The Role of AI in Network Slicing: Unlocking Vertical Industry Transformations

The telecommunications industry stands at a strategic crossroads with AI-driven network slicing emerging as a critical differentiator in the 5G era.

This technology enables operators to transform from connectivity providers into strategic digital partners through highly customized network services.

The 5G network slicing market is projected to grow from $840 million in 2025 to $5.07 billion by 2030 at a CAGR of 43.3%, creating an unprecedented opportunity for those who act decisively.

This article examines the critical executive decisions that will determine market leadership, presents transformative case studies across key vertical industries, and provides a strategic framework for implementation success.

The Strategic Divide: A Tale of Three Executives

Imagine a telecommunications landscape where three competing CEOs faced identical decisions about AI-driven network slicing investments in 2023. Their divergent strategies illustrate the critical importance of timing and focus in adopting emerging innovations.

• The FIRST EXECUTIVE committed fully, integrating AI throughout their network operations and developing industry-specific solutions. This approach aligns with industry reports highlighting how early adopters of AI-driven network slicing achieve up to 40% better resource utilization and significant operational efficiencies
• The SECOND EXECUTIVE adopted a cautious "wait-and-see" approach, making minimal investments while focusing on traditional services. This conservative approach mirrors challenges faced by operators struggling to justify upfront costs without clear ROI projections.
• The THIRD EXECUTIVE created a hybrid strategy, selectively deploying AI capabilities in high-value segments while maintaining existing operations elsewhere. This balanced approach reflects real-world examples where operators focus on industries like manufacturing and healthcare, which offer proven ROI for network slicing implementations

𝗧𝘄𝗼 𝗬𝗲𝗮𝗿𝘀 𝗟𝗮𝘁𝗲𝗿: 𝗔 𝗦𝘁𝗮𝗿𝗸 𝗥𝗲𝗮𝗹𝗶𝘁𝘆.  

𝗧𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲'𝘀 𝗰𝗼𝗺𝗽𝗮𝗻𝘆 𝗲𝗺𝗲𝗿𝗴𝗲𝗱 𝗮𝘀 𝗮 𝗺𝗮𝗿𝗸𝗲𝘁 𝗹𝗲𝗮𝗱𝗲𝗿, 𝗹𝗲𝘃𝗲𝗿𝗮𝗴𝗶𝗻𝗴 𝗔𝗜 𝘁𝗼 𝗱𝗲𝗹𝗶𝘃𝗲𝗿 𝗵𝗶𝗴𝗵𝗹𝘆 𝗰𝘂𝘀𝘁𝗼𝗺𝗶𝘇𝗲𝗱 𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 𝗮𝗰𝗿𝗼𝘀𝘀 𝘃𝗲𝗿𝘁𝗶𝗰𝗮𝗹𝘀 𝗹𝗶𝗸𝗲 𝗮𝘂𝘁𝗼𝗺𝗼𝘁𝗶𝘃𝗲 𝗮𝗻𝗱 𝗵𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲. 𝗧𝗵𝗶𝘀 𝗮𝗹𝗶𝗴𝗻𝘀 𝘄𝗶𝘁𝗵 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻𝘀 𝘁𝗵𝗮𝘁 𝗲𝗮𝗿𝗹𝘆 𝗮𝗱𝗼𝗽𝘁𝗲𝗿𝘀 𝘄𝗶𝗹𝗹 𝗱𝗼𝗺𝗶𝗻𝗮𝘁𝗲 𝘁𝗵𝗲 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝗲𝗱 $5.07 𝗯𝗶𝗹𝗹𝗶𝗼𝗻 𝗻𝗲𝘁𝘄𝗼𝗿𝗸 𝘀𝗹𝗶𝗰𝗶𝗻𝗴 𝗺𝗮𝗿𝗸𝗲𝘁 𝗯𝘆 2030.  

𝗧𝗵𝗲 𝘀𝗲𝗰𝗼𝗻𝗱 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲'𝘀 𝗳𝗶𝗿𝗺 𝘀𝘁𝗿𝘂𝗴𝗴𝗹𝗲𝗱 𝘁𝗼 𝗰𝗼𝗺𝗽𝗲𝘁𝗲, 𝗹𝗼𝘀𝗶𝗻𝗴 𝗺𝗮𝗿𝗸𝗲𝘁 𝘀𝗵𝗮𝗿𝗲 𝗮𝘀 𝗿𝗶𝘃𝗮𝗹𝘀 𝗰𝗮𝗽𝗶𝘁𝗮𝗹𝗶𝘇𝗲𝗱 𝗼𝗻 𝗔𝗜'𝘀 𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝘁𝗼 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗲 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗮𝗻𝗱 𝗴𝘂𝗮𝗿𝗮𝗻𝘁𝗲𝗲 𝘀𝗲𝗿𝘃𝗶𝗰𝗲 𝗾𝘂𝗮𝗹𝗶𝘁𝘆.  

𝗧𝗵𝗲 𝘁𝗵𝗶𝗿𝗱 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲 𝗮𝗰𝗵𝗶𝗲𝘃𝗲𝗱 𝗺𝗼𝗱𝗲𝗿𝗮𝘁𝗲 𝘀𝘂𝗰𝗰𝗲𝘀𝘀 𝗯𝘂𝘁 𝗳𝗮𝗰𝗲𝗱 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 𝘀𝗰𝗮𝗹𝗶𝗻𝗴 𝘁𝗵𝗲𝗶𝗿 𝘀𝗲𝗹𝗲𝗰𝘁𝗶𝘃𝗲 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁𝘀 𝘁𝗼 𝗺𝗲𝗲𝘁 𝗴𝗿𝗼𝘄𝗶𝗻𝗴 𝗱𝗲𝗺𝗮𝗻𝗱 𝗮𝗰𝗿𝗼𝘀𝘀 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗲𝘀        

As we progress through 2025, telecommunications stand at the intersection of strategic necessity and unprecedented opportunity. The integration of artificial intelligence with network slicing represents far more than incremental progress—it signals a fundamental shift in how networks are managed, monetized, and experienced by users across vertical industries.

Technological Foundations: The AI Revolution in Network Slicing

Network slicing technology revolutionizes telecommunications by enabling multiple virtual networks on a single physical infrastructure, each tailored for specific service requirements. The integration of AI transforms these virtual networks from static partitions into dynamic, self-optimizing entities. Each slice operates independently, with its own security protocols and performance parameters, enabling operators to guarantee specific service levels for different applications.

At its core, AI transforms network slicing through:

• Predictive Analytics: AI systems now predict resource requirements with up to 95% accuracy, enabling proactive allocation adjustments before performance issues arise. This predictive capability has reduced service disruptions by 70% and improved user experience across all network slices.
• Resource Optimization: AI-powered slicing achieves up to 40% better resource utilization compared to traditional approaches. Advanced machine learning models analyze traffic patterns and user behavior to optimize slice configurations dynamically, resulting in a 50% reduction in resource waste.
• Security Management: By 2025, AI is expected to manage 75% of network security operations autonomously, processing up to 1 million threats per second and reducing incident response times by 96%. This automation dramatically enhances the security posture of network slices without increasing operational overhead.
• Service differentiation is progressing from basic QoS tiers to highly personalized, intent-based networking that aligns precisely with business needs.

The decisions made today about AI-driven network slicing investments will determine competitive positioning for years to come.

𝗔𝘁 𝗠𝗪𝗖 2025 𝗕𝗮𝗿𝗰𝗲𝗹𝗼𝗻𝗮, 𝗕𝘂𝗯𝗯𝗹𝗲𝗥𝗔𝗡 𝗱𝗲𝗺𝗼𝗻𝘀𝘁𝗿𝗮𝘁𝗲𝗱 𝘁𝗵𝗶𝘀 𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝘁𝗵𝗲𝗶𝗿 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗧𝗲𝗹𝗲𝗻𝗼𝗿 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 & 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗡𝗩𝗜𝗗𝗜𝗔, 𝘀𝗵𝗼𝘄𝗰𝗮𝘀𝗶𝗻𝗴 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜-𝗯𝗮𝘀𝗲𝗱 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗻𝗲𝘁𝘄𝗼𝗿𝗸 𝘀𝗹𝗶𝗰𝗶𝗻𝗴 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗳𝗼𝗿 𝗽𝗿𝗶𝘃𝗮𝘁𝗲 5𝗚. 𝗧𝗵𝗲𝗶𝗿 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗹𝗲𝘃𝗲𝗿𝗮𝗴𝗲𝘀 𝗹𝗮𝗿𝗴𝗲 𝘁𝗲𝗹𝗲𝗰𝗼𝗺 𝗺𝗼𝗱𝗲𝗹𝘀 (𝗟𝗧𝗠𝘀) 𝘁𝗵𝗮𝘁 𝗻𝗼𝘁 𝗼𝗻𝗹𝘆 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝘁𝗿𝗮𝗳𝗳𝗶𝗰 𝘁𝘆𝗽𝗲𝘀 𝗯𝘂𝘁 𝗮𝗰𝘁𝗶𝘃𝗲𝗹𝘆 𝗮𝗽𝗽𝗹𝘆 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀 𝗿𝗲𝗾𝘂𝗶𝗿𝗲𝗱 𝘁𝗼 𝘀𝘂𝗽𝗽𝗼𝗿𝘁 𝗱𝗲𝘀𝗶𝗿𝗲𝗱 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗼𝗳 𝘀𝗲𝗿𝘃𝗶𝗰𝗲 𝗮𝗰𝗿𝗼𝘀𝘀 𝗲𝗻𝗱-𝘁𝗼-𝗲𝗻𝗱 𝗻𝗲𝘁𝘄𝗼𝗿𝗸𝘀.        

The Executive Decision Framework: Strategic Positioning

The most successful telecommunications executives are approaching AI-driven network slicing through a structured decision framework that balances short-term results with long-term strategic positioning.

This framework encompasses critical dimensions:

Investment Timeline and Resource Allocation

• Early market leaders are dedicating 15-20% of their technology budgets to AI capabilities for network slicing
• Successful implementations show strategic phasing rather than big-bang approaches
• The hybrid approach requires careful orchestration between traditional operations and innovation initiatives

Vertical Industry Prioritization

• 6 top industries account for 90% of addressable revenues, representing approximately $200 billion in potential revenue for communications service providers
• Automotive, manufacturing, and healthcare consistently demonstrate the highest ROI for specialized network slice implementations
• Strategic partnerships with industry leaders accelerate time-to-market and strengthen competitive moats

 Business Transformation

• Vertical-Specific Solutions: AI-powered network slicing enables the creation of tailored network environments for specific industries. Companies are developing industry-specific network slice templates powered by AI, offering pre-configured solutions for sectors like healthcare, manufacturing, or gaming. These specialized implementations can reduce deployment time by 75% and implementation costs by 50%.
• Slice-as-a-Service Platforms: Entrepreneurs and enterprises can capitalize on this convergence through slice-as-a-service offerings, enabling businesses to lease customized network segments on-demand. This model revolutionizes how industries access network resources, creating new recurring revenue streams for operators.
• AI-Driven Optimization Services: Organizations are developing specialized services that help enterprises maximize their network investments through continuous monitoring and adjustment of slice configurations. These services ensure optimal performance while minimizing costs, with early adopters seeing up to 40% reduction in network operating expenses.

 Organizational Transformation

• Leading companies are evolving from traditional telcos to "techcos" with AI capabilities embedded throughout the organization
• Talent acquisition and development in AI and machine learning has become a critical competitive differentiator
• Cultural transformation is essential for successful adoption of AI-driven network slicing as an organizational capability

𝗧𝗵𝗲 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲𝘀 𝘄𝗵𝗼 𝗵𝗮𝘃𝗲 𝗻𝗮𝘃𝗶𝗴𝗮𝘁𝗲𝗱 𝘁𝗵𝗲𝘀𝗲 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 𝗺𝗼𝘀𝘁 𝘀𝘂𝗰𝗰𝗲𝘀𝘀𝗳𝘂𝗹𝗹𝘆 𝗵𝗮𝘃𝗲 𝗺𝗮𝗶𝗻𝘁𝗮𝗶𝗻𝗲𝗱 𝘂𝗻𝘄𝗮𝘃𝗲𝗿𝗶𝗻𝗴 𝗳𝗼𝗰𝘂𝘀 𝗼𝗻 𝘃𝗮𝗹𝘂𝗲 𝗰𝗿𝗲𝗮𝘁𝗶𝗼𝗻, 𝘃𝗶𝗲𝘄𝗶𝗻𝗴 𝗔𝗜-𝗱𝗿𝗶𝘃𝗲𝗻 𝗻𝗲𝘁𝘄𝗼𝗿𝗸 𝘀𝗹𝗶𝗰𝗶𝗻𝗴 𝗻𝗼𝘁 𝗺𝗲𝗿𝗲𝗹𝘆 𝗮𝘀 𝗮 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗯𝘂𝘁 𝗮𝘀 𝗮 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗰𝗮𝘁𝗮𝗹𝘆𝘀𝘁.        

Vertical Industry Transformations: The Real-World Impact

The abstract promise of AI-driven network slicing becomes concrete when examining its transformative impact across vertical industries.

The following case studies demonstrate how this technology is creating measurable business value today:

Maritime Transportation: BubbleRAN, Telenor Research & Innovation, and NVIDIA

In a groundbreaking maritime implementation, BubbleRAN collaborated with Telenor Research & Innovation and NVIDIA to create an intelligent network slicing solution for maritime transportation.

The challenge involved ensuring mission-critical traffic flows received priority treatment in a cruise ship environment while still accommodating robotic remote inspection information and passenger broadband data. This complex scenario required effective implementation of network slicing within the ship's private network.

BubbleRAN's solution leveraged NVIDIA NIM microservices and NeMo microservices to create and train large telecom models (LTMs). These models powered network agents that could understand different traffic types and continuously apply reconfiguration, management, and control actions to meet desired quality of service levels.

The implementation deployed three network slices that were autonomously operated with resources dynamically allocated over time as requirements evolved. The results included:

• Significantly improved quality of service for end users
• Streamlined joint RAN and core network slicing through a Kubernetes Slice Operator
• Replacement of pre-configured schedulers with dynamic, AI-driven resource allocation

This case study demonstrates how AI-driven network slicing can maintain service quality for critical systems while optimizing the overall passenger experience, creating a win-win scenario for both the operator and end users.

Port Operations: Deutsche Telekom and Nokia at Port of Hamburg

The Hamburg Port Authority (HPA), Deutsche Telekom, and Nokia partnered to test 5G applications in the industrial environment of the Port of Hamburg. The project installed a 5G radio base station on an existing television tower to provide connectivity across the 8,000-hectare site.

Focusing on testing network slicing against diverse requirements of complex industrial applications, the implementation addressed three distinct use cases:

• Mobile data sensors on three HPA ships enabled real-time environmental monitoring
• The Port Road Management Centre connected with traffic lights through a network slice for remote control capabilities
• AR applications provided the port's engineering team with access to necessary data when on-site

The project demonstrated the flexibility and adaptability of network slicing in real-life conditions, serving as a "blueprint for the industrial use of 5G technology". The implementation showed how multiple distinct use cases with varying network requirements could be supported simultaneously on shared infrastructure.

Manufacturing: Ericsson, TIM, and Comau Factory of the Future

In June 2022, three innovative applications were tested in the "factory of the future" using 5G network slicing at Comau's headquarters in Turin, Italy. The implementation, deployed by TIM and Ericsson, demonstrated the benefits of slicing functionalities specifically for industrial contexts.

The three applications showcased different aspects of industrial transformation:

• Real-time synchronization between physical robots and their digital twins, enabling modeling of production processes for optimization insights
• Asset monitoring using data from various sensors collected in a central application, providing insights for predictive maintenance and quality control
• Enhanced support for on-site staff using AR and digital tutorials, connecting staff to remote experts through high-bandwidth video transmission

The implementation demonstrated how AI-driven network slicing could address multiple manufacturing challenges simultaneously, from production optimization to maintenance to workforce augmentation. The factory saw significant improvements in operational efficiency, with early reports indicating up to 30% better resource utilization.

Implementation Challenges: Navigating the Path Forward

Despite the compelling business case, telecommunications executives face significant challenges in fully implementing AI-driven network slicing. Addressing these obstacles is crucial for realizing the technology's full potential.

Key challenges include:

Technical Complexity and Integration

• End-to-end network slicing requires sophisticated orchestration across access, transport, and core networks
• Legacy systems often lack the programmability needed for dynamic slice management
• Each network domain (RAN, transport, core) requires specialized approaches to slicing, as highlighted by Nokia's domain-specific solutions

Talent and Expertise Gap

• The specialized knowledge required for AI-driven network slicing implementations remains scarce
• Organizations must upskill existing staff while competing to attract new talent with expertise in both telecommunications and artificial intelligence
• Boston Consulting Group suggests that successfully implementing AI at scale requires embedding machine learning in every operational process

Commercial Model Evolution

• Traditional pricing models are poorly suited to the value-based potential of network slicing
• Service providers must develop new frameworks for monetizing quality of service, security, and performance guarantees
• Three key 5G service types (eMBB, URLLC, and mMTC) require different commercial approaches

Investment Prioritization

• While the long-term benefits are clear, telecommunications operators must justify significant upfront investments in AI capabilities and slicing infrastructure. This requires careful calculation of ROI and strategic alignment with overall business objectives.

Regulatory Considerations

• As network slicing enables more granular service differentiation, operators must navigate evolving regulatory frameworks
• Data privacy, network neutrality, and security requirements vary by region and continue to evolve
• Proactive engagement with regulatory authorities is essential for market leaders

Call to Action: Securing Your Competitive Position

The convergence of AI and network slicing represents both the greatest opportunity and the greatest challenge that telecommunications companies have faced in recent history. Those that embrace this convergence will help build the foundation of a multi-billion-dollar digital economy and secure their place within it.

To capitalize on this momentous opportunity, telecommunications executives must take decisive action:

1. Define your strategic position

Assess your organization's current capabilities and competitive landscape to determine whether to pursue market leadership, selective implementation, or strategic partnership approaches. This positioning will guide resource allocation and implementation priorities.

2. Prioritize high-value vertical industries

Focus initial implementations on industries with proven ROI, including automotive, manufacturing, and healthcare. Develop deep domain expertise in selected verticals and create specialized network slice templates that address specific industry challenges.

3. Build your AI foundation

Implement the core AI capabilities required for dynamic network slicing, including predictive analytics, autonomous operations, and security automation. This foundation will enable more sophisticated implementations as your organization's capabilities mature.

4. Develop new commercial models

Create innovative pricing strategies that capture the value of specialized network services rather than simply charging for connectivity. Experiment with outcome-based pricing, tiered service levels, and partnership-based revenue sharing.

5. Foster ecosystem partnerships

No single entity can realize the full potential of AI-driven network slicing in isolation. Develop strategic relationships with technology providers, industry leaders, and specialized AI companies to accelerate your implementation.

6. Implement Proof-of-Concept Projects

Organizations should start with targeted implementations that demonstrate clear ROI while building internal capabilities. These initial projects can provide valuable learning experiences and tangible success stories to support broader deployment.

Conclusion: The Future Belongs to the Decisive

The data is unequivocal: AI-driven network slicing delivers unprecedented improvements in network efficiency, resource utilization, and service quality while enabling new revenue streams worth billions by 2025. AI-driven network slicing delivers 40% improvement in resource utilization while reducing operational costs by 35% through automated management systems. These efficiencies translate directly to enhanced customer experiences and new revenue opportunities that weren't possible with traditional networking approaches.

The evolution of telecommunications business models is accelerating, with differentiated services emerging as key revenue generators. Industry experts predict that AI-driven network slicing will catalyze a new era of customized communication solutions that transform operators from connectivity providers into strategic enablers of digital transformation.

𝗔𝘀 𝘄𝗲 𝘀𝘁𝗮𝗻𝗱 𝗮𝘁 𝘁𝗵𝗶𝘀 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗷𝘂𝗻𝗰𝘁𝘂𝗿𝗲, 𝘁𝗲𝗹𝗲𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲𝘀 𝗺𝘂𝘀𝘁 𝗮𝘀𝗸 𝘁𝗵𝗲𝗺𝘀𝗲𝗹𝘃𝗲𝘀 𝗻𝗼𝘁 𝘄𝗵𝗲𝘁𝗵𝗲𝗿 𝘁𝗵𝗲𝘆 𝗰𝗮𝗻 𝗮𝗳𝗳𝗼𝗿𝗱 𝘁𝗼 𝗶𝗻𝘃𝗲𝘀𝘁 𝗶𝗻 𝗔𝗜-𝗽𝗼𝘄𝗲𝗿𝗲𝗱 𝗻𝗲𝘁𝘄𝗼𝗿𝗸 𝘀𝗹𝗶𝗰𝗶𝗻𝗴, 𝗯𝘂𝘁 𝘄𝗵𝗲𝘁𝗵𝗲𝗿 𝘁𝗵𝗲𝘆 𝗰𝗮𝗻 𝗮𝗳𝗳𝗼𝗿𝗱 𝗻𝗼𝘁 𝘁𝗼.        

The future of telecommunications belongs to executives who recognize that network infrastructure is no longer just about connectivity—it's about creating intelligent, adaptive digital environments that enable new business models and enhanced user experiences across every industry sector.

References

1. Globe Newswire, "5G Network Slicing Market Report 2025-2030: CAGR of 43.3", February 2025
2. Mischa Dohler, "Shaping Tomorrow's Connectivity with AI-Driven Network Slicing", December 2024
3. Mischa Dohler, "AI in Telecommunications Enhances Network Slicing in 5G", December 2024
4. Mischa Dohler, "The Future of 5G Network Slicing and AI in Telecom - Limitless Potential", December 2024
5. Total Telecom, AI-driven 5G Network Slicing for Maritime Communication, February 2025
6. NETSCOUT, "Service Provider Success in 2025: One Step Forward…12 Months Back", January 2025
7. STL Partners, "5G network slicing examples", July 2024
8. Futurism Technologies, "Transforming Telecom with AI-Driven Network Slicing", December 2024
9. Fierce Network, "AI-driven Network Slicing Technology at MWC by BubbleRAN, Telenor Research & Innovation and NVIDIA", February 2025
10. Nokia, "Network slicing", February 2025