AI for Cloud-Based SaaS Applications To Enable Efficient Remote Work in 2022
Looking for an intelligent solution for remote work on your cloud-based SaaS applications? In this article, learn how AI can help you with remote capabilities.
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The recent pandemic has emphasized the need for remote work. Especially for businesses that did not have remote capabilities, the need arose for reliable SaaS-based solutions to meet immediate demand. Migration to the Cloud and SaaS-based solutions has been pivotal to remote working capabilities, which is why significant IT expenditure is driven towards it.
According to Gartner, the worldwide spending on cloud-based SaaS applications was $120,686 in 2020 and will rise to $171,915 in 2022. However, integrating SaaS-based solutions into your existing system is not that easy, and several repetitive tasks increase the cost.
For example, different testing features that will improve remote capabilities will perform repetitive tasks like writing test cases for various functions. Here AI-based automation can help reduce such repetitive tasks and save resources for enhanced operations. Artificial Intelligence has been at the heart of such innovations.
Here, we will discuss a different aspect of remote work enhanced through SaaS-based applications powered by AI.
The increasing adoption of SaaS and cloud-based solutions are boosting the consumption of enterprise content. This shift in content consumption and demand for scalable SaaS platforms has led to increased demand for AI-based operations or AIOps.
AIOps is a multi-layered technology platform that goes past current SaaS-based IT capabilities. So, if you are wondering how to develop a SaaS-based solution powered by AI, leveraging AIOps can take your remote working capabilities to the next level.
It encapsulates analytics and Machine Learning algorithms to offer intelligent operational excellence. First, the AIOps platform leverages Big Data and aggregates data from several resources across your organization. Next, it deploys ML algorithms to enable real-time actions to sudden changes in the SaaS-based operations with detailed analytics on different parameters.
AIOps works on two primary components: Big Data and ML. These platforms need data sources beyond logs and records of monitoring tools. Therefore, they also aggregate engagement data from sources like CRM tools, operational analytics, and even security systems.
AIOps can not only track data and engage in making quick changes for optimizations but also detect anomalies to enhance the security of your systems. This is crucial, especially when most of your employees work remotely and access systems through unsecured networks at their location.
Another essential factor to consider for AI-based SaaS application development to improve the remote capabilities is integrations.
2. Remote Integrations
A SaaS-based enterprise platform needs several integrations related to different functionalities like security, CRM, and even communication. For example, major enterprises use Communication as a Services (CaaS) integration for enabling calling, instant messaging, and VoIP (Voice over IP) features.
Similarly, there are different integrations that every enterprise-grade application needs to add new functionalities and enhance user experiences.
AIaaS can enable enterprises with reliable third-party integrations to their SaaS applications. Let’s take an example of a marketing solution of reputation management that you want to integrate into your CRM applications.
An AI-based algorithm can help you design custom APIs or Application Programming Interfaces based on the vendor’s environment and create seamless integration to your existing SaaS-based CRM software.
Most organizations leverage the development of custom APIs for such integrations. However, with each integration, you will have to create a new API from scratch. AI can help you create reusable scripts that can accommodate minor changes as per the new environment and reduce the time for developing APIs.
However, some frameworks offer such reusable scripts, but they are highly opinionated and not flexible to adjust according to different environments. Apart from these, remote integrations need the ability of enterprises to deploy these APIs over the cloud-based platform, which requires smarter functionalities and close monitoring of assets.
AI-based SaaS monitoring tools can help your organization keep track of deployments remotely across different environments. Similarly, executing remote deployments of apps is also a challenge that AI can help through smarter CI/CD pipelines.
4. Remote Deployments
Deploying your SaaS applications remotely is not that easy as there are core services you may want to store at localized data centers for better security and uptime. Here, you can employ a hybrid cloud approach for the execution of deployments. However, deployments need continuous integration and delivery to be streamlined.
Take an example of a chatbot implementation for your SaaS application. It is a computer program that mimics human-to-human communication for better engagement. For the deployment of a chatbot, you will have to configure several trigger functions that enable storage, analysis, and data processing.
The chatbot needs AI-based algorithms at its core to function, but at the same time, you can also use AI technologies for deployments. For example, an AI algorithm can orchestrate a deployment pipeline and streamline everything from design to testing SaaS applications.
When it comes to remote deployment, another critical aspect is to deliver content effectively across platforms while creating and collaborating the content remotely.
5. Remote Content Delivery
Enterprise-grade applications need reliable solutions to deliver content across platforms and at the same time ensure that the user experience is enhanced. Let’s take an example of the images and pictures in your content that need a reliable delivery network or can slow the loading times.
Content delivery is an essential part of your business and needs an efficient delivery network for high performance. Enterprise-grade applications leverage Content Delivery Networks (CDN) to handle slower loading problems and performance bottlenecks.
AI-based CDN systems can help your SaaS applications deliver content on the go dynamically with faster loading and enhanced customer experience. You can leverage tier-based content delivery through an automatic deployment pipeline triggered by Machine Learning algorithms. For such a CDN system, you need a trigger function to customize as per business needs.
6. SaaS Customizations
When you want to customize trigger functions for your applications, there needs to be an elaborate SaaS-based strategy. Once you have strategized on the AI integrations, the next step is to assess the existing applications and define critical functions for which you need a trigger.
For example, if you are developing a SaaS-based application for marketing purposes, you need to define trigger functions like auto-replies, follow-up emails, and more. Here, you can leverage a SaaS consultant’s expertise to identify, assess, and develop essential trigger functions for enhancing the customer journey.
Cloud adoption is a business decision, and when you need to integrate remote working capabilities, AI can enable intelligent features. However, the configuration of essential functions, development of custom APIs, and remote deployment become quintessential to AI-based SaaS applications.
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