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8 AI-Enabled Software Development Tools for Faster Business Processes – Analytics Insight

AI-enabled software development tools enable businesses to perform tasks with efficiency and accuracy. 

Software application contributes significantly to the routine activities across organizations. From searching a product over the internet to sending emails to clients and colleagues, the utilization of software in business has accelerated. Though the software is a compelling entity, its development is a tough task. It is a complex process that requires ideation, product definition, strategic designing, coding, quality assessment, and coding. Additionally, if any step in the software development goes wrong, the entire process needs to be started again. Also, software development is driven by changing marketing trends; hence there is no room for distraction in software development.

To remedy this and the challenges associated with the traditional process in developing the software, many organizations are embedding artificial intelligence and machine learning for better outcomes. Though technology is nascent, it is making the software development tools more efficient and accurate.

Google’s ML Kit

Google’s ML kit is the most common AI-enabled software development tool available in the market. ML Kit brings Google’s machine learning expertise to mobile developers in a powerful and easy-to-use package. It can be used on both Android and iOS devices for a more engaging and personalized experience. ML Kit’s processing happens on-device, making it fast with real-time use cases like camera-input processing. It also works offline and can be used for processing images and text that need to remain on the device. The kit is a combination of best-in-class machine learning models with advanced processing pipelines and offers easy-to-use APIs to enable powerful use cases in your apps. The Video and Image analysis APIs enables image labeling, barcode scanning, and facial detection.

IBM Watson

This is amongst the best AI-driven software development tool for businesses demanding faster and better results. Watson comes pre-integrated and pre-trained on a flexible information architecture optimized to accelerate production and deployment of AI. It helps the businesses to make more accurate predictions, automate processes, interact with users and customers, and augment expertise. It has the developer tools that make it easy to incorporate conversation, language, and search into your applications. Watson gives the client access to detailed developer resources for faster documentation, accelerated R&D, enriched interactions, the anticipation of market trends and mitigating risks, amongst others.


H2O is an open-source, distributed in-memory machine learning platform with linear scalability. H2O supports the most widely used statistical & machine learning algorithms, including gradient boosted machines, generalized linear models, and deep learning, amongst others. Build with mobile applications; it aids in programming languages like Java, R and Python. Its use cases are data analysis, customer intelligence, and risk analysis, amongst others. H2O works on existing big data infrastructure, on bare metal or existing Hadoop, Spark or Kubernetes clusters. It can ingest data directly from HDFS, Spark, S3, Azure Data Lake or any other data source into it’s in-memory distributed key-value store.

Infosys NIA 

Infosys Nia, is the next-generation Artificial Intelligence Platform build with the combination of the AI platform, Infosys Mana, and Robotic Process Automation (RPA) solution, AssistEdge. Its capabilities include socialization of organizational knowledge, deep analytics, service automation, automated incident root cause analysis, and others. Infosys Nia, tackles break-through business problems such as forecasting revenues, forecasting what products need to be built, understanding customer behavior, deeply understanding the content of contracts and legal documents, understanding compliance and fraud.


TensorFlow is an end-to-end open-source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers quickly build and deploy ML-powered applications. Through Tensorflow, the businesses can easily train and deploy models in the cloud, on-premise, the browser, or on-device. It builds and trains ML models using easily intuitive high-level API like Keras with eager execution, making for immediate model iteration and easy debugging.


The Accord.NET Framework is a .NET machine learning framework combined with audio and image processing libraries completely written in C#. It is a complete framework for building production-grade computer vision, computer audition, and signal processing and statistics applications for commercial use.


Mxnet is an open-source deep learning framework suited for flexible research prototyping and production. It establishes scalable distributed training and performance to optimize the research and production through parameter server and hooved support. Its use-cases involve computer vision, NLP, and time series, amongst others due to its thriving ecosystem.

Google Assistant

Counted amongst the most common AI-driven software development tools, Google Assistant provides faster ways for users to access your Android app. It supports various languages, browses any commands, set reminders, and have conversations. It can be used to enhance the efficiency and productivity of the business team.

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