AI’s importance in enterprise IT

AI’s importance in enterprise IT

Artificial Intelligence has real-world uses in many enterprise systems especially those based on anomaly use cases and analytics

Every organization requires Artificial Intelligence to succeed. AI provides business value but doesn’t solve the organizational problems magically. It can be used to boost the organization’s enterprise IT and business operations when implemented rationally.

Confusing AI and automation

CIOs should understand the difference between automation and AI before giving the go-ahead to vendors. Logic or sophisticated algorithms in the software for common use cases, which execute tasks faster and more accurately than people are automation and not AI. By itself, automation is beneficial for the organization but machine intelligence-based automation will prove to be problematic in the long run.

AI and Automation Have a Key Role in Driving the New Normal

AI is different as it decides for itself and that is not always necessary in most business cases. A CIO would not want the AI system to be taking crucial decisions related to finances, product management, recruiting, or network planning.

AI system should be implemented focused on analytics and anomaly detection

CIOs should use AI technologies exclusively used for identifying abnormal methodology in human decision making. Automation can be used for handling known patterns. Machine learning and other forms like artificial learning and deep learning are required to discover unknown patterns. Analytics-based AI identifies unknown patterns more efficiently and faster than a human can. The final decision on the action to be taken however is dependent on human intelligence based on the data from analytics.

IT leaders state that majority of the AI technology implemented currently is anomaly detection based analytics. Software vendors have implemented this integration for popular business processes and use-cases.

The most common issue that CIOs have to be aware of is that data science is the route for fusing analytics with intelligence. The setback is that data scientists are not trained for either business analysis or decision-making.

AI analyst Kjell Carlsson has stated that engineers access ML via AutoML. This framework removes the need to construct ML models from scratch. Process improvement teams well-versed in Lean and Six-Sigma are best suited for ushering in AI into Analytics.

AI has more exploratory forms like “augmented intelligence”. It has logical, useful use cases for a wide variety of enterprise systems: IT systems, logistics, marketing, document processing, and user interfaces as well.

Implementation of AI in business systems

CIOs mostly use AI in the applied analytics basis to enterprise systems which deal with uncertain or changing environments, a large volume of data and rapidly changing process, etc. Document processing is a rarely used domain of AI which CIOs will find to be useful.

AI used in IT systems: AIOps

AI operations is another domain that CIOs are highly interested in, as it promises to reduce IT workloads by diagnosing issues in networks, permitting automation to suggest solutions, and business process flows. AIOps is however still a less mature version of other enterprise AI areas.

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