The Journey to Autonomic IT: Why Enterprises Must Let Go to Learn 

Several of our recent blog posts have introduced the characteristics of each phase of the Autonomic IT maturity model, from Siloed IT to Coordinated IT (an essential foundation for Autonomic IT) and the transition to Machine-Assisted IT and AI-Advised IT.  

We explored how you can identify where your organization stands on this transformative journey, why you might not be as far along as you believe, and what is needed to advance your journey.  

Now we arrive at IT nirvana: Phase 5, Autonomic IT.  

The Ultimate Destination for Tech Leaders

Autonomic IT represents the pinnacle of IT evolution, offering a tantalizing vision for CIOs, CTOs, and technologists alike. In this futuristic landscape, machines do more than just identify issues and alert teams. They continually learn from logs, incidents, and the actions of engineers, aggregating data from across hybrid IT and making data-driven decisions with unprecedented accuracy, creating a self-optimizing data fabric that’s so comprehensive and intelligent that it can: 

• Perform automatic root cause analysis when incidents occur. 

• Identify unknown unknowns before they cause incidents. 

• Make intelligent, independent decisions to resolve complex issues in microseconds. 

• Continuously self-heal and self-optimize. 

• Deliver an always-available, always response, always seamless self-managing IT environment. 

But Autonomic IT is more than just a framework in which machines fix themselves. It’s a paradigm shift that promises highly efficient, automated processes, lower total cost of ownership (TCO), and more time to innovate and explore new revenue opportunities. 

A Self-Sustaining IT Ecosystem

At its core, Autonomic IT is an ecosystem of technology capabilities – AI, ML, predictive analytics, and automation – that work in harmony to detect anomalies in the IT estate, analyze patterns to anticipate potential issues in advance, and resolve the problem autonomously – even going as far as switching systems and automatically replacing them.  

Picture this scenario: AI/ML detects that an application database is at risk of failure. It then uses financial models to calculate potential outage costs. Instead of waiting for the system to fail, it procures new hardware, installs it automatically, and seamlessly updates the environment to monitor the new database. 

While we’re still a few years away from realizing this visionary state, the ScienceLogic AI Platform enables AI-advised IT today. Skylar AI – our suite of advanced AI capabilities – has already introduced a new industry paradigm. Skylar delivers accurate predictions, tailored recommendations, and intelligent automations that drive business efficiency and innovation. 

Skylar AI harnesses the power of generative AI and unsupervised ML combined with human-in-the-loop automation training models to revolutionize IT operations. By automating complex troubleshooting tasks, Skylar unburdens human experts from spending time on routine operational tasks, freeing up more time for innovation.  

The Skylar AI suite and the ScienceLogic AI Platform deliver the essential building blocks of Autonomic IT. But technology alone won’t propel organizations to Autonomic IT. Periods of technological advancement can be tumultuous. New technologies have the power to change jobs and industries, and AI has its share of critics. Just as some people are hesitant to trust autonomous vehicles to travel long distances without human intervention, IT teams may also be reluctant to relinquish control of their systems to machines. 

For these reasons, the journey to Autonomic IT will also require growth as humans.  

Realizing Autonomic IT: Letting Go of the Wheel

The journey to achieving Autonomic IT is well within our technological capabilities today. In fact, technology is already advancing more rapidly than human readiness. The real challenge lies in building trust and embracing new ways of doing things.  

To fully embrace Autonomic IT, significant organizational change and education are required. Individuals need to be open to learning, investing time, trusting technology to do its job, and to letting go. 

Learning to let go doesn’t mean engineers will lose control. Just as drivers have control of their choices when operating an autonomous vehicle, in Autonomic IT, ITOps teams will continue to guide the AI, stay engaged, and remain vigilant. And this brings added benefits. Instead of reacting to every alert and incident around them, the shift from human-driven to machine-driven IT enables engineers to step back and see the big picture.  

With routine tasks automated, they can broaden their awareness of the entire IT environment, bring a better understanding of context, improve decision-making (without top-down direction), be more strategic, and ensure better customer experiences.   

And, as engineers shift their focus and trust more over time, they can incorporate more machine-learned information, let go a little more, and facilitate even greater automation. 

Ultimately, the journey to Autonomic IT is about trusting the process and the technology – monitoring and maintaining it attentively – but not letting it overwhelm you. It’s about letting go to learn.  

With ScienceLogic, Autonomic IT is Within Reach

It takes a lot of growth for humans to realize true Autonomic IT. But with ScienceLogic, its precursor, Phase 4: AI-Advised IT – and its promise of near-zero outages – is already within reach for many organizations.   

We’ve harnessed the power of familiar (yet siloed) automation tools that engineers have used for decades and consolidated them into a modern, centralized platform that unlocks automation and applies ML/AI to fix complex problems at scale across hundreds of machines. Essentially, we’ve taken what every engineer already knows and automated it.  

Next comes refinement. In true Autonomic IT, organizations have reached a stage where they have fine-tuned their technology to the point that they have zero outages. Now, they can turn their sights to focus on using autonomic processes and AI to deliver next-level self-optimization capabilities that drive sustainability and waste reduction and new cost efficiencies across the business.  

Manage the Transition to Autonomic IT

New technologies, like AI and ML, are revolutionizing how work is done across industries. Yet, the trajectory of AI depends on how organizations choose to manage it. To be successful, they must adopt new technology policies and effectively implement change management across people and processes. 

ScienceLogic enables this transformation. Our Advisory Services team assists people at all levels in preparing for and navigating the transition to Autonomic IT, with compounding benefits at every step of the way. 

To help you understand your current position and outline a roadmap for progressing to Autonomic IT, contact us today. Our Advisory Services team is here to help.