Hyperautomation in Telecom: RPA & AI-Driven Process Automation

Telco automation has come a long way, but it still feels like something’s missing for many brands. 

Certain tasks continue to slip through the cracks due to the current limitations of automation. For instance, while traditional systems may successfully route a customer issue to the appropriate department, the resolution often still requires manual intervention.

Or a network alert system might flag a potential outage, but it still requires an engineer to diagnose the issue and trigger a fix, slowing down response time and impacting service quality.

These are the types of challenges hyperautomation solves. Unlike traditional automation, hyperautomation combines AI, robotic process automation (RPA), and other advanced tools to go beyond routing tasks. It can predict, make decisions, and take action across key telco processes.

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What Is Hyperautomation?

Gartner first coined the term hyper-automation in 2019. Since then, many industry thought leaders have tried to define it. 

However, one definition that stands out is that of Beatrice Ortega, EMEA Telco Business Development Manager at Red Hat.

She says, “hyperautomation is a business-driving strategic approach adopted by organizations to rapidly identify and automate as many business and IT processes as possible.”

For instance, imagine a customer trying to set up a new internet connection. With hyperautomation, everything from verifying their identity, checking service availability, processing the payment, and scheduling the installation can be handled by hyperautomated tech. 

There will be no need to speak to an agent or fill out multiple forms. AI, bots, and backend systems work together to make the process smooth, fast, and error-free. 

Core Components of Hyperautomation

‍How It Works in a Telco Environment

Hyperautomation in telecom builds on existing automation by adding more advanced tools to improve efficiency. The aim is to reach full autonomy or enable systems to manage more complex, end-to-end tasks with little human input.

This flexibility gives telcos several paths to hyperautomate their operations. Most have started by targeting CRM systems, billing, ticketing, or incident management, where automation can quickly reduce manual workload and improve response times.

AI’s function in telecom is enabling hyperautomation to deliver real-time recommendations that support more informed, data-driven decision-making.

For example, AI can suggest optimizing network routes, flagging, and prioritizing a high-value customer’s support request. Once triggered, they can launch workflow engines or RPA bots to act immediately on the fly. 

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‍Why Hyperautomation Is a Game-Changer for Telcos and Digital Transformation

Digital transformation in telecom is about breaking away from siloed processes and embracing agile, software-defined operations. Hyperautomation aligns directly with this vision by integrating AI, RPA, and orchestration to streamline complex workflows. 

Instead of treating automation as isolated pilots, telcos are embedding it across OSS, BSS, and customer-facing systems, which accelerates service rollouts and improves operational resilience. This approach aligns with the goals of Telco Digital Transformation, where automation is not just about efficiency, but also about enabling new service models and enhancing the customer experience. 

Hyperautomation is therefore best seen as a practical step within the broader transformation journey, ensuring telcos modernise faster and at scale.

Volume and Complexity of Telco Operations

Here’s a breakdown of the volume of operations that telco brands have on their hands. 

The global mobile data usage is 200 exabytes per month, with the average user consuming 23 GB per month. Furthermore, 5G networks are expected to account for 80% of total mobile traffic by 2030, with 5G subscriptions projected at 2.9 billion by the end of 2025.

To make things even harder, telcos are juggling many different types of networks, high service expectations, and huge amounts of data. The real challenge is handling all that data in real time, especially for things like streaming, IoT, cloud services, and critical communications. 

Hyperautomation tackles these challenges by offloading tasks that don’t need much human involvement. 

And it’s not just limited to operations. Speed and consistency also significantly influence marketing automation, lead management, and customer support.

Hyperautomation in the 5G and 6G Era

As 5G networks scale and 6G research accelerates, telecom providers face unprecedented complexity in managing data flows, latency, and the increasing number of connected devices. Hyperautomation plays a crucial role here by enabling real-time monitoring, automated spectrum allocation, and predictive network optimisation. 

For example, automated workflows can reroute traffic during congestion or launch self-healing routines when anomalies occur. These capabilities reinforce the promise of the 5G & 6G Evolution in Telecom, where ultra-reliable, low-latency communication demands intelligence at every layer. 

Hyperautomation ensures that next-generation networks remain efficient and resilient, giving operators the ability to deliver new services without overwhelming human teams.

‍Pressure to Reduce Costs and Boost Efficiency

The telecom operations management market is valued at over $72 billion in 2024 and is expected to surpass $77 billion in 2025. 

This gives telcos a sense of how many resources go into managing complex processes in telecom. The market is also crowded with multiple providers, which drives down average revenue per user (ARPU). 

With growing pressure from investors to cut costs and improve ROI, hyperautomation becomes a practical solution. 

It reduces manual, repetitive work, streamlines entire processes, and improves efficiency. The result is lower overhead and the profitability that telcos need to stay competitive.

‍Need for Real-Time Decision-Making

One of the most effective ways for telecom companies to stay ahead is by leveraging social listening to capture customer sentiment, identify emerging trends, and respond swiftly with data-informed decisions. 

Whether the goal is to engage Gen Z as a telco, improve customer experience, or refine product offerings, social listening provides a competitive edge in an increasingly dynamic market.

AI is central to hyperautomation, especially with predictive analytics and automated decision-making technologies. 

Predictive analytics, for instance, allows telcos to process data from network sensors, CRM systems, and billing records to spot patterns and anticipate issues before they arise. AI takes it further to generate clear recommendations with the insights. 

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4 Key Use Cases of Hyperautomation in Telecom

The sections cover some of the ways telco brands use hyperautomation. 

Back-Office Operations

A lot happens behind the scenes before telecom brands can deliver a seamless customer experience while keeping operations smooth.

However, many of these processes are still manual and time-consuming. For example, activating a new customer line often requires an agent to verify documents manually, cross-check account details across multiple systems, and update records. The result is a slowed-down service delivery which increases the risk of human error.

Hyperautomation addresses this challenge by streamlining manual processes through advanced automation. For example, an automated billing system can handle tasks such as invoicing and order entry, reducing the need for human intervention.

‍Customer Service Automation

Research by Circles revealed that nearly 40% of telecom users prefer brands that recognize and reward their loyalty. Additionally, four in ten customers expressed a desire for better treatment from the companies they remain loyal to.

While there are various ways to boost customer experience, leveraging technology to streamline interactions has become the latest advancement in customer service. Since entering the industry, Circles has identified five core pillars that define an exceptional customer experience.

These include:

Using the right technology, brands can automate these pillars. For example, AI-driven chatbots can provide real-time, personalized customer support, automate ticketing processes, and offer users clear and transparent billing information.

The goal is to find the right balance and use the technology to automate the process where it has the greatest impact.

Network Operations and Monitoring

Telco companies spend hours of manpower to monitor and repair faulty network lines. Machine Learning (ML) and automation work together to detect faults, classify incidents, and trigger real-time self-healing actions. 

For example, a company can design an ML model that analyzes network data to spot anomalies, like unusual latency or dropped calls, and automatically classifies the issue based on severity or root cause. 

Once identified, automation tools can kick in to reroute traffic, reboot network components, or alert the right teams. This resolves issues before customers even notice a problem.

Sales and Marketing Optimisation

Marketing automation leads the way, with companies using advanced tools to run and manage omnichannel campaigns. With minimal manual effort, these systems help ensure messages reach the right audience at the right time.

That’s how Zain Jordan did it. They used marketing automation to run targeted, cross-channel campaigns that improved personalization and drove profitability. It’s a good example of how telcos can simplify their marketing workflows and respond more quickly to customer behavior. 

Other use cases include lead scoring, churn analysis, and delivering targeted offers using behavioural insights.

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Implementation Roadmap for Hyperautomation in Telecom

The path to hyperautomation must be clearly planned to avoid roadblocks that could disrupt operations. This section outlines the key steps telcos should follow to implement hyperautomation.

Process Discovery and Assessment

The first step is to assess telco operations to identify repetitive, manual tasks that present opportunities for automation. It's important to note that hyperautomation goes beyond basic rule-based automation. 

Through process discovery, organizations can gain a more comprehensive view of workflows and determine where advanced automation technologies can deliver the most impact.

Look for a series of connected tasks, some of which need to happen in a specific order and others that run in parallel. It’s about mapping out the full process, not just individual steps.

Consider the customer onboarding process. Rather than a single trigger such as 'customer signs up, then activates service,' it typically involves a series of interconnected steps that span multiple systems and teams. These might include:

Some steps, like KYC and network checks, must happen in sequence, while others, like sending the welcome email or updating CRM records, can run in parallel. Hyperautomation helps coordinate all of this in one seamless flow, across systems and departments, without manual handoffs. 

Pilot and Scale Approach

After identifying tasks suitable for hyperautomation, the next step for telcos is to conduct thorough testing. For instance, if the focus is on billing processes, this may involve evaluating specific workflows such as order handling during provisioning or invoice reconciliation.

The company can then implement automated solutions, such as RPA bots enhanced with AI, on a limited scale to assess their effectiveness, accuracy, and overall impact on process efficiency and speed.

Next, they’ll closely monitor performance metrics, gather feedback from users and stakeholders, and address any bottlenecks or errors to ensure the automation runs smoothly.

After a successful pilot, the final step is to scale the solution across additional processes and departments. This involves expanding coverage, refining workflows, and integrating advanced capabilities like AI-driven decision-making and workflow orchestration.

Tools, Platforms, and Vendors

There are multiple tools specifically built to help telco hyperautomate their processes. Some of these include:

Workforce Enablement and Culture Change

Hyperautomation is driven by people, so its success relies on getting a team on board. This involves investing in training and encouraging a mindset that sees it as a valuable tool rather than something to fear.

It is also essential to foster AI literacy, upskill teams, and redefine roles to enable employees to collaborate effectively with technologies such as RPA and AI. This approach helps reduce resistance to change and promotes greater transparency across the organization.

Operators like Vodafone and SK Telecom are strong examples. They’ve launched AI academies and internal programs to upskill their teams and embed AI into daily operations. 

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Ethical, Regulatory, and Security Considerations

Implementing hyperautomation requires staying compliant with relevant laws and regulations. In this section, we’ll cover key regulatory and ethical considerations to remember during adoption to avoid legal risks.

Job Displacement and Workforce Transition

One of the most common concerns from employees is the fear of job loss, which can lead to lower morale and reduced productivity.

To prevent these issues, it’s important to ensure employees understand their role in meeting compliance requirements. For example:

To reduce compliance risk, training platforms should include modules on new AI, privacy, security, and telecom-specific regulatory frameworks. 

Another critical consideration is maintaining oversight of all automated tasks. Organizations should train employees on how to effectively monitor and audit AI-powered workflows to ensure compliance, accuracy, and accountability.

Data Privacy and Model Governance

Mishandling customer data can lead to severe regulatory penalties and financial losses.

Regulatory boards like GDPR require telcos to report data breaches within 72 hours and implement strong security measures; failure to do so can result in fines of up to 4% of global turnover or $20 million.

Hyperautomation initiatives must align with telecommunications regulations to ensure compliance and avoid potential legal or regulatory issues. 

Regular audits are essential if a telco brand uses AI or ML systems that process sensitive data, especially when customer information is involved. Auditing helps catch potential data leaks early and ensures systems comply with data protection standards.

Responsible AI and Algorithmic Bias

AI bias has appeared in several telco use cases, with fraud detection systems being one of the most common.

Bharti Airtel and Orange use AI to scan millions of call records daily for suspicious activity. While effectively saving the companies millions of dollars, there have been concerns that the systems can unintentionally flag certain customer groups or regions more often due to biased historical data, raising concerns around fairness and discrimination.

Even when unintentional, such issues can negatively impact a company’s brand reputation. Therefore, it is crucial to conduct regular reviews and audits of AI systems to ensure they are functioning as intended and remain compliant with established standards.

Cybersecurity Considerations in Hyperautomation

Expanding automation across telecom environments increases efficiency but also widens the potential attack surface. 

Hyperautomation must therefore integrate tightly with security frameworks to ensure data protection and compliance. RPA bots and AI engines require constant oversight to prevent misuse, while immutable logs and automated monitoring strengthen trust in critical processes.

This mirrors the strategies explored in Cybersecurity in Telecom, where layered defences combine encryption, monitoring, and governance. 

For telcos, the key is treating cybersecurity as a built-in feature of hyperautomation rather than an afterthought, ensuring that automation enhances resilience instead of introducing new vulnerabilities.

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Real-World Examples and Results

The next section focuses on real-world examples of brands actively using hyperautomation in their systems. 

1. Telkomsel + UiPath: Back-Office Transformation

Telkomsel partnered with UiPath to automate repetitive back-office tasks, starting with invoice handling. 

They then deployed RPA bots that cut down manual processing time and improved data accuracy. This freed up staff to focus on higher-value tasks, enabling the company to scale without expanding its workforce.

The company achieved three things from the collaboration. 

According to Muhammad Dodi Darmawan, the General Manager, “The implementation brought good experiences through new ways of working, where employees work together with robots to deliver better and more efficient results.”

2. Vodafone’s TOBi: Conversational AI for Customer Service

Vodafone had long aimed to deliver a consistent and distinct customer experience across all channels to meet user needs while staying aligned with the company’s broader mission. 

Partnering with Conversation Design Institute, They rolled out TOBi, a virtual assistant that uses natural language processing to manage customer service inquiries. 

TOBi now manages millions of interactions across platforms like chat and WhatsApp, handling routine queries and escalating more complex issues when necessary. This helps reduce wait times and eases the workload on human support teams.

3. Verizon: RPA in Network and Field Ops

Verizon needed a reliable RPA platform with the technical infrastructure, security controls, governance framework, and operational support required to deploy automation at scale.

They partnered with Radiant Digital to achieve this. The RPA system they created sped up fault detection, ticket generation, and technician dispatch in its network and field operations. 

The automated workflows enable the system to detect anomalies, create service tickets, and dispatch technicians in real time. This has improved service uptime and allowed field teams to respond quickly to outages and maintenance tasks.

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The Future of Hyperautomation in Telecom

Looking ahead, hyperautomation’s growth will have a massive impact on telcos that want to automate their operations fully. Here’s a breakdown of what to watch.

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FAQs on Hyperautomation in Telecom

How is hyperautomation different from basic RPA?

Hyperautomation takes basic RPA a step further by adding intelligence through AI, machine learning, and process mining tools. While RPA is limited to handling repetitive, rule-based tasks, hyperautomation can manage entire workflows and make informed decisions using data. It works by linking different technologies to streamline processes across teams and departments.

What types of telco processes are best suited for hyperautomation?

Repetitive, rules-based, and high-volume processes are ideal for hyperautomation in telecom. These include customer service, billing, CRM updates, network monitoring, incident management, and lead routing. It’s also effective in marketing automation, fraud detection, and service provisioning.

Is hyperautomation feasible for mid-sized telcos?

Yes, hyperautomation is a realistic option for mid-sized telecom companies. With scalable tools and cloud-based platforms, they can begin by automating a few critical processes and gradually expand their efforts as the benefits become clear.

Does automation lead to job losses in telecom?

Hyperautomation can change roles but doesn’t always lead to job losses. It often shifts employees away from repetitive tasks and into higher-value work like analysis, strategy, or customer engagement. Success depends on reskilling and involving teams in the transition.

From Process Automation to Competitive Differentiator

The question has always been if hyperautomation will replace automation. 

Given the clear benefits, such as cost savings, faster operations, greater scalability, and room for innovation, it’s easy to see why many telecom companies are shifting towards it. 

Staying ahead means getting started early, but that doesn’t mean going all in from day one. The innovative approach is to begin with a small, focused rollout, build internal expertise, and gradually scale using platform-based tools.

Explore how a telco can take a practical, phased approach to transformation and position itself for long-term success.