In two years, thanks to the advances of generative AI and ChatGPT, autonomous operations will be a reality

Estimated read time 5 min read

In two years, thanks to the advances of generative AI and ChatGPT, autonomous operations will be a reality

We are at the dawn of the AI revolution. ChatGPT and other large language models (LLM) allow you to interact directly with AI in several common use cases. For the first time, most consumers are seeing that AI can change the world.

Even if many use cases of generative AI are new, it has been contributing significantly to powering the backbone of the internet for consumers for years. Thanks to what is called AIOps, or the AI that is used to improve IT operations, IT teams can optimize the functioning of websites, applications, databases and infrastructure, including the underlying networks. In practice, AI identifies the origin of failures or performance problems to allow IT teams to remedy them. For example, AI can already analyze the root cause of problems by examining logs and indicators. We can also see that, gradually, AI is increasingly being integrated into mitigation strategies aimed at correcting problems affecting IT environments, as they occur.

So far, these AI capabilities applied to computer operations are developed on small custom-made language models trained to meet specific needs, namely the identification and resolution of computer problems. However, with the advent of large language models such as ChatGPT, the technology community is realizing that there are unique use cases that go beyond the most common uses for consumers, including creating content or answering questions.

The OpenAI Codex model is trained on billions of lines of code and would master dozens of programming languages. GitHub® has also launched the Copilot system which is based on the OpenAI Codex model and can create code by listening to natural language prompts, then detect errors while suggesting recommendations to solve them. Forrester® analysts also predict that AI will be able to perform other software engineering tasks, such as generating tests.

More than ever, autonomous operations, during which AI and machine learning (AA) can take over the forecasting and decision-making tasks entirely in place of IT teams to ensure the proper functioning of IT environments, are becoming a reality.

When AI ensures the management of increasingly complex IT environments, in particular of several networks, applications, databases, infrastructures, etc., the future obviously seems exciting, but also worrying. Since autonomous operations will relieve IT team members of a huge burden, the question that is often asked is whether these people will keep their jobs. This concern is certainly understandable but erroneous, and for two reasons. First of all, companies are already experiencing difficulties in hiring enough employees to manage increasingly complex environments. These environments are going to become more and more complicated and many technology professionals cite the acceleration of hybrid computing as the cause of this increased complexity. Then, for overworked IT teams, autonomous operations will only replace simple and uninteresting tasks, rather than their employment.

Just as the printing press has relieved the task of scribes in charge of copying manuscripts by hand, to allow them to devote themselves to their own writings, autonomous operations will free up IT professionals who will thus be able to innovate and create new products while benefiting from new rewarding professional experiences. They will also give them the opportunity to realize the enormous untapped potential due to the endless responsibilities of complying with their service level agreements (or SLAs).

So, what will the autonomous operations consist of? Thanks to a combination of tailor-made models dedicated to computer operations and the extraordinary new capabilities of large language models, autonomous operations will seem very “normal”. While many of us perceive the future with AI as a science fiction universe, with a floating orb endowed with consciousness and infinite knowledge, which answers the questions of developers related to IT, the interactions with the AI of autonomous operations will take place via processes that we already use daily.

Let’s get ready to converse directly with your AI-optimized observability systems via Microsoft Teams® or Slack® to transmit our requests in simple language. IT professionals will simply communicate their SLA contracts and operational requirements via a simple message and the AI platform will automatically execute them. This frees up time for technology professionals and ensures the consistency of the company’s products and services in terms of quality and delivery.

The advantages of autonomous operations do not stop at the management of the IT environment. After performing monitoring and error correction tasks on the technology stack, the AI in the background can also generate information reports on these tasks. She can thus create presentations for the board of directors or customers to present them the performance of the environment and indicate that the company respects its SLA contracts.

Since autonomous operations make it possible to converse in simple language in an interactive way, I really think that they will become part of our daily lives within two years. Thanks to the power of generative AI, users can enter plain text and receive the content they need, namely reports, lines of code or anomaly analysis. This is the paradigm shift that the sector has been waiting for for a long time.

Here are some examples of using standalone operations. SentinelOne® recently launched a new threat detection platform that uses layers of AI technology to ensure security and trigger autonomous, real-time responses to threats and attacks as they occur across the enterprise. This represents a major turnaround in cybersecurity, because AI detects problems and triggers corrective actions and interventions throughout the environment.

Autonomous operations can offer a very high scalability. For example, reports have indicated that IT and cloud teams spend about 44% of their time on routine tasks to ensure that everything is working properly. This is a remarkable potential that will emerge from the additional time that computer scientists will benefit from. Let’s imagine all the possible innovations with this time now available. Autonomous operations will make it possible to optimize speed, agility, scale and security in the IT field, and the resulting impact will be considerable.

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