Not meeting SLA targets? AI-driven predictive automation could help

A service-level agreement (SLA) is the ultimate kind of promise. It’s your word to your customer, and your business’s reputation rides on consistently following through.

Despite the high-stakes nature of SLAs, failing to meet them is a common problem for IT automation departments. The ripple effects on customer trust and loyalty are significant. 

To improve your SLA performance, it’s essential to investigate why your team is coming up short and develop a strategy for meeting every SLA, no matter how much your business grows or how complex your processes become.

Why do SLAs fail?

There are many reasons why you may not be able to meet your customers’ expectations. We’ll cover a few of the most common ones.

Inadequate tools

Often, the issue begins with not having the appropriate tools — limited communication channels,  insufficient job scheduling software or insufficient systems for predicting and remediating automation issues, for example. There are so many steps that contribute to successful SLA outcomes, and each one must execute perfectly. 

Without proper data analysis tools, it’s almost impossible to identify where a workflow or process went off track and get a clear sense of the scale of your SLA non-compliance. Team members may also be discouraged and less productive: According to a survey by Airtable, employees mainly disengage from a task because it’s too hard to find the data they need to complete the job. 

Lack of visibility

Even when the right tools are in place, you could lack proper visibility into your automated processes if they don’t work well with one another. Disjointed notifications cause sheer overwhelm and make it hard to determine the root cause of SLA failure. Making informed decisions also becomes a challenge.

Information silos

Compartmentalized tools and processes lead to siloed information, which complicates SLA management. Communication between your teams could be fragmented, which delays your response to SLA-related issues. Redundancy can also crop up and waste resources. Plus, each silo might collect and store data differently, skewing your SLA insights. 

The strain of managing SLAs at scale

When you can focus on providing on-time service to just a few customers, it’s possible to address any issues as soon as they appear.  But scale up, and you’re likely to run into roadblocks:

• Data overload: Managing hundreds or thousands of SLAs requires continually collecting and analyzing real-time data. It’s critical to derive meaningful insights to ensure compliance and optimize SLA performance. However, the effort required for extensive data handling and analysis diverts your IT team’s time and brain power away from strategic, value-added work.
• Resource allocation pressure: Only when you apply resources optimally can you feel confident that you won’t miss deadlines or fail to deliver on what your customers expect. Human capital and non-human resources must be distributed wisely to support SLAs, but without the right tools, that ideal distribution won’t be obvious.
• Shifting priorities: As your organizational strategies evolve, so must your SLAs. However, with numerous agreements in place, it’s hard to keep track of which are outdated. Without dynamic monitoring and management systems, your SLAs can quickly become irrelevant and create misunderstandings with customers.
• Stressful compliance tracking: Staying on top of compliance requirements for multiple SLAs can be overwhelming, especially if you’re in a heavily regulated industry. Without efficient tracking mechanisms, it’s easy to overlook milestones that could result in penalties and damaged relationships.

Predictive analytics for SLA optimization

Automation offers a way to overcome these challenges — specifically, workload automation (WLA) technology that’s poised for the artificial intelligence (AI) wave.

WLA software can be your foundational tool for modernizing your tech stack, in turn improving all the factors that contribute to SLA management. Today, WLA solutions are evolving to provide built-in AI features or integrate with AI, such as predictive analytics tools.

With predictive analytics, you can leverage historical data to forecast future events, including SLA issues. The ability to anticipate potential failures and make decisions to prevent them is a competitive differentiator in today’s business climate. Moreover, by analyzing trends and patterns over time, AI tools can signal when an SLA is misaligned with current business objectives or customer needs. 

The combination of predictive modeling and machine learning algorithms can significantly enhance the utility of WLA platforms. From predictive maintenance of systems for avoiding downtime to improved outcome prediction, these emerging technologies offer a future-proofing opportunity for organizations ready to drive efficiency and increase visibility.   

The best automation platforms are those that innovate to enable you to:

• Get an early warning if critical deadlines are predicted to slip, which allows your team to address potential issues before they impact the business. 
• Configure process SLAs and thresholds with customizable escalations and alerts to ensure the right people have the right information at the right time.
• Leverage dynamic scheduling capabilities to ensure your at-risk SLA processes meet their deadlines.

Getting proactive with SLAs using workload automation

There are clear benefits of using WLA enhanced with AI capabilities to improve SLA performance. Here, we’ll look at practical use cases in various industries.

1. Financial institutions: A multinational bank faces an unexpected surge in transaction processing due to a market event, which requires a high volume of record processing jobs to be completed for end-of-day reporting SLAs.
   
   A critical job failure prediction model powered by AI within WLA software identifies potential failures in the job queue that could put SLAs at risk. By alerting operators in advance, the system enables preemptive action to reroute or reprioritize tasks and ensure compliance despite the sudden increase in demand.
2. Healthcare: A large hospital network experiences an overload of patients one day. Although patient intake finishes within SLA parameters, the excess pressure on the system delays the compliance jobs that run overnight. Thus, they do not meet compliance SLAs.
   
   WLA software could prevent this scenario by scheduling and running data updates during off-peak hours to maintain system performance during high-traffic times. The network can supplement its automations with predictive analytics to better allocate resources and keep up with SLAs despite spikes in demand.
3. IT services: A leading IT service provider notices some automations are running slow on a machine with a new version of anti-virus software. They have trouble identifying whether this will create a problem downstream for SLAs.
   
   The team could use WLA to manage software updates and security patches across thousands of endpoints and incorporate AI to anticipate security risks and predict when it’s most efficient to deploy updates. They can rest easy knowing all client systems are up to date and that they’ll be alerted in the event of equipment failure.

Protect your SLA commitments 

Attempting to improve your SLA performance with piecemeal automation tools isn’t an effective long-term answer to the universal SLA problem. Today, smart enterprise teams implement a WLA platform with a centralized portal to monitor automations across their tech stacks.

RunMyJobs by Redwood offers advanced SLA management with automatic alerting for missed milestones, detailed process execution tracking and end-to-end visibility of all business and IT processes.

WLA can act as a catalyst, accelerating your decision-making and scalability and boosting your ability to deliver an exceptional customer experience. Demo RunMyJobs today.