When UPS implemented an AI route optimization engine, they started saving 100 million miles of driving and 10 million gallons of fuel annually.

American Express uses AI to detect 99.9% of fraudulent transactions in real time, while Netflix claims to have saved $1 billion in customer turnover after switching to AI-based recommendations.

AI has been in use for some time now, and we can observe its tangible impact. The truth is that AI isn’t just for big tech companies. It’s a practical tool that businesses can introduce to solve some real problems.

In this article, we’ll walk you through how to develop AI software and showcase some examples we, here at Techstack, have delivered with our partners. Let’s dive in and learn how to create AI software!

Defining Artificial Intelligence and Its Operating Mechanisms

Essentially, Artificial Intelligence is about building systems that can analyze data, find patterns, and make decisions for different scenarios. Think of it as teaching a computer to learn from examples.

When you scroll through your social media feed, AI looks at your past behavior: what posts you’ve liked, how long you’ve watched certain videos, and which articles you’ve shared. It then uses all this data to predict what content will keep you engaged. The same principle applies to fraud detection in banking to quality control in manufacturing.

What makes today’s AI so powerful is its ability to process vast amounts of data. A human analyst can only spot trends across a few variables. On the other hand, AI can analyze hundreds of variables at the same time to find subtle correlations and patterns.

Modern AI is available to businesses of all sizes thanks to powerful tools and frameworks. Cloud services from providers like AWS, Google, and Microsoft offer pre-built AI capabilities that can be integrated into applications with minimal coding. Open-source libraries like TensorFlow and PyTorch allow you to build custom AI solutions without creating everything from scratch.

Key Concepts Behind AI

At its heart, AI systems operate on three fundamental principles: data, training, and prediction. AI needs examples to learn from and a way to test its understanding.

Data is the foundation of any AI system. When Amazon recommends products, it's using patterns found in millions of customer interactions. But you don't need massive datasets to start—even a few thousand examples can be enough for many apps.

Training is where AI develops its "intelligence." During this phase, the system analyzes data to spot patterns. For instance, a fraud detection AI learns to spot suspicious transactions by studying past examples of both legitimate and fraudulent activities.

Finally, prediction is where AI puts what it’s learned into practice. This could be classifying images, generating text responses, or making recommendations.

In fact, it looks very similar to how a seasoned professional makes decisions based on experience, except AI can process thousands of factors at once.

The principles above are used by key types of AI systems: Machine Learning, Deep Learning, Natural Language Processing, and Computer Vision.

Although some of these technologies are subsets of others, let's look at the most widespread and common-known AI approaches:

Machine Learning models excel at finding patterns in structured data, such as spreadsheets, databases, or any other type of organized information. This is what powers Netflix's recommendation engine or fraud detection systems at banks.

Deep Learning, a specialized subset of machine learning, works particularly well with unstructured data like images, audio, or text. These neural network-based models form the backbone of many modern AI systems.

Natural Language Processing (NLP), largely powered by deep learning techniques today, focuses on understanding and generating human language. Modern AI assistants like ChatGPT combine NLP with Deep Learning and other generative AI techniques to enable natural conversations.Computer Vision, often implemented through deep learning architectures, enables machines to understand visual information. From quality control in factories to medical image analysis, these systems can spot details that might escape the human eye.

AI Compared to Conventional Programming

Traditional programming follows strict rules that rarely change. Making an AI program is different because it learns from examples you feed it and adapts automatically.

Learning how to build AI software begins with understanding whether you need custom development or, indeed, AI integration.

Regular code needs constant updates, while AI improves with more data. Overall, it’s best to use traditional programming for precise tasks and AI for pattern recognition.

Neither approach is universally better, and each has its own strengths. Learning how to make an AI program requires understanding when each approach works best.

Conventional programming remains perfect for tasks with clear, unchanging rules, like processing payments. AI excels at handling ambiguity and discovering patterns in complex data. Modern software often combines both approaches for optimal results.

Important Considerations Before Implementing AI in Your Business

Before jumping into developing an AI, we recommend businesses to focus on three critical questions:

• the problem you’re solving;
• the data you have;
• the resources you need.

First, take your time to define your problem. Rather than "we want to develop a piece of AI software," identify specific challenges: "We need to predict inventory levels" or "We want to automate customer support for common questions." The clearer your goal, the better you can measure the outcome’s success.

The next critical element for developing an AI is quality data. When you develop AI software, you'll need thousands of clean, organized data examples.

The third integral element is sufficient computing power and AI expertise or access to development tools.

Once you’re all set, don’t rush into big projects. Begin with focused pilot projects that align with clear business values.Remember: Not every problem requires developing AI software. Sometimes, traditional programming or even manual processes might be enough. AI shines at handling complex patterns and predictions, but it's not a magic wand for every business case.

Business Challenges That AI Can Address

When deciding to develop an AI solution, we need to identify where it can deliver real value. Here are tasks where AI consistently shows strong returns:

Pattern Detection and Prediction

• Sales forecasting: Predict future revenue based on historical data, market trends, and customer behavior
• Equipment maintenance: Anticipate issues before they happen by analyzing sensor data
• Customer retention: Identify customers likely to leave based on engagement patterns

Process Automation

• Document processing: Extract key information from invoices, contracts, and forms
• Customer support: Handle routine inquiries through AI chatbots
• Quality control: Spot defects in manufacturing through computer vision

Data Analysis

• Market research: Analyze customer feedback across social media and reviews
• Competitive intelligence: Track market trends and competitor activities
• Resource optimization: Better allocate staff and resources based on demand patterns

Personalization

• Product recommendations: Suggest items based on browsing and purchase history
• Content delivery: Customize website content for different customer segments
• Email marketing: Tailor messaging and timing to individual preferences

The key to successfully developing an AI system is to start with a clear, measurable business problem.

Remember: The best AI solutions often target specific, well-defined problems, rather than trying to solve everything at once. Start with one area where you can clearly measure success, then expand based on results.

Advantages of Integrating AI into Your Business

You can see AI’s business impact across five dimensions: efficiency, insight, scalability, innovation, and customer experience. Here are a few examples of the benefits AI can potentially bring to your business:

Speed and Scalability

• Process millions of transactions and data in real-time
• Scale up during peak times

Deeper Insights and Predictions

• Forecast with more accuracy
• Spot opportunities and risks earlier

Operational Excellence

• Automate complex workflows with high accuracy
• Run 24/7 without extra cost

Financial Aspects

• Automate operational costs
• Drive revenue through personalized customer experiences
• Get better ROI on marketing and sales efforts

Ongoing Improvement

• Systems that adapt to your business needs
• Quick response to market changes and new challenges

The real power of AI is to augment, not replace human capabilities. AI helps you out by handling data-heavy tasks and complex calculations, so you can focus on creative and strategic solutions instead.

Many of these benefits are already accessible through existing AI services and platforms. Instead of thinking about how to build an AI platform from scratch, businesses can leverage the following:

• Cloud providers like AWS, Google Cloud, and Azure offer pre-trained AI models and services for everyday business needs
• Industry-specific SaaS solutions that have AI capabilities built-in for tasks like customer service, sales forecasting, or inventory management
• APIs from established AI companies that provide specific functionalities like text analysis, image recognition, or recommendation systems

This approach can reduce implementation time and costs while still delivering the benefits of AI to your business.

Example of AI Software Developed by Techstack

Building an AI-Powered Virtual Assistant for Healthcare

A US-based healthcare provider faced a major challenge in delivering timely and accurate cancer risk assessments.

With an overwhelming volume of patient data and a shortage of oncology specialists, they needed a solution to scale their expertise while maintaining assessment quality.

We partnered to enhance their existing system through AI integration.

Challenges

As we investigated the struggles our partner experienced, we outlined the critical challenges our team eagerly tackled:

• Manual processing created significant delays in patient evaluation.
• The assessment methods weren't consistent.
• Limited specialist availability impacted service delivery.
• The existing systems didn't cope with the growing patient data volumes.

We set a two-week timeline to deliver an MVP that profoundly changed our partner's assessment approach.

Technical Spotlight

We developed a sophisticated solution integrating OpenAI's GPT-4 with existing medical systems. Key technical solutions included:

• Custom prompt engineering for medical context processing
• React/TypeScript frontend with Material UI for rapid deployment
• AWS-based infrastructure ensuring HIPAA compliance and scalability
• RAG implementation for real-time medical knowledge integration

Business Impact

The transformation and impact was immediate:

• Reduced assessment time from hours to minutes
• Enabled consistent evaluation standards across all cases
• Expanded specialist reach through AI assistance
• Created a foundation for scalable patient care

The AI assistant now processes thousands of patient records while maintaining high accuracy and security standards.

This successful case has built a solid foundation for expanding the system into a comprehensive patient management platform, showcasing how AI can effectively augment healthcare professionals in delivering better service.

A Step-by-Step Guide to Developing AI-Driven Software

Now that we have covered the basics, let’s walk through the AI creating software process.

Step 1: Problem Identification and Goal Setting

As we’ve previously discussed, this is your starting point in your journey to learn how to build AI software. Consider these questions your kick-off checklist.

Define your goals

• Identify a specific business challenge
• Decide what success looks like
• Set clear, measurable goals

Research the following questions

• Can AI actually solve this problem better than traditional alternatives?
• Do you have enough data in-house to train an AI?
• What's your target accuracy or performance level?

Set Clear Metrics

• Define target improvement numbers
• Establish timeline
• Calculate expected ROI

For example, instead of “We need AI to get customer insights,” break it down further into “We need to have our customer reviews analyzed to identify top 3 product improvement priorities.”

The more specific your requirements are, the easier it becomes to craft your software.

Step 2: Gathering and Preparing Data

Data is your AI's foundation. Here's how to get it right:

Data Requirements

• Quality matters more than quantity
• Aim for diverse, representative samples
• Ensure data is legally compliant and ethical

Common Data Sources

• Internal databases and CRM systems
• Customer interactions and feedback
• Sensor readings and logs
• Public datasets and APIs
• Third-party data providers

Data Preparation Checklist

Clean Your Data

• Remove duplicates and errors
• Handle missing values
• Standardize inputs

Structure Your Data

• Organize into clear categories
• Label data accurately
• Create training and testing sets
• Balance your dataset

Step 3: Choosing the Right Tools and Platforms

Think of AI tools like choosing the right equipment for building a house: each has its purpose, and picking the right ones makes your job much easier.

Essential Development Tools:

• Jupyter Notebook: Like a playground for testing AI ideas
• Docker: Packages everything neatly for deployment
• Git: Keeps track of your code changes
• MLflow: Helps monitor how your AI models perform

Choose Based On:

• Your team's comfort with coding
• Your project's size and budget
• Where you'll deploy your solution
• What other tools you need to work with

Pro Tip from Techstack: Don't get overwhelmed by options. Start with user-friendly tools like Fast.ai or Azure ML Studio. As your needs grow, you can always switch to more sophisticated tools.

Step 4: Designing the Algorithm or Choosing the Model

You can choose an AI model that will work well for you, depending on your goals. Here's how to pick the right one:

Selection Criteria:

• Data type (text, images, numbers)
• Problem complexity
• Training data size
• Performance requirements
• Computing resources

Pro Tip from Techstack: Start with simpler models that meet your minimum requirements. You can always make them more complex if needed. Often, a basic model that works is better than a perfect model that's still in development.

Step 5: Training the AI Model or Algorithm

Training your AI model is like teaching a student. It needs relevant examples, proper guidance, and regular testing to ensure it's learning the right way.

Training Process Essentials: Your first step is splitting your data—typically 70% for training, 15% for validation, and 15% for testing.

Think of this as having study materials, practice tests, and final exams. Initial training starts with small batches of data, letting your model gradually learn patterns. You'll then validate its performance on new data to ensure it's learning properly, not just memorizing.

Key Metrics to Watch:

• Accuracy: How often your model makes correct predictions
• Processing Time: How quickly it can make predictions
• Resource Usage: Computing power and memory needed

Pro Tip from Techstack: Start small. Train your model on a subset of data to quickly test and fine-tune your approach before scaling up to complete training. This saves time and resources and helps identify problems early on.

Step 6: Assessing the AI System's Performance

When creating an AI system, thorough testing is where major work happens. Here's how to evaluate your AI’s model efficiency:

Technical Performance

• Accuracy: How often your model gets it right
• Speed: Response time under different loads
• Scalability: Performance with increasing data

Business Impact

• Cost savings compared to traditional methods
• Time saved in operations
• Revenue generated or preserved
• Customer satisfaction

Red Flags to Watch For:

• Inconsistent performance across different user groups
• Unexplainable results
• High failure rates with slight data variations
• Resource usage spikes

Pro Tip from Techstack: Remember that immaculate accuracy isn't always necessary when learning how to create an AI system. Focus on meaningful improvements over current solutions.

A model that's 80% accurate but processes thousands of cases per minute might be more valuable than a 99% accurate model that takes hours to run.

Success Checklist:

• Meets or exceeds defined goals
• Performs reliably under stress
• Stays within resource budgets
• Provides clear business value

Step 7: Implementing Your AI Solution

To successfully create Artificial Intelligence that delivers value, deployment needs to be as thoughtful as development. Here's your implementation roadmap:

• Start with a pilot program
• Roll out in phases
• Monitor performance closely
• Have fallback options ready

Integration Checklist:

Technical Integration

• API endpoints and documentation
• Data pipeline connections
• Security protocols
• Monitoring systems

Process Integration

• Update existing workflows
• Train end users
• Document new procedures
• Set up support channels

Performance Monitoring

• Real-time analytics dashboard
• Error logging and alerts
• Usage metrics tracking
• Cost monitoring

Risk Management:

• Backup systems ready
• Data privacy compliance
• Regular security audits
• Performance thresholds with alerts

Pro Tip from Techstack: Successful preparation often takes longer than development. Plan for extensive testing and adjustment periods. Start small, prove value, and then scale up.

Step 8: Ongoing Monitoring and Updates

An AI system is never truly finished: it needs continuous attention to maintain and improve performance. Here's how to keep your system operating at its best:

Monitor these areas daily:

• Performance metrics and accuracy levels
• Processing speed and resource usage
• User feedback and system interactions
• Error rates and unusual patterns

Create clear protocols for different scenarios:

• Performance drops below thresholds
• Unexpected system behavior
• Data quality issues
• Security concerns

Pro Tip from Techstack: Build a monitoring dashboard that alerts you to issues before they impact users. Focus on metrics directly tied to business value, not just technical performance.

Remember: good monitoring is proactive, not reactive. Regular small adjustments prevent the need for major overhauls and keep your AI system delivering consistent value.

Common Challenges in AI Software Development

Let's explore the key hurdles that come up when creating AI software.

Ethical Considerations

AI's growing impact raises some questions, and for good reason.

How do we protect user privacy? What about bias in AI decisions? How can one develop artificial intelligence software that follows ethical requirements?

Users deserve to know how AI affects their lives, especially in critical areas like healthcare and banking. Smart companies build ethical guidelines from day one and regularly check that their AI stays fair and transparent.

High Computational Demands for AI Training

Training AI is resource-demanding. Even smaller projects need serious computing power. While cloud services do help a lot, costs can add up fast. Success means finding the right balance between AI power and practical budgets.

AI’s Limited Understanding of the Code It Produces

Working with AI systems requires a lot of attention to data management and continuous updates.

The models need high-quality, well-structured data to perform well, and keeping these models up-to-date with new information is an ongoing task. This is why skilled engineers are essential: they ensure proper data pipelines, update mechanisms and maintain the system's reliability over time.

Risks of Errors and Potential Damage

AI mistakes can have big consequences. For example, a glitch in AI trading could cause millions in losses in mere minutes. A healthcare AI error could affect patient care. As you will no doubt understand, a minor discrepancy can cause a domino effect that impacts your product.

So, thorough testing, safety checks, and human oversight are necessary for responsible AI development.

Challenges in Integrating AI with Current Systems

Adding AI to existing systems can indeed be tricky. Old and new technologies don't always play nice, and real-time AI can often overwhelm older systems.
Keep in mind that companies sometimes need to upgrade their entire technology foundation to make AI work effectively.

Scarcity of Skilled AI Developers

The best AI developers are problem solvers who understand complex mathematics, software architecture, and the business side of the product.

It’s often a good strategy to outsource key senior roles, while your in-house developers gradually grow into the role.

Final thoughts

When smartly used and with tangible outcomes in mind, AI integration can be a deal-breaker for your company’s growth. We’ve seen it work for various businesses across a few domains and can help you decide if that’s your go-to option.
Drop us a line to book a free consultation and learn how we can use your data to your advantage.