5G and 6G – How will ultrafast networks change business applications?

The digital transformation of businesses is entering a new era thanks to ultra-fast telecommunications networks. The development of 5G technology and research into the future generation of 6G are introducing significant changes in the way companies design, deploy and use business applications. This breakthrough is not limited to faster data transfer, but includes a significant evolution in the architecture of IT systems, opening the door to innovations hitherto limited by the technical barriers of network infrastructure. It should be noted, however, that the pace and scope of these changes will vary regionally and depend on a number of regulatory, economic and technical factors.

5G networks represent a significant step forward not only because of increased capacity, but primarily because of changes in architecture. Unlike previous generations, 5G introduces the concepts of Software Defined Networking (SDN) and Network Function Virtualization (NFV), enabling dynamic management of network resources. These technologies allow the creation of flexible network structures tailored to specific business needs.

However, it should be noted that the technical parameters of 5G vary significantly depending on the variant implemented:

– 5G mmWave (millimeter wave, 24-100 GHz): Offers the highest throughput (theoretically up to 20 Gbps), but has very limited range (200-300 m) and poor obstacle penetration. Deployment example: Verizon is using this technology in Chicago business centers, providing download speeds of 1-3
Gbps, which has allowed logistics companies to reduce visual data processing time in warehouses by 60%.

• 5G mid-band (3.5-6 GHz): Provides a balanced combination of coverage and speed (1-6 Gbps), representing the most popular deployment option. Deutsche Telekom in Germany uses this spectrum to support smart factories, where it has achieved a 40% improvement in the efficiency of industrial sensor data transfer.
• 5G low-band (600-900 MHz): Offers the best coverage, but throughput only slightly higher than 4G LTE (250-300 Mbps). T-Mobile in the US has deployed this version in rural areas, supporting precision agriculture and enabling small businesses to reduce connectivity costs by about 15-20%.

6G technology, whose research is in its preliminary stages and commercial deployment is not expected until 2028-2030 at the earliest, could potentially bring another leap in performance. In theory, speeds of up to 1 Tbps are being considered, but these are hypothetical values, not established standards. Research is focused on the use of terahertz bands (above 100 GHz) and new conductive materials that could change the way signals propagate. It should be emphasized that the technology is still in the conceptual stage, and its final parameters could differ significantly from current predictions.

One direction for future networks is deeper integration with AI algorithms. While 5G-Advanced (an evolution of the 5G standard planned for 2023-2025) introduces elements of network intelligence, the 6G concept assumes that AI algorithms could become an integral part of the infrastructure itself, enabling autonomous management and optimization. However, this is still a research concept, not an approved specification.

Key technological differences

• 4G LTE: Throughput up to 1 Gbps, latency 50-100ms, consumer-oriented architecture
• 5G low-band: throughput 250-300 Mbps, range comparable to 4G, latency 15-20ms 5G mid-band: throughput 1-6 Gbps, range 1-2km, latency 5-15ms 5G mmWave: throughput up to 20 Gbps (theoretical), range 200-300m, latency 1-10ms
• 5G-Advanced (2023-2025): An improved version of 5G with better integration with AI and IoT
• 6G (concept, 2028-2030+): Theoretical throughput of up to 1 Tbps, potential latency of less than 1ms, research into terahertz band communications

How will ultra-low latency in 5G/6G will affect real-time applications?

Lower latency is one of the important features of 5G networks, potentially expanding the capabilities of real-time applications. Latency (latency) is the time it takes to send a data packet from the sender to the receiver and back – a key parameter for applications where responsiveness is critical. While 4G offers latency of 50-100 ms, selected 5G implementations can reduce this parameter to 5-20 ms (mid-band) or even 1-10 ms (mmWave under ideal conditions).

For the business perspective, it is crucial to understand which industries and processes can really benefit from this reduction in latency, and for which it is an unnecessary parameter. Here’s how different sectors can take advantage of lower latency:

Industrialmanufacturing (large enterprises): Bosch Rexroth deployed a private 5G network to control production lines at its smart factory in Germany. With delays of less than 10 ms, they achieved a 25% improvement in the responsiveness of safety systems and a 30% reduction in downtime related to communication delays. ROI for this investment (€900k) was achieved after 20 months due to increased productivity.

Financial sector (all sizes of companies): For most banking and payment operations, the difference between 50 ms and 10 ms latency is imperceptible and does not bring significant business value. The exception is high-frequency algorithmic trading (HFT), where even small reductions in latency can bring significant benefits. Trading firm Citadel Securities invested in 5G mmWave connections between key data centers, which reduced transaction times by 2-3 ms, translating into an estimated 3-5% increase in profits in selected trading strategies.

Medical (hospitals and medical centers): Telemedicine and remote consultations gain quality at lower latencies, but most applications work satisfactorily at 4G latencies (50-100 ms). Barcelona Hospital Clinic has implemented a 5G-based remote diagnosis system, enabling specialists to analyze ultrasound images in real time, which has reduced diagnosis time by 60% in emergency cases. However, full remote surgery, often cited as an example, remains a distant prospect due to regulatory challenges and legal liability.

Small and medium-sized entrepreneurs: For most SMEs, investment in ultra-low latency infrastructure rarely yields a reasonable return on investment. The exception is companies operating in technology niches, such as AR/VR software developers or industrial automation.

A guide to the real benefits of low delays

• Business-critical (1-10ms): Industrial automation, security systems, HFT algorithmic trading, real-time remote control
• Significant improvement (10-20ms): Advanced AR/VR applications, treatment telemedicine, real-time monitoring systems
• Slight improvement (20-50 ms): HD video conferencing, remote office, standard cloud applications
• No noticeable difference (>50 ms): Email, web browsers, most SaaS business applications

Practical tip: Before investing in low-latency technologies, conduct an audit of your applications to identify those that truly require such performance. Often, you will find that only 5-10% of business processes yield measurable benefits from reducing latency below 20 ms.

How How will 5G accelerate the development of the Internet of Things (IoT) in business?

5G technology has the potential to significantly enhance the development of the Internet of Things, especially in applications requiring a large number of connected devices or the transfer of significant amounts of data. The 5G specification envisions the ability to support up to one million devices per square kilometer – compared to about 100,000 for 4G networks. This connectivity density is available in specific 5G mMTC (massive Machine Type Communications) deployments, but it is worth examining the actual benefits for different sectors and enterprise sizes.

Large manufacturingcompanies : Ford’s Valencia plant, for example, has deployed a private 5G network to manage a fleet of autonomous vehicles inside the factory and comprehensively monitor the production process. By connecting more than 12,000 sensors per square kilometer, a 35% improvement in quality defect detection and a 28% reduction in downtime was achieved. The total investment (€1.2 million) paid for itself within 18 months through operational savings.

Medium-sized logisticscompanies : GEODIS in France has implemented a 5G-based tracking system in its distribution centers. Implementation costs (€400k for a 45,000 sq. ft. center) initially seemed high, but a 40% increase in inventory accuracy and a 65% reduction in shipment location time yielded a return on investment within 24 months. The system uses flexible “network slicing” (network slicing) to isolate critical traffic from standard traffic.

Small and medium-sized enterprises: For many SMEs, especially with limited IT budgets, the benefits of deploying dedicated 5G IoT solutions may not offset the costs. Alternative LPWAN (Low-Power Wide-Area Network) technologies, such as LoRaWAN, Sigfox or NB-IoT, often offer more cost-effective solutions for specific IoT applications. For example, a network of small vineyards in Spain (averaging 15-50 hectares) deployed a LoRaWAN-based monitoring system for 1/5 the cost of a 5G solution, achieving 90% of the functionality required for their needs.

The key industries that stand to benefit the most from 5G IoT are:

1. Manufacturing: comprehensive monitoring of machines and processes
2. Logistics and supplychain management : Real-time asset tracking
3. Smart cities: urban infrastructure management (for local governments)
4. Precision agriculture: For large farms (>100 hectares)
5. Energy: Transmission infrastructure monitoring

A practical guide IoT implementations

Large enterprises (>1000 employees):

• Consider a private 5G network for critical IoT applications (cost: €750k-€2 million)
• Typical payback period: 18-36 months
• Key success factor: Integration with existing systems (ERP, MES)

Medium-sized enterprises (100-1,000 employees):

• Hybrid approach: 5G for critical applications, cheaper alternatives for others
• Typical cost of implementation: EUR 200-500 thousand.
• Payback period: 24-48 months

Small businesses (<100 employees):

• Consider alternative technologies (LoRaWAN, NB-IoT) or 5G services from operators
• Estimated cost: EUR 30-150 thousand.
• Focus on one key business process instead of a comprehensive transformation

Will edge computing revolutionize data processing thanks to 5G and 6G networks?

Edge computing is a model that moves some computing from central data centers closer to data sources. When combined with 5G, it can bring significant benefits to applications that require fast processing of large amounts of data with minimal latency. To assess the real business value of this combination, it is worth examining specific deployments and their financial results.

Large manufacturingcompanies : Siemens Group at its semiconductor plant in Dresden integrated a private 5G network with edge computing infrastructure, moving production quality analysis systems from a central cloud to the edge of the network. The results:

• Reduction in processing latency from 75 ms to 12 ms
• Decrease in production defects by 27%
• Reduce data transmission costs by 62%
• Total cost of implementation: €1.8 million
• Payback period (ROI): 22 months

Retailcompanies : Carrefour in France has deployed an edge computing solution combined with 5G for in-store video analytics:

• 32 analytics cameras per store generating 1.2 TB of data per day
• Local processing instead of uploading to the cloud
• Reduction in data transfer costs by 74%
• Improved precision of customer behavior analysis by 40%
• Implementation cost per store: EUR 120-180 thousand.
• Payback period: 36 months

SMEs (Small and Medium Business): For smaller companies, end-to-end deployments of edge computing and 5G often do not make economic sense. For example, a chain of 25 small hotels in Italy considered implementing a smart building management system based on 5G and edge computing. The analysis showed that:

• Total cost of implementation: EUR 45-60 thousand per location
• Expected savings: EUR 12-15 thousand per year
• Payback period: more than 4 years

The company opted for a hybrid model, implementing edge computing only for selected critical functions and leaving the rest of the processes in the cloud, which reduced the turnaround period to 30 months.

Challenges in evaluating the economic benefits of edge computing include:

Hidden costs: In addition to hardware (an average of $700-800K per location for a large deployment), significant costs are generated:

• Software and integration (~35% of budget)
• Staff training (~15% of the budget)
• Maintenance and upgrades (~20% of budget per year)

Alternative solutions: In many cases, cloud application optimization or dedicated industrial networks can provide comparable benefits at a lower cost.

A Practical Guide implementation edge computing with 5G

Step 1: Identify applications that are critically sensitive to latency (latency <20 ms) or that generate huge amounts of data

Step 2: Conduct a financial analysis that takes into account:

• Total cost of ownership (TCO) for 5 years
• Direct savings (data transfer, performance)
• Indirect business benefits (quality, response time)

Step 3: Consider the implementation model:

• Large companies: Own edge infrastructure and private 5G network
• Medium-sized companies: Hybrid model with edge computing for critical functions
• Small businesses: Edge computing services from vendors as an alternative to full deployment

Key indicator: If the total cost of implementation does not pay for itself within 36 months, consider alternative approaches or phased implementation

How industries such as logistics or manufacturing will benefit from ultrafast networks?

The logistics and manufacturing industries have the potential to gain significant benefits from 5G technology, but the spectrum of these benefits varies depending on the size of the company,
the nature of operations and specific business needs. Analyzing specific deployments helps to understand the real impact on specific market segments.

Large logisticscompanies : DHL has deployed 5G technology at its logistics center in Hamburg, integrating it with autonomous transport vehicle (AGV) systems and advanced video analytics:

• Increase center throughput by 32% due to better coordination of the 120 AGV fleet
• Reduce picking errors by 41% with AI-assisted vision systems
• Reduce lead times by 15-20%
• Total investment: €2.2 million
• Payback period: 26 months

Key success factor: Comprehensive integration with existing WMS A medium-sized manufacturer (automotive sector): Brembo, a brake systems manufacturer in Lombardy, implemented a hybrid solution combining a private 5G network with upgrades to existing industrial networks:

• Cost of implementation: EUR 840 thousand.
• Payback period: 31 months
• Key benefits:
  • 28% reduction in downtime with predictive maintenance
  • 17% improvement in overall equipment effectiveness (OEE)
  • Integration of 180 robotic workstations with the central quality control system

Small and medium-sized logisticscompanies : A consortium of 12 smaller logistics companies in Spain (averaging 20-50 vehicles each) has invested in a common 5G-based platform for managing last-mile deliveries:

Cost per company: EUR 65-90 thousand.

Payback period: 38 months for a medium-sized company

Benefits:

• Real-time route optimization improving fleet utilization by 24%
• Reduction of empty runs by 31%
• Integration with e-commerce platforms to ensure accurate tracking of shipments

A comprehensive sector analysis shows a nuanced picture of potential benefits:

Industrial production:

• Large enterprises (>1000 employees): High return-on-investment (ROI) potential for private 5G networks – an average of 22-36 months of returns
• Medium-sized companies (100-1000 employees): Moderate ROI potential, payback in 3048 months, hybrid implementation recommended
• Small companies (<100 employees): Mostly no business case for dedicated 5G deployments

Logistics and Supply Chain:

• Large logistics centers: High ROI especially with automation and AGVs – payback in 24-36 months
• Medium operations: Moderate ROI for selected applications (fleet tracking, smart warehouses)
• Small operators: better results with shared models or 5G public services

Decision matrix implementations 5G in logistics and manufacturing

For large enterprises:

• High ROI: Production automation, AGV fleets, comprehensive quality monitoring
• Average ROI: Digital twins, augmented reality in maintenance
• Risky investments: Total digital transformation of all processes simultaneously

For medium-sized companies:

• Incremental strategy: Deploy technology in the most critical areas
• Hybrid approach: Combining 5G with existing networks for cost optimization
• Typical first step: Resource monitoring and tracking, quality analysis

For small businesses:

• Consider shared services: Shared platforms with other companies
• Take advantage of public 5G networks: Instead of investing in private infrastructure
• Focus on applications: Business value comes from software, not infrastructure

Will cyber security going to be a challenge in the era of 5G/6G-based applications?

The deployment of 5G networks and future communications technologies poses significant cyber security challenges that directly impact business decisions and security strategy. The distributed architecture of 5G networks based on network functions virtualization (NFV) and software-defined networks (SDN) introduces new attack vectors that require a modified approach to protection.

Actual incidents and costs Security incidents:

A large manufacturing company in Germany deployed a private 5G network without adequate virtualization layer security, leading to a security breach:

• Attackers gained access to the network by compromising NFV management functions
• Production downtime: 16 hours
• Direct financial losses: €1.8 million
• Cost of remediation and security enhancement: EUR 420 thousand.
• Implemented solution: Comprehensive virtualization layer monitoring system with automatic anomaly detection

A medium-sized logistics operator in Sweden has experienced an attack on its 5G-based fleet management system:

• Attack vector: Insufficient isolation between network slices (network slicing)
• Time to detection: 72 hours
• Direct costs: EUR 290 thousand.
• Implemented security: Advanced microsegmentation with continuous monitoring of flows between segments

Analysis of security costs in different market segments:

Large enterprises (>1000 employees):

Average cost of comprehensive 5G private network security: €350-500k.

Annual security maintenance costs: 15-20% of the initial investment

Recommended security investment structure:

• NFV/SDN infrastructure protection: 40% of the budget
• Monitoring and incident detection: 30% of the budget
• Application layer security: 20% of the budget
• Training and awareness: 10% of the budget

Medium-sized enterprises (100-1000 employees):

• Typical security budget: EUR 180-320 thousand.
• Potential savings through shared model or managed services: 30-40%
• Key investment areas: network edge protection, identity management, incident detection

Small businesses (<100 employees):

• Recommended approach: security as a service (SECaaS) from third-party providers
• Estimated monthly cost: €5-15 thousand depending on the scale of the operation
• Necessary minimum: access management, data encryption, basic monitoring

A Practical Guide security 5G for decision makers business

Risk assessment and planning:

• Identify critical business assets and processes using 5G
• Estimate the potential financial loss in the event of a breach for each resource
• Determine the “acceptable level of risk” for different systems

Securityinvestment strategy :

• Traditional model: 10-15% of total 5G investment allocated to security
• Risk-based model: Match investments to the estimated risk of each system
• Cyber insurance: Consider in addition to technical security

Key securitytechnologies by priority:

1. Advanced network microsegmentation with automatic policy enforcement
2. Continuous monitoring and analysis of anomalies in real time
3. Data encryption at rest and during transmission
4. Advanced authentication and identity management
5. Automatic management of security updates

How the integration of 5G with AI will enable intelligent business process automation?

Integrating 5G networks with artificial intelligence technologies creates synergies that can significantly impact business process automation. By combining the high bandwidth and low latency of 5G with advanced AI analytics, organizations can deploy intelligent systems that work on massive data sets in real time. To better understand the practical benefits of this combination, it is worth examining specific deployments in different sectors and enterprise sizes.

Large manufacturingcompanies : The ABB conglomerate has deployed a comprehensive autonomous quality control system combining 5G, edge computing and deep machine learning at its plant in Switzerland:

System: 64 high-resolution (4K) cameras monitoring the production line

Data load: 38 TB of data per day analyzed at the network edge

Business results:

• Improved defect detection by 48% (compared to manual inspection)
• Reduce quality control costs by 37%
• Reduction in customer complaints by 29%

Implementation costs: EUR 2.4 million

Payback period: 22 months

Mid-sized retailer: A chain of 35 supermarkets in the UK has implemented a system for analyzing customer behavior and dynamic inventory management:

Technology: AI cameras with edge processing connected via 5G to a central system

Investment per store: EUR 110-140 thousand.

Business results:

• Reduction of incomplete inventory on shelves by 62%
• Increase sales conversion by 18% by optimizing store layout o Reduce wait time at checkout by 26%

Total payback period: 28 months

Challenges: RODO compliance and customer privacy (solved by processing data locally and aggregating anonymous results)

Small logistics company: a courier company (45 vehicles) in the Netherlands has implemented a route optimization system based on 5G and AI:

Investment: €95,000.

Components: 5G mobile terminals, AI system for dynamic route optimization, predictive platform for estimating delivery times

Results:

• Increase shipments per vehicle by 27%
• Reduction in fuel consumption by 18%
• Improved compliance with delivery dates by 32%

Payback period: 19 months

Analysis of various deployments shows that the greatest benefits of 5G and AI integration are in systems that require:

1. Real-time analysis of large amounts of data (e.g., video streams)
2. Make decisions in millisecond intervals
3. Coordinate a dispersed fleet of equipment or vehicles
4. Process and analyze data close to its source (at the edge of the network)

A practical guide to 5G and AI integration

Stage 1: Evaluation of use cases

• Identify processes that require real-time analysis
• Estimate the potential return on investment for each case
• Assessing the scale of data generated – does it require high 5G bandwidth?

Stage 2: Architecture selection

• Large enterprises: Hybrid model with edge and central processing
• Medium-sized companies: Selective implementation for critical processes
• Small businesses: Consider SaaS solutions using public 5G networks
  

Stage 3: Cost management

• 5G infrastructure: 35-45% of the budget
• AI hardware and software: 30-40% of the budget
• Integration with existing systems: 15-25% of the budget
• Training and change management: 10-15% of the budget

Key successindicator : Payback time of less than 30 months

How how will augmented reality (AR/VR) gain new applications with 6G?

Augmented (AR) and virtual reality (VR) technologies are evolving rapidly, and future communication networks could significantly impact their capabilities and business applications. Currently, AR/VR deployments in business are constrained by network bandwidth, latency and mobility, which affects the quality and scope of applications. While next-generation communications technologies can overcome some of these barriers, the actual benefits and return on investment vary significantly by sector and scale of operations.

Cost-benefit analysis in different market segments:

A practical guide to AR/VR implementations in business

Assessment organizational readiness:

• Infrastructure: Private 5G/Wi-Fi 6 network with dedicated edge resources
• Integration: API to CAD, ERP, CRM and product databases
• Competencies: Team with experience in 3D, UX and systems integration

Strategies implementations by size companies:

• Large enterprises: Start with a pilot in one area with critical processes
• Medium-sized companies: Consider partnering solutions with AR/VR vendors as a service
• Small businesses: Focus on front-end applications (customer presentations, visualizations)

Key KPIs to monitor:

• Adoption rate among employees (% of team actively using technology)
• Measurable improvements in process efficiency (time, quality, cost)
• Total cost of ownership (TCO) vs. business benefits gained
• User satisfaction index (internal and external)

How 6G capabilities in the area of holographic communications differ from 5G solutions?

Holographic communication is one of the frequently mentioned potential application areas for future communication technologies, but the difference between current 5G capabilities and future 6G concepts is significant. To understand the real prospects for business applications, it is worth analyzing the current state of the technology, existing pilots, and the economic and technical aspects of implementation.

Current state of technology (5G):

Despite theoretical 5G speeds of up to 20 Gbps, actual holographic transmission capabilities are severely limited. Ericsson conducted pilot tests of holographic conferences using 5G mmWave in 2022:

• Bandwidth requirements for a single low-quality hologram: 300-500 Mbps
• Achieved image quality: low-resolution holograms (640×480 pixels) at 15 frames per second
• Latency: 80-120 ms (too high for smooth interaction)
• Limitations: Quality insufficient for professional applications, inability to render complex scenes

Specific Example of pilot implementation:

Orange Business Services and NTT DATA have conducted a pilot project on holographic communication in the medical industry:

• Cost of pilot implementation: EUR 1.3 million
• Infrastructure: Dedicated 5G network, 8 depth-sensing cameras, real-time data compression, specialized displays
• Results: Possibility of holographic consultation between 2 specialists, but with significant delay (>80 ms) and quality insufficient for diagnostic applications
• Main limitations of 5G: Bandwidth (even with a dedicated mmWave network), latency, connection stability

Technical requirements of high-quality holographic communication:

Economic analysis of implementations (based on feasibility studies):

1. Large multinational corporation considering holographic board meeting systems:

• Estimated cost of implementation for 5 sites: EUR 8.2 million
• Annual maintenance costs: €1.7 million
• Potential savings from travel reductions: €1.2 million per year
• Conclusion of the analysis: Project considered uneconomic with current technology

Architectural firm planning holographic presentations of projects:

• Cost of the system for a single studio: EUR 420,000.
• Estimated return on investment: Unattainable with current quality of holograms o Selected alternative: Advanced VR system (cost: €85k) with a payback period of 22 months

A realistic look at holographic communication

Short-term perspective (1-3 years):

• Low-resolution holography limited to controlled environments
• High implementation costs without business justification for most companies
• Better alternatives: Advanced VR/AR systems, 3D video conferencing

Medium-term outlook (3-7 years):

• Gradual development with transition technologies (5G-Advanced)
• Potential niche applications in medicine and high-value industries
• Still high costs with limited ROI

Long-term outlook (7+ years):

• Future technologies can reduce the cost barrier
• Standardization of holographic protocols and formats
• Need for a comprehensive ecosystem (displays, sensors, software)
  

How prepare a company’s IT infrastructure for 6G technology deployment?

Preparing IT infrastructure for the development of communications technologies requires strategic planning with a multi-year perspective. While full deployment of 6G remains a distant prospect (probably after 2030), businesses can take preparatory steps now that will benefit in the short term with 5G while laying the foundation for future technologies. However, the approach varies significantly depending on the scale of operations, the industry and specific business needs.

Practical examples of infrastructure transformation in various market segments:

A large multinational financial corporation (>15,000 employees) has embarked on a 5-year program to modernize its IT infrastructure:

Strategy: Phased transition to cloud-native architecture

Key elements of implementation:

• Conversion of 75% of applications to microservices model (cost: €42 million over 5 years)
• Implementation of a hybrid multi-cloud environment with Kubernetes orchestration (€11.5 million)
• Construction of 4 regional edge centers (edge) (€28 million) o Modernization of internal network to SDN architecture (€16 million)

Business results of the first 24 months:

• Reduction in time to implement new functionality by 68%
• Reduce IT operating costs by 22%
• Improved fault tolerance from 99.95% to 99.99% availability
• Ability to handle 3.8x more transactions during peak hours

ROI: 40% of the investment paid off in the first 30 months

A medium-sized manufacturing company (850 employees) has implemented a 3-year modernization program:

Strategy: Selective modernization of key systems

Implementation projects:

• Deployment of a private 5G network at a major manufacturing site (€1.2 million)
• Upgrading 40% of applications to a containerized architecture (€1.8 million) o Implementing edge computing infrastructure for critical systems (€950,000)
• Implementation of the Zero Trust model for security (€720,000)

Business results:

• Increase production efficiency by 17%
• Reduction in unplanned downtime by 42%
• Reduce IT maintenance costs by 28%

ROI: Total return on investment after 42 months

The small technology company (48 employees) took a pragmatic approach:

Strategy: Use managed services instead of building your own infrastructure

Implementation:

• Migration to SaaS platform with selective use of edge computing (€120k)
• Integration with public 5G services for key mobile applications (€65K)
• Implementation of Zero Trust security model (EUR 85 thousand).

Business results:

• Reduce IT costs by 32% compared to an in-house infrastructure model o Increase service scalability during peak periods

ROI: 22 months

Based on these examples and industry analysis, here is a three-level strategy for preparing for future communication technologies:

Strategic plan for IT infrastructure transformation

Level 1: Upgrading foundations (1-2 year horizon)

• Large companies: Start a gradual migration to microservices architecture
• Medium-sized companies: Identify critical applications to upgrade
• Small businesses: Consider SaaS solutions and managed cloud services
• Expected costs: 2-5% of annual revenue for large companies, 1-3% for medium and small companies

Level 2: Implementation advanced capabilities (2-3 year horizon)

• Large companies: Build hybrid edge/cloud infrastructure, deploy private 5G networks
• Medium-sized companies: Selective edge computing deployments for critical areas
• Small businesses: Leverage specialized edge services as a service (EaaS)
• Key investments: Containerization, orchestration, automation

Level 3: Preparation for future technologies (3-5 year horizon)

• Large companies: Development of advanced distributed resource orchestration models
• Medium-sized companies: Selective implementation of Software Defined Everything components.
• Small businesses: Work with technology partners offering the latest capabilities
• Budgetingprinciple : 70% (maintenance) / 20% (improvements) / 10% (innovations).

Key Success Indicators:

• Reduction in average time to implement new functionality
• Ability to dynamically scale resources
• Fault tolerance and self-repair capability
• Total cost of ownership (TCO) versus business value

Will 5G/6G will enable full decentralization of cloud services?

Decentralization of cloud services is a trend that is growing independently of advances in communications technologies, but 5G and future technologies could significantly affect its reach and effectiveness. To assess the real potential and return on investment, it is worth analyzing specific deployment cases and the specific needs of different types of organizations.

Analysis of actual implementations of decentralized cloud models:

Example 1: A large European bank (>100,000 customers) has implemented a decentralized hybrid cloud model using 5G networks:

Implementationstructure :

• Central cloud environment for undemanding applications
• 8 regional edge centers for latency-sensitive applications
• 5G as access technology for branches and mobile workers

Implementationcosts :

• Total investment: €28.5 million
• Operating costs: 42% higher than centralized model

Business results:

• Reduction in latency for transactions from 95 ms to 18 ms
• Compliance with local data processing regulations
• Improved service availability during regional outages from 98.5% to 99.98% o Reduced risk of data loss through geographic redundancy

ROI: 38 months (longer than originally expected)

Major challenges:

• The complexity of managing a distributed infrastructure
• The need to standardize platforms and tools
• Higher costs of shore infrastructure components

Example 2: A medium-sized manufacturing company implementing a decentralized cloud model for factory monitoring systems:

Solutionstructure :

• Private 5G network for communications
• Local edge processing for critical systems
• Hybrid architecture with selected services in the public cloud

Investment: €2.4 million

Business results:

• Reduction in production downtime by 27%
• Data transfer savings: EUR 340,000 per year
• Increase reliability of critical systems

ROI: 32 months

Key success factor: Selective approach – only critical applications in a decentralized model

Example 3: Small e-commerce company (35 employees) considering a decentralized cloud model:

Options analyzed during the feasibilitystudy :

• Full decentralization: Costs €450-720k, ROI not achievable
• Hybrid model: Costs €180-230k, ROI >48 months
• SaaS solutions with selected edge services: EUR 65-90k, ROI 24 months

Choice: Third option as most economical

Implementation: Leverage CDN services and publicly available edge points of presence (PoPs) of cloud providers

Business results:

• 32% improvement in page load speed o 17% increase in e-commerce conversions o Maintain RODO compliance at a lower cost

Comparison the economics of cloud models in different sectors:

A practical guide to decentralizing the cloud

Assessment readiness organizational readiness:

• TeamCompetencies : Experience in managing distributed environments
• Geographic scale: Presence in multiple regulatory regions
• Application Profile: Percentage of applications sensitive to delays
• Process maturity: Advanced orchestration and automation

Recommendations by segment market:

Large companies (>1000 employees)

• Optimal strategy: hybrid model with selective decentralization
• Staged deployment: Start with critical workloads and regions
• Internal benchmark: Track the TCO of each workload across locations
• Successrate : Reliability and performance vs. maintenance costs

Medium-sized enterprises (100-1000 employees)

• Optimal strategy: micro-regional approach (edge clusters)
• Selective decentralization: only for applications with proven ROI
• Consider partnerships: Shared shoreline infrastructure
• Successrate : Reduction in latency of critical applications

Small businesses (<100 employees)

Optimal strategy: Use of commercial edge services

Priority: Availability and reliability over data location

Cost alternative: CDN and edge services as a SaaS solution

indicator Success: Improved efficiency with minimal cost increase How will ultrafast networks affect the development of autonomous systems in transportation?

Autonomous transportation systems are one of the most discussed potential applications of advanced communications technologies. However, actual implementations show the complexity of such solutions and the significant difference between theoretical possibilities and practical realizations. Let’s examine specific pilot projects and implementations to better understand the current state and development prospects in this area.

Example 1: Port of Hamburg – deployment of autonomous transport vehicles (AGVs) using a private 5G network:

Scale of deployment: 25 autonomous container transport vehicles

Communication technology: Dedicated 5G (mid-band) network with guaranteed quality of service

Investment: EUR 3.2 million (including 5G infrastructure and AGV adaptation)

Business results:

• Increase terminal throughput by 21%
• Reduction in operating costs by 19%
• Reduce CO₂ emissions by 26%.

Key challenges:

• Operational area limited to controlled port environment
• Need to maintain human supervision despite automation o High initial costs requiring a long payback period (46 months)

Example 2: A medium-sized logistics company testing semi-autonomous vehicles using 5G:

Pilotproject : 12 delivery vehicles with advanced driver assistance systems

Technology: public 5G network supplemented by local edge nodes

Cost of pilot implementation: EUR 850 thousand.

Pilot results:

• Reduce fuel consumption by 16% with real-time route optimization
• Improved safety – 62% fewer traffic incidents
  Partial automation of “last mile” delivery

Technological limitations:

• Insufficient 5G coverage in rural areas (availability in only 48% of routes)
• Significant data transmission delays in areas with high building density
• Reliability problems in weather conditions (rain, fog)

Example 3: City of Helsinki pilot project for autonomous shuttle buses:

Scope: 3 autonomous minibuses on a 2.5 km route in an urban area

Communication technology: Hybrid – 5G complemented by dedicated base stations

Total pilotcosts : €1.7 million (12 months)

Results:

• Autonomous driving efficiency: 83% of the time without operator intervention
• The need to keep the safety operator on board at all times
• Speed limits (max 18 km/h) due to system response time limits

Key findings:

• Communications technology was just one of many challenges
• Legal aspects, accountability and social acceptance remain key o Actual autonomy remains limited even with ideal connectivity

A comparison of the different levels of of transport autonomy and their requirements communication:

A practical guide to transport autonomization

Recommendations for various sectors:

Logistics operators

• Optimal short-term approach: Advanced support systems (level 2-3)
• Areas of highest ROI: Distribution centers, controlled warehouse areas
• Key investments: Fleet management systems with real-time route optimization
• Implementationmodel : Incremental adoption in controlled areas
• Payback period: 24-36 months for supporting systems, 36-60 months for partial autonomy

Ports, airports and closed areas Industrial

• Most profitable applications: Autonomous transport vehicles (AGVs)
• Infrastructure requirements: Dedicated 5G networks with guaranteed capacity
• Key success factor: Comprehensive digitization of the entire operational area
• Payback period: 36-48 months

Public transport

• Recommended approach: Hybrid solutions with a security operator
• Most realistic applications: Shuttle buses on dedicated routes
• Non-technological challenges: Regulation, accountability, public acceptance
• Economic Forecast: Hardly achievable positive ROI over 5 years

How how will 6G accelerate big data analysis in business applications?

Big data analysis is the foundation of modern business, and its effectiveness depends not only on the communications infrastructure itself, but also on the entire technology ecosystem. Current deployments demonstrate both the capabilities and limitations of existing networks in the context of analyzing huge data sets. Future communications technologies have the potential to solve some of these challenges, but the impact will vary depending on the industry and specific applications.

Example 1: A large manufacturing company implementing a predictive infrastructure maintenance system:

Scope of deployment: 12,000 IoT sensors at 3 manufacturing sitesTechnology: 5G private network + edge computing + advanced AI analytics

System architecture:

• Data collection: 4.5 TB per day from vibration, temperature and pressure sensors o Edge processing: Pre-filtering and data analysis (95% reduction) o Central processing: Advanced predictive analytics

Investment: EUR 3.8 million

Business results:

• Reduction in unplanned downtime by 47%
• Extend the life of key components by 23%
• Annual savings: EUR 2.7 million

Technological limitations:

• Delays in analyzing high-definition video streams
• Need to compromise between data quality and speed of analysis
• Problems with integrating data from different source systems

Example 2: A medium-sized retail chain (120 stores) implementing a customer behavior analysis system:

Infrastructure: Hybrid of 5G and wired networks with local edge nodes

Data scope:

• 380 cameras with video analysis
• POS transaction data
• Data from mobile app (850,000 users)

Implementationcosts : €1.6 million

Business results:

• Increase in-store conversions by 14%
• Inventory optimization resulting in 8% cost reduction
• Personalization of offers to increase average basket value by 11%

Technology challenges:

• Real-time analysis limited to basic metrics
• Full analysis with 24-hour delay for advanced metrics o High cost of edge infrastructure relative to ROI

Example 3: Real estate company (management of 45 office buildings) implementing a smart energy management system:

Data:

• 8,500 IoT sensors (temperature, humidity, air quality, presence)
• 250 GB of data per day

Communication technology: Combination of Wi-Fi, 4G/5G and LoRaWAN

Cost of implementation: EUR 950 thousand.

Business results:

• Reduction in energy consumption by 23%
• Improved environmental conditions to increase tenant productivity
• ROI: 28 months

Technological aspects:

• Higher costs of 5G compared to LoRaWAN did not justify full 5G deployment
• Selected 5G applications only for systems requiring low latency

Based on these analyses, conclusions can be drawn about the impact of communication technologies on the efficiency of big data processing:

A strategic guide to big data for different segments market

Strategies for multi-level data analysis:

Large enterprises

• Recommended architecture: Hybrid analysis model (edge-fog-cloud)
• Key investments: Edge infrastructure with AI for pre-processing
• Communications model: Dedicated 5G networks for critical applications
• Estimated expenditures: EUR 2-5 million for comprehensive implementation
• Typical payback period: 24-36 months
• Successrate : Reduction in analytical latency by min. 60%

Medium-sized enterprises

• Recommended architecture: Selective edge computing with central analytics
• Optimal model: Hybrid communications infrastructure (5G + alternative technologies)
• Investment strategy: phased implementation starting with the highest business value
• Typical budget: 800,000 – 1.5 million euros.
• Payback period: 18-36 months
• Successrate : Improvement in accuracy of predictive models by min. 25%

Small businesses

• Recommended architecture: Analytics-as-a-Service solutions
• Optimal approach: Using public 5G infrastructure and public edge services
• Priority investments: Data integration and analytical tools instead of infrastructure
• Typical budget: €100-250 thousand.
• Payback period: 12-24 months
• Successrate : Business value of specific use cases

Success factors independent of communication technology:

• Clear data strategy and clearly defined business objectives
• Team competencies (data science, data engineering)
• Quality and availability of source data
• Integration of analytical results with business processes

Will 6G networks will they remove geographic barriers to accessing specialized services?

New communications technologies have the potential to reduce some of the barriers to accessing specialized services, but it is worth maintaining realistic expectations about the scale and
pace of these changes. Access to specialized services faces numerous challenges beyond communication infrastructure issues, including legal, regulatory, economic, linguistic and cultural aspects. Technology can support overcoming some of these barriers, but is rarely a stand-alone solution.

In the healthcare sector, communication technologies with higher bandwidth and lower latency can improve telemedicine, enabling better quality video consultations, more efficient transmission of diagnostic images or more advanced remote monitoring. However, regulatory and legal issues, such as licensing of physicians limited to specific jurisdictions, liability for remote diagnoses, protection of medical data, or differences in insurance systems, remain important constraints. These aspects often present a greater barrier to inter-regional service delivery than the communications infrastructure itself.

The concept of remote control of surgical robots, while technologically exciting, faces fundamental challenges that go far beyond connectivity issues. Even with theoretically ideal network parameters, medical procedures require backup systems in case of loss of connectivity, local specialists overseeing the procedure, compliance with local standards of care, and clearly defined legal responsibilities. These aspects mean that full democratization of access to specialized medical procedures through communications technology remains a distant prospect, even assuming optimistic infrastructure development.

In the field of education and specialized training, communication technologies can support more interactive forms of remote learning, potentially incorporating elements of augmented or virtual reality. However, it is important to remember that effective education requires not only efficient transmission of information, but also appropriate pedagogical methods, a supportive learning environment, social interaction, and assessment and accreditation systems. Technology can support these processes, but is not a substitute for a comprehensive educational ecosystem tailored to specific disciplines and levels of education.

Realistic perspectives on the democratization of professional services

• Telemedicine: Potential for improvements in consultation, diagnosis and monitoring, with regulatory barriers persisting
• Engineering and manufacturing: ability to provide better remote support and access to expertise in controlled environments
• Specialized education: Development of more interactive forms of distance learning to complement traditional methods

Key barriers beyond communications technology:

• Local regulations and professional jurisdictions
• Legal liability for services provided remotely
• Linguistic and cultural differences
• Issues of availability and cost of specialized terminal equipment
  

How will the the enterprise application market in the era of widespread access to 6G?

The market for enterprise (enterprise) applications will undergo a significant evolution with the development of new communications technologies. In order to properly understand the directions of these changes, it is useful to analyze current digital transformation trends and the first deployments using advanced 5G networks, and then extrapolate these conclusions to future scenarios. Specific deployment examples show how business applications are adapting to new technological opportunities.

Example 1: A multinational manufacturing company introducing a “seamless applications” architecture using 5G and edge computing:

Scope of transformation: Converting 60% of operational applications to a microservice architecture

Technologies: 5G private network, local edge computing, Kubernetes containers, orchestration

Investment: €5.2 million (3-year program)

Business results:

• Reduction in time to implement new functionality by 78%
• Dynamic reallocation of computing resources to increase efficiency by 34%
• Higher fault tolerance from 99.9% to 99.997% availability
• Reduce the total cost of ownership (TCO) of applications by 22%

Key success factor: Full orchestration of computing resources between the cloud and the network edge

Example 2: A medical services company (35 facilities) implementing an advanced telemedicine system:

Solutionarchitecture : Hybrid with local edge nodes at each site

Application model:

• Application core in the central cloud
• Critical components (image analysis, patient monitoring) at the network edge
• Dynamic migration of components based on user location

Implementation costs: EUR 2.4 million

Business results:

• Increase consultation throughput by 35%
• Reduction in patient waiting times by 42%
• Reduce diagnosis time by 28% with local analysis of medical images

Major challenges:

• Comprehensive integration with existing systems
• Ensure compliance with medical data regulations
• Managing data consistency between distributed nodes

Example 3: A financial company implementing groundbreaking software licensing models:

New model: “Compute as a Service” with dynamic resource allocation

Technologies: Hybrid cloud, edge computing, advanced orchestration

Business Benefits:

• Reduction in license costs by 31% due to precise use of resources
• Reduce peak infrastructure demand by 47%
• Increased flexibility during periods of high load

• Implementationchallenges :

• The complexity of modeling resource use
• Need to renegotiate contracts with software vendors
• Investment in advanced monitoring tools

Key trends in enterprise application transformation in the context of context new communication technologies:

Practical recommendations for different types of organizations:

Large companies (>1000 employees)

• Immediate action (1-2 years): Convert key applications to a microservice architecture
• Medium horizon (2-4 years): Implement advanced orchestration between cloud and edge
• Long-term investments: Comprehensive distributed application management platforms
• Key SuccessIndicator : Time-to-Market for implementing new functionality.
• Expected return on investment: 30-48 months

Medium-sized enterprises (100-1000 employees)

• Priority actions: Identify critical processes for modernization
• Recommended strategy: Hybrid transformation – selective modernization of applications
• Optimal approach: Using off-the-shelf SaaS platforms and components
• Key success factor: Balance between cost and business value
• Typical payback period: 24-36 months

Small businesses (<100 employees)

• Recommended approach: Prioritize applications that generate the most value
• Optimal strategy: Using applications delivered as a service (SaaS)
• Key investments: Inclusive competencies instead of infrastructure
• Most profitable innovations: New user interfaces, personalization
• Typical payback period: 12-24 months

Universal recommendations independent of the size of the organization:

• Work with software vendors on new licensing models
• Build competence in managing distributed applications
• Develop infrastructure step by step, starting with critical processes
• Invest in distributed architecture security