Trouble with SLA Breaches? AI Can Help

Service Level Agreements (SLAs) are the backbone of any successful IT operation. But meeting SLAs can be a daunting challenge, especially in complex IT environments. Frequent SLA breaches lead to financial loss, reputational damage, and eroded customer trust.

The state of workload automation tools

Workload automation tools and schedulers have long been a staple in IT operations. They’re designed to automate routine tasks, schedule event-driven workflows, and manage batch jobs. But the IT ecosystem has become so complex that an ever-critical tool like this often doesn’t have the perspective needed to provide the observability IT needs today.

IT environments have become more diverse and dynamic. Mergers and acquisitions result in conflicting workload management solutions and staff turnover that creates knowledge gaps in how each solution automates the business. While many companies consider the consolidation of their workload management solutions, they first need to know how all workloads historically operate in order to consolidate successfully. How does IT determine which systems are managing which SLAs? How do they plan for change to ensure no negative business impact?

The complexity, risk, and cost associated with changing workload automation solutions often results in a capitulation to the status quo. Instead of preempting challenges, organizations react to SLA breaches by sitting their experts around a table to solve each problem as it arises. But absorbing the seemingly modest cost of five, ten, or even 20 people spending an hour or two per month putting out fires — potentially 40 hours of labor every month — detracts from the value-added contributions for which those experts were hired: moving the business forward!

If this sounds like a familiar challenge, lets switch from reactive to proactive. And AI can help.

Attended AI versus unattended AI

There are different kinds of AI, and it is important to consider the power and impact of each. No matter which kind we use, we must always remember that AI is a helper, an addition to our team that assists us in completing our work more efficiently.

We like to think of AI in two categories: Attended and Unattended.

• Attended AI is an exciting opportunity for a person to set a few parameters so that the GenAI will create something — code, stories, answers to questions, and more. But the core perspective of this tool is that a person has to ask GenAI to perform a task.
• Unattended AI operates autonomously. It is always working to proactively monitor its area of focus, and then automatically provides results such as an alert, email, or ITSM ticket. This version of AI works without being prompted and, instead, autonomously corrects problems or notifies teams as needed to get involved when there is no clear direction.

How AI can help in achieving SLAs

In the case of workload management, an unattended AI is the best choice for monitoring SLA breaches because it runs autonomously. It addresses SLA breaches by continuously evaluating an entire ecosystem of jobs, finding jobs at the root of a potential breach, and taking corrective action to prevent the breach. What is even more powerful is that a well-trained AI can perform these corrective actions without any human intervention.

Here are some AI capabilities that free your team up for more value-added tasks:

Predictive analytics: AI can analyze historical data to identify patterns and predict SLA misses before they breach. This capability empowers IT teams to proactively address potential issues, preventing service disruptions and ensuring a faster mean time to resolution (MTTR).

Intelligent workload scheduling: AI can consider factors like resource availability, dependencies, and SLAs while optimizing workload scheduling and reducing the risk of delays and bottlenecks. Its comprehensive analysis of various factors enables IT teams to shift their attention from routine scheduling tasks to value-driven projects.

Root cause analysis: AI can help pinpoint the root cause of new challenges, enabling faster resolution and preventing reoccurrence.

Observability and predictability: With AI and ML, you gain insights into your workload ecosystem that were previously impossible to obtain. By analyzing historical data and real-time information, AI can identify patterns and trends that lead to more efficient SLA compliance.

From siloed systems to observability

For effective workload management, AI must oversee the entire ecosystem. Whether managing mainframes, open-systems, or multiple workload management platforms, AI provides essential visibility and true observability. At Rocket Software, we implement an agnostic solution, meaning it talks to our solutions and others that may be in in the ecosystem. Rocket^®AI Predictive Pulse is an unattended AI that can be added to any of Rocket Software’s WLM solutions and build a cohesive blueprint across all of your automation solutions.

In a bid to respond faster and work smarter, customers are increasingly turning to AI for unparalleled efficiency in workload management. Here are some results AI Predictive Pulse users are achieving:

• Faster responses with an average 4 hours look-ahead time for missed SLAs
• Reduced time to plan for changes from weeks to hours
• Predictions of delays from batch jobs with 90% accuracy

Learn more

To understand more about how AI is helping customers, download our datasheet here or request a demo.