In previous blog posts, we introduced the Autonomic IT maturity model and discussed the characteristics of the early stages of that journey, progressing from “Siloed IT” to “Coordinated IT” and then to “Machine-Assisted IT.”
Wherever your organization is on that journey, there is likely still work to be done. In fact, most organizations are currently in Phase 1 or 2 of their Autonomic IT journey and still struggle with disparate tools, fragmented data, and isolated teams. Some have already begun addressing these issues and laying the groundwork for future phases of Autonomic IT by merging and standardizing data from across the IT systems for better observability, more meaningful analysis, and effective troubleshooting and remediation.
Now we reach Phase 4, the penultimate phase of the Autonomic IT journey. In this phase – which we call “AI-Advised IT” – organizations embrace advanced AI and machine learning (ML) to empower their engineers to handle tasks more efficiently, liberating the IT function, lowering costs, and propelling businesses forward toward elevated performance and greater potential.
Let’s look at the characteristics of Phase 4 and how and how the ScienceLogic AI platform and Skylar AI suite support them.
Transitioning to Phase 4: AI-Advised IT
To understand Phase 4 of the Autonomic IT journey, it’s important to grasp the concept of AI-Advised IT and how it differs from Machine-Assisted IT (Phase 3).
In Phase 3, organizations leverage ScienceLogic’s powerful machine learning capabilities to further advance the progress achieved in Phase 2. They utilize centralized and contextualized data from all areas of their IT infrastructure (including events, logs, and other relevant data points) to train machines in recognizing patterns and repetitive workflows.
These insights are then used to perform automated actions that free engineers from time-consuming, highly manual troubleshooting and incident response tasks such as root cause analysis, CMDB data population and synching, ticket creation and enrichment, and more.
Phase 4 is when AI really shines. During this phase, AI provides accurate, automated guidance – including critical insights into root cause analysis, predictive analytics, and real-time recommendations (presented in human-friendly language through Skylar Advisor), so engineers can optimize operations and proactively anticipate and prevent issues.
This is not about machines or AI replacing humans; it’s about helping users at all skill levels swiftly resolve complex IT challenges. For instance, with Skylar AI’s plain language recommendations, lower-level engineers are empowered to tackle issues – at the rate of millions of triage events per day – freeing Level 3 engineers to focus on innovation. In addition, over time, the future of IT jobs will change and evolve as Level 1 and 2 engineers become more skilled and capable of handling Level 3 work.
The results speak for themselves. A Forrester Total Impact™ Study found that AI and automation can help organizations realize $1.2 million in saved effort and shave 20,100 hours off the time that IT support analysts spend on unnecessary trouble tickets.
Going Beyond AIOps
Organizations are already starting to leverage AI in ITOps – aka AIOps – but ScienceLogic is going a step further than current AIOps solutions.
By making IT autonomic, ScienceLogic establishes a self-empowered operational environment that monitors, optimizes, and heals technology investments while it runs (we’ll get to that in Phase 5). It replaces manual cycles with real-time data capture, analytics insights, and AI and ML techniques to transform how IT issues are recognized, understood, and remediated.
Autonomic IT delivers an IT environment that can understand its real-time state and offer intelligent insights and recommendations. It is designed to autonomously restore and optimize operations, leveraging generative to create a more user-friendly IT experience.
Where Does Your Organization Fall on the Journey to AI-Advised IT?
Assessing your progress on the Autonomic IT maturity model is crucial for planning the advancements you must make. Here are some basic questions to consider:
- Have you achieved automated issue identification, remediation recommendations, and initiated actions?
- Are you using generative AI to advise lower-level engineers on how to resolve issues previously requiring top-tier expertise?
- Are you leveraging Autonomic IT to go beyond traditional operations, provide clear fix recommendations, and enable automated resolution?
To help you understand your current position and outline a roadmap for progressing to AI-Advised IT and beyond, contact us today. Our Advisory Services team is here to help.
