- For managed service providers (MSPs) to remain competitive during COVID, they must offer more compelling value beyond low-cost device management.
According to this article in MSP Insights, although the IT industry remains strong, the pandemic is forcing companies to scrutinize their tools and technology to better serve their customers. This shift is putting MSPs under pressure from low-cost competitors: value-added resellers are moving rapidly to introduce managed service offerings, while global systems integrators are beginning to target smaller enterprise customers outside the Fortune 1000 — with the economies of scale to squeeze MSP margins.
So how can MSPs better differentiate their services? By refreshing their monitoring systems to include an AIOps-centric, business services approach. Instead of selling services based on a “price per managed device,” MSPs can now move the discussion to one centered on business outcomes — such as ensuring the customer’s eCommerce platform is always on, or reducing the impact of application downtime on customer service, or ensuring less downtime for key business services such as email, telephony, or accounting.
- AIOps could save you millions a year in change management.
According to this article in DZone, while making actual software changes can take a few hours, getting approval to go live can take almost two weeks. Instead, with the help of AIOps, the next time someone asks for the status of a bug fix, the answer would be: “we’ve just made the change, the change request was automatically approved, and the estimated go-live time is 10 minutes. You will get a notification on your phone when it’s ready to test in prod, as you are a pilot user.”
In order to get millions of changes approved and deployed to production safely in a year, change management needs to be automated, federated, and delegated to delivery teams for the majority of changes, while there also needs to be a good level of audit and traceability.
- The value of artificial intelligence and machine learning to improve IT infrastructure is just beginning to be realized.
According to this article in Inside Big Data, there is a big opportunity for organizations around IT infrastructure monitoring and workload planning for self-healing, self-optimizing, and self-protecting automation.
While workload planning is very hard to do in storage due to a storage environment’s need for new requirements every day, infrastructure admins can anticipate those requirements with planning and tuning. This can take hours, which can lead to underutilization or overuse. But machine learning models can predict load and capacity, finding the ideal locations for workloads and minimizing risk. Machine learning enables organizations to forecast and simulate the impact of changing components on load and capacity.
- AIOps restructures the modern IT ecosystems to leverage the best in artificial intelligence and machine learning for business transformation.
According to this article in Analytics Insights, the explosion of big data happening across enterprises is an excellent opportunity to drive intelligence, automation, effectiveness, and productivity with AIOps—freeing enterprise IT operations by inputs of operational data to achieve the ultimate data automation goals.
AIOps bridges the gap between ITSM, performance management, and automation within the IT eco-system to accomplish the continuous goal of ITOps improvements. And AIOps creates a game plan that delivers within new, accelerated IT environments, to identify patterns in monitoring, service desk, capacity addition, and data automation across hybrid, on-premises and multi-cloud environments
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