Organizations are attempting to optimize their operations by putting everything on autopilot that does not require the attention of a committed human operator. It all comes down to money and effectiveness. It’s an issue of repurposing expensive human resources to do what they do best: think and solve problems rather than perform dull, repetitive chores. Artificial Intelligence provides a natural evolution in this setting. It promises to help businesses keep track of their operations and untangle some of the ever-increasing IT problems.
The ecosystem of AI platform suppliers will grow increasingly cluttered as more companies enter the market. AI-powered implementations provide analytics solutions that can assist humans in decision-making while also performing security alert triage and taking automated steps for some aspects that demand conventional replies.
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Domain-centric or domain-agnostic platform?
There is no such thing as a one-size-fits-all solution. Each has advantages and disadvantages. After a thorough examination of the project’s requirements and limits, a decision will be made only after examining the capabilities of the perfect system.
Domain-centric platforms are a subset of tools and apps that are laser-focused. Their mission is to give an in-depth performance analysis of a particular topic or use case. A domain-centric solution should be less difficult and less likely to fail, with a quick return on investment. Domain-centric systems have limited application because they lack the capacity to correlate operations data and events with the overall IT ecosystem.
Domain-agnostic platforms, on the other hand, work well with a variety of data sources across a wide range of sectors, as well as across all segments and functions within those industries (such as IT, manufacturing, security, and so on). Domain agnostic AI-powered platforms are beneficial for making connections between diverse sections of a large IT system and undertaking root cause analysis to pinpoint the system’s failure causes.
What type of data is used for the decision process?
Adoption of AI process automation technologies leads to data-driven solutions. The first step is to collect all of the necessary data in one place and then analyze it. The greatest platform designs are domain-agnostic, which means they can function with any data source and format. This trait allows them to function in a variety of verticals and industries, not only IT, though it is well-suited to this industry.
What is the expected ROI of switching to AI-based platforms?
Most AI-based solutions are not inexpensive, but they reach breakeven quickly and provide a high return on investment in less than a year, assuming that the organization uses operations monitoring to fill in the gaps between the present and intended states, and there is a clear flow for this.
To make an accurate assessment, examine each process you wish to automate and consider whether it is feasible to eliminate the human component and replace it with AI.
How difficult is it to integrate an AI solution with an existing system?
The implementation of AI does not start with a clean slate. It must smoothly interact with legacy systems and make full use of them. When looking for an AI platform, make sure to inquire about connectivity and compatibility with your present setup.
Make a list of any applications for which you want to track performance. Thread restarting without restarting the entire application is a basic AI application for this.