To develop AI software, is it a luxury or a must? Let’s look into whether or not it’s a beneficial idea for your business.

AI, or artificial intelligence, is a useful tool in many fields.

In 2024, the AI market will be worth 184 billion dollars. By 2030, it will be worth 826.7 billion dollars.

Companies won’t be able to avoid using AI solutions in their business processes, such as when they make AI products, because of this trend.

However, using AI in software development comes with new problems, and the costs that come with it are one of the biggest ones. This article goes into detail about the factors that affect the cost of developing AI software and gives you information to help you decide if investing in AI is a good idea for your business.

AI Development Process And Cost To Develop AI Software

Many steps go into making AI software, and each one has its problems and costs. To make a good budget and plan, you need to know about these steps and how much they cost.

Step 1: Evaluate And Make A Plan

In this first step, goals are set, feasibility is evaluated, and a plan for integrating AI is made.

These costs may apply:

  • AI experts will be paid to help define and evaluate the AI plan
  • Checks to see how ready the business and technology are
  • Setting aside money and time for planning and writing up

Workshops and thorough presentations may be needed for big projects.

The cost range for evaluating and planning an AI project is thought to be between $5,000 and $25,000 or more.

Step 2: Design And Build The Model

Here is the most important part of the AI development process: teams turn ideas into technology that can be used. Based on the type of growth you want to do, this step can be done in a few different ways.

To be exact, you can pick between:

  • Building special AI models,
  • AI models already trained,
  • Third-party AI companies

Custom Training And Application Of AI Models

It takes a lot of resources to get through this time. It includes tasks like getting the data ready, building the model, and deploying it. The following sub-steps make up the whole process, and each one affects the total cost of AI

Getting And Preparing The Data

You will need extra resources to name the data, get rid of useless information, and make a good training dataset if you don’t already have datasets that are ready to use for training models.

Building The Model’s Design And Teaching It How To Work

Building the model design and training it, which requires a lot of computing power, is done by specialists, usually data scientists or sometimes ML engineers.

Tests And Validation

The model is put to the test, and the team makes any necessary changes to make it work better.

Setting Up And Maintaining

The team puts the model into a production setting and keeps it in good shape. In this case, the high prices of infrastructure need to be taken into account.

The cost range for custom model training and operation is thought to be between $50,000 and $300,000 or more.

Using Models That Have Already Been Trained

Pre-trained models can be a cheap way to use AI, especially if your operations don’t need complicated models that are made just for them.

In this case,

  • The team finds the right pre-trained model and sets it up so that it works with the system.
  • The model is being added to systems that are already in place, which usually means making some changes. Read our other blog post to learn more about how to add AI to an app.

The AI models that have already been trained are thought to cost between $35,000 and $150,000 or more.

Step 3: Ongoing Updates And Improvements

The last step makes sure that AI systems keep working well and can change based on new data or requirements.

  • AI experts keep AI models up to date and tune them so they work better and are more accurate.
  • The system is watched by teams so that problems with performance can be fixed quickly or so that it can be updated with new operational data.
  • All those who are responsible keep everyone in the loop about the newest AI developments and system changes.

You can hire someone in a country with lower hourly rates to do this step, which will save you some money. 

The post-deployment stage is expected to cost between $20,000 and $80,000 or more.

Cost-Effective Approaches To Testing AI Before Full Investment

Cost To Develop AI Software

  1. Exploring AI solutions gradually before committing fully can be a smart, budget-friendly strategy. Building a data-driven, cost-aware culture within the development team encourages the creation of high-quality AI solutions while keeping development expenses under control.
  2. Using lean methods and scalable AI implementations can save costs and reduce the need for major rework as the project grows. Selecting widely used AI tools and frameworks with strong community support can also help cut down on development time and expenses compared to custom-built options.
  3. Applying agile methodologies in AI projects improves cost management and enhances project outcomes by allowing early identification and resolution of potential issues, especially with efficient AI algorithms. Outsourcing tasks like data labeling to specialized providers can further lower development costs.
  4. Additionally, employing modular and reusable components along with microservices architecture can save both development time and costs. Leveraging third-party AI software also simplifies the process.

Creating a Proof of Concept (PoC) for your AI or ML project allows you to validate your idea early in development, ensuring feasibility and effectiveness before making a larger commitment

Predictions For The Future Of AI Development

As AI technology continues to grow, some major trends are expected to shape the future of AI software development, affecting both costs and returns on investment:

Reduced Hardware Costs

With AI becoming more integrated into various sectors, there will be a push toward creating specialized hardware that reduces overall costs. Innovations, such as neuromorphic computing and photonic chips, are set to make AI systems both cheaper and more energy-efficient. This decrease in hardware expenses will make it easier for businesses to implement AI technologies like AI healthcare chatbots.

Expanding AI Adoption Across Industries

AI is likely to become more widely used across industries, leading to custom AI solutions designed to address unique challenges and opportunities within each sector. From AI in fintech to healthcare chatbots and customer service automation, AI will offer specific tools for different business needs, increasing efficiency and problem-solving capacity.

Improved ROI Tracking Tools

New and advanced tools will help companies accurately measure the return on investment (ROI) of their AI projects. With real-time analytics, businesses will gain immediate insights into how well their AI solutions are working. This will empower organizations to make informed decisions on AI spending and improve overall performance.

Wider Access To AI Resources

Open-source software, partnerships, and training resources will remove obstacles to AI use and increase adoption. The industry can close the skill gap by providing more training and resources to help more organizations design and maintain AI systems. As a result, more companies will be able to adopt tools like AI healthcare chatbots and AI call receivers in-house.

Focus On Ethical AI And Regulation

Governments and regulators are likely to introduce standards for AI development to ensure fairness, transparency, and accountability. Compliance with these ethical standards may raise costs initially but will support long-term growth and sustainability. With these new regulations, businesses will be encouraged to focus on developing ethical AI practices, especially as the demand for responsible AI solutions like AI in fintech increases.

Conclusion

While setting up and maintaining an AI system can be expensive due to the costs of development, upkeep, and hiring skilled professionals, it’s important to remember that these costs can be kept under control with good planning and resource management.

However, AI development can give a big return on their money because it can lead to more work getting done, better decisions, and better experiences for customers.

Appic Softwares can help you with AI and machine learning, or you can give us a call if you need a reliable partner to start your project.