How AI-driven network automation transforms modern IT systems: TMA case study

According to Grand View Research, the AI-driven network automation market was valued at 8.67 billion USD in 2023. It is expected to reach 60.60 billion USD by 2030, with an average annual growth rate of 32.5%. At the same time, Ericsson reported that nearly 48% of telecom service providers worldwide have already used AI in their network operations. These numbers show that AI-driven network automation is becoming a natural trend. Let’s explore how this technology is changing modern IT systems in the article below.

1. What is AI-driven network automation?

AI-driven network automation means using artificial intelligence in network management to handle tasks like traffic monitoring, problem detection and performance tuning. By combining machine learning, Big Data analytics and predictive models, the system can learn from real data, forecast risks, and flexibly adjust operations. This helps improve the experience for end users.

2. Why do businesses need AI-driven network automation?

As the need for connection, security, and smooth operations grows, AI-driven network automation becomes the solution that helps businesses stay ready.

Increased network reliability

AI-driven network automation keeps monitoring the network, spots unusual signs early, and can redirect or balance traffic when needed. This reduces failures and cuts downtime.

Improved performance for AI workloads

AI workloads often need high bandwidth, low latency and fast data transfer. Network automation can prioritise important data flows, balance traffic and optimise paths. This ensures AI models and apps like chatbots, video analytics, and machine learning training run without delays or congestion.

Scalability for growing AI needs

When businesses deploy more AI apps (on edge, in cloud, or on devices), the amount of data and connected devices grows fast. AI-driven network automation allows the network to expand flexibly without heavy manual setup, keeping the system stable while scaling.

Enhanced security and compliance

Many AI systems handle sensitive data such as personal information, internal algorithms, or customer records. Network automation helps keep device settings updated, tracks abnormal activity, and manages access control consistently.

Reduced IT workload and human error

Manual network management requires much effort, from setup and configuration to monitoring and troubleshooting. AI-driven network automation automates repeated tasks like manual checks or late responses to alerts. This lowers the risk of mistakes, allows IT teams to focus on strategies, and keeps the network more stable.

3. Real-world use cases of AI-driven network automation

AI-driven network automation has been widely applied across many industries:

3.1. In Life Sciences

AI is changing how healthcare works, from patient care to data management. Key use cases include:

• 24/7 remote patient monitoring: Connected to medical devices and wearables, the system collects health data in real time. AI detects early abnormal signs and sends instant alerts.
• Smart health kiosks: Patients can check vital signs such as blood pressure, heart rate, and SpO₂ without the need for medical staff.
• Data integration & AI health analytics: Data from medical devices, prescriptions (OCR), and health records are combined. AI models then predict risks and support doctors in decision-making.

Example: The mCare solution by TMA is a strong case. It connects with more than 40 devices and processes real-time data using OCR. Hospitals and families can track patients continuously, spot risks early, and shorten reaction time.

3.2. In Financial Services

The finance industry uses AI to automate workflows and reduce risks. Typical applications include:

• Automated financial reporting: Systems collect data from multiple sources such as bank transactions, ledgers and accounting systems, check consistency, and generate scheduled reports.
• AI credit scoring and risk models: Based on history, customer profiles and behaviour, AI assigns credit scores and risk levels.
• Transaction monitoring and anomaly detection: AI tracks transactions across internal networks, APIs, and ATMs to detect fraud, network failures, or attacks. It then triggers automatic responses such as isolation, alerts, or rerouting.

Example: In the project Enhance Credit Risk Analysis With AI/ML Technologies, TMA built a risk assessment model using AI and Machine Learning algorithms. The outcome was higher accuracy in classifying high-risk customers, leading to faster and more reliable credit evaluation.

3.3. In Telecom

Telecom providers use AI to optimise infrastructure and keep services stable. Key examples include:

• Self-healing networks: When bottlenecks or transmission errors appear, the system automatically reroutes traffic, balances loads, or restores connections without manual work.
• Intent-based networking: Engineers only set goals (for example, “increase bandwidth for the VN region” or “apply video priority policy”), and AI translates them into network configurations and deploys them.
• Edge service quality management: AI improves fast switching between Wi-Fi, 5G, and wired networks. It also monitors edge apps like gaming, AR/VR, and IoT to ensure smooth user experiences.

Example: At its Telecom Solution Center, TMA has developed solutions such as Seamless Handover, Adaptive Wi-Fi Connection and 5G Network Slicing Apps. These solutions keep connections stable while users move, improve internet quality in crowded areas, and allocate network resources fairly for each service (for example, video calls or AR/VR).

3.4. In Manufacturing

AI is shaping smart factories by streamlining processes and reducing failures. Typical use cases are:

• Real-time machine monitoring: Sensors collect data, display machine status, measure performance and detect early signs of issues.
• Predictive maintenance: AI analyses sensor data such as vibration, temperature, and sound to predict failures and schedule maintenance before breakdowns happen.
• Production optimisation and smart resource management: AI controls equipment, saves energy, tracks environmental data (temperature, humidity) and reacts automatically to changes.

Example: TMA built an AI Sound-Based Fault Detection system to monitor machines through audio sensors. The system records and analyses noise, vibrations and sound frequencies. When unusual patterns are detected, it instantly sends alerts to the maintenance team.

4. AI-driven vs traditional network automation

To give a clearer view, the table below compares AI-driven network automation and traditional network automation based on key factors in IT infrastructure management.

5. Steps to implement AI-driven network automation

To apply AI-driven network automation successfully, businesses should follow these steps:

Step 1: Check current setup

First, review the entire network infrastructure, from devices and architecture to the APIs in use. The IT team must assess data collection ability and AI/ML skills. If there are gaps, fill them with training or new hires before starting the project.

Step 2: Define clear goals

Set clear objectives such as reducing incident response time, keeping the network stable, improving performance for key apps or lowering operating costs. Each goal should have measurable indicators to track progress and results.

Step 3: Choose tools and technologies

Consider AIOps for network data analysis, cloud or hybrid cloud for scalability, and HPC or edge computing for intensive processing. Make sure tools support API integration, have high reliability and match long-term strategy.

Step 4: Build a scalable data infrastructure

A solid data foundation is key for AI. Companies need continuous data collection, centralised and scalable storage, and standardised and encrypted data. Once data is clean and secure, AI models can provide accurate insights.

Step 5: Run small tests

Instead of rolling out the whole system at once, start with a limited network area. Automate simple tasks like traffic adjustment or fixing common errors. After testing, record real results, compare them with goals and learn before scaling up.

Step 6: Make automation event-driven

Set up scenarios where AI reacts instantly when network events happen. This includes access control, anomaly monitoring, data encryption and regular audits. Also, follow data protection rules like GDPR or HIPAA.

Step 7: Measure, repeat, and improve

Build performance tracking to compare results with goals. Keep fine-tuning AI models with new data. Encourage technology trials, invest in AI/ML training for staff, and maintain flexibility to stay competitive long-term.

To deploy AI-driven network automation successfully, the implementation team needs strong and deep knowledge in several areas:

• Networking: Understand network architecture design, routing and transport protocols, and be familiar with hardware configuration and operations.
• Data Science & AI/ML: Be able to handle large volumes of data, filter and normalize it, and build and train machine learning models to support decision-making.
• Software Development: Have practical programming skills, use network APIs, and write scripts or automation workflows to integrate into daily operations.

Reasons why TMA is the right partner for businesses:

• Over 28 years of technology experience with hundreds of successful international projects.
• A team of 4,000 expert engineers with combined skills in networking, data, and AI/ML.
• Deployment processes aligned with global standards, proven by leading certifications (CMMI, ISO, Agile).

6. Challenges of AI-driven network automation and key future trends

AI-driven network automation not only creates many current challenges but also opens important trends for the future.

Challenges

During the implementation of AI-driven network automation, businesses may face several key challenges, such as:

Data issues and integrating legacy systems

Network data is often scattered, inconsistent, and difficult to synchronise. Legacy systems usually do not support open APIs or real-time data, which makes AI integration complicated. This problem slows down the process of using data and limits the ability to operate automation based on AI.

Initial investment costs and ROI

The initial cost of AI-driven networking is often very high, from hardware infrastructure and software licences to staff training. Businesses find it hard to define a clear ROI if they do not have strong baseline data. This leads to concerns about payback time and the risk of spending beyond the planned budget.

Ensuring security and regulatory compliance

Applying AI in networks expands the attack surface and increases the risk of data leaks. The system also has to meet strict regulatory and security standards. If these requirements are not fulfilled, organisations may face legal risks, financial penalties and a loss of customer trust.

Needs better wording & coherence

AI-driven automation requires staff with deep knowledge of networking, data, and software. Many organisations lack people with enough cross-disciplinary skills. The difficulty lies in the need to adapt quickly while technology keeps changing, which makes the skills gap even bigger.

Future

Along with the challenges, AI-driven network automation also opens important trends for the future, such as:

Emerging technology trends (Intent-based networking, AIOps)

New trends like Intent-based Networking and AIOps are shaping the future of AI-driven networking. They promise to improve performance and upgrade the way networks are managed, as business goals can be translated into automatic policies and big data can be processed in a smarter way.

The impact of AI-driven networking on the labor market

AI-driven networking not only changes technology infrastructure but also affects the labor market. Applying AI in network management transforms business operations, which leads to a shift in workforce demand.

Forecasting the development and wide adoption of AI in Network Management

The rapid growth of AI shows that AI-driven networking will become more and more common. In the future, this technology will not only be widely used but may also become an essential part of modern network management.

7. 2 frequently asked questions about AI-driven network automation

1 - Which AI tool is best for networking?

In fact, there is no single “best” AI tool. The right choice depends on your goals. For IT network infrastructure, platforms like Juniper Mist or Cisco DNA Center can be used for management and automation. For professional connections, Crystal Knows and NetworkAI help analyse profiles, while Poised supports real-time communication skill improvement.

2 - Does AI-driven automation replace human network engineers?

AI-driven automation does not fully replace network engineers. It mainly handles repetitive tasks such as monitoring, log analysis and anomaly detection. Human experts are still essential for designing architecture, making strategic decisions, and solving complex cases that AI cannot yet automate.

AI-driven network automation is becoming a key foundation in modern IT infrastructure management. Although challenges remain in cost, data, and workforce skills, its rapid growth shows that this technology will soon be an effective solution for helping businesses keep their competitive edge in the digital era.

If you want to bring AI-driven network automation into your IT infrastructure, contact TMA Solutions today to get a tailored deployment roadmap.

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