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Context & Scope
Network optimisation is a critical business function that involves monitoring, analysing, and improving network performance and reliability. Traditionally, human network engineers perform this role by manually reviewing network metrics, identifying bottlenecks, and implementing configuration changes.
- Manufacturing: Optimising factory floor networks to ensure seamless communication between automated machinery and control systems.
- Healthcare: Enhancing hospital networks to support real-time patient monitoring and telemedicine services.
- Financial Services: Improving trading network performance to minimise latency and maximise transaction speeds.
- Logistics: Optimising supply chain networks to ensure real-time tracking and efficient coordination of shipments.
- Education: Enhancing campus-wide networks to support simultaneous high-bandwidth activities like online lectures and research data transfers.
AI Solution Overview
- AI continuously monitors network performance metrics in real-time
- AI analyses data to identify patterns, anomalies, and potential issues
- AI predicts future network demands based on historical data and current trends
- AI generates optimisation recommendations for network configurations
- AI implements approved changes automatically or alerts human operators
- AI measures the impact of changes and adjusts strategies accordingly
- AI provides detailed reports on network performance and optimisation efforts
If needed at any point: • AI can alert human operators for manual intervention • Human operators can override AI decisions and implement manual changes • AI can simulate proposed changes before implementation to assess potential impacts
Human vs AI
| Human Intelligence (HI) | Artificial Intelligence (AI) |
|---|---|
| HI can only monitor a limited number of network metrics simultaneously | AI can monitor thousands of metrics across the entire network in real-time |
| HI may miss subtle patterns or correlations in network data | AI can detect complex patterns and relationships across vast datasets |
| HI requires time to analyse data and formulate optimisation strategies | AI can analyse data and generate optimisation recommendations instantly |
| HI can only implement changes during scheduled maintenance windows | AI can make continuous, incremental optimisations 24/7 |
| HI may struggle to predict future network demands accurately | AI can forecast future demands with high accuracy based on historical and real-time data |
| HI can become fatigued during long monitoring sessions | AI can maintain consistent vigilance and performance indefinitely |
| HI may take longer to identify the root cause of complex issues | AI can quickly isolate and diagnose problems across the entire network |
| HI relies on experienced staff, which can be costly and in short supply | AI can scale its capabilities across multiple networks cost-effectively |
Addressing Common Concerns
Reliability of AI decision-making: The AI system is trained on vast amounts of network data and industry best practices. It continuously learns from its actions and outcomes, refining its decision-making process. Human oversight is always available for critical decisions.
Security implications: The AI system is designed with security as a top priority. It operates within strict parameters and cannot make changes that would compromise network security. All actions are logged and auditable.
Integration with existing systems: The AI solution is designed to be compatible with a wide range of network hardware and software. It can integrate with existing monitoring and management tools, enhancing rather than replacing current systems.
Handling of unique or unexpected situations: While the AI is highly capable, it's designed to recognise situations outside its training or confidence level. In these cases, it alerts human operators for manual intervention.
Job displacement concerns: Rather than replacing network engineers, the AI system augments their capabilities. It frees up skilled staff to focus on strategic initiatives and complex problem-solving, while handling routine optimisation tasks.
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