You know technology is moving fast when new terms are introduced to describe its multiple emerging use-cases. No sooner had the industry found its footing with the concept of generative AI than “agentic AI” entered the conversation. Agentic AI refers to AI systems with the autonomy to make decisions, take actions and pursue goals independently beyond simple prompts or scripted tasks. Its potential cannot be overstated.
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At the end of 2024, the agentic AI market was valued at $5.1 billion. According to Capgemini, that figure is set to compound annually by 44% and reach a staggering $47 billion by the end of the decade.
For telecom operators, the biggest challenge in adopting agentic AI isn’t ambition — it’s direction. Many start their journey aiming straight for full autonomy, envisioning a sophisticated “Skynet” system that can think, act and self-optimize like something out of science fiction. Yet, as with most powerful technologies, without a clear roadmap, these projects collapse under their own weight.
The terms “agent,” “assistant,” “autonomous” and “AI-powered” are used interchangeably in a lot of boardrooms. This leads to confusion about capabilities and expectations and a lack of clear direction. The best path for operators is to start with simple, focused tools that solve specific problems and help employees to do their jobs better. This allows the tools to gather data, demonstrate ROI and build credibility.
From there, operators can evolve tools into co-pilots or collaborators that guide and augment decisions, and eventually into true agents that act independently across systems. Launching an advanced AI agent without the proper foundational support is premature — and a potential waste of resources. Effective deployment starts with small wins. And those who move thoughtfully will be best positioned to successfully scale agentic AI.
AI and Legacy Systems
With a clear direction determined and a stepped strategy in place, the next question is often about AI and legacy systems, such as business support systems (BSS) or other monetization systems. Keep in mind, innovation isn’t about discarding everything you’ve built. Rather, it’s about amplifying its value with intelligence.
True innovation is evolutionary, not destructive. It’s about introducing agentic intelligence on top of your existing infrastructure. Think of it as building a smart home — not by demolishing the foundation, but by digitally augmenting it with IoT, AI and automation.
Similarly, agentic AI augmentation is the fastest path to transformation for telecom operators. For example, telecom BSS are often battle-tested, deeply integrated and functionally rich, providing a firm foundation. Therefore, leveraging agentic AI doesn’t require overhauling BSS or other monetization systems. Instead, innovation respects what works and injects intelligence where it matters most — at the point of interaction, decision-making and service — smartly implementing AI.
What About the Workforce?
While agentic AI is quickly becoming essential, its adoption won’t look identical across all operators or regions. Some markets, where labor is abundant, digital literacy is lower, or customers strongly prefer human interaction, may find limited immediate value in certain agents, particularly those focused on customer care or sales. Similarly, operators with indirect revenue models or low churn pressure may deprioritize sales agents.
But these are the exceptions, not the rule. And agentic AI isn’t limited to customer service. It can also assist with network planning, resource allocation, security operations and the delivery of services and content. Across the board, telcos will need to embrace a “dual workforce” model, combining human expertise with digital agents to stay competitive. Opting out entirely is not realistic.
So, what should this dual workforce look like? While ambitions are high, what are the must-have agents that today’s telcos cannot afford to miss out on? For telecom organizations, there are five strategic domains: employee productivity, customer experience, product innovation, operational agility and revenue growth. The usage of AI in each of these domains is imperative
1. Employee Productivity Agent
For many telecom operators, knowledge is scattered across time zones, teams and tools. An employee productivity agent bridges these divides. It gives staff a way to instantly access institutional knowledge without relying on meetings, message threads, email chains or internal directories. Whether it’s onboarding a new hire, solving a technical issue or understanding a product’s history, this agent becomes a real-time resource that reduces delays and boosts output.
Beyond convenience, it also helps train the broader AI ecosystem. By interacting with employees daily, it learns the company’s language, workflows and pain points, building the contextual awareness that future agents will rely on. It’s the ideal starting point for any operator serious about deploying AI: low risk, high return and foundational for everything that follows.
2. Customer Care Agent
Customer care is often the largest line item in an operator’s operating budget, and one of the ripest areas for automation. A customer care agent can handle the most common queries instantly: account balances, data usage, plan comparisons and even minor technical issues. This offloads a massive volume of repetitive tasks, freeing up human agents for complex or emotionally sensitive cases where empathy and judgment still matter.
More than a cost-saver, it also enhances the experience. Customers get faster responses, fewer transfers and resolutions on their own terms, often through conversational interfaces that allow them to “self-serve” instead of waiting for a human interaction. For operators looking to modernize without sacrificing service quality, this agent is a potential game-changer. And in markets where digital self-service is culturally embraced, it’s quickly becoming indispensable.
3. Product Configuration Agent
Product innovation in telecom often lags behind market demand. That’s not because of a lack of ideas, but because of the months and months it typically takes to configure, test and launch new offerings. A product configuration agent accelerates this process by recommending configurations, validating them against existing inventory and policies and predicting uptake based on historical patterns.
So, instead of waiting months for post-launch analytics to reveal what went wrong, operators can adjust their direction in real time. This is particularly valuable in hyper-competitive markets with active MVNO ecosystems, where speed and relevance determine success. With the right agent in place, product teams can shift from reactive to proactive, cutting lead times and capturing value before the window of opportunity closes.
4. Operations Agent (AIOps)
Behind every service outage or customer complaint is an operations team racing to identify the root cause. An operations agent, trained on system logs, alarms and past incident data, can detect, diagnose and even resolve issues far faster than even the most effective human team. It’s the operational backbone of a telco’s AI ecosystem, offering real-time insights and reducing mean time to resolution (MTTR) across the board.
The best part is, over time, this agent becomes predictive. It learns to recognize patterns that precede failures — whether it’s storage degradation, call drops or throughput bottlenecks – and alerts teams before users are impacted. For operators juggling legacy systems, vendor dependencies and growing complexity, an AIOps agent becomes central to building resilience and maintaining subscriber trust.
5. Sales Agent
Last but not least, sales. Sales at scale is one of the industry’s most persistent challenges. With hundreds of products and potentially millions of subscribers, even the most sophisticated segmentation strategies fall short if the right offer can’t be made at the right moment.
These windows of opportunity are so narrow, and so individual, that it’s impossible to please every user. Even users in the same narrow segment will need different upsell prompts, discounts or bundles at different times depending on their own unique situation.
An AI sales agent solves this by using fine-tuned language models to match offers to individuals and initiate human-like conversations, guiding customers toward whatever it is they might need. This level of hyper-personalized engagement simply isn’t possible with human teams alone. The sales agent brings together customer behavior, usage data and commercial goals to optimize outreach. And most importantly, it does it at scale. This isn’t selling harder; it’s selling smarter, turning interest into action.
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Human intelligence is vital
To be clear, agentic AI isn’t just another layer of automation. It’s the setting of a new operational benchmark for the industry. Human intellect is the most beautiful thing on earth, and AI cannot replace it. AI needs people, and data created by people. AI should not be framed as a replacement for people—it’s a partner in productivity.
When the goal becomes 100% automation, you lose the soul of your organization. Human intuition, creativity and empathy remain irreplaceable. The telcos that thrive in a post-AI world won’t be the ones who take a “Skynet” approach and deploy the biggest, flashiest tools, but the ones who build patiently, align culturally and scale intelligently.
