AI at scale

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