The rollout of new technology can come with hesitation and even fear, and generative AI is no different. The New York Times in February 2023 dubbed the popularity of ChatGPT a “phenomenon” that started an AI arms race – one that wasn’t exactly welcomed by news writers, marketing agencies, or business leaders.
After all, generative AI was blamed for CNET’s recent controversy, in which the publication’s staff said it had quietly used AI for months to publish articles that were pained with factual error. CNET has worked to clear things up since the public outcry, but about 50 percent of its editorial team was cut on March 2.
Samsung in early May put a stop to all ChatGPT use on its internal networks and company-owned devices, according to Bloomberg, after growing concern that when its employees communicated with generative AI, security and sensitive information leaks became too risky. Samsung leaders said it would work to prepare measures, and that the restriction order was temporary.
Even Sam Altman, CEO of Open AI said he’s not fearless when it comes to adoption of ChatGPT, telling ABC7 San Francisco that yes, he’s a little bit scared. But Altman also said his reservation shows that he and his company can be trusted.
Early Users Embrace Generative AI
Generative AI is here to do good things, despite what those who weren’t early fans had to say.
From robot-written college papers for students, to brilliant advertisement copy for a startup’s tiny marketing team, it’s easy to see why generative AI had its fair share of early adopters. It makes things easier and can do so at record-breaking speed.
An average of about 13 million unique visitors had entertained ChatGPT per day in January. To put that into perspective, TikTok reached 100 million users nine months after its launch, and it took Instagram 2 1/2 years to hit that mark.
Adoption of AI has doubled over the last five years, McKinsey found in 2022, and the pace of AI investment is neck-and-neck with its popularity. According to McKinsey, machine learning (ML) enables the development of AI through models that “learn” from data patterns (without the help of humans) that are too large for humans to manage. It’s about teaching a model to take in such massive amounts of data so it can become able to come up with accurate predictions—including a variety of them—so they seem human-like.
The potential of ML and the need for generative AI are increasing at never-before-recorded rates. Outside of simple things like entertainment, generative AI can mean big benefits for businesses. For IT teams, generative AI can almost instantly produce accurate and correct code.
Generative AI for IT
Simply put, IT teams deserve generative AI. After all, they’ve pretty much been using it for a while now to automate scripts and make their daily to-do lists easier. As with any evolving technology, adoption opens doors for there to be a better way. And here at Resolve, we believe we found it!
We’ve launched the all-new AI Code Translator, now available for IT teams!
Read more about the launch of Resolve’s OpenAI/ChatGPT Translate Code.
What all does this mean for your IT Team? It allows them to harness the power of the latest, resounding disruption in AI (you guessed it, ChatGPT).
IT teams commonly extend Resolve’s platform to orchestrate their existing scripts and scriptlets. For example, Resolve can integrate with Ansible playbooks to automate network configuration or chef or puppet scripts for CI/CD-related activities.
To further this flexibility, Resolve now allows IT teams to incorporate with ChatGPT. To do so, Resolve Automation Exchange holds the AI Script Converter needed to turn your existing scripts into Resolve Actions tasks. From there, you can grow a collection of automation tasks that can be reused, as well as version controlled and secured. With added governance you can now make this available to your teams for use, promoting ideal automation best practices.
RELATED BLOG: Just Stick to the Script?
Generative AI at Work
There are a handful of valuable ways IT teams can apply generative AI to make their operations better:
Log Analysis and Anomaly Detection
Generative AI can be used to analyze log files produced by various IT systems, such as servers, network devices, and applications. The knowledge gained from historical log data enables generative models to identify normal log patterns and detect anomalies or potential issues, which helps IT operations teams quickly detect and troubleshoot problems; and therefore, improve system reliability and minimize downtime.
IT Incident Response
When IT incidents occur, generative AI can assist by automating incident response processes. In lending a hand, generative models examine and assess past incident data and associated resolutions so that current incident resolution becomes faster, human error is reduced, and overall IT service management is enhanced.
IT Capacity Planning
Generative IT models can help IT operations teams with capacity planning by creating and rolling out synthetic workload data based on historical patterns; and then, they can simulate various scenarios, predict response requirements, and assist in optimizing infrastructure provisioning. By accurately forecasting capacity needs, IT operations teams can prevent resource bottlenecks and ensure smooth operations.
IT Infrastructure Configuration Optimization
When it comes to IT infrastructure configuration optimization, generative AI can study and learn from existing IT configurations and their performance metrics – and then enhance them by applying the new knowledge. Generative models can produce optimized configuration settings for IT systems, like server setups, network configurations, and cloud infrastructure parameters. For IT operations teams, it means finetuning their infrastructure for improved efficiency, scalability, and cost-effectiveness.
IT Service Performance Prediction
As mentioned, generative AI is good at learning from the past, and so it can also be used for IT service performance prediction. Basically, generative AI can leverage historical performance data to predict future service performance metrics. IT operations teams; therefore, gain insights into service response times, resource utilization, and system capacity requirements. From there, they can proactively address potential issues, optimize resource selection, and deliver reliable IT services.
Generative AI can help scale your IT operations, and Resolve can show you how. Get your personal demo!