In this episode of the Beyond the Horizon podcast, N-able’s Andrew Burton and Stefan Voss discuss the role of AI and machine learning in backup products for MSPs. They highlight the importance of technician efficiency and the need for tools that can help MSPs do more with less. They specifically focus on the addition of machine learning capabilities to recovery testing in COV and how it improves accuracy and efficiency. They also address concerns about AI adoption and emphasize the importance of responsible use and partnering with the right vendors.

Related asset:

Blog: How, and Why, We Applied Machine Learning to Cove Continuity, Part 1 – https://www.n-able.com/blog/how-and-why-we-applied-machine-learning-to-cove-continuity-part-1

Blog: How, and Why, We Applied Machine Learning to Cove Continuity, Part 2
https://www.n-able.com/blog/how-and-why-we-applied-machine-learning-to-cove-continuity-part-2

Whitepaper: AI for MSPs https://www.n-able.com/resources/ai-for-msps

Key talking points:

• MSPs are looking for AI and machine learning tools to improve technician efficiency and do more with less.
• The addition of machine learning capabilities to recovery testing in backup products can improve accuracy and efficiency.
• AI adoption should be done in a responsible manner, and it is important to partner with vendors who prioritize responsible use.
• Choosing the right partner is crucial when implementing AI and machine learning tools.

Disclaimer: This podcast provides educational information about issues that may be relevant to information technology service providers. Nothing in the podcast should be construed as any recommendation or endorsement by N-able, or as legal or any other advice. The views expressed by guests are their own and their appearance on the podcast does not imply an endorsement of them or any entity they represent. Views and opinions expressed by N-able employees are those of the employees and do not necessarily reflect the view of N-able or its officers and directors. The podcast may also contain forward-looking statements regarding future product plans, functionality, or development efforts that should not be interpreted as a commitment from N-able related to any deliverables or timeframe. All content is based on information available at the time of recording, and N-able has no obligation to update any forward-looking statements.

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