Understanding AI Error Rates: Best Practices for MSPs in Workflow Automation and Project Management with Mike Psenka

Business of Tech: Daily 10-Minute IT Services Insights

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8d ago 17:05

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Mike Psenka, CEO of Moovila, discusses the limitations and challenges of large foundational AI models, particularly in the context of their application in complex tasks. While these models have shown impressive capabilities, there is a growing concern about their reliability, especially when it comes to analyzing intricate data structures like graph data. Psenka emphasizes that these models excel at generating content but struggle with tasks that require deep understanding and analysis of relationships and dependencies within data.

The conversation highlights the importance of recognizing the error rates associated with AI models. Psenka points out that while advancements have been made in areas like 2D and 3D vision, certain domains, such as graph data, still exhibit unacceptably high error rates. This limitation raises questions about the trustworthiness of AI in critical applications, where a single mistake can lead to significant consequences. He urges caution in relying solely on generative AI for complex evaluations and suggests a hybrid approach that combines traditional deterministic AI methods with generative models.

As the discussion progresses, Psenka shares insights on the emerging trend of agentic AI, which aims to automate decision-making processes. He argues that while agentic AI can handle small, discrete tasks effectively, it falls short in managing complex workflows that require nuanced understanding. The need for a structured framework that integrates both deterministic and generative AI is emphasized, allowing for more reliable outcomes in complex scenarios. This hybrid model could help mitigate the risks associated with AI errors and improve overall efficiency.

Finally, Psenka offers guidance for managed service providers (MSPs) and IT service providers on workflow automation. He stresses the importance of identifying which processes can be automated while ensuring that human oversight remains integral to the workflow. By developing flexible templates that can adapt to various customer needs, MSPs can enhance their service offerings and improve operational efficiency. The conversation concludes with a call to action for organizations to build AI capabilities and stay competitive in a rapidly evolving landscape.

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