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At DTW Ignite, the autonomous telco will be a major theme as communication service providers look to accelerate their AI journeys. Oracle’s Tony Gillick discusses why autonomy must extend beyond the network, the challenges operators face in operationalizing AI, and how CSPs can turn intelligence into measurable business outcomes.
Tony Gillick
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Why the autonomous telco is the next operating model for CSPs
Tony Gillick is Vice President, Product Management, at Oracle Communications Applications. He argues that the autonomous telco offers a practical path to greater agility, efficiency and customer-centricity. He explains how AI, real-time data and cross-domain automation can help CSPs align decisions and actions with business intent.
TG: When we talk about the autonomous telco, we are talking about a shift in how CSPs operate their business. The industry has spent years improving individual domains, but many critical processes remain fragmented, reactive, and dependent on manual coordination. The opportunity now is to move toward an operating model where the business can use real-time and near-real-time signals to understand what is happening, anticipate what is likely to happen next, decide on the right course of action, and execute with increasing levels of automation.
TM Forum’s work on autonomous networks has been very important in moving the industry in this direction. It has helped create a common language around concepts such as closed-loop control, intent-driven operations, standardized interfaces, and self-optimizing network domains. That work gives CSPs a practical foundation for reducing manual intervention and improving network performance, resilience, service quality, and efficiency.
Our view is that this foundation now needs to extend beyond the network. Customer and business intent should drive what happens across the telco, and that intent often cuts across network, operations, business systems, and customer engagement. The autonomous telco builds on the industry’s work around autonomous networks and operations, then extends those principles into autonomous business and autonomous customer experience, so the organization can align around the desired outcome, monitor for deviations, and take closed-loop remediation actions when needed.
That is where the model becomes especially powerful: it helps CSPs close the gap between what they know, what they intend to deliver, and what they can act on in the moment.
TG: The autonomous telco is the right operating model because the basis of competition in telecom is changing. Network quality, scale, and cost discipline remain essential, but the new competitive battleground is increasingly customer experience. To win there, CSPs need to move faster, use assets more effectively, improve efficiency, and deliver more relevant, responsive, and personalized engagement despite continued economic pressure.
That is difficult to achieve when data is spread across OSS, BSS, network, IT, and business domains, and workflows stop at system boundaries. Decisions are often made after the fact, with people reconciling information across applications, functions, and teams. The result is slower response, higher cost-to-serve, underused assets, and inconsistent customer experiences.
AI is foundational because it allows CSPs to interpret real-time and near-real-time signals in context, understand customer and business intent, anticipate impact, and recommend or trigger the next best action. In the autonomous telco, AI is not a separate tool or experiment; it is embedded into how the business operates. That is what enables networks to become more self-optimizing, operations more predictive, business functions more adaptive, and customer engagement more personalized and context-aware.
The value is not simply that existing tasks can be done faster. It is that CSPs can better align decisions and actions to the customer and business outcomes they intend to deliver.
TG: The biggest challenge is that most telecom environments were not designed for this level of autonomy. Data is often inconsistent, delayed, or trapped in disconnected systems. Legacy architectures make integration difficult, and many operators are still building the skills and operating practices needed to put AI into production. That is why so many AI initiatives remain pilots rather than becoming part of live operational workflows.
CSPs also need to manage a careful architectural balance. Multi-vendor flexibility remains important, but too much fragmentation creates cost and complexity, especially when AI-enabled processes need to coordinate across network, service, customer, revenue, and back-office domains. This is where TM Forum standards such as Open Digital Architecture and Open APIs are important. They help preserve flexibility while giving the industry a more consistent way to connect systems, exchange context, and coordinate action in multi-vendor environments. We are also encouraged by TM Forum’s expanded mission around trustworthy AI and data, because moving AI into production will require common approaches to data, governance, APIs, responsible AI, talent, and measurable business outcomes.
CSPs should take a pragmatic approach. They need to start with a unified, governed, real-time data foundation, then focus on use cases where the value is clear, such as customer engagement, service assurance, or billing operations. From there, the priority is to embed AI into the workflows where decisions are made and actions are executed.
The goal is to move from AI experimentation to AI operationalization. The CSPs that make the most progress will be those that can connect data, decisions, and execution across the business while maintaining the control and governance their operating environments require.
TG: “The Future. Faster” captures the urgency many CSPs are feeling. The industry understands where it needs to go: more intelligent networks, more predictive operations, more adaptive business functions, and more personalized customer engagement. The challenge is getting there while managing cost pressure, architectural complexity, and rising customer expectations.
An autonomous telco operating model makes that transition more achievable. It moves the discussion beyond fragmented modernization programs and isolated AI pilots toward a model where intelligence is embedded across the domains that determine business performance: network, operations, customer experience, and business systems.
Speed in telecom is no longer only about launching services more quickly. It is about detecting change sooner, understanding business and customer impact, making better decisions in the moment, and acting with less manual coordination. An autonomous operating model helps compress the time between signal, intent, decision, and action.
That is what “The Future. Faster” means in practical terms. It is about helping CSPs shorten the distance between what is happening, what the business intends to deliver, and what the organization can act on, so they can respond with more speed, confidence, and consistency.
TG: At DTW Ignite, attendees will see the autonomous telco operating model in several ways: our customer session with KPN, the catalyst project on agentic network resilience and lead-to-quote, and a panel discussion on trust by design and responsible AI.
Our customer session with KPN will show how the model extends beyond the network into the systems and processes that shape the customer journey. KPN will share its approach to AI and its use of Oracle enterprise applications, data intelligence, and billing solutions. The session will offer a practical view of how CSPs can embed AI into core processes and align it more closely to customer and business outcomes.
We will also showcase the catalyst project, “Agentic Network Resilience and Lead-to-Quote Using AI-Driven Digital Twin,” with CSP champions Entel, Vodacom, Du, Bell Canada, and Sasktel. By linking network resilience with lead-to-quote, the catalyst demonstrates the cross-domain nature of the model. It shows how AI-driven digital twins and agentic workflows can help CSPs assess changing conditions, understand business and service impact, and coordinate action across technical and commercial processes.
In addition, we will participate in a panel discussion on trust by design and responsible AI. This is central to the autonomous telco discussion because speed and automation cannot come at the expense of governance. As AI becomes embedded across operational and business workflows, CSPs need explainability, risk controls, and clear guardrails so teams can make informed decisions with confidence.
Together, these examples show what the autonomous telco operating model looks like in practice: AI connected to the processes that shape customer experience, autonomy applied across technical and commercial workflows, and governance built into how decisions are made.
