AI might be everywhere these days, promising efficiency and automation, but not all AI solutions are suitable for MSPs. Generic AI tools often fall short of the precise data accuracy that MSPs need to effectively manage their support operations.
When you ask generic generative AI to classify, prioritize, or estimate the budget for tickets, you’ll always get an answer, but there’s a catch. That answer is frequently incorrect. Typical accuracy rates for off the shelf AI hover around 50% or even less. 50% is a failing grade! If you’re managing critical customer support workflows, that means half of your automations could be steering you in the wrong direction.
For MSPs, inaccurate ticket classification or prioritization isn’t just an inconvenience; it creates real problems. Incorrect tickets slow down resolution times, increase escalations unnecessarily, and lead to frustrated customers. Misjudging budgets can also disrupt your resource planning and directly hurt your profitability.
That’s why it’s essential to rely on AI solutions built specifically from your own operational history. CrushBank leverages machine learning based on your company’s actual ticket history, ensuring accuracy levels consistently hit between 90% and 95% or better.
When CrushBank utilizes your historical data, your automation triggers, ticket classifications, prioritizations, and budgeting estimates become dependable. This accuracy drastically cuts down operational disruptions, improves customer satisfaction, and streamlines your overall efficiency.
Don’t settle for the “coin toss” accuracy of generic AI. Boost your business with reliable and precise results by putting your own data to work with CrushBank.