According to recent findings, 60% of telecom operators consider AI a driver of profit for their sector, but only 25% are confident about scaling the technology. Furthermore, nearly 43% of AI initiatives are anticipated to fail in the long run, exposing the existing gap between ambition and execution. This gap, in turn, is often the result of adopters practicing tool-first thinking rather than strategy-first thinking. Understanding the flaws of the former and embracing the latter is the key to successful implementation and scaling.
This article explores the importance of AI and automation advisory in telecom, dissecting common mistakes and providing strategic tips from vetted SMEs.
What is tool-first thinking and why is it not enough?
Before explaining advisory, it makes sense to dissect tool-first thinking and the problems it entails. In essence, tool-first thinking is when executives focus their attention and effort on the technology first—and build from it after the technology is already implemented and running. The appeal is understandable. There are many vendors providing tools that are no longer generic and industry-agnostic. Instead, they’re tailored to telecom’s OSS/BSS constraints, complete with network optimization and predictive maintenance capabilities that are in synergy with telecom infrastructure’s needs. At first glance, it seems like vendors have already taken care of the details and provided a fit, so investing in such a ready-made platform feels intuitive and safe.
This isn’t just a theory. One of the most well-known examples on the market is Ericsson’s OSS platform and Nokia’s Net Act. They are widely promoted as a robust fit for 5G capacity planning and core network operations. When large brands provide what looks like a full kit for telecom modernization, adopters feel confident about beginning with the tool. However, the pitfalls are always there.
The problem is that getting a platform from a vendor doesn’t answer all the complicated, enterprise-specific questions. A vendor can’t identify the pain points of an individual enterprise or assign responsibility within this enterprise. A vendor isn’t held responsible if their tool outputs conflict with other systems in the enterprise. And, finally, no vendor can provide a detailed picture of success for the enterprise or plan a roadmap.
All these steps and actions must be taken by executives before the tool is bought and implemented. Otherwise, they end up resolving a number of rapidly emerging issues in real time, while enterprise AI tools start counteracting each other.
How does tool-first thinking impact OSS/BSS modernization?
To further understand the dangers of tool-first thinking, there is a point in exploring how it can jeopardize the most sensitive part of telecom, OSS/BSS modernization efforts. Since 72% of service creation and revenue enablement efforts depend on the successful transformation of these systems, the stakes are particularly high, so any sequencing mistake is particularly costly.
- Vague migration strategy
Around 83% of data migration projects end up unsuccessful due to running out of funds or lack of proper tools. This statistic shows how migration is a full-on business, technological, and financial strategy rather than an afterthought. Therefore, telecom operators are supposed to plan thoroughly and execute their strategy gradually (setting up microservices in parallel to existing legacy systems, performing traffic routing in increments, and replacing legacy component by component). However, within the tool-first thinking operators buy the platform first and decide on the strategy later, which often leads to disproportionate changes. As a result, budget runs out and nothing gets done. - Rushed lift-and-shift
As 57% of telcos practice a hybrid cloud approach when planning OSS/BSS cloud migration, the cloud-native OSS/BSS market is seeing a spike in demand and displays potential for improving agility and reducing costs. But these outcomes can only be secured through a strategic session that identifies fitting candidates and establishes latency-critical workloads. Relying only on a platform demo in decision-making results in increased expenses, greater latency, and severe data residency issues. - Wrong vendor evaluation criteria
Finding itself under the pressure caused by mass vendor exodus, the telecom sector is prompted to reassess its strategy of choosing suppliers and lean towards value-based partnerships. However, the shrinking pool of vendors can often lead to a distorted view of available platforms and ways of assessing them. Ideally, operators shouldn’t stop at features and price and should go deeper, exploring how a specific platform would interact with their billing, dispatch, provisioning, and network monitoring. But within tool-first thinking, a platform is often evaluated as a disconnected piece without any ties to specific organizational operations or pain points.
These challenges show that tool-first thinking only works when you have a small, localized problem. But when you work with a system as complex and interconnected as telecom operations and consider such an autonomous technology as AI, you shouldn’t start with the best tools on the market. Instead, you should start with the best AI and automation advisory for your telecom network.
What is AI and automation advisory in Telecom?
In telecom, AI and automation advisory is a set of practices for helping operators define the best strategy for leveraging AI and automation for telecom infrastructure and operations. The goal of these practices is to enable operators to discover business value and identify clear areas for improvement before they invest in AI or automation.
Why is it important? Imagine a telecom operator decides to leverage AI for their organization. They look at existing business cases on the market. They add an AIOps platform to the stack, integrate a copilot into their NOC dashboard, and insert a couple more tools into adjacent domains. On the surface, everything goes smoothly: there are no delays, and everything moves fast. But then months go by, and the platform just sits there. It’s unclear who owns the outcomes, what this platform is supposed to optimize, or how AI tools interact with each other. As a result, there is no ROI and no outcome at all.
This is what happens when you buy capability before you define intent—and this is the kind of mistake AI and automation advisory prevents
AI and automation advisory in telecom covers every phase that concerns strategy, deployment, and execution.
Network operations
- Facilitating decision-making on how and where AI and automation can improve network performance (predictive maintenance, anomaly detection, self-healing networks, traffic optimization).
- Identifying how AI-powered capabilities can pave the road towards autonomous operations.
OSS/BSS modernization
- Providing advice on integrating AI in billing, provisioning, legacy-to-cloud-native migration, and service orchestration.
- Outlining a roadmap on what should be modernized first and how it should be modernized.
Customer experience and commercial operations
- Establishing ways to apply AI agents and automation to personalization, revenue assurance, contact centers, and churn prediction.
- Preventing isolated optimization hurdles by onboarding operators on how agentic systems coordinate with network-side AI.
Governance and risk
- Enabling autonomous decision-making through building guardrails (identifying who is accountable for AI actions, outlining permissions, building governance).
- Preparing AI-powered telecom operations to be compliant with AI Act-style requirements, data sovereignty, and relevant risk practices.
Organizational readiness
- Evaluating the readiness of workflows, data, and talents for AI and automation.
- Removing obstacles in such areas as ownership, change management, and data quality.
When done well, AI and automation advisory allows telecom operators to develop and execute a fully fleshed-out strategy where every detail—from what problem AI is supposed to solve to how it interacts with other systems— is clear. As a result, telecom executives ensure a productive investment while timely preventing all potential issues.
How AI and automation advisory shapes strategy-first thinking
All challenges mentioned above point to the root of the problem—and it’s not outdated legacy anymore. Instead, we are looking at a newer, more relevant problem: disconnected intelligence. Telcos are now routinely investing in digitization, autonomous network solutions, and cloud migration. The change is manageable, no longer intimidating. But there is a coordination barrier that prevents tools working just fine in isolation from making sense as a whole ecosystem.
Only 16% of deployed AI projects succeed at targeting network-wide pain points and yield tangible outcomes. While this looks like a contradiction to 66% of telecom operators reporting active use of AI, the disparity becomes more understandable through the disconnected intelligence lens.
An organization can successfully implement and use an AI model for predictive maintenance, a copilot for customer service and interaction personalization, or an AI system for anomaly detection. There might even be small, optimistic outcomes adopters see and feel reassured about the course of their AI strategy. But the truth is, there is no strategy—because nobody has a vision of how these tools connect.
On the one hand, it doesn’t seem like an issue. Not all tools have to interact with each other. On the other hand, when we’re talking about agentic AI, things become more complicated.
For example, you have several AI agents in your system: a network optimization agent, a customer experience agent, a sustainability agent, and a field operations agent. All of them have different tasks: traffic rerouting recommendations, premium customer prioritization, energy consumption reduction, enterprise service promotion, and maintenance.
Logically, their tasks conflict with each other—the customer experience agent wants to retain premium customers by granting them access to better offerings, which increases traffic. That action triggers the network optimization agent and sustainability agent that try to maintain optimal balance. They start negotiating—or outright working against each other in order to achieve their assigned objectives.
What follows next is a cascade of errors and context drift—a direct result of disconnection and lack of strategy that could oversee their potential interactions or limit them through guardrails
How can strategy-first thinking address and prevent such issues? The goal of AI and automation advisory is to provide the right sequencing for decision-making by establishing several important pillars:
- Decision ownership
AI can impact many areas, like revenue assurance, field operations, and network performance. However, in every domain there should be an owner, a person in charge of addressing the outcomes of systems conflicting and preventing such developments in the first place. AI and automation advisory decides who should be tracking agent behavior and where, what indicators they should use, and how they should intervene. - Coordination within the architecture
Modern AI and automation for telecom does more than determine the number of AI agents a system needs. Its main goal is to provide detailed instructions for how different areas are going to negotiate with each other, what kind of data contracts will be shared, and what escalation protocols should be deployed. It sees a robust and well-thought-through governance layer as the future competitive edge in the AI-native telecom industry. - Migration and integration planning
AI and automation advisory for telecom covers every aspect and component of a successful and fail-proof data migration and system modernization. This is where advisors plan out the rollout phases, workload placement, and the order of decommissioning legacy system components. - Connecting KPIs to business outcomes
The common mistake adopters make is defining success through how well a tool is utilized. But the end goal runs much deeper than how well the technology performs. Real success lies in how the innovation improves customer experiences, how it reduces expenses, or how it changes network uptime. Establishing this connection also allows adopters to achieve balanced tokenomics and avoid instances of token overconsumption as the false flag of productivity.
Another important part of AI and automation advisory in telecom is how it helps telcos establish the right culture within their organization. Some managers put active AI use among their employee productivity assessment criteria, which leads to employees trying to create as many interactions as possible instead of using AI for real value. The real outcomes begin when you stop thinking about how often your teams use AI and focus on how and where AI made work easier.
Establishing AI and automation advisory in telecom: How to add real value?
The crucial part of implementing the right strategy is to slow down where others rush in. Adopters should assess their readiness and start conversations about ownership and coordination before they invest. The general framework is usually the following:
Getting sponsors
- Establishing the executive sponsor (CTO, COO, CDO) for supporting and reviewing procurement decisions by the advisory function.
Mapping existing intelligence
- Getting a list of AI tools already used in OSS/BSS, network optimization, and customer interactions.
- Important for monitoring shadow AI tool use and untracked pilots.
Defining the operating model
- Understanding how AI will be operating within a system (whether it should be a federated model or whether CoE should handle developing, running, ownership, and governance)
- The most balanced option is usually embedding domain specialists in OSS/BSS, customer experience, and network operations, while keeping central governance and standards.
Assembling an advisory board
- Gathering representatives from different domains (Legal, Finance, Security, Network, OSS/BSS).
- Necessary for discovering conflicting initiatives and establishing priorities before AI adoption begins.
Building a business case
- Implementing an approach for assessing potential AI or automation initiatives.
- Allows businesses to focus on KPIs that matter (integration complexity, potential business outcomes, data preparedness, regulatory compliance) instead of going for the most impressive demo.
Establishing governance
- Outlining ownership, data access permission levels, logging and audit protocols, and where agent autonomy ends and human oversight begins.
- Provides enterprises with a playbook to follow for every next initiative.
Proving the operating model
- Selecting from 2 to 3 impactful initiatives that can be led through the entire advisory process.
- Crucial for proving the functionality and efficiency of the chosen operating model.
Scaling and standardization
- Making advisory coverage a standard for new initiatives across the organization.
- Enables organizations to keep up with evolving standards and growing AI maturity.
Effective AI and automation advisory in Telecom or any other industry is about change management, first and foremost. As AI expands its capabilities, enterprises are expected to follow suit, which often requires refreshing their knowledge and keeping their mind open to new developments. To reduce the overwhelm, it’s often recommended to involve outside expertise that can provide vital and relevant AI knowledge and consulting that can be combined with years of business acumen and enterprise knowledge.
If you are interested in detailed and results-rich readiness assessment before building a coordinated, AI-driven network, let’s chat! `
Trinetix has been enabling successful change management and transformation for organizations and business across every sector. With our in-depth consulting and outcome-driven approach, we will make sure your AI for Telecom initiative hits every milestone.
