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Challenges in AI Software Development and How to Overcome Them

Artificial intelligence has been catapulted to center stage in the tech industry over the last year and a half. ChatGPT shook the market with its launch in late 2022, and now generative AI (the tools that can write text or create artwork when you feed them specific prompts) is at the top of most users’ minds when they hear the term “artificial intelligence.” But, AI is much more than that. AI software development has been around for decades, innovating the way we solve problems and increasing the capabilities of the technology we use every day. 

ai software development

AI Software Development and Its Unique Challenges

Projects integrating AI software development have their own unique set of challenges for developers. At DAS Labs, we integrate augmented reality and artificial intelligence to create groundbreaking solutions, and we're intimately familiar with the hiccups in the road that come with AI software development. By nature, effective AI models need robust computational capabilities. When working with massive datasets and training a model to interpret large amounts of data in specific and nuanced ways, developers might run into hurdles like data scarcity, model interpretability, and maintaining model accuracy over time.

Challenge 1: Data Scarcity 

High-quality data is gold in the world of artificial intelligence and machine learning, a subset of AI. The quality, quantity, and relevance of available data is one of the most crucial pieces to effective AI software development. Without enough reliable data, the model’s performance, accuracy, and reliability will suffer right from the beginning. Data is the way developers “teach” a model to detect patterns, make predictions, and improve over time via continuous training. 

There are a few common strategies that experienced developers might use to overcome data scarcity: synthetic data generation, data augmentation, utilizing public datasets, and data sharing collaborations. The first involves carefully creating new data points from existing ones with algorithms. However, when we don’t know enough about the statistical properties of the data to synthetically generate new data points, data augmentation comes into play. Data augmentation is a procedure which artificially generates more data points by performing subtle changes to our available data. For example, if we are working with images, by rotating the original images by 90, 180 and 270 degrees, we increase the size of our dataset 3 times. When the project truly requires real-world data, engineers might source publicly available datasets or form collaborative partnerships with other organizations who might have relevant data. 

Challenge 2: Model Interpretability 

The degree to which a person can understand why an AI model made a certain decision is called interpretability. Since artificial intelligence and machine learning are becoming more prevalent in sensitive settings like healthcare and finance, many users need to understand the model’s process in order to trust it - sometimes, with their lives. 

To help people to understand the models that they rely on, there are a few methods that prove helpful: 

  1. Feature Importance: By ranking an algorithm’s features based on their importance in improving the model’s predictions, engineers can explain which features are being used most in decision-making. 

  2. Visualization Tools: Visual aids are a powerful tool to make complex concepts more easy to understand. Techniques such as partial dependence plots (PDPs), decision trees visualization, and neural network graph tools help in demystifying a model's internal workings, and visually show how inputs are transformed into outputs.

Challenge 3: Maintaining Model Accuracy Over Time

As the world changes, the data that a model was trained on might become outdated. The phenomenon is called “concept drift,” and it happens when the statistical properties of the target variable (the one we’re trying to predict) change over time. When this happens, the assumptions at the foundation of the AI model no longer align with reality, and results can start to get strange and inconsistent. 

The first step in combating this phenomenon is to be aware of it and plan for it. AI is not a “set it and forget it” type of software, and once you understand the risk of neglecting maintenance, you can plan to maintain its relevance and effectiveness. We recommend a combination of these techniques, based on the software’s needs and availability of resources: 

  1. Continuous monitoring and model updating 

  2. Automatic retraining and versioning

  3. Implementing feedback loops from end-users

Credible AI Software Development Companies will be Thoroughly Familiar with Solving These Challenges

When considering an AI software development company, intentionally vetting their capabilities and experience with these challenges can help to avoid pitfalls down the road. At DAS Labs, planning for and responding to these challenges is a common part of our process. If you have a project you’d like to discuss with us, please set up a free consultation with our team. 


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