In today’s competitive and software-driven era, businesses must make better decisions to stay ahead in the market. The IT infrastructure of a business is responsible for essential business processes. Businesses invest more in the management and security of their IT framework so that their essential operations do not stop. However, with the growing business data and customer needs, managing IT infrastructure has been tougher than ever.

AI tools in IT operations management offer enterprises a way to achieve better results.

The need for a responsible AI framework

The traditional IT framework is not able to match the huge volumes of business data produced every day. Business data needs to be analyzed and protected from intruders. Traditional software systems cannot upscale themselves automatically with the growing need. You will have to restructure your IT framework frequently as per growing demands.

A framework is a foundation on which business applications and other components of IT infrastructure are built. An AI framework is scalable and requires less human intervention to operate. IT automation with AI tools will also enhance the productivity of an organization.

Things to know before building a robust AI framework 

1. Building a dependable AI framework isn’t easy 

AI adoption can be complicated as its impact is tricky to measure. An organization can go for hyper-automation, but that will involve high investment. Implementing a complete AI framework for each of your business processes will help you achieve hyper-automation. It is important to note that firms that achieved hyper-automation also started small.

Analyze which IT operations are most vulnerable and demand a scalable solution. Once you have identified them, apply AI only to those IT operations. There is no point in exhausting your funds at once by going for a full-fledged AI solution. Once AI is successful for initial test cases, you can start building your AI framework piece by piece.

2. Leverage the power of AIOps platforms 

AIOps (Artificial Intelligence for IT Operations) is a scalable solution to automate and enhance the productivity of your IT operations. AIOps platforms are also helping organizations monitor remote work extensively. The recent COVID-19 pandemic has forced organizations to look for automated IT operations. The main benefits of using AIOps for your business are as follows:

• AIOps managed infrastructure services help reduce the need for human intervention.

• With a reliable AIOps based analytics platform, you can identify the incidents within the IT infrastructure easily.

• AIOps platforms use AI/ML algorithms to provide actionable insights for solving an incident within the IT framework.

• You can use AI for application monitoring amidst the remote work culture. AIOps platforms offer enhanced observability into software systems to monitor their performance. Virtual desktop infrastructure solutions powered by AIOps are also available in the market to adapt to the WFH (Work from Home) culture.

• With AIOps, you can identify vulnerabilities associated with the software systems like exhaustive capacity, outage prediction, and much more.

• AIOps platforms for your IT framework will help the IT teams collaborate quickly. The disorientation among the IT teams will be drastically reduced by adopting AIOps.

• AIOps platforms can offer real-time information about cyber-attacks to boost cybersecurity.

You can identify the skill gaps within your organization and implement AIOps to fill them. System administrators, CIOs, or CTOs always aim to monitor the performance of software systems end-to-end. With AIOps, not only can you monitor the software systems rigorously, but also resolve incidents faster within the IT framework.

3. Decide your performance metrics

Benchmarking is critical for identifying the impact of AI on a business. While designing an AI framework for an organization, IT leaders should decide on performance metrics to be used. Some of the best performance metrics for measuring the impact of AI on an organization are as follows:

• Service availability: The availability of your applications and systems to provide essential services to customers translates into business reliability. With AI adoption, you can significantly enhance your service availability.

• MTTD: MTTD (Mean Time to Detect) is the average time taken to identify the root cause of an incident within the IT framework. With AI adoption, you can decrease the MTTD.

• MTTR: MTTR (Mean Time to Resolve) is the time taken to fix an issue within the IT framework. A reduction in MTTR will denote the positive impact of AI on your organization.

• MTBF: MTBF (Mean Time Between Failures) is the average time between IT outages. For example, if the software systems of an organization fail three times after operating 300 hours, we can say the MTBF is 100 hours.

Some other metrics for measuring the performance of AI-powered systems are service reliability, MTTA, the ticket to incident ratio, automated versus manual workload, and many others. Deciding the metrics for measuring the performance of AI-powered systems should be done while implementing an AI-led IT infrastructure.

4. Look for a responsive and reputed AI firm 

There is a skill gap in the industry when it comes to AI experts. Organizations find it hard to ensure ethical use of intelligent AI algorithms and high-end analytics. Organizations are already using traditional monitoring systems and are not sure about the algorithmic fairness of AI platforms.

You should use AI monitoring tools provided by reliable AI firms. Pre-made AI solutions are designed according to industry standards. With premade AI solutions, you can decide the extent of automation to be induced in the IT framework.

In a nutshell 

More than 50% of businesses have reported that adopting AI has boosted their productivity. Implementing a responsible AI framework for your company will help you cut costs in the long run. Start exploring AI-based platforms for your company now!