How to Create AI Software Step by Step: An Implementation Guide

The growing range of AI use cases and applications makes many organizations wonder how to create artificial intelligence software.

According to a flash survey by McKinsey among 100+ organizations with $50M+ in revenue, 63% of respondents gave AI technologies a high and very high priority. However, 91% are not completely prepared for responsible AI implementation. 

It’s no surprise that implementing AI can be difficult. Alongside immense opportunities, AI hides challenges and risks that the project team must handle.

Let’s analyze specific details of AI software development in this guide for business owners.

When building AI software, organizations can choose from numerous types of AI and specific AI technologies. The most essential branches of artificial intelligence include:

ML covers a variety of algorithms that can learn from data. These algorithms can be used in recommendation systems, spam detection, forecasting, and other use cases.

Deep learning focuses on building neural networks that function similarly to the human brain. It is used for image recognition, natural language processing (NLP), predictive analytics, and more.

NLP is used to understand, process, and interpret human languages, including through speech recognition, translation, and sentiment analysis.

Robotic technologies engage with building and operating machines to automate processes in manufacturing and transportation.

Expert systems reproduce human decision-making processes, usually based on expert knowledge in a specific industry; they can be applied to planning, service, predictions, etc.

Computer vision focuses on image and video recognition. It is a key technology for AR, defect detection, facial recognition, and autonomous cars.

The most recent and advanced technology, generative AI is used to understand a user’s request and generate a relevant text, voiced answer, or image in response.

Your organization can make your own AI application for the following processes:

Source: Microsoft Learn 

Some of the many aspects where organizations can solve problems with the help of AI software include:

AI can take over important but low-added-value tasks, such as generating meeting notes, screening CVs, or working on initial ideation.

AI/ML solutions can detect fraud and cyberattacks in real time.

AI automates time-consuming operations such as route generation, payroll, or big data analysis, decreasing the probability of human error.

Generative AI can answer questions from prospects and current customers, helping with purchasing and troubleshooting.

AI can efficiently process hundreds of parameters and quickly build detailed reports.

Cost is one of the key factors to consider for every successful artificial intelligence implementation.

Still, estimating costs takes time and effort. The expected outlay to create AI applications can vary from $5,000 for a simple chatbot integration to $300,000 for a custom AI-powered automation system.

An estimate for creating AI software depends on several factors:

Let’s analyze these factors in detail:

As with any other software development project, building AI software varies in complexity depending on various factors:

Here are four categories of AI solutions based on their complexity:

Integrate existing AI tools as is, with minimal fine-tuning and pre-set information.

Examples: AI prototypes, chatbots

Select and fine-tune an existing AI technology while using your data. 

Example: Background check module for an HRM system

Integrate a fine-tuned AI model with other complex solutions, such as an AR/VR system.

Example: AR/VR virtual assistant

Work on the best approach and create your tailored AI model based on unique requirements and specific data. AI software is tightly integrated with other software your company uses.  

Example: AI analytics and forecasting system

When you think about how to create artificial intelligence software, you should focus specifically on selecting an AI model.

You can build a custom AI model from scratch. However, it may cost you six to seven figures.

Alternatively, you can integrate a prebuilt AI model. In this case, you save on upfront development costs but pay a subscription/license fee. Also, specific usage conditions, a minimum contract period, and other limitations may apply.

Ultimately, you can even use an AI service as is. ChatGPT and similar services are free to use via their apps, which your employees can use to perform their tasks (e.g., generate source code) but you cannot integrate a free app.

If you want to use an AI model internally, you will also need high-end hardware for efficient calculations, which you need to include in your initial cost estimate. 

You can deploy your infrastructure in the cloud and pay as you go for computing time. Also, scaling is simpler in the cloud than when using internal servers.

Choosing an in-house team versus an outsourcing team is an important decision for managing the costs of building AI software. 

An in-house team ensures instant communication and improved cost control. However, it can be difficult to assemble an in-house team due to talent shortages. It can take 45 days to 5 months to fill a specific position, while you may need a team of 5 to 15 engineers. Also, keeping an in-house team is expensive.

Source: Indeed

In contrast, an outsourcing team can start building AI software for you immediately while managing all technical aspects of AI development, deployment, and maintenance. And with outsourcing, you will pay a lower hourly rate. You will also save on hiring and overhead. 

Still, it is crucial to collaborate with a reliable vendor that has positive reviews from real clients. Otherwise, you risk wasting time and money on implementing an irrelevant AI solution.

Last but not least, you should expect to spend 15% to 20% of your initial investment in AI software annually on updates and maintenance. 

This money will pay for:

If you plan to further expand your software product after its initial launch, that will likely be a separate project. Unlike maintenance, the costs of feature expansion are hard to predict. The team must estimate requirements and prepare a new plan that meets your AI software expansion needs. 

Be ready to transition to new infrastructure or a new AI service when your current AI solution outgrows a specific service’s capabilities, or when the service shuts down. As with software expansion, the team must prepare a transition plan and estimate the cost of implementing it.

Whether you are building a fraud detection system or a customer support bot, the AI/ML lifecycle covers the following key activities:

Before AI development starts, your organization needs a holistic view of how to create AI software and how it affects the company’s processes. 

Your organization will need a cross-functional team comprising managers, software engineers, lawyers, and end users to understand various aspects of AI software development:

The team needs to determine the project’s feasibility and estimate ROI, ensuring that the savings from building AI software will justify the development costs. The team must also understand possible negative impacts on the entire organization, employees, customers, and the community.

They must check regulatory requirements and devise practical steps to encourage fair use and transparency, fostering trust from system users and confidence in the technology.

When you are ready with the preparation step, you can move on to building AI software.

Next, you can focus on developing a solution based on your plan for how to create artificial intelligence software efficiently:

The quality of the dataset is critical for the accuracy of the results. Hence, it’s crucial to decide on data types and volumes. The team also determines how it collects, normalizes, filters, and processes relevant data, ensuring the data set is clean, representative, and pertinent to the project goals.

At this step, the team should clearly identify success metrics which indicate that the AI software is ready for implementation. They must be specific and measurable (for example: 95% result accuracy, 20% decreased operational time, 7.5% false positive reactions).

Features are individual attributes of a particular piece of data (for example, a customer’s age range). The team must work with subject matter experts to determine how data features could affect the results.

It is also possible that the team must optimize data using specialized services when available data is insufficient. Extra data evaluation may be required in this case.

The project team can develop an AI model. However, building an AI model from scratch can be expensive. When resources are limited, the team can adapt a prebuilt model or integrate one via an API and fine-tune it.

The team should prioritize a model with transparent data processing.

For successful model training, the team should split the entire dataset into two parts: training data and test data. These two datasets should be combined wisely and not intersect. The team will feed the prepared training dataset to the AI model.

The team works in fast-paced iterations, evaluating results after each, adjusting both the AI model and dataset, adding new data, or even switching the AI model when results are unsatisfactory.

When the model provides results, the team evaluates them to ensure they meet expectations. It then decides either on deploying the model to production when it does or moving to the next reiteration if improvement is required.

It’s crucial that the team uses real data but works in a training environment, which is isolated from the live environment and does not affect the company’s processes.

Once the AI model regularly provides accurate and interpretable results, the team can move it to the live environment. 

To deploy the developed AI solution, the team needs to package it into containers that connect to user software, ensuring end users can start completing their everyday tasks with the help of AI. 

The team can deploy the AI solution to the cloud, deploy it to internal services, or exchange data with the AI model via its API. 

It is important to introduce the best DevOps (MLOps) practices to an AI project to streamline AI software management and updates. DevOps practices, including a CI/CD pipeline, centralized code/dataset management, and shared environments enable the team to maintain the project codebase in a good state and deliver updates quickly.

Let’s summarize. Here is your list of things to keep in mind when thinking about how to create AI software:

Prepare clean, structured, and machine-readable data. It must be relevant to the problem you are solving and cover required scenarios.

Identify the problems AI software will solve to understand what tangible results you can expect. 

Set up the legal framework for collecting and processing user data while adhering to regulatory and ethical principles.

Find an AI/ML model capable of producing relevant results at a competitive price.

Install high-end servers or purchase cloud time to train, run, and optimize the AI model.

Hire an in-house team or opt for artificial intelligence consulting to design, customize, and deploy AI software.

Be ready to iterate to reach desired results and retrain the model to adapt to a constantly changing environment.

With the help of the described steps, you can create artificial intelligence software that meets your business’s needs and end-users’ expectations.  

Go through the steps to determine your tech needs, find the best technology, tune it, and integrate it into your processes. Several iterations can be required to produce the expected results. 

Also, remember to constantly retrain your AI solution, which is crucial for remaining at the top of the competition.

An expert engineering team is crucial for the success of any digital project, and AI software is no exception.

IT Craft engineers can add missing AI expertise, helping you design a plan for how to create AI software cost-effectively, integrate AI technologies, monitor app health, and more. 

Check out IT Craft’s artificial intelligence development services.