AI Software Development: 2024 Handbook for Business Owners and Executives

Artificial Intelligence in Software Development: Highlights for Business Apps

At a glance, you can see the main points to consider when building AI-based applications for businesses.

Types of AI in Software Engineering

To navigate the variety of AI, it is appropriate to know, in particular, about its three basic types:

• Narrow AI (ANI): Powers specific tasks like virtual assistants and AI chatbots
• Artificial General Intelligence (AGI): Theoretical AI matching human cognitive abilities
• Super AI: Future concept surpassing human intelligence capabilities

AI’s field includes several powerful subsets and subfields you can use for your business. For instance, machine learning (ML) helps your systems learn from data and improve over time. Deep learning, a subset of machine learning, uses neural networks to allow your apps to process information like the human brain and excels at complex pattern recognition.

Also, when creating AI-powered business apps, software engineers often use the technologies of Computer Vision (CV), Natural Language Processing (NLP), automatic speech recognition (ASR) or speech-to-text (STT), Generative AI (GenAI), and others.​

Custom and Ready-made Solutions in AI-based Software Development

The choice between building from scratch and applying ready-made solutions in AI projects includes the choice between AI model types, which are the following:

1. Pre-trained models

These models have been trained in advance to address specific business needs using a set of collected data. While pre-trained models come with baseline capabilities, they can be retrained on custom data and fine-tuned for specific outputs, a task typically handled by data scientists.

2. Pre-trained foundation models

Introduced recently, these models are trained on vast datasets and can handle various applications. An example is ChatGPT, a language model adaptable to a wide range of tasks. Similar generative models exist for audio, video, and images. Foundation models are usually accessible via APIs, and while they can function without heavy customization, additional training is often required for domain-specific tasks. This makes them relatively easy to integrate with minimal input from data scientists, provided no custom adjustments are necessary.

3. Custom models

Custom models offer the highest level of flexibility, as they can be fully tailored and optimized for specific client needs. However, a major hurdle in custom AI development is data availability, which can be challenging for smaller businesses or startups with limited operational history.

This circumstance is essential, and you should realize that the relevant data source issues are crucial for any AI project. Is the date sourcing problem insurmountable? No, you can solve this task even for newly launched projects and companies.

First, free data sets are available in some areas. Also, you may use opportunities to crowdsource such data sets. In addition, you can consider ways such as synthetic data creation, data purchase, and web scraping.

Salient Features of AI-driven Software Development

Here are some principal features that make AI software development unique.

Data dependency

AI systems depend heavily on data quality and quantity. Software developers build AI-based apps around data collection, processing, and analysis for AI models. The more data AI systems have, the more accurate their predictions and insights, making data quality a priority.

AI model adaptability, training, and self-learning

AI solutions can adjust their responses based on new data, making them highly adaptable to changing conditions. For example, machine learning models “learn” from data over time. These models improve with each training cycle, enabling continuous enhancement of software capabilities.

Continuity of AI development projects

AI solutions need continuous fine-tuning and improvement. Such a feature requires good arrangement and smooth operation of the data working process, including gathering, updating, processing, evaluation, etc. Therefore, AI software product development projects may not have an ultimate endpoint.

Without going too far into technical details, business owners and managers should still understand the main differences between AI software development and traditional software development.

In essence, AI development relies on dynamic models that improve through continuous interaction with data, while traditional software relies on pre-set instructions and requires manual adjustments for updates. AI development suits environments needing flexibility and adaptability, while traditional methods are generally preferred for straightforward tasks or highly regulated systems.

How AI Software Engineering Benefits Your Specific Industry

AI software development affects different industries in unique ways. Each sector finds new ways to use these technologies to stay ahead of competitors. Look closely at the typical solutions in the industries in which your business operates.

Healthcare

The AI healthcare market is expected to grow from $19.27 billion in 2023 to a 38.5% annual rate through 2030​. AI assists with precision medicine, diagnostics, and predictive analytics, using machine learning to interpret medical images, predict patient outcomes, and support personalized treatment plans:

• Predictive analytics: AI helps forecast patient outcomes, anticipate disease outbreaks, and allocate resources.
• Diagnostic imaging: AI analyzes medical scans to detect conditions like cancer accurately.
• Virtual health assistants: Bots provide real-time patient assistance, monitor symptoms, and facilitate follow-up care.
• Medicolegal information systems: AI enables medical summarization software and similar analytics solutions.
• Personalized treatment plans: Machine learning tailors care to individual patients by analyzing genetic and lifestyle data.

E-Learning

Research shows 99.4% of higher education institutions consider AI a vital factor for their competitiveness in the next three years. AI in education personalizes learning through adaptive algorithms that adjust content difficulty based on individual progress, while AI tutors offer on-demand assistance and predictive analytics help support student success:

• Adaptive learning platforms: AI customizes learning content based on student progress and comprehension.
• AI tutors: Virtual tutors provide one-on-one assistance and answer questions in real-time.
• Smart content creation: AI generates quizzes, flashcards, and study guides from course materials.
• Sentiment analysis: Tracks student engagement and feedback to refine course materials.
• Predictive analytics for student performance: Identifies students at risk and tailors support efforts.

Sports & fitness

In sports & fitness, AI-driven apps and devices enable personalized workout plans, real-time feedback, and interactive coaching. With capabilities like computer vision for real-time feedback on exercise form and wearable tech integration, AI makes workouts safer and more effective, driving the $1.3 billion AI fitness industry:

• Personalized training programs: AI adapts workouts based on progress and goals.
• Real-time exercise feedback: Computer vision provides form corrections for safer exercise.
• Diet and nutrition guidance: Apps analyze dietary habits and offer tailored suggestions.
• Wearable integration: AI tracks health metrics like heart rate and steps for comprehensive fitness insights.
• Virtual coaches: Offer motivation and guidance, mimicking a real trainer’s support.

iGaming

AI enhances user experiences in iGaming through personalized recommendations, virtual reality environments, and anti-fraud systems that detect suspicious betting patterns, securing operators and users while promoting fair play. Real-time personalization in iGaming adds value by adapting to player preferences​:

• Behavioral analysis: AI personalizes game recommendations and adjusts gameplay to user preferences.
• Anti-fraud detection: AI spots suspicious betting patterns to maintain fair play.
• Customer support chatbots: Assist players with inquiries around the clock.
• Predictive maintenance for game servers: AI minimizes downtime by identifying potential server issues early.
• Virtual Reality (VR) and Augmented Reality (AR): Enhance immersion by creating interactive gaming experiences.

Fintech

The global AI in fintech market will reach USD 61.30 billion by 2031, which shows massive growth potential. AI boosts fraud detection, risk assessment, and credit scoring, allowing companies to analyze large datasets in real-time. Chatbots and virtual assistants are transforming customer service, while robo-advisors personalize investment strategies for users, adding convenience and security to financial services:

• Fraud detection: AI detects anomalies in transactions to prevent fraud.
• Robo-advisors: Offer personalized financial advice based on user data.
• Customer service chatbots: Provide instant support for common financial inquiries.
• Credit scoring: AI evaluates creditworthiness by analyzing more complex data than traditional methods.
• Algorithmic trading: AI monitors markets and executes trades in real-time to optimize gains.

Manufacturing

AI-driven applications include predictive maintenance, quality control, and automated inventory management. By analyzing sensor data and visual information, AI reduces equipment downtime and improves safety:

• Predictive maintenance: Detects issues in machinery before they cause downtime.
• Quality control: AI inspects products for defects, improving accuracy over manual inspection.
• Supply chain optimization: AI manages inventory levels and forecasts demand.
• Process automation: Robotics and AI streamline repetitive manufacturing tasks.
• Worker safety monitoring: AI identifies hazards and ensures compliance with safety protocols.

Clean energy

AI solutions in energy include predictive maintenance and emissions monitoring to reduce environmental impact. Energy companies can predict consumption patterns, optimize resource allocation, and improve distribution efficiency with AI:

• Energy forecasting: Predicts energy demand and adjusts supply from renewable sources.
• Grid optimization: AI manages power distribution to improve efficiency.
• Predictive maintenance for power plants: Minimizes downtime by anticipating maintenance needs.
• Carbon emission monitoring: AI tracks emissions to help reduce environmental impact.
• Digital twins: Creates virtual models of equipment for optimized asset management and operational insights​

Construction

In construction, AI optimizes project management by forecasting delays, improving safety with real-time hazard alerts, and reducing costs through automated site monitoring and resource allocation. These technologies minimize operational risks and help construction projects stay on schedule:

• Project scheduling and forecasting: AI helps predict project timelines and budget requirements.
• Site monitoring with drones: AI analyzes drone footage to assess site progress and safety.
• Risk management: Identifies potential project risks, such as supply chain delays.
• Autonomous machinery: AI-powered machinery assists with excavation and transportation tasks.
• Building Information Modeling (BIM): AI optimizes designs and material usage for cost savings.

As you can see, businesses can gain significant competitive advantages while solving industry-specific challenges through smart AI software development implementation. Success comes from choosing solutions that match your operational needs and growth goals. AI strategy consulting services help to evaluate which tech solutions can primarily benefit specific industries and create a comprehensive AI adoption strategy.

Artificial Intelligence in Software Engineering: Shortlisted Solutions for Your Business

Your company’s AI strategy may include creating custom comprehensive platforms that cover all aspects of operations and AI tools that may boost individual business processes and provide clear ROI potential. Take a look at examples of cutting-edge AI solutions that can help you solve a bottleneck or win over your target audience by offering improved CX.

Digital twin solutions help you create virtual replicas of your physical assets or processes. Such digital twins let you simulate scenarios, predict maintenance needs, and track performance live. According to Gartner, 75% of organizations implementing IoT projects will use digital twins.

AI-powered inventory management systems can bring your company the following:

• Predicting demand patterns with 95% accuracy
• Optimizing stock levels automatically
• Reducing carrying costs by up to 25%
• Preventing stockouts and overstock situations

Generative AI chatbots have changed how businesses engage with clients. These solutions, for example, handle up to the majority of routine customer queries, freeing your team to focus on complex issues.

AI recommendation systems are advanced tools designed to predict and suggest items, from products – to movies, that users may find appealing based on their previous behavior and preferences. Such systems assist in improving user engagement and satisfaction through personalized offers.

AI identity verification solutions enhance security while streamlining user experience. Your business can benefit from reduced fraud rates and faster onboarding processes, with verification times dropping to mere seconds.

AI sales forecasting and churn rate prediction are great ways to strengthen your company’s market position. These tools analyze historical data, market trends, and customer behavior patterns to predict future outcomes.

AI data entry and invoice processing solutions can shorten manual processing time by up to 80%. These systems automatically extract, categorize, and confirm information to minimize errors and speed up workflows.

AI voice agents can transform your customer interactions completely. This technology makes natural customer conversations possible and smoothly handles everything from appointment scheduling to complex questions.

You can examine the features of AI-powered tools of this type in an example of Voice.AI, a whitelabel solution from Riseapps.

Voice.AI excels at understanding context and keeps conversations flowing naturally. Such functionality is precious for businesses that want to scale their customer service operations quickly.

The video below will give you an idea of ​​the capabilities of Voice.AI.

Addressing Common Challenges in Artificial Intelligence Software Development Projects

‘AI software development comes with its own set of unique challenges. What are the most critical hurdles you’ll need to overcome?

Software engineering and AI bias

Research shows that 68% of enterprises spend almost 30% of their IT budget on data storage and management, yet bias continues to be a stubborn problem. The case of Amazon’s recruiting engine showing bias against women is widely known. In the end, this company preferred to decommission this system rather than redevelop it. Biases in the training data sets affect the AI systems created with their help.

Solutions:

• Applying fairness-enhancing techniques such as pre-processing, which eliminates bias in the data before training
• Using post-processing for adjusting model outcomes to align with fairness goals after initial predictions
• Conducting regular bias audits and impact assessments
• Testing and refining AI systems with diverse training data sets

Data quality and silos

Data quality determines the levels of efficiency and accuracy of the AI ​​model. In turn, data silos create a significant challenge as information stays isolated across departments, causing inconsistencies and inefficiencies.

Solutions:

• Implementing a unified data strategy that maintains data integrity
• Creating data governance frameworks that unify data sources and improve overall data consistency, especially when dealing with high-stakes AI apps
• Adhering to data standardization practices and ensuring cross-functional data access through cloud-based data warehouses to overcome silos

Overcoming implementation challenges

AI implementation faces several obstacles along the way. 52% of organizations point to talent shortage as their main concern. Setting up reliable interaction of AI products with other software used by the organization is also a complex issue.

Solutions:

• Regular monitoring of AI systems and fine-tuning them across all project phases
• Ensuring seamless integration of AI solutions with existing software systems
• Training employees on AI tools and processes
• AI consulting support for the flawless operation of implemented systems

Ethical Considerations and regulatory compliance

AI adoption needs strict ethical guidelines and regulatory requirements. Studies show that over half of organizations lack designated roles for responsible AI oversight.

Solutions:

• Set up clear AI governance policies
• Put robust data protection protocols
• Ensure transparency in AI decision-making
• Run compliance audits regularly

Employing transparency tools like “model cards,” which outline the training and testing processes of AI models, helps ensure they meet ethical standards.

Remember that implemented AI software systems become an integral part of your company, so make sure they are transparent, ethical, and aligned with your business objectives. By addressing the described challenges proactively, you can tap into AI’s full potential while mitigating the associated risks.

Best Practices for AI-powered Software Development from Scratch

AI software development needs a solid foundation of technical best practices. Here’s a look at the essential components that help your AI project succeed.

Containerization excellence

AI-powered apps require consistency across environments. Containerization provides faster deployment cycles and ensures your AI models run the same way from development to production. Through Docker and Kubernetes, you’ll achieve:

• Consistent environment management
• Rapid deployment capabilities
• Efficient resource utilization
• Enhanced collaboration potential
• Simplified scaling operations

Robust logging systems

A detailed logging system fuels AI system transparency. Your logging system should capture both system events and model behavior, enabling effective debugging and performance optimization. Studies show that organizations with strong logging systems cut their troubleshooting time by up to 80%.

Version control mastery

Version control in AI development goes beyond code and includes data and models. You may implement a distributed version control system that tracks:

• Model parameters and hyperparameters
• Training data sets and their versions
• Model performance metrics
• Dependencies and environment configurations
• Deployment configurations

AI management system integration

The AI management system can give you detailed oversight of model development, deployment, and monitoring. This system becomes your central hub for managing model lifecycles and maintaining consistent performance across deployments.

Audit and evidence logs

Your AI system’s decisions and actions need detailed audit trails. Research indicates that 52% of companies consider risk factors crucial when implementing new AI use cases. Your audit logs should track:

• Model predictions and decisions
• Data access patterns
• System modifications
• User interactions
• Compliance adherence

Human-in-the-loop implementation

Can AI replace software engineers? There are a lot of discussions on this issue today.

Currently, while AI provides powerful insights, human oversight remains vital. Systems where human software engineers validate AI recommendations before execution work better, especially in critical decision-making processes. This approach reduces error rates compared to fully automated systems.

Cutting Costs for Founders by Using AI in Software Development

AI technologies can not only benefit your software product but also shorten and cheapen the way you get it. AI-powered approaches can reduce your development costs by up to 40% and speed up project delivery. Here’s how you can use AI to get the most from your development budget.

AI-driven code generation and refactoring

Your development team can now generate and optimize code automatically. AI coding assistants analyze requirements and create production-ready code that reduces development time by 30-40%. This approach speeds up development and delivers higher code quality and consistency.

Automation of repetitive tasks

Your development workflow becomes smoother when AI handles routine tasks. Team can focus on strategic work while AI manages:

• Code documentation generation
• Bug detection and fixes
• Code review automation
• Test case generation
• Deployment processes

AI-assisted quality assurance (QA)

AI-powered testing tools improve your QA processes by catching bugs before they reach production. These systems cut testing time by up to 90% while providing better coverage and accuracy.

Natural language processing (NLP) for UI/UX design

NLP-powered design tools simplify UI/UX development. They turn natural language descriptions into functional interfaces, reducing design time and costs while ensuring consistency across apps.

Predictive analytics for project planning

AI-powered predictive analytics help make informed decisions about resource allocation and risk management. Project managers can:

• Forecast project timelines accurately
• Identify potential bottlenecks early
• Optimize resource allocation
• Avoid project overruns
• Improve budget management

Enhancing SDLC with AI

It is possible to set up an AI model to analyze historical project data to optimize your development pipeline throughout the software development lifecycle. This results in 25% faster delivery times and lower operational costs.

Many subtleties regarding the application of AI in software engineering can help you streamline the development process and cut costs. According to recent studies, programmers who started using AI can write a quarter more code than before in the same period. The key is selecting the right combination of solutions that align with your specific project needs and business objectives.

AI and Software Development: Tech Stack for Your Product

The success of an AI software development project depends on picking the right tech stack. You may pay attention to some powerful tools that can boost your software product and shorten its time to market.

Amazon BedRock gives software developers a foundation for building and scaling generative AI applications. This serverless platform offers access to leading foundation models, enabling the rapid creation of sophisticated AI solutions while keeping data privacy.

TensorFlow may serve as a comprehensive machine learning platform. Its strong ecosystem lets software engineers to:

• Deploy models across multiple platforms
• Access pre-trained models
• Employ extensive debugging tools
• Scale from research to production
• Utilize GPU acceleration

ML Kit becomes your vital companion when developing mobile apps. This mobile SDK brings Google’s machine learning expertise to Android and iOS apps, offering features like text recognition and face detection with minimal setup.

ChatGPT integration can reshape your app’s user experience. Its API enables natural language processing capabilities that enhance user engagement and automate support functions.

GitHub Copilot works as your AI pair programmer to accelerate development cycles. It suggests code completions and entire functions that can boost your development speed by 55%.

LLAMA (Large Language Model Meta AI) gives AI development founders an open-source alternative to proprietary models. This is a family of versatile LLMs, suitable for various tasks, from creating texts to solving mathematical problems. The LLAMA flexibility allows for customization while maintaining high-performance standards.

Tech stack choice should match specific business goals and technical needs. As you can see from the examples given, the range of technologies for AI-based software development is constantly expanding, increasing the flexibility and variability of projects.

Core Steps for Applying AI in Software Development Projects

Five essential steps will help you embark on your AI software development journey. Research shows that organizations implementing a structured approach to building AI software solutions are 2.3 times more likely to succeed in their projects.

As often happens on the paths of software development, the next stage will be facilitated if everything was done correctly in the previous one. It is possible to follow the sequence of actions of applying AI in the software development process using the example of a large language model (LLM).

Discovery phase

It is advisable to begin developing your AI product with a comprehensive discovery phase. This significant stage involves:

• Business challenge identification
• Data infrastructure analysis
• Success metric definition
• Technical feasibility assessment
• Project timeline establishment

During the discovery phase, you need to make a pivotal choice for your project: between the custom development of an AI model or the use of one of the existing ones.

The pre-trained AI model you select will also substantially affect your project’s outcome. Consider these critical factors when choosing the right AI model:

• Alignment with business objectives
• Scalability requirements
• Resource constraints
• Implementation complexity
• Maintenance requirements

Data collection and preparation

Quality data is the foundation of your AI software solution. Your data assets need proper gathering and preparation. Research reveals that 85% of AI projects fail because of poor data preparation. Your key focus areas should include:

• Data sourcing and validation
• Cleaning and preprocessing
• AI-compatible data structuring
• Quality assessment
• Privacy compliance verification

PoC development

Proof of Concept development ensures grounded responses and reduces misconceptions. A well-designed PoC should showcase feasibility and meet your business requirements. Research indicates that effective PoCs help organizations reduce their implementation risks by up to 60%.

In many cases, at the stage of PoC development, software engineers need to apply such techniques to improve the accuracy and reliability of the AI ​​model as Retrieval Augmented Generation (RAG) and Prompt Engineering.

Data tuning and additional data collection

You can create conditions for enhancing your AI model through iterative data improvements. When necessary, leverage synthetic data generation to augment your training data sets. Such data can improve model accuracy by up to 50% and ensure privacy compliance. Gartner estimates that, soon, most of the data for AI will be synthetic.

Training and fine-tuning of the AI model

This stage involves training and fine-tuning your AI model, in our example, the Large Language Model (LLM). This process requires:

• Parameter optimization
• Performance monitoring
• Iterative improvements
• Validation testing
• Production deployment preparation

Successful AI implementation isn’t just about technology—it’s about aligning AI capabilities with your business objectives. Fine-tuning allows you to adjust the model to your data and specific tasks.

AI Software Development: Riseapps’ Success Stories

Riseapps has been providing advanced AI-based software solutions since 2016, and its track record includes many vivid examples of clients achieving their business goals with the help of AI capabilities. Discover how leading companies are leveraging AI software development to transform their operations through these real-world success stories from Riseapps’ portfolio.

Case Study: BlackSwan AI Risk Management Operating System

BlackSwan Technologies partnered with Riseapps to upgrade its ELEMENT™ platform, an enterprise-level AI-powered operating system used in risk management. The core feature of the project was the use of multiple AI technologies, including machine learning, natural language processing, and knowledge graphs.

Customer Requests:

• Scaling the AI software engineering team
• Streamlining deployment
• Enhancing data security

Project Challenges:

• High deployment complexity
• Security issues
• AML (anti-money laundering) compliance
• Long processing times for transactions

Riseapps’ Solutions:

• Abstract architecture
• Cloud migration of infrastructure and CAIOS
• Transaction monitoring module
• Enhancing transaction processing capabilities

Project Results:

• $28 million in funding and new clients such as PepsiCo and J&J
• 6 million processed transactions per hour
• 50x decrease in deployment time
• 55% reduction in cost per alert
• 50% drop in AML-related false positives

Case Study: Black Doctor 24/7 Telemedicine Platform with Personal AI Assistance

Dr. Bill Releford, founder of Black Doctor 24/7, envisioned a telemedicine app that would address healthcare disparities in the African-American community.

Customer requests:

• Launching a cross-platform app within six months
• Integrating telemedicine with seamless video calling
• Implementing an AI virtual assistant for patient support
• Connecting the app with third-party services
• Ensuring full HIPAA compliance

Project challenges:

• Developing an all-encompassing platform under a tight timeline while complying with stringent HIPAA standards.
• Implementing complex functionality, including a 24/7 AI assistant

Riseapps solutions:

• AI assistant development
• Comprehensive UI/UX design
• Physician web platform
• Secure patient mobile app
• Advanced third-party integrations
• HIPAA compliance and security measures

Project results:

• Launch of the feature-rich platform in 6 months
• Full HIPAA compliance
• 24/7 personalized medical assistance with zero downtime
• 65% reducing diagnosis time and service time
• 92% patient satisfaction rate
• 40% cutting in сare delivery overheads