How to Build Your Own AI Software With An In-House AI Team

The emergence of artificial intelligence (AI) technologies is undeniable. AI capabilities will gradually become the golden standard many software development projects want to build. For businesses wishing to leverage AI, there are two options: 

• Buy AI solutions, i.e., AI as a Service(AIaaS) from large tech enterprises such as Microsoft, Amazon, IBM, or Google. 
• Build your own AI software

Read here for some machine learning use cases that have benefited many businesses. Interested in building your own AI software? It will be a significant investment that, once successful, will bring lasting impact.

When Should You Build Your Own AI Software?

You should build your own AI software when AI capabilities form the backbone of your business. To make this decision, consider the following supporting reasons.

• The subscription model of AIaaS, like any SaaS, is known to cost more in the long term.
• AIaaS puts a data limit that obstructs you from scaling up.
• A custom AI solution that best fits your business needs without waiting for customer service and complete control over
• More control over risks such as data security and storage, technical issues, and downtime. This is important not just for ESG reporting; your customers and shareholders will expect better risk management in turbulent times.
• You own the AI technology, data, and assets, and can modify or rent them out as needed.

If you find these reasons convincing enough, read the rest of the article to see the main steps in building an AI software product, especially building an AI team.

How to Build AI Software

While every AI project is different, these are the typical phases in building an AI software product. It is recommended to start small with a prototype to test your ideas and earn more budget that would support more ambitious goals.

1. Clarify Your AI Business Objectives

Don’t just chase the shiny new trend. Analyze how emerging AI technologies can truly bring value to your business. Do they solve any of your pain points that cannot be solved otherwise? What are their genuine contributions?

A lot of businesses fail in their AI undertakings because they let FOMO (fear of missing out) squander away their resources on costly but poorly fitted and futile AI projects. When it seems everyone else is doing it, think critically about why you want your own AI software. The above section may help.

2. Evaluate Available Data

Once you have nailed down the justifiable business objectives, consider your available data. Is your data siloed or inconsistent? The current state of your data will affect your choice of the tech stack and the skills you will need. Your business objectives and the available data significantly impact the scope of your project, and thus, the amount of investment. Therefore, ensure the first two steps are properly done - they justify the “why” of your project.

3. Choose The Right Tech Stack

The right tech stack should satisfy your project requirements. This includes the programming languages, development tools, testing tools, cloud services, and big data solutions. You can only narrow down your options when you are clear about your project requirements. Your AI team (see below) should be able to advise on these AI solutions.

4. Invest in IT Infrastructure

Training machine learning models eat up a lot of computational resources. You may need to invest in extra servers, storage, and network to ensure the AI operation does not disrupt your current operation. If your business already has in place updated technologies such as cloud computing and data analytics infrastructure, then scaling them up to make space for AI capabilities is a reasonable expectation. Meanwhile, those with legacy systems will need to modernize before they can get started with AI projects.

Moreover, having an AI-ready infrastructure makes it easier to attract and build an AI team.

5. Build an AI Team

Three types of roles comprise a comprehensive AI team: business, data analytics, and software engineering.

The business and engineering people may be familiar with working together on previous non-AI projects, but the data specialists are likely new to the team. Consequently, the AI team needs to figure out how best to work together, by answering questions such as:

• How should AI projects be managed?

Consider this within the context of your company. If your company favors the Waterfall project management methodology, will it be sustainable for AI projects with high risks? To manage the risks of AI project failure, how big is the expected learning curve for your organization?

• How experienced are your team members in working with AI?

AI is still a new technology requiring a major shift in mindset; adding on to this is a global AI talent shortage. It is a big ask to expect that your company can compete against Big Tech for an entire team of AI experts. It is normal to have team members who will be working on an enterprise-level AI project for the first time. Make sure you do have some experienced AI team members working alongside with others who are willing to start their own AI journey.

Jump to the section below for more details about the key roles in an AI team.

6. Big Data Analytics

To illustrate the steps of data analytics in which your data goes through before it can be deployed into AI software development, let’s consider the Data Science Hierarchy of Needs.

Your company’s data goes through each level of the pyramid to reach the top which is AI software development. Depending on the state of your data, you will need to set up the following framework, with several data specialists moving your data from raw to AI deployment:

• A data engineer collects structured and unstructured data.
• A data scientist cleans and prepares the data for analysis.
• A data analyst interprets and visualizes data for insights and decision-making.
• A machine learning (ML) engineer trains ML models with data to make predictions.

7. Deploy ML Models into AI Software Development

The software engineers deploy ML models to build AI software during this phase. Since the risks in AI projects are high, adopting Agile into the software development cycle is better to manage risks at every phase. Specifically, the AI team should follow MLOps.

MLOps provides the framework for a software team to incorporate ML models into development. MLOps is an extension of DevOps, a practice that improves software quality with continuous feedback between operations and development.

Essentially, MLOps facilitates the collaboration between the AI software team and operations, enabling a comprehensive workflow that traverses software, hardware, and IT support. Its ability to break down silos between departments is a quality that will benefit AI software development.

8. Testing of Software and ML Models

Just as software improves in quality through rigorous testing, ML models also benefit from continuous testing. This is because ML models are continuously updating itself with new data without human interference. Without frequent monitoring, datasets will degrade, and biases may be introduced into the datasets.