In today’s world of technology, artificial intelligence is a new buzzword. From chatbots to smartphones, AI technology is prevalent in multiple aspects of our daily lives. Moreover, a reputable AI development company helps you automate your business operation & overcome critical business challenges efficiently. AI helps to integrate fully automated solutions for effective business opportunities.
AI development companies help both product organizations and mid-scale enterprises to plan and develop full-fledged data-driven AI solutions for multiple industries like healthcare, manufacturing, energy, retail, finance, and more. As the demand for AI solutions continues to grow, understanding the scope and capabilities of Generative AI development becomes crucial for organizations aiming to stay at the forefront of technological advancements.
No doubt, artificial intelligence has a huge potential to create new sources of business growth & change the way of workflow. Statistics show that AI may contribute up to $15.7 trillion to the world economy by 2035. Continue reading to learn more about AI development services and their scopes.
Real-Life Use Cases in Multiple Sectors
An AI software development company elevates a number of industries or sectors to another level of success with an array of solutions. Here is a list of real-life use cases
1. Healthcare Industries
No doubt, healthcare is a booming industry and Artificial Intelligence plays a very significant role in evolving every stage of modern healthcare solutions. AI is used to make sophisticated machines for detecting diseases and cancer cells. As the demand for intelligent healthcare solutions grows, investing in AI Chatbot Development becomes pivotal for staying at the forefront of this rapidly advancing industry. Moreover, AI-enabled solutions can accurately analyze medical data that ensure quick diagnosis, and promotes personalized treatment. Other perks include:
- Artificial Intelligence supports decision-making as well as research
- AI offers content-rich disciplines for the scientific medical communities
- Automates tasks and helps to analyze big data sets
2. Robotics
Artificial Intelligence is extensively used in robotics fields. AI technology offers appropriate computer vision & motion control to robots for better understanding and acting accordingly. Moreover, AI-powered robots utilize real-time updates to identify obstacles & instantly pre-plan their journey. AI robots are mainly controlled by multiple AI programs and technologies like computer vision, Machine learning, RL learning, etc. AI-powered robots can be used for:
- Delivering goods in factories, hospitals & warehouses
- Cleaning large equipment and offices
- Inventory management
3. Automotive Industries
Today’s car manufacturers are deeply adopting AI applications in every stage of the automotive value chain such as design & manufacturing, production, supply chain, post-production, and more. Moreover, Artificial Intelligence is also widely used in making self-driving vehicles. It also supports the vehicle’s camera, emergency braking, blind-spot monitoring, cloud services, GPS, control signals, etc. Common benefits of AI applications in automotive industries include:
- Promotes autonomous driving
- Advanced driver assistance systems
- Supports predictive maintenance
- Automotive testing
- Helps in driver monitoring systems
4. E-Commerce Industries
AI offers the e-commerce sector a very competitive advantage. Shoppers can utilize Artificial Intelligence technology to identify related items in their preferred color, size & brand. Let’s take a look at some major applications of Artificial Intelligence in E-commerce.
Personalized Shopping: Artificial Intelligence is mainly used to build recommendation engines that allow users to engage with their potential customers more efficiently. These suggestions or recommendations are usually based on browsing history, interests, and preferences. It also helps in enhancing the interaction with customers & the brand loyalty of a user.
AI-Powered Assistants: Chatbots or virtual shopping assistants enhance user experience during online shopping. Natural Language Processing or NLP is mainly used for making a human-like audio sound. In addition, these voice assistants have the capability to improve real-time engagement with consumers.
Fraud Prevention: Nowadays, fake reviews and credit card fraud cases are both common challenges that e-commerce businesses are presently dealing with. Fortunately, Artificial Intelligence technologies help to minimize the risk of fraud detection. Moreover, many buyers prefer to purchase any product or avail of any service depending on customer reviews. AI technology helps to identify and detect fraudulent reviews.
5. Finance Sectors
Artificial intelligence is effectively shaping the finance industry. Whether it is corporate finance or personal finance, AI can significantly improve a vast range of financial solutions. Moreover, AI-based financial solutions streamline and optimize business procedures. They also help in financial risk management, financial advisory services, and quantitative trading. A few examples of finance AI include:
- Artificial intelligence in personal Finance
- Artificial intelligence in Corporate Finance
- Artificial intelligence in Consumer Finance
Apart from that, artificial intelligence helps banks and financial organizations with fraud detection task automation and assessing loan risks.
How to Develop Artificial Intelligence Software?
The sequence and duration of a development process mainly depend on the basic software functionalities & AI technology. Below we are presenting a generalized process of artificial intelligence development for your convenience.
Step 1: A Feasibility Study
- Developers outline all software requirements
- They accurately estimate the ROI of AI implementation
- Makes a PoC (proof of concept) for AI to verify the economic and technical feasibility of the software, calculate the work scopes, budget, timeline & risks
Step 2: Business Analysis to Obtain AI Requirements
Developers define detailed non-functional and functional AI requirements, like the required AI accuracy level, fairness, explainability, privacy & response time. In some cases, the already value is driven with 65% to 80% of accuracy.
Step 3: Solution Architecture Design
The duration of this step mainly depends on the complexity level of software functionality.
Artificial intelligence developers choose integration procedures and patterns. They design the solution architecture with AI module integration.
Looking for The Best Artificial Intelligence Development Company?
Amplify your business performance and productivity with our data-driven artificial intelligence development solutions.
Step 4: Business Processes Preparation
Developing AI-enabled business-critical software may need some organizational changes to boost the chances for its successful adoption as well as implementation. In this stage, experts help in business process preparation and promote frequent collaboration between tech stakeholders and businesses.
Step 5: Software and AI Module Development
In this stage, the developers start making the software’s front end and back end. They also work on important APIs for artificial intelligence module integration. After that, throughout the AI development, they run all QA procedures for validating the software quality. Moreover, AI module development is divided into two categories: data preparation and ML model training.
Data preparation: This method is essential for increasing AI deliverables quality. In the data preparation process, developers consolidate data from internal & external data sources. Now, they work on exploratory data analysis to find out useful patterns and identify errors, anomalies, etc. After that, they standardize and replace variables.
ML model training: Developers choose ML algorithms & develop machine learning models. They train the ML models with a dataset then they boost their performance through fine-tuning hyperparameters. The high-performing ML models can be integrated into a model to minimize the separate model error rates. The ultimate machine learning model is validated against a dataset in a pre-production ecosystem.
Step 6: AI Deployment
The configuration of artificial intelligence deployment depends on AI outputs the results. In this stage, the developers mainly integrate the final model with the software and then thoroughly test it to make sure that it gives accurate results. During the deployment process, developers focus on the following points:
- They continuously monitor and measure the model performance
- Define the baseline for measuring future model iterations
- Keep modifying the model to enhance model performance with data
Types of AI Development Tools
Developers use a number of AI tools and frameworks to facilitate a number of data processing in several industries. Some of the most popular and powerful AI development tools include:
1. PyTorch: PyTorch is one of the well-recognized artificial intelligence tools used for ample applications like natural language processing, computer vision, etc. This is open-source software originally built by Meta AI & now it’s a part of the Linux Foundation umbrella. It enables quick debugging with Python tools. Key features of PyTorch include:
- Tensor computation
- Dynamic graph computation
- Automatic differentiation
- Python support
2. H2O AI Platform: H2O is a business-oriented open-source AI tool that helps to make insightful decisions on businesses. This deep learning platform is written in Java & it uses interfaces like Scala, Python, Java, R, CoffeeScript & JSON. H2O AI is widely used for predictive modeling, insurance analytics, and risk analysis.
3. OpenNN: Open Neural Networks or OpenNN is an open-source library written in C++. This simulates the neural networks for deep learning research. Open Neural Networks use ML techniques to solve predictive analytics in multiple fields. It also allows developers to upgrade advanced analytics.
4. TensorFlow: TensorFlow is an end-to-end open-source and free software library for artificial intelligence and machine learning applications. It is used for several tasks but it mostly focuses on deep neural network inferences. TensorFlow is considered an excellent AI tool that makes numerical computation easier for more accurate future predictions.
5. Tellius: Tellius is a well-known business intelligence and analytics tool relying on AI to help businesses to build new strategies and increase growth rates. As an AI-driven platform, it allows users to get quicker insights from the data.
Searching for a Reliable Artificial Intelligence Development Company?
Our end-to-end artificial intelligence (AI) development services automate your business operations & open multiple opportunities.
Final Words
AI for software development has the power of streamlining business processes & aggregating data. An Ai development company allows businesses to effectively automate iterative tasks and minimize downtime for critical processes & boost decision-making. If you would like to amplify productivity and increase sales growth, leverage top-notch AI development services to transform your business into a next-gen enterprise. For further information and queries, stay in touch with Sphinx Solutions.
