So, you’ve taken a long look around you. The truly endless companies utilizing artificial intelligence (AI) or preparing to introduce it have gotten your attention. You’ve noticed big brands like Netflix jump aboard, and you’ve read about superhuman robotics in healthcare.
Maybe you’re getting FOMO. Or, perhaps, the benefits of implementing AI have become too good to ignore. Whatever the reason might be, you’re asking yourself the question: how am I supposed to build AI software? Before you get started, you have a lot to learn.
It’s important to be fully clued up before you take on such a big investment. You need to be confident you’re getting it right so your resources, like money and employee time, don’t go to waste. With our ultimate guide to AI software development, you’ll be empowered to make the best decisions for you.
Why should you build your own AI software?
As a business owner researching AI implementation, you will have noticed two options: buying existing AI software or building your own. The decision is yours to make, but here are the most useful advantages of each to help you make a more informed choice.
|Buying an existing AI software
|Building your own AI software
|Some businesses do not have the funds or capacity to create something from scratch. Despite upfront costs, this approach will be more cost-effective and quicker to set up.
|With this method, you will be in control of the design and algorithms. This makes it better suited to your specific needs and will likely offer a better solution.
|Buying from an external company often comes with benefits like maintenance services and backend developer support. Some of the bigger burdens of AI product building are reduced or removed.
|When you build your own software, you have something none of the other companies in your market have. This could potentially give you a unique selling point and a competitive advantage.
|The software for sale has been refined and tested by professionals. It won’t be your responsibility to make sure it works well because this will have been done already. You’ll even have a friendly user interface.
|You’ll have the flexibility to grow. The solution can be scaled and adapted as your business needs change. You won’t have to retrain your in-house team or learn something new entirely.
|The creators of these AI applications are likely to be authoritative professionals in industries like computer science and deep learning. Why not learn from the best?
|You gain more independence because you don’t have to rely on a third-party developer to provide updates, introduce new advancements, or fix issues quickly.
Building AI software isn’t for everyone. That being said, it might be for you if you have unique requirements, innovation-driven goals, or existing AI knowledge. If you have your eyes set on the future and how to fit into it, this option is likely the better one for you.
The 6 steps of building AI software
Knowing how to build AI software is often complicated. Having a systematic approach is the best way to make it less daunting and ensure you haven’t missed any crucial course of action. Below we’ve broken down the tasks into manageable tasks to get the ball rolling.
1. Define your business problem
If you’re seeking AI assistance, you’re probably already aware of what you need to improve, whether it’s ineffective customer service, lagging workflow, or unengaging user experience. Before you commit to a solution, it’s important to define your problem clearly.
Doing so ensures your AI technology aligns with your business objectives and values. It also makes sure you move in the right direction towards effective results. There are plenty of ways you can identify your pain points and gain clarity on the situation. Here are a few.
- Ask employees to fill out surveys. Employees see more than you might think. As the people who work closest with the operations, such as the project manager, probably have a lot to tell you about where improvements need to be made.
- Conduct a gap audit. This process uses the current position of a company and its expected performance to discover what needs to change to get there. This is achieved with comparison and looking for where the differences are.
- Try sentiment analysis. Machine learning models can be found online, giving you insight into what your customers think about your brand. For example, they can analyze the emotions from text on social media to see if it’s positive or negative.
- Measure performance with metrics and KPIs. By examining things like customer reviews, market share, and return on investment (ROI), you can track progress towards goals and see how well business processes are performing over time.
Once you have all the information under your belt, you can develop a problem statement to explain what the concern is, why it’s a problem, and what needs to be done about it.
Your AI solutions can be designed to address a singular issue or multiple. However, you don’t want to bite off more than you can chew. It makes sense to focus on a concerning or complex problem if you discover it. The statement you create will help you to think about your highest priorities and how many resources need to be allocated before tackling them.
2. Gather the data
The data you use to train AI systems is all they know. Whatever you input becomes their entire universe. Big data, beyond what humans are capable of sifting through, can be processed with clever AI technology and algorithms. They learn from them and adapt to new information depending on what they find. Their expertise grows beyond what they were originally fed.
Now you know how important it is, how are you supposed to get it? Collecting data for developing AI software involves several steps. The process is a long and detailed one, but here is a general overview of what you’ll need to do and consider to get the best results.
- Choose your data sources. These should be related to your company specifically. For example, internal databases, time-series data, external APIs, and news articles would be a good place to start. Remember, the more information the machine learning algorithms have, the better they will be able to perform.
- Identification. You’ll need to decide if structured or unstructured data is best for you. If you wanted to delve into natural language processing (NLP) to create a chatbot, unstructured would capture a wider range of human expressions and reactions. Let’s stick with customer experience for the next part. In this case, textual data, such as customer support tickets, would be the most useful to include.
- Collect data. Think about how you’re going to access what you need. There is an abundance of methods to pick from, such as web scraping or data extraction from external sources. Remember, you’ll also need to choose an appropriate infrastructure to store it. Many software engineers would choose a cloud-based method for efficiency and simplicity of use.
- Cleaning and preprocessing. Data cleaning is the process of finding and removing errors. There are many techniques to do this, including fixing typos, removing unnecessary values, and translating language. Doing these tasks ensure the information you give your AI system is as high-quality as possible.
If your artificial intelligence software requires real-time knowledge and decision-making, continuously or periodically gathering data becomes essential. Patterns change and evolve, especially when dealing with volatile factors like markets, trends, and customer behaviors.
3. Choose an appropriate AI model
Here’s the good news. AI software development doesn’t necessarily mean you have to create a model from scratch. While that is an option for those who want the full custom experience, other methods can make the job a little bit easier.
As you now know, you can buy software instead of building your own. But that doesn’t give you any room to get creative. However, when it comes to AI models, there’s a lot more wriggle room. To show you what we mean, here are the main types worth considering.
- Custom model: this approach would require selecting appropriate algorithms and training the model on your selected data alone. This choice is best for those who have some sort of knowledge of AI, data science, and programming languages.
- Open-source model: Some models have already been designed and trained. These are adaptable, so you can use them with your datasets whilst also benefiting from other people’s contributions.
- Fine-tuning a model: this is where you take a pre-trained model and train it even more with your data, helping it to adapt specifically to your problem. It provides you with more flexibility than an existing model that cannot be modified.
- Model libraries: Certain AI platforms have been developed to handle user data and offer a collection of pre-built AI tools and functionalities. Microsoft Azure, for example, offers models for advanced speech recognition. Java integrations can be used to customize them and Python makes them easy to understand. Other platforms with these features include TensorFlow, PyTorch, IBM, and Scikit-learn.
Don’t be afraid to ask for help. You can consult with AI technology experts, development companies, and data scientists who have experience and valuable wisdom to share. Leveraging this additional expertise will ensure you implement the most feasible model for your software.
4. Build and train the AI model
Training is the foundation of your AI model. Without it, your software wouldn’t know what to do with the precious data you’ve given it. It’s an unskippable step. Doing so determines your AI capabilities and allows the system to perform a task accurately, make predictions based on unseen information, and recognize patterns that could present solutions to your most difficult business problems.
As usual, there are things you need to know before you get started. We promise this is one of the last hurdles before you have your final work-in-progress. So, without getting too deep into the nuances of the process, below is how you achieve success in your AI app.
- Set the initial values for the model’s parameters before training happens.
- Add in your training data and compare the predictions made with the known truth.
- Measure the difference between this comparison with loss functions. These evaluate how well your algorithm models your dataset.
- Attempt to minimize the loss by adjusting the model’s parameters.
- Repeat steps 2-4 as you deem necessary.
- The model (ideally) will gradually begin to improve, learn, and reduce loss. This will ultimately guide the incredible technology to perform successful data analytics.
Eventually, your model will reach a point where you think its performance has improved enough. When this happens, you can use it on new and unseen data. That’s when things get exciting! This will take time, so be patient and know it will be worth it afterwards.
Note that the process does depend on the model you chose. Also, if you opted to take advantage of one of the earlier mentioned libraries, make the most of the documentation and guidance. There you’ll find training instructions, which will be invaluable for a startup.
5. Consider the ethics
AI development demands a lot of skills, as you’ve seen from the discussions above. However, software demands more than just technical talent. You need to take measures that protect the overall well-being of humanity to allow for truly positive advancements.
In other words, with great power comes great responsibility. As exciting as these technologies are, they can do more harm than good when left unchecked. To foster innovation, trust, and equity, be aware of the impacts your AI project might have.
Various factors will affect your impact, such as your customers, intended usage, and the data your systems have been trained with. Therefore, we can’t tell you what to do. What we can do is give you some general ethical dilemmas to put some careful thought into.
- AI systems are not all-knowing and can be subject to inaccuracies.
- The conclusions the models come to can not always be explained.
- Some AI implementations can disrupt the job market and access to employment.
- It isn’t always clear who should be held accountable if something goes wrong.
- There is a chance the outcome will be harmful, biased, or discriminatory.
- Real-world values, such as cultural norms, won’t be considered unless trained to.
- The personal and sensitive information in datasets isn’t always well protected.
A good place to begin is documentation. Define the reasons for development, brainstorm guidelines, and lay out values. Eventually, an ethical framework will form specific to your company. This can be shared with stakeholders, internal team members, and freelancers.
6. Conduct ongoing maintenance
The advancements we’ve seen in AI already are breathtaking. And they just don’t seem to stop. The research lab OpenAI is set to release GPT-5 technology this year, which could be indistinguishable from a human. Measures like the Turing test could become obsolete.
The industry is not going to sit still. It’s more likely that AI will continue to take giant strides ahead and grow at a rapid pace. But what does this mean for your software development? If you don’t keep it up-to-date, someone else will do it for you. Your product will either pale in comparison to your competition, or it will become outdated and, soon enough, useless.
If you want your AI model to be successful, the work shouldn’t end when it reaches the shelves. The research, idea generation, and experimentation will be an ongoing effort. Maintenance allows you to keep up with new use cases, regulations, and evolving needs.
Below are the best practices for the longevity and viability of your product.
- Hire someone specifically to conduct routine checks. This may not be the best method for staying ahead of the curve, but it’s ideal for a busy company.
- Implement a machine learning method called predictive maintenance. Certain parameters will help the system with forecasting when changes or updates will be needed. It can also alert the user to likely failures and breakdowns.
- Educate your team. Training on emerging technologies will make them more likely to engage with news, trends, and the latest research. Also, encouraging them to participate in AI courses or communities keeps their knowledge and skills fresh.
- Seek collaboration. Those involved in AI are always brimming with ideas. Doing so will help you uncover new potential, team up on ambitious projects, and possibly find funding to build even more amazing things.
Ready to start building?
The optimization of business through AI software has huge potential. It enhances human capabilities through features such as automation and decision-making. But also, it will leverage you as someone who is future-proof, forward-thinking, and a powerful leader.
Not everyone who builds AI succeeds. We can’t all be Amazon or Google. Hopefully, now you can set realistic expectations and clear goals and embrace the necessary changes every company must succumb to. We think you’re ready for this big but rewarding challenge.
Inspiration for your next big adventure can be found everywhere. But if you want to see how the best of the best do it, have a gander at the AI-powered applications on Top Apps.