operators, the questions are practical. How much revenue leakage can be identified and recovered? How much investigation effort can be reduced? Can teams resolve discrepancies faster and focus their expertise on the issues that matter most? And what will it cost to integrate, operate and maintain these capabilities?

These questions need clear answers before pilots can translate into scaled adoption.

The value of agentic business assurance should therefore be assessed against measurable outcomes, including:

• Revenue recovery: Identifying leakage that existing controls or manual processes may miss, and helping teams prioritise opportunities for recovery.
• Investigation efficiency: Reducing the time spent gathering evidence, correlating data and tracing the causes of discrepancies.
• Faster resolution: Helping teams move from identifying an anomaly to understanding its cause and determining the next action.
• Operational scalability: Enabling teams to investigate growing volumes of transactions and exceptions without a corresponding increase in manual effort.
• Cost and payback: Evaluating integration, infrastructure, ongoing operating costs and the time required to realise measurable returns.

The business case will differ by operator, depending on the scale of the opportunity, the quality and accessibility of data, existing systems and the maturity of operational processes.

There is no single path to adoption

Some operators may be ready to explore agentic capabilities across multiple business assurance workflows. Others may benefit more from starting with a clearly defined, high-impact use case.

A focused starting point can help establish a baseline, demonstrate measurable improvements and build confidence before expanding to additional workflows. Use cases such as root-cause investigation of revenue discrepancies, prioritisation of leakage opportunities or automated checks during billing close can provide practical starting points, depending on an operator’s needs and data readiness.

This approach also helps organisations identify the operational changes required to make adoption successful. Agents need access to relevant data, clear process boundaries, appropriate permissions and well-defined escalation paths. Their recommendations must be explainable, and consequential decisions should remain subject to the right human controls.

In other words, successful adoption requires more than deploying an AI capability. It requires fitting that capability into the way revenue assurance teams work, measure performance and act on findings.

The considerations for Africa and other emerging markets

Across Africa and other emerging markets, telecom operators are pursuing growth through mobile money, digital services and expanding partner ecosystems. These opportunities can also introduce additional transaction volumes, commercial relationships and operational complexity.

For revenue assurance teams, the challenge is to protect margins while supporting growth, often within tight cost and resource constraints. This makes the economics of agentic business assurance especially important.

A compelling proposition must demonstrate not only what the technology can do, but where it can deliver value, how quickly that value can be realised and whether the benefits justify the cost and complexity of implementation.

A phased approach, grounded in operator-specific priorities and measurable outcomes, can help balance the opportunity for innovation with commercial realities.

Proving value before scaling

Agentic business assurance has the potential to connect activities that have historically operated in separate steps—from detecting an exception to investigating its cause and recommending what should happen next. But that potential must translate into tangible operational and financial benefits.

Operators evaluating adoption should begin by defining the business problem, establishing a baseline, identifying the data and integration requirements, and agreeing on how success will be measured. A focused implementation can then provide evidence to guide the next investment decision.

Ultimately, the question is not simply whether AI agents can transform revenue assurance. It is where they can make a measurable difference, under what operating conditions, and at what cost.

The operators most likely to realise value will be those that pair the potential of agentic AI with a clear business case, disciplined implementation and a practical path to scale.