Driving operational excellence with AIOps

AIOps-enabled automation: Making an impact on the bottom line 

• How to optimize network and service performance through intelligent assurance and AI in a timely manner
• Incorporating legacy technology with AI/ML capabilities
• Developing self-learning algorithms to automatically detect anomalies in near real-time and resolve issues more quickly
• Using AI systems to prioritize responses based on the quantity and importance of customers

Ways of working and implementing AI to achieve operational excellence 

• Driving operational excellence, customer experience, and profitability with ML (machine learning)
• The challenges with data literacy and data management and how to overcome them
• Engaging different stakeholders across the organization to become data-driven
• Overcoming the ‘trust in AI’ issue

AIOps-enabled automation – Making the case for wider adoption   

• Establishing the business case for AIOps is critical for cost reduction and monetizing services
• Adoption and real-world implementation of AIOps
• Where can AI be usefully introduced and how?
• Why new technologies and autonomous networks are major drivers of AIOps
• Sharing the benefits of automated networks enabled by AIOps
• What are the challenges of implementing AIOps?

AIOps for your business and the maze to navigate 

• Investment in AI is expensive - How to exploit AI safely and properly
• What are the risks in scaling AIOps?
• How to exploit the full potential of AI and deliver growth
• Closing the gap between traditional operations and AI software to enable AI-driven operations
• How to plan and prepare your AI products and services