Artificial Intelligence (AI) is rapidly becoming an essential component of the telecommunications industry, driving significant changes in how networks are managed, optimized, and maintained. With the growing complexity of telecom networks, coupled with the rising demand for seamless connectivity, AI offers a range of solutions to address these challenges. From predictive maintenance and network optimization to enhancing customer service, AI is poised to transform telecom operations.

In the fast-paced world of ecommerce, efficiency is everything. The Global Operations Center (GOC) TOO team constantly battles with ecommerce store orders that get stuck, causing delays and resulting in customer dissatisfaction. Here’s a look at how one retail organization transformed their order troubleshooting process using automation.

In today’s rapidly-evolving retail landscape, automation and orchestration have become pivotal in driving efficiency and innovation. The retail sector, especially during and post-COVID-19, has undergone a massive transformation. With technological advancements over the past five years surpassing those of the previous 25, retailers are leveraging automation to streamline operations and enhance customer experiences.

In the ever-evolving landscape of network operations, the role of Network Operations Centers (NOCs) has become increasingly complex and demanding. Traditional monitoring and observability tools have been crucial in identifying and alerting teams to potential issues. However, as networks grow in complexity with the advent of IoT, 5G, and other digital advancements, the sheer volume of data and incidents can overwhelm even the most robust monitoring systems.

In the ITSM world, automation is no longer just a luxury—it’s essential. Automation empowers organizations to enhance operational efficiency, reduce expenses, and boost service reliability. That said, achieving true, sustainable results with automation can be challenging and often comes at a significant cost. Successful delivery of automation requires a clear vision and the strategic adaptation to align with evolving goals and incorporate agility.

Robotic process automation (RPA) initiatives often falter because enterprise application leaders choose use cases that involve intricate business logic, extended workflows, and require significant orchestration or additional technologies to deliver business value. In essence, RPA is not well-suited for complex workflows, lengthy processes, and those requiring extensive coordination.