Dynatrace today revealed it has added generative artificial intelligence (AI) capabilities to its Davis engine it uses to apply machine learning algorithms to its observability platform.
Steve Tack, senior vice president for product management at Dynatrace, said that hypermodal approach to AI will allow DevOps teams to take advantage of probabilistic AI capabilities provided by Davis CoPilot that are enabled by large language models (LLMs) originally developed by OpenAI.
The generative AI capabilities effectively provide DevOps teams with a tool that surfaces workflow recommendations via a natural language interface. The recommendations are based on historical data such as sales and customer experience trends, seasonality and cloud application health. It is also able to analyze dependencies across large sets of data while retaining context accessed via the Dynatrace Grail data lakehouse and the Dynatrace Smartscape application visualization and mapping framework.
Dynatrace is now making a case for a multimodal AI platform that uses a common set of metrics, traces, logs, user behavior and topology to enable machine learning algorithms to predict anomalies and causal-AI dependencies to determine the root cause of an IT issue and then leverage generative AI to provide recommendations to resolve it. The company is forecasting IT teams will achieve 10% to 20% productivity improvements year-over-year as a result.
It’s too early to determine the full extent to which a multimodal approach to AI will transform IT management, but it’s clear that as IT operation teams become more productive, more time will be freed up for innovation, noted Tack. Many of the manual tasks that IT teams still need to perform divert time away from building and deploying additional applications, he added.
Naturally, there’s a lot of speculation concerning the impact AI will have on DevOps. Some contend it’s only a matter of time before the entire software engineering process is automated. As those advances are made, the cost of building applications will effectively drop to zero. Others argue that AI will augment DevOps teams in a way that makes it simpler to manage IT at scale using either the same number or possibly even fewer engineers.
The one thing that is certain is that AI will soon be pervasive, which will drive the adoption of automation frameworks as organizations look to operationalize the insights surfaced by AI platforms. As rich as the insights any AI model surfaces might be, they only provide real value when integrated with an automation framework, noted Tack.
Ultimately, most DevOps professionals will not want to work for organizations that don’t provide them with AI tools that make it more likely for them to be successful. Organizations that embrace AI may not have a sustainable competitive advantage when every other organization is doing the same, but those that don’t will soon be left behind. Those that succeed will benefit from managing application environments at a previously unprecedented level of scale at minimal additional cost. The challenge is determining which tasks will be automated with an eye toward planning how DevOps roles will need to evolve.
