Microservice architecture works on the principle of displaying only the relevant details to the end-user. It conceals the complexities associated with software and hardware, operating systems, and development toolkits inside a standard service available to employees on network.

IT personnel refer to this functionality as an “abstraction layer”. If an employee is using a certain application and even if its vendors completely modify the physical location of the data center, hardware, or the programing language, the productivity of the application will not be affected in any manner. CIOs will find that for an internal software application, they no longer have to worry about the time-consuming and expensive labor of rewriting complex connections and interfaces between systems when using microservice architecture. The architecture runs on a standardized order management process and will deliver the exact results regardless of the application used, and the shift to a different platform will be seamless for any application that uses the service.

Best ways to implement a microservice architecture

IT leaders state that the best way to integrate the service is by using them in the organization’s service architecture. Most business applications and modern end-user applications are high-level logic and end-user interface that interacts via multiple microservices.

CIOs must be aware that these services require keys or registration and some services require payment after a point. This investment is however, less compared to building custom codes and maintaining them. Employees working on building microservices can identify the key services that the organization can deploy either externally or internally. Some examples of internal microservice include customer information that can be utilized by the organization’s customer support team, call center, and logistics application.

Microservices as a part of the technology tack

CIOs acknowledge that microservices have leveled the technology playing area to a huge extent. They are considering investing in it, as a measure to decrease the dependency on the legacy and proprietary systems. Using internal microservices ensures that organizations are independent of particular software or hardware third-party vendor and can easily upgrade parts of the infrastructure without affecting other applications.

Many IT leaders have put the concept of exploring microservices on hold as they consider the idea to be confusing and complex, but the service is pretty easy to be implemented.

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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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