MSPs' Secret Weapon: AI-Powered Observability

By Chad Reese

Chad Reese

The IT environments of modern enterprises have become increasingly dynamic and complex. Cloud migration, digital transformation and distributed, remote workforces accessing company applications and data from anywhere in the world have all contributed to this complexity.

Hybrid IT and the need to run applications and workloads across cloud and on-premises infrastructure is challenging alone. Add to that significant budgetary constraints, and companies struggle to manage their environments effectively.

IT teams’ pain points in managing this complexity significantly impact the bottom line. A SolarWinds survey found that 75% of tech professionals said the return on investment (ROI) was impacted during an IT project they oversaw due to increased IT complexity.

The result is that companies are increasingly turning to managed service providers (MSPs) to help design and manage their IT environments. The MSP market was valued at more than $267 billion in 2022 and “is expected to grow at a compound annual growth rate (CAGR) of 13.6% from 2023 to 2030.”

The reality is that MSPs must overcome the same challenges their customers are turning to them to address. As MSPs manage increasingly complex IT environments for their customers, the potential for downtime goes up, and their ability to maintain their SLAs goes down. Because companies hire MSPs to deal with IT issues quickly and effectively, this increased complexity is a significant risk to any MSP unable to rise to the challenge.

Reasons Behind AI-Powered Observability’s Growth

To continue serving existing customers and scale to grow, MSPs are increasingly turning to AI-powered observability solutions. Here are the top five reasons AI-powered observability solutions are becoming a core component of MSP offerings this year.

AI-powered observability solutions use machine learning algorithms to continuously monitor and analyze large amounts of data from various sources, such as servers, applications, networks and databases. The algorithms can quickly detect anomalies or unusual patterns that may indicate a problem or potential issue, even in complex and dynamic environments, enabling MSPs to proactively address issues before they escalate into major incidents or downtime. This increased efficiency is critical for MSPs looking to maximize profit.

Automated remediation actions also can adjust configurations, restart services or perform proactive measures to prevent potential issues. By automating remediation actions, MSPs can significantly reduce the mean time to resolution (MTTR) for incidents. The MTTR is the time elapsed between detecting an incident and resolving it. A shorter MTTR means incidents are resolved faster, which leads to less downtime, fewer disruptions and improved overall system performance.

Automating remediation actions can reduce the operations team’s workload, as the AI algorithms can perform routine tasks automatically. This allows MSPs to focus on more critical and complex tasks, such as analyzing data trends, identifying patterns and developing predictive models.

AI-powered observability solutions offer MSPs advanced capabilities and improve overall efficiency. This supports MSPs in addressing the complexities of the modern IT landscape. By harnessing the potential of AI-driven observability, MSPs can take proactive measures to address potential issues before they escalate into major incidents or downtime. These advancements also allow MSPs to focus on strategic initiatives and drive innovation, unlocking new opportunities for growth in an increasingly competitive market.

Chad Reese is president of Americas sales and global channel at SolarWinds. A technology industry veteran with 25 years of experience serving in leadership positions at IBM and, most recently, VMware, Reese leads sales at SolarWinds in the Americas and is responsible for the company’s global channel ecosystem. You may follow him on LinkedIn or @solarwinds on Twitter.