About this video

• Video Title: AI Boom or Bust? Rethinking Roles, Outcomes, and AI's Impact on MSPs: Session 1
• Channel: NinjaOne
• Speakers: Jonathan Crowe, Alex Farling, Elliot Heyman, Neil Yenni, May Jane, Peter Melby, Stephen Frein
• Duration: 01:26:53

Overview

This video is the first session of a series discussing the impact of AI on Managed Service Providers (MSPs). It features a panel of industry leaders from companies like Lyra Technology Group, Shield Technology, Ntiva, and New Charter Technologies. The discussion focuses on the current state of AI adoption within MSPs, client expectations versus MSP-led initiatives, the hype versus reality of AI, and how MSPs are integrating AI into their operations and service offerings. The speakers share their experiences, strategies, and insights on build versus buy decisions, organizational changes, and the future role of AI in the MSP landscape.

Key takeaways

• Client Demand vs. MSP Initiative: The majority of MSPs are leading the AI conversation with clients, as most clients are aware of AI but lack a clear understanding of how to implement it in their businesses.
• "Build vs. Buy" Dilemma: MSPs face a choice between building their own AI solutions or buying from vendors. Large MSPs are leveraging internal resources and acquisitions to develop custom solutions, while vendors often cater to the lowest common denominator.
• Orchestrating Innovation: MSPs are increasingly taking ownership of orchestrating their future, rather than solely relying on vendors for innovation, especially during periods of rapid technological disruption.
• Organizational Impact and Upskilling: AI adoption necessitates organizational changes, with a focus on upskilling existing staff and hiring for soft skills and customer service rather than just technical expertise. The goal is to automate lower-level tasks, allowing technical talent to focus on more complex and impactful work.
• Monetization and Value Creation: The pricing and monetization of AI services are evolving. While recurring revenue is a preferred model, the early stages of AI adoption emphasize consulting and delivering tangible business value to clients, with higher margins possible due to the current scarcity of AI expertise.

Clients are primarily concerned about understanding how to implement AI in their businesses and what specific applications can provide value. They are aware of the AI phenomenon, often through media like the Wall Street Journal, but lack practical knowledge.

MSPs are addressing this by:

• Educating clients: Acting as educators to help clients understand AI and its potential.
• Leading conversations: Proactively discussing AI with clients rather than waiting for client-initiated requests.
• Developing AI readiness programs: Offering assessments and structured programs to evaluate a client's AI readiness and identify use cases relevant to their business challenges.
• Providing consultative services: Offering bespoke solutions and guidance, acting as trusted advisors to help clients navigate the complexities of AI implementation and data security.

Yes, the transcript provides actionable intel on how MSPs are using AI internally and what was required.

Here are some key points with links to highlights:

• Ntiva's "Natalie AI":

  • What it is: An internal AI engine developed by Ntiva named "Natalie" (Artificial Thought and Language Intelligence engine).
  • Internal Use: It's used for processing tickets, prioritizing tasks, providing troubleshooting steps, and performing sentiment analysis.
  • Impact on Roles: Ntiva's goal is to automate Tier 1 and Tier 2 level work through generative AI, freeing up technical talent to focus on higher-level tasks that only they can do, allowing them to grow faster. They aim to eliminate the need for traditional Tier 1 roles by the second half of next year, focusing on upskilling existing staff and hiring for soft skills and customer service.
  • What was required:
    • Internal Challenge & Play: Challenging their team to explore and leverage generative AI tools to make jobs easier, more efficient, and effective. This started about two years ago.
    • Self-Education: Initial exposure was through simple actions like selling Copilot licenses, but they moved to self-education via YouTube and reading to gain deeper knowledge.
    • Team Involvement: Initially, the AI tool was deployed top-down, causing fear among techs. Ntiva paused the rollout and involved the team in the design process, emphasizing the "why" and how it aligned with organizational goals. This led to better adoption.
    • Focus on Purpose: Reinforcing the company's purpose of "growing people" and using technology as an accelerator.
    • Overcoming "Build vs. Buy" Hesitation: They initially paused their internal development to investigate vendor solutions (like ConnectWise's RPA/Sidekick), but found vendor offerings lacking in actionability and cost-effectiveness for their specific needs, leading them back to their internal build.
  • Highlight: [01:00:10] "Natalie was rocking and rolling, doing lots of amazing things, processing tickets, prioritizing, giving the last troubleshooting steps, doing sentiment analysis."
  • Highlight: [01:08:44] "our goal is to not need tier one texts by the second half of next year. I'd like to do it sooner. And that does not mean we are exiting tier one texts from our organization. In fact, we are already focusing on upskilling..."

• Lyra Technology Group's Approach:

  • Internal Use: While not detailing a specific internal tool like "Natalie," Lyra focuses on empowering their operating businesses to adopt AI. They've spent significant time on-site with their portfolio companies asking about AI implementation and understanding client needs.
  • What was required:
    • Education and Awareness: Starting the conversation internally and externally, ensuring their teams know how to "spell AI" and are positioned as knowledgeable advisors.
    • Focus on Customer Success Function: Looping AI initiatives under the customer success function, drawing parallels to the adoption curves of security and cloud.
    • Recurring Solutions: Aiming to build repeatable and scalable AI solutions rather than purely bespoke ones, with a goal of creating an "agent store" where common AI tasks can be customized to client systems.
    • Agility: Emphasizing that smaller organizations have an advantage in agility and can experiment more freely than larger, established ones.
  • Highlight: [28:39] "start the conversation our job is to be first educators um and to make sure that we are you know that uh our customers know that we know how to spell AI"

• Shield Technology's Strategy:

  • Internal Use: Shield embeds engineers into their partner MSPs to develop AI solutions and software that the entire portfolio can leverage. This implies internal development and sharing of AI capabilities across their acquired companies.
  • What was required:
    • Centralized Resources: Utilizing funding and expertise from investors (like Thrive, an investor in OpenAI) to bring in specialized talent and embedded technologists.
    • Building Best-in-Class Software: Shield's mandate is to build the best-in-class AI software platform for MSPs, rather than relying solely on existing vendor products.
    • Partnership Model: A partnership-based approach where they offer engineers and talent to MSPs, working with them rather than pushing solutions.
  • Highlight: [36:23] "It's not our vendor's job. Um, and so it's our job to put the puzzle pieces together. And the vendors are really good whenever there's disruption and you know new things that need to be innovated and they see market opportunity they say here's a puzzle piece..." (This highlights their proactive stance on innovation).
  • Highlight: [33:40] "the shield mandate has actually crystallized to to not just be providing the best customer service to our partners but actually building the bestin-class software platform for MSPs period"

• New Charter Technologies' Approach:

  • Internal Use: New Charter acquired an AI startup six months prior to the discussion to integrate AI as core internal technology. Their focus is on owning the next 18 months of innovation.
  • What was required:
    • Acquisition: Purchasing an AI startup to gain internal AI capabilities.
    • Owning Innovation: Taking control of their own innovation roadmap to ensure they can adapt and lead rather than be disrupted by external forces.
    • Internal Core Technology: Treating the acquired AI platform as internal technology, not a product to be resold externally, allowing for focused development.
    • Agility over Scale: While having significant advantages from their size, they acknowledge that smaller organizations have greater agility to experiment.
  • Highlight: [37:39] "So six months ago we acquired um an a an AI startup and it was a startup that was initially aimed at the broad industry and focused on that..."
  • Highlight: [39:20] "So it's this rolling, you know, flywheel that we have to keep owning in that. And so owning our own team in that is is critical to our ability to to just keep our eyes to the future, you know, of especially what clients need."

In summary, internal AI adoption requires a willingness to experiment, invest time in self-education, involve the existing team, and potentially acquire or build specialized capabilities. The focus is shifting from simply fixing computers to becoming trusted advisors and innovators, automating routine tasks to elevate technical talent and create new service opportunities.