How artificial Intelligence makes network deployments faster, safer and smarter

The rapid advancement of Artificial Intelligence (AI) has revolutionised many industries and processes. AI-powered systems and Machine Learning (ML) algorithms are being increasingly deployed to enhance the accuracy, efficiency, and safety of field operations across diverse sectors.

When it comes to rolling out networks, it’s important that all parties have a deep commitment to ensure the health and safety of all the employees and partners involved. The aim is simple: everyone should go home safely at the end of the day. AI and ML play a big role in transforming the way we monitor and ensure the safety of everyone in the field. In addition, they make the quality verification process faster and more accurate right from the start, while helping optimise network performance. In this way, artificial intelligence is a true game-changer for network deployments.

In this article, I will explore the applications and benefits of AI in quality verification and how it can help ensure the health and safety of those working in field activities of mobile network deployments.

Embracing AI and ML for deployment services

The validation of site quality as well as ensuring compliance with health and safety regulations are of critical importance in network deployments. Both are prone to human error and subjectivity, which can lead to rework, site revisits, increased cycle time and increased risk of operational hazards.

Embracing artificial intelligence and moving away from manual work helps minimise human error and enhance the accuracy of the verification processes. At the same time, it’s necessary to acknowledge that the adoption of AI and ML in network deployment operations is not without its obstacles.

This is true with all disruptive technologies. In my experience, these four aspects can greatly contribute to a positive result:

1. Applying standard processes everywhere

A uniform way to work on all site deployments helps optimise operations and establish important routines. If processes vary between different sites, it may lead to oversights, which can then result in unfortunate safety hazards.

2. Ramping up expertise in data analytics

When adopting new digital tools, it is important to provide the users with appropriate competence development and opportunities to ramp up their skills. Otherwise, the benefits of the new technologies are left unexploited.

3. Adopting a positive mindset

There can be resistance to digitalisation especially when the old manual-intensive tasks are proven to work well enough. However, AI can take away the repetitive and the most time-

consuming part of the workload, letting human personnel concentrate on more motivating and value-adding tasks.

4. Ensuring data security and legislative compliance

Data security is a valid concern, and embedding security thinking into all products and solutions will help to navigate a complex landscape. For example, in some regions, AI-related legislation designed to protect personal privacy may limit the extent to which AI can be leveraged when processing data. Trying to find the best solution in compliance with local regulations will really help to improve the chance of success.

Achieving enhanced quality with First Time Right approach

Utilising AI and ML for RAN deployments in Europe produces great results, increasing First Time Right achievements by up to 30 percent. A ‘First Time Right’ approach means, for example, that the amount of site re-visits decreases, resulting in concrete cost savings.

With the further enhancements of digital platforms and the increasing expertise in leveraging artificial intelligence, this can improve even more.

With AI, quality verification can also be completed more quickly than with traditional methods - up to 25% faster, in my experience. The beauty of AI is that it can achieve time savings across the entire deployment process, making the deployments much faster and more cost-effective.

Artificial intelligence has allowed us to institutionalise a new and improved way of working. In short, we leverage AI and ML for these five critical tasks:

● Technical site surveys

● Site quality self-assessments conducted by the field crew

● Site quality audits conducted by a supervisor

● Customer acceptance checks for site quality

● Health and safety checks prior to starting field activities

AI/ML-based automation is essential for the safety and quality of modern field operations

The industry-first approach to using AI and ML set out in this article enhances the safety, speed and quality of network deployments. The benefits are multifold: Artificial intelligence enables automated inspections by analysing vast amounts of data collected from field activities. Machine learning algorithms can identify defects, anomalies, or deviations from desired quality standards and customer-specific checklists. AI-powered systems can monitor compliance with health and safety regulations such as the use of personal protective equipment as well as hazard recognition in real-time.

As Artificial Intelligence continues to evolve, I believe that it has an ever-growing potential to transform critical processes in network deployments. Leveraging AI and ML technologies in field activities can drive efficiency, reduce costs, and most importantly, protect the well-being of workers.