Automation Before AI

Software engineers are experts at managing shifting paradigms. Within the last few years, they managed the infrastructure shift to remote work which entailed enormous digital infrastructure upgrading and maintenance over a short time. Through this shift, they continued to incorporate new cloud technologies to support evolving team needs and customer demand. 

However, as infrastructure complexity grows so does the risk of disruptions, rising 13% last year. Companies without strong operational maturity often learn about issues from customers directly. This leads to a reactive approach where support needs to manually inform engineering teams of reported service degradations, which in turn delays resolution. 

With AI increasingly in demand, bringing new services online is like running before learning to walk. Without automation to support the engineering team in managing operations, there isn’t the bandwidth to safely and securely manage AI for the enterprise at the necessary speed and reliability.

Abstract the Toil

Engineers must be able to manage break-fix emergencies. Removing noise and toil via automation allows them to devote themselves to the strategic operations required to ensure AI services get the attention needed.

Automating some of the work of digital operations means engineering teams can acknowledge incidents and mobilize responders faster, resolving incidents quicker for fewer hours of downtime. It’s something that can be taken on at higher levels of digital operations maturity, as well as something that can help achieve higher levels.

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The digital operations maturity model, moving from manual to reactive, responsive, proactive and preventative, maps the stages of moving towards a state of reliable consistent customer experience. Preventative teams use predictive issue remediation machine learning insights and can forecast the future impact of any planned changes. They have highly automated processes that eliminate escalations and engineering toil, meaning they can embrace a culture of continuous learning, improvement and prevention. Automation also offers an opportunity to encode better practices.

Build on Automation

Many organizations have been building data-intensive features for some time and aren’t starting from zero with generative AI. A strong data architecture foundation is critical to moving quickly with AI and effectively harnessing LLMs. Storage, often a data lake, scalability accommodating variable workloads and a well-designed API layer allow seamless LLM integration. Comprehensive automated monitoring, logging and cost management systems help maintain infrastructure health and optimize expenses.

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Artificial Intelligence for IT Operations (AIOPs) can supercharge IT operations by bringing together data from a variety of sources across an environment and consolidating it into consumable forms. Automating the toil from the response process in this way requires a workflow. Incoming data consolidates into one engine. That deduplicates events and adds additional context to normalize the information. 

Non-relevant alerts are suppressed or paused, while related alerts are grouped into a single incident and routed to the correct team. From there, machine learning provides triage context on the incident and automation sequences can kick off, pulling diagnostic information or even resolving incidents. Without AI, this tracking down of context and cleaning up data, let alone triaging, would be a highly manual process consuming precious hours better spent delivering. 

Be Mature: Automate Before AI

Reactivity is key, making engineering talent vital in an AI service culture. Without automation that talent will be squandered, losing time and cognitive attention and failing to deliver the services that power growth and trust.