The rise of artificial intelligence (AI) technologies is undeniable, and the integration of AI capabilities will increasingly become the golden standard for numerous software development projects.
Businesses seeking to harness the power of AI often have two options:
Concentrating on AI software strategy is a crucial step towards outpacing your competitors. Read the article to understand the use cases and key steps that corporations need to consider when developing an AI-based software solution.
With the emergence of modern technology, the imperative to leverage the power of artificial intelligence to enhance and empower the functionality of software systems has also increased, resulting in the creation of apps that incorporate AI capabilities.
AI-based software development refers to the creation of new software or the evolution of existing software to provide users with AI-driven analytics results (e.g. demand prediction) and/or trigger certain actions based on those results (e.g. blocking fraudulent transactions in banking apps).
With the support of AI, mobile apps can automate business processes, customise service delivery, and generate operational-specific insights. According to Deloitte, 94% of business leaders believe that “AI is essential to business success over the next five years”. Moreover, the AI market is anticipated to reach $1,597.1 billion by 2030.
For the financial services industry, there are several use cases of generative AI, such as:
Read more: Implementing Generative AI in Financial Software Development
In the realm of healthcare, AI technologies are leveraged due to the complexity and sensitivity of data and the demand to proceed in an orderly manner. Prosthetics, implantation, and robotics are some prominent expected use cases of AI in healthcare. These advancements in AI technology hold promise for improving patient care, enhancing medical procedures, and facilitating innovations.
AI software development has had a profound impact on the retail industry, revolutionising many aspects of operations, customer experience, and decision-making. Some key applications of AI in retail software development include recommendation systems, demand forecasting, inventory management, personalised marketing, virtual shopping assistants, and customer sentiment analysis.
Developing AI software follows a distinct process that sets it apart from traditional software development. While the conventional stages of research, design, idea testing, development, support, and others still apply, an additional stage must be included: the AI creation stage.
Opting for the dedicated software development team model has proven to be highly effective when it comes to integrating or building AI software, especially if you lack ideas or experience in this field.
At this initial stage, the fundamental purpose is to align the client's business objectives and the AI program's intentions. The discovery phase is essential since it helps understand the business requirements, goals, and desired outcomes of the AI application. Key activities include:
This additional step can help you develop traction-promising AI software. In fact, the Proof of Concept (PoC) serves as the foundation for the development of machine learning platforms. During this pivotal phase, the primary tasks involve constructing an AI algorithm and thoroughly testing the chosen AI models. The process begins with training your own AI using pertinent data and observing the subsequent outcomes.
Considering that AI encompasses smart algorithms, we can anticipate expert systems to autonomously determine the most suitable approach for processing tasks, transitioning from less optimal to more optimal methods.
It’s vital to check the technical and economic feasibility of enriching software with AI. Building software from scratch involving advanced technologies like deep learning, natural language processing, neural networks, robotics, and others is a costly endeavour, leaving little room for tolerating errors at the initial stages of the software development process.
The architecture of an AI software solution is what makes it stand out. However, when it comes to an AI system, the design process involves creating only part of the design at a time. Instead, the concentration is on developing the key screens and features essential to the AI system's functionality and effectiveness.
This is an iterative and experimental phase aimed at validating and refining the proposed design concept, and functionality, helping improve the interface to make the system as user-friendly as feasible. The IT team can design the architecture of the software with integration points between its modules, comprising integration with an AI module.
If you have picked the appropriate technological stack, engaged the appropriate dedicated software team, trained the AI algorithms, and chosen all the essential components to equip your soon-to-be artificial intelligence system, the coding process will go without a hitch. Key considerations in this phase include:
To effectively streamline this time-consuming stage, utilising automation tools is worth considering.
2. ML model training:At this stage, the software development team selects suitable machine learning algorithms and constructs ML models. These models are trained using a designated training dataset and tested against a separate validation dataset. The performance of the models is further improved by fine-tuning their hyperparameters.
These models are trained using a designated training dataset and tested against a separate validation dataset. The performance of the models is further improved by fine-tuning their hyperparameters.
This phase concentrates on deploying ML models to develop AI software. Due to the high risk associated with AI initiatives, incorporating Agile into the software development cycle is preferable for risk management at each phase. In particular, the AI team should adhere to ML operations.
Since AI software is constantly updating itself with new data without human interference, automation testing is needed to increase test coverage and accelerate the testing process. Without regular monitoring, datasets will degrade, and biases may be introduced into the datasets.
Tracks and resolves bugs and possible AI integration problems, optimises software performance and enhances UI according to user input, creates new features and expands the capability of AI-enabled systems by taking into account changing business or user requirements.
Maintenance of AI involves a separately controlled process. This aims to identify any signs of 'drift', which refers to a decline in accuracy and an increase in bias as the data being processed by the AI system diverges from the initial training data.
These services are designed to help businesses harness the power of artificial intelligence to drive innovation, improve efficiency, and achieve their strategic objectives.
Our consultants, with expertise in AI technologies and software development, provide strategic guidance and advisory services to help businesses identify and evaluate opportunities for leveraging AI in their software solutions. Here’re our offerings:
Our dedicated software development team offer end-to-end creation of AI-powered software solutions tailored to specific business needs. Instead of relying solely on in-house resources, you can choose to outsource the development of AI software to leverage the expertise and capabilities of specialised teams. Here are some key aspects of outsourced custom AI software development:
Since artificial intelligence is drastically reshaping the business world, there are several reasons to develop your own AI software. While it is feasible to adopt this innovative technology independently, there are numerous challenges that may arise, particularly if you lack experience in the field and need to focus on your core business operations.
A tried and practical option is to empower your AI software project with an experienced software development team. KMS Solutions’ team of experts is well-versed in leveraging the power of AI to develop innovative and intelligent solutions for various industries, especially the BFSI sector. With a deep understanding of the latest AI technologies and best practices, we are dedicated to delivering cutting-edge software solutions that drive business growth and enhance customer experiences.