In tandem with network cloud transformation, communications service providers (CSPs) are evaluating opportunities to integrate artificial intelligence (AI) into their networks. Their goal is to dramatically enhance operational efficiency and customer experience. AI can help CSPs by, among other things, automating processes, adopting autonomy in their operations and providing real-time insights into network performance and operations. Adopting AI and Data Management in their networks is a key ingredient in the CSP’s journey towards Autonomous networks.
AI can optimize processes and service quality in telecom networks by:
- Driving network performance: AI can revolutionize network management through tools to enable predictive maintenance, allowing CSPs to foresee equipment failures and mitigate downtime. This proactive approach not only reduces maintenance costs but also enhances network reliability. Additionally, AI algorithms can analyze traffic patterns to optimize resource allocation, improving overall service quality.
- Boosting the bottom line: Data-driven insights help prioritize network investments, allowing CSPs to scale their infrastructures to meet rising demand for new services. And CSPs that have already integrated AI into their network operations are reducing expenses through automation and resource management optimization. For example, AI is enabling capabilities such as network slicing – prioritizing critical traffic – and digital twins to simulate network scenarios to support decision-making.
Getting started: Building an effective business case
A 2024 study ranked automation and AI as two of the priorities that will have the greatest impact on the telecommunications industry over the next five years. To leverage AI successfully, CSPs must underpin their AI adoption strategies with robust business cases that clearly define objectives and outcomes.
Embarking on an AI integration journey requires a comprehensive strategy that encompasses changes in both processes and mindset. The following focus areas can build the foundation for a robust business case for AI in the network:
- Evaluate operational readiness: Begin by assessing existing systems to identify areas ripe for AI integration. For example, call center automation and network troubleshooting are two areas where AI can drive greater efficiencies and reduce total cost of operations. This foundational step ensures that subsequent strategies are tailored to your organization’s most pressing needs.
- Define objectives and KPIs: Clearly outline the desired outcomes of the AI initiative by establishing key performance indicators to measure success. This should also include well-defined return on investment metrics.
- Develop pilot programs: Start with small-scale pilots targeting specific use cases within a single domain. This approach allows CSPs to validate the business case before scaling efforts across the network.
- Upskill the workforce: As AI technologies evolve, so too must the skill set of the workforce. CSPs should consider a mix of hiring new talent with AI, data management and cloud-native technology experience and upskilling existing employees to bridge knowledge gaps.
- Leverage ecosystem partnerships: Collaborating with technology partners can provide the necessary expertise and resources to effectively test, validate and scale AI use cases. Open ecosystems provide greater access to innovation and flexibility, allowing a CSP to take advantage of the depth of expertise each ecosystem partner brings to the table.
- Monitor and refine: Continuous monitoring of AI applications is crucial. Gathering feedback and refining the deployment will ensure optimal performance and alignment with business objectives.
Making your case
If your organization has not begun planning for AI in your network, the time to start is now. You can position your organization to gain competitive advantages in the rapidly evolving CSP market by building sound business cases for AI that will ultimately deliver enhanced network efficiency and lower operating costs.
Vendors with expertise in developing AI applications and operations can help CSPs develop strategic objectives and identify and prioritize use cases for manageable pilot programs.
Choosing partners
When evaluating potential partners, look for organizations with proven experience helping CSPs implement high-priority AI use cases. Partners should provide expert guidance, tailored services, and validated solutions to accelerate operational results at scale. To ensure access to innovation and flexibility, choose partners with a history of nurturing a broad, open AI ecosystem.
CSPs are adopting strategic approaches to AI integration by recognizing the need for both process and mindset shifts. By collaborating with strong ecosystem partners, CSPs can leverage AI to optimize network performance, enhance customer experience, and unlock new opportunities for growth and innovation.
