Expected to grow up to $27.1 billion by 2031, the AI telecom solutions market marks the speed at which the sector is progressing from AI curiosity to AI integration. As noted by NVIDIA, telecom leaders maintain high confidence in the technology, with most of them already using some form of AI daily. While this momentum is optimistic, the pitfalls are still there: over 80% of AI projects fail to reach meaningful production deployment, which is twice the number of failed non-AI IT projects. Such findings underscore the importance of treading with caution and taking a good look at potential candidates prior to making the first step. This article explores the key insights and steps for evaluating a telecom AI transformation use case before a single dollar leaves the budget.
State of telecom in 2026
What makes AI such an attractive investment for telcos? Research by McKinsey and other vetted analysts confirms that there is a wide gap between AI transformation leaders and laggards. This is noteworthy for two reasons. First, few other sectors show a similarly wide disparity. Second, this makes it possible to identify the clear, measurable value telco AI transformation has gleaned from the technology.
From improved connectivity to lower churn rates, telcos see the proof of how AI-led capabilities can enable a competitive edge. The pinnacle of this confidence is the increasing investment in business model innovation. As the percentage of communication service providers planning to redesign their business core is expected to hit 19% in 2027, the shift in how the telco sector sees and captures value is evident.
However, what does actual technological maturity in telecom look like?
The complicated part is that tech maturity in telecom is complex. There are levels to it. Understanding these levels means understanding where real strategic value lives. This knowledge also helps with keeping expectations real and not giving in to the competitive overwhelm. So, let’s take a closer look at these maturity levels.
Level 1: Actual maturity
Technologies with proven efficiency and existing integrations (AI-powered predictive maintenance and churn prevention, tools for network optimization and fraud detection, customer service assistants).
Level 2: Rapid scaling
Commercially fleshed out solutions that still lack tools for proper integration within complex infrastructures (agentic AI for troubleshooting, intelligent SON, GenAI for personalized marketing).
Level 3: Experimental
Technologies still in research and available within specific demos, mostly covering end-to-end AI automation (Fully AI-automated network optimization and operations, customer management supported by AI agents, AI-led 6G network).
Level 4: Visionary
AI-native telecom networks where every process is built around tailored AI systems trained on exclusive telecom data.
Within these levels of maturity, it’s clear that verified, established value exists in levels 1–2. Solutions mentioned there are well-known and have been available on the market for at least five years. Level 3 is where blind spots appear because, while the commercial side is strong, there are no existing telecom AI transformation use cases to build from, which complicates decision-making and puts every bold statement under great scrutiny. Logically, only a small fraction of adopters claims to have reached the fourth level of AI maturity, where the technology drives value from compute to distribution.
This classification shows where the solid, tested ground ends and terra incognita begins. Because while levels 3 and 4 don’t provide marketable proof, it doesn’t mean these solutions don’t exist—or aren’t being explored by competitors right now. These levels are also where AI technology becomes more personalized, which means there will be fewer success cases you can try out, and every true benefit is learned through discovery and experimentation.
This is also a subtle reason why AI project abandonment has increased from 17% to 42%. Some adopters make a decision to invest in a tried Level 1-2 solution, but it fails to respond to their specific pain point. Other adopters decide to budget bold end-to-end AI transformation, but their enterprise is not prepared for such a massive overhaul and would have benefited more from a narrower and more targeted change. The key to finding the right balance begins with identifying the area that will benefit from AI adoption the most.
For telecom as an industry with large operational stakes and potential upside, selecting the right candidate for transformation is particularly important. As the sector is dealing with the mounting pressure of growing data traffic and maintaining quality of service KPIs, seizing AI capabilities isn’t a mere exploration of a novel technology. Instead, it’s a way to keep vital infrastructure stable, adaptive, and available to clients across the globe.
Selecting telecom AI transformation use cases: How to tick every box?
According to recent studies, 44% of telcos are still weighing their options and estimating the potential value of AI instead of taking first steps toward adoption. How can adopters avoid turning their deliberations into a dead end and move on with candidate selection?
Time is of the essence: when a telecom operator discovers that an AI initiative was the wrong bet, millions have usually already been spent. Similarly, when a telecom operator finally makes investment decisions, their competitors are already one step ahead. This is why having a clear candidate selection roadmap is so important: you don’t waste time thinking about what else to take into account. You just tick off the boxes and proceed.
The process of identifying telecom AI transformation use cases can be divided into five steps.
1) Start with the business case
The first mistake adopters make is paying too much attention to technology and not enough to their underlying business problems. At the point of planning, technology and its capabilities will always look good on paper, but business problems cause operational performance to deteriorate right here, right now. You must understand how to convert capabilities into outcomes.
To get a better understanding of how AI intersects with business pain points, leaders should be able to answer a number of explorative questions:
Question
Insight
“What is this costing us today?”
Finding actual numbers, such as the number of hours spent on inefficient tasks, churn rate, or dollars wasted.
“Who feels this pain, and how often?”
Identifying teams affected by the problem and evaluating how it impacts their performance.
“What does “solved” look like?
Visualizing the to-be state, how performance or KPIs should look like when pain points are gone.
There is a reliable way to check whether you’re on the right course. Remove the words “machine learning” and “AI” from the pitch: is the remaining business case worth funding? If you still feel positive about it, then the answer is yes.
2) Build from readiness, rather than ambition
The promise of improvement that AI brings to the table is appealing. However, if the enterprise isn’t ready culturally, structurally, and data-wise, adopters will end up with zero impact and risks skyrocketing. For that reason, the next step should trigger a detailed, thorough readiness audit of a telecom AI transformation use case. In general, adopters should pay close attention to four areas:
- Data maturity
Preparing data for AI optimization remains a cornerstone of successful transformation. Verified, validated, and organized data provides a clear and easy-to-navigate environment for the chosen AI model. AI must be able to access and capture information easily because when it’s forced to look for it across legacy systems that don’t interact with each other, it will only lead to context drifting and severe operational errors. Therefore, adopters must review the data strategy of the chosen candidate. If it’s outdated and data sets can’t be used for model training, there is no point in making an investment. - Process repeatability
AI works well with simple, repeatable, pattern-based decisions. But when it’s entrusted with judgement-heavy activities that rely on critical thinking and decades of exclusive experience, it struggles and fails to generate meaningful impact. This is what adopters should keep in mind when evaluating the candidate’s workflows. If they involve ticket classification, analytical forecasting, and fraud detection, the candidate will benefit from AI. Accordingly, if candidate’s workflows center around regulatory disputes and bespoke contracts, AI will add to the complexity rather than resolve it. - Risk tolerance
Adopters should take into account that AI can make mistakes. While it’s an undesirable scenario, it still can happen. The question is: how will the candidate fare if it happens? What will the blast radius be? Will the damage control be easy to initiate? - Organizational readiness
Ultimately, it all boils down to owners’ willingness to embrace a new tool. If the team that will be working with AI tool the most isn’t part of the decision, doesn’t get involved during the implementation process, then the initiative is doomed to stall. Adopters should establish direct communication with the candidate’s teams, assess their needs, and preparedness to work with AI for resolving these needs.
Within this readiness roadmap, evaluating telecom AI transformation use cases becomes a clearer process. If it scores well across all these areas, then there is a synergy worth exploring. However, if some of the prerequisites are missing, then the candidate should be taken off the lists and revisited later after introducing necessary improvements.
3) Invert the expertise
While it's logical for adopters to approach their projects with success in mind, leaving the comfort zone and thinking about how and why the project can fail is important for gaining a better perspective.
Within this approach, you should sit your team down and go through the following scenario: the demo failed in a year and half after launch, why? The point is not to torture your team, but to go through every possible problem that can hypothetically lead to such a development. For example, a business problem was miscommunicated from the beginning. Or the wrong metric was used. Or the model was made for a wrong operational workflow.
If your team can immediately name two or three plausible failure modes from this list, it doesn’t mean you should kill the initiative. You now have an information that helps you understand what to validate or contractually protect against before committing real budget.
4) Validate against real constraints
Demos that run on clear, curated data will always perform well on the surface but fail to make any meaningful difference. AI capabilities should always be applied to a unique enterprise environment, and specific constraints before adopters can decide about the potential candidate. Proper validation should leverage the following aspects:
Enterprise data
Evaluating how AI interacts with a limited sample of relevant information that will be used in an actual workflow.
Real edge case
Assessing how AI works with a real, difficult process (a hard-to-classify network anomaly, an evasive fraud pattern, a confusing support ticket type).
Scope
Rating solution’s ability to operate under enterprise’s specific, time-boxed scope with enterprise data and environmental pressures.
This is particularly important if you’re working with a vendor and looking at their demo. Vendors usually provide showcase data that allows for impressive demo performance but offers little substance. So, if a vendor agrees to validate the demo against your candidate’s constraints, it means they’re confident about the pilot’s quality and ability to meet your needs. If they insist running pilot on their curated data, it’s a red flag.
5) Be ready to reallocate capacity
Adopters often see AI as a technology that adds capacity. However, they rarely think about what they’re willing to stop doing after AI is introduced to the enterprise system. This is relevant even during relatively minor changes. A triage system for automated ticket resolution only pays off if the leaders know what to do with liberated time and saved expenses.
If the transformed candidate performs successfully, are you prepared to redeploy the people and budget it frees up into higher-value work? If you’re not ready to answer this question, then the candidate isn't ready for investment, even if the technology performs exactly as promised.
Ultimately, selecting right telecom AI transformation use cases doesn’t require an exotic framework. The foundation is discipline. Adopters should be prepared to interrogate their enthusiasm, add “and...?” to each optimistic statement. Every revolutionary AI capability should be anchored to actual business constraints and needs, allowing stakeholders and leaders to see how it will fit in the system—and where it will drive the biggest value.
Telecom operators who lead the adoption race didn’t get there by adopting as many use cases as possible. Usually, they’re the ones who had the confidence to say “no” when they didn’t have enough prerequisites and data, preserving budget and resources for candidates capable of going beyond superficial impressiveness and generating actual outcomes.
If you’re looking to close the selection and framing gap with a winning AI transformation candidate for telecom, let’s chat!
At Trinetix, we have over 15 years of experience driving digital transformation and enabling innovation-ready discipline for our Fortune 500 clients. Within our insights-rich guidance, you will be able to map out the perfect strategy for identifying and transforming the most vital areas of your telecom enterprise, overcoming pressures and gleaning new value.
