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This report provides insights into the challenges CSPs and their vendor partners will face when deploying AI within the CSP network environment. It also describes best practices that CSPs must bear in mind to manage these challenges.


  • There are several challenges CSPs must face before deploying AI to their network operations: they include lack of AI skills, access to the right data sets, fear of job losses, immaturity of AI systems, and the inability of current IT systems to support AI initiatives.
  • To address these challenges, CSPs should develop an AI strategy that aligns with overall corporate strategy, move AI models quickly into production, develop a robust AI application development lifecycle, and collaborate with peers and industry players.

Features and Benefits

  • Identifies key obstacles encountered by CSPs investing in AI to improve their network operations.
  • Highlights key steps CSPs should bear in mind to succeed with their AI strategies.

Key questions answered

  • What steps should CSPs priortize when implementing AI?
  • How can CSPs remain successful when implementing AI into their network operations?

Table of contents


  • Catalyst
  • Ovum view
  • Key messages


  • Recommendations for CSPs
  • Recommendations for vendors

Challenges with deploying AI use cases for network operations

  • Technology challenges
  • People challenges
  • Process/operations challenges

Best practices to consider when deploying AI in network operations

  • Develop an AI strategy that aligns with overall corporate strategy
  • Set up a plan to move AI models quickly into production
  • Develop a robust AI application development lifecycle
  • Provide access to high-quality data
  • Collaborate with industry players and organizations
  • Select your vendor solutions for AI use cases wisely
  • Explainable AI must be prioritized


  • Methodology
  • Further reading
  • Author

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