AI For Cloud-based SaaS Apps: How it Enables Remote Work in 2022?

Some years ago, sitting in your pajamas and solving the enterprise problems may seem like a vague idea. However, in 2020, everything changes with the onset of restricted physical work. According to BBC, the post-pandemic workforce will see more of a hybrid approach in the offices.

Here, remote working technologies will play a key role. Everything will be remote, from video conferencing to virtual meetings, project management, and operations.

What has fueled the remote capabilities of most businesses in recent times is Software as a Service(SaaS) apps. It is an approach to delivering applications over the internet.

So, for an organization looking for remote work solutions, the delivery over the internet seems perfect. In addition, mainly cloud-based SaaS apps help scale the remote capabilities across operations.

Source

We see several remote solutions providers like Zoom and Atlassian on the 50 biggest SaaS companies list. However, cloud-based SaaS apps for remote work have specific challenges like all technologies. This is where artificial intelligence or AI can help.

It can reduce repetitive tasks and create adaptive workflows to improve SaaS efficiency. Finally, we will discuss different cloud-based SaaS app challenges and how AI helps overcome them.

Top cloud-based SaaS app challenges & AI solutions

SaaS apps provide the flexibility of deploying apps with cloud-native capabilities but have challenges in a hybrid approach. For example, organizations with legacy infrastructure need to have compatibility to integrate cloud-based SaaS apps. So, primarily for hybrid working capabilities, it becomes essential.

Flexibility and agility

SaaS applications can power your remote working capabilities. However, one significant challenge is flexibility and agility to cope with changing market demands.

Take an example of an accidental SaaS app that became one of the most used communication tools for different project teams- Slack! It was initially built to solve the communication problems of developers working on Glitch-an online multiplayer game.

Slack has become the go-to app for several remote teams that helps share files over the cloud.

Today, apps (and the approach to developing them) have become more structured, following specific steps for SaaS app success. The best part about such cloud-based SaaS apps has been scaling capabilities.

It comes with tons of features for teams to customize remote communications. Similarly, you need an agile and flexible SaaS app for your organization to cope with hybrid environments' prerequisites.

AI can help cloud-based SaaS apps through scalable platforms which adjust to scaling needs. Organizations can design AI-based algorithms that automate feature updates according to the communication scale. In other words, you can automate the virtualization of resources for increased capacity through algorithms.

Workflow Adaptability

Organizations looking to leverage hybrid working capabilities need to adapt their workflows. This is where lies a significant challenge for most cloud-based SaaS apps. Take a simple example of a cloud-based PDF editing tool like Lumin PDF editor. Such tools help organizations with features to edit PDF files on the go with cloud-based features.

Integrating such tools needs adaptability if you are an enterprise with essential documents stored on-site with a slew of data centers. Especially the entire workflow for teams will need adaptability.

For example, if you are to integrate a PDF editing tool for encrypted documents for production and testing teams, access authentication is essential. Here, AI can help through automated user authentications and access policies.

AI-based algorithms can help companies to deploy specific trigger functions. These functions will trigger the authentication process for data access as per the security policy. However, ensuring security policy deployment is also a challenge for many cloud-based SaaS applications.

Security policies

From user authentication to data access restrictions, security policies help ensure that cloud-based SaaS apps are secure. Organizations need to use a hybrid cloud approach spanning several environments, which include,

• Public cloud environments
• Private cloud environments
• On-premise data centers

AI-based algorithms can help deploy security policies and monitor environments for anomalies. For example, if the existing on-premise data center uses a legacy system with password-based security, integrating a safe password tool can help.

Companies can use third-party service APIs and custom security policies to ensure secure data access. However, regulating data access is one thing, and managing data quality is another.

Data quality management

SaaS applications with cloud-native capabilities help scale data aggregation, storage, processing, and analysis. However, though Machine Learning algorithms enable SaaS apps to manage data, the challenge of maintaining information quality is still a massive issue.

So, how do we overcome data quality issues?

The best way to ensure that data quality remains high is by analyzing the source and identifying its characteristics. This is where organizations can use deep learning algorithms that help in finding specific source issues for each data. It also impacts the service delivery of cloud-based SaaS applications, which is another critical challenge.

Service delivery

Customer service (CX) delivery is essential for businesses. As a result, companies' new norm is to work towards enhanced customer journeys. According to a customer experience survey, 45.9% of companies will focus on CX improvements for the next five years.

Source

So, there is no denying that a higher customer experience will be right there at the top of your checklist. However, it becomes challenging to provide enhanced customer services without effective service delivery.

Cloud-based SaaS companies have been helping organizations deliver services efficiently. In addition, providing customer experiences has been on the mark with integrations to innovative cloud-based services.

However, it’s the personalization of these experiences that AI can help. Therefore, providing remote customer support and personalizing the experience needs advanced AI capabilities.

Take, for example, the challenges of the integrated cloud-based call center. Cloud-native capabilities of SaaS applications do help in delivering remote customer services. However, AI adds efficiency into the mix!

A simple way to enhance the customer experience for your organization is to integrate an AI-based chatbot with cloud-based call centers. Here are some benefits of using chatbots for your SaaS apps,

• Enables personalized communication with users
• Provides data that helps in developing specific services
• Allows enhanced resolutions to customer queries
• Assists in delivering better customer experiences
• Enhances the review and feedback aggregation

Cloud-based SaaS applications are not limited to infrastructure management and IT operations. It has varied applications in manufacturing, healthcare, architecture, etc. This is where AI has been making a huge difference.

Remote healthcare

We see lots of discussion around how AI can help organizations with remote capabilities through advanced SaaS apps. However, one of the most significant impacts of AI for SaaS apps has been in the healthcare industry. Cloud-based SaaS apps have enabled healthcare professionals to diagnose patients remotely and provide services.

According to PRNewswire, the global remote healthcare market will reach $23.9 billion by 2028. It is growing at a CAGR of 17% and is fueled by accelerated remote healthcare services.

Especially for patient monitoring services and remote tracking, cloud-based SaaS apps provide flexible architecture.

However, where AI can enhance patients' entire remote healthcare experience is personalized health plans. For example, algorithms designed to track patients' behavior, symptoms, and eating habits will help with specific plans.

These personalized health plans are backed by goals prescribed by healthcare professionals to improve patients’ overall well-being. Another critical challenge of cloud-based SaaS applications is release management.

Managing releases with AI

SaaS-based applications provide the much-needed agility for teams to release features quickly. However, you need intelligent solutions when it comes to remote release management. For example, releasing versions that are backward compatible for multiple devices across environments can be challenging. Further, if you have to manage these releases remotely, it becomes a tedious task.

AI-based predictive analytics and real-life simulations can help in managing releases. It allows quicker assessments of the releases across environments. Further, AI’s predictive analytics also provide insights into the remote execution of codes and app behavior. This way monitoring the releases remotely, making changes on the fly, and deploying versions becomes efficient.

Signing Off

With cloud-based offerings and innovative integrations, SaaS applications make remote working a reality. Most importantly, it transforms several business domains like healthcare, IT, manufacturing, and more.

Where AI comes into play are SaaS app challenges that need intelligent solutions. These AI-based SaaS apps are designed for business intelligence and help improve ROI. So, start integrating AI into your SaaS apps and see the difference!