7-Steps Guide for Technical Lead to Develop AI-Powered Software

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:

• Purchase AI solutions, i.e. AI as a Service (AIaaS), from giant technology enterprises like Microsoft, Amazon, IBM, or Google.
• Develop their own AI software tailored to their specific needs and requirements.

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.

The Core of Building Software with AI Capabilities

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.

AI Software Development in Different Industries

1. Banking and financial services

For the financial services industry, there are several use cases of generative AI, such as:

• Conversational finance software: currently, the integration of AI-powered chatbots in financial software can respond to various questions quickly, effectively, and individually, enabling users to notice information and conduct transactions using conversational interfaces.
• Financial analysis and anticipating: AI can be harnessed to analyse financial data, identify patterns, and calculate statistical measures, all serving for making better financial decisions.

Read more: Implementing Generative AI in Financial Software Development

2. Healthcare

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.

3. Retail

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.

7 Key Steps to Develop AI-Powered Software 

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.

Step 1: Understanding AI Business Objectives 

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:

• High-level software requirements outlining
• Problem understanding
• Data assessment
• Feasibility study
• Risk assessment
• Solution exploration

Step 2: Proof of Concept (PoC) Creation 

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.

Step 3: Solution Prototype Development

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. 

Step 4: AI Platform Coding 

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:

• Project management practice: By implementing the Agile methodology, the AI development process guarantees transparency and flexibility. With customer expectations constantly evolving, an agile development team equipped with AI expertise can effortlessly accommodate product changes whenever necessary.
  
  
• Developing an AI system from scratch: It’s essential to understand that engineers may need to generate neutral networks or train AI models suitably.

• AI module integration: The upcoming AI system's minimum viable product (MVP) is initially built with essential features, while non-essential features can be fulfilled using off-the-shelf options. This approach helps to save time and budget resources.

Step 5: AI Module Development

• Consolidating information from various relevant sources (internal and external, available via subscription or one-time purchase).
• Conducting exploratory analysis of data to uncover valuable patterns, and identify evident errors, outliers, anomalies, and other significant insights.
• Cleansing data: This process entails standardising, replacing missing data or deviating variables, eliminating duplicate entries, and anonymising sensitive information.
• The resulting data is divided into training, validation, and test sets for further processing and evaluation.

To effectively streamline this time-consuming stage, utilising automation tools is worth considering.

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. 

Step 6: AI Software Deployment and Testing

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. 

Step 7: Maintenance and Evolution of AI Software

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.

Professional Services for AI-Powered Software Development 

These services are designed to help businesses harness the power of artificial intelligence to drive innovation, improve efficiency, and achieve their strategic objectives.

1. Consulting: Software development with AI capabilities 

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:

• A feasibility analysis on AI integration into businesses’ existing software (potential advantages, costs, risks, etc.)
• A risk management approach to minimise AI-related risks
• A comprehensive development, deployment and integration plan
• Selecting the optimal sourcing strategy
• Providing appropriate technology stack for AI-based software.

2. Outsourced Custom AI Software Development 

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:

• Feasibility study with PoC
• Eliciting requirements of AI-powered software
• Software and AI development: data preparation, ML model development, training and tunning
• AI integration and testing
• AI Training course
• Software maintenance and evolution. 

Choosing KMS Solutions to Deliver AI-Powered Software

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.