Generative AI offers transformative potential and many MSPs have been trying it out, but Gartner predicts that 30 percent of these projects will be abandoned after the proof-of-concept stage by the end of 2025. For MSPs, adopting generative AI can enhance service delivery and streamline operations but some pilots with public AI systems have been unsuccessful. CrushBank addresses the common challenges faced by MSPs by leveraging a private data lake built from its own data and integrating IBM watsonx retrieval-augmented generation to improve response accuracy.
The Allure and Reality of Generative AI for MSPs
Generative AI can revolutionize the way MSPs operate, from accelerating customer support to optimizing client communication about the status of issues. However, many projects face technical complexity, data quality issues, and integration challenges. Gartner’s report highlights these obstacles, emphasizing the need for strategic planning and robust data management. Often, the software system that MSPs use to run their support have had AI bolted on, but the results have mixed, particularly because as these public models are not able to access the MSPs own data and history. This has been especially true when trying to classify support tickets using public AI.
A Different Approach
CrushBank’s private data lake is built by continuous ingestion of the MSPs own data from across all their systems. By using the MSP’s own proprietary data, CrushBank reduces risks associated with external data sources and brings real client information into the interactions. The integration of IBM watsonx retrieval-augmented generation further improves the accuracy and relevance of AI responses, directly benefiting MSP operations. CrushBank Neuro uses conversational search and retrieval-augmented generation to answer IT support questions from the MSP’s own data in the data lake. It leverages the data lake to provide precise, contextually relevant responses, enhancing the efficiency and effectiveness of IT support. The same data can be used to summarize, classify and budget tickets based on the MSP’s own model and desired framework.
Addressing Key Challenges for MSPs
Simplified Technical Processes: The private data lake streamlines data ingestion, reducing technical complexity and speeding deployment.
Staff Turnover: By bringing all of the company’s information into one accessible place, technicians have a greater sense of achievement as they escalate fewer tickets and get more done themselves. It also makes for much faster on-boarding for new hires, as they don’t have to keep asking for help.
Enhanced IT Support: CrushBank Neuro utilizes the data lake to offer conversational search capabilities, allowing IT support teams to quickly find accurate answers using their own data.
Conclusion
Generative AI’s potential is immense for MSPs, offering opportunities to enhance service quality, improve operational efficiency, and drive innovation. However, success requires addressing key challenges. CrushBank’s private data lake, IBM watsonx integration, and CrushBank Neuro provide a robust solution, ensuring high-quality data and accurate AI responses tailored to the needs of MSPs. This strategic approach not only mitigates risks but also sets a foundation for sustainable and impactful AI-driven innovation in the managed services sector.