McKinsey Global Survey report indicates that more than 40% of organizations are ready to increase their investment in AI. This growing interest in AI underscores its transformative potential in multiple industries, from healthcare and finance to entertainment and logistics. 

However, as this technology progresses, considerable hurdles are closed. Despite the elevated interest and investment, developing quality AI applications is complex. The juggling between data, algorithms, and real-world applications brings a suite of challenges for developers. 

In this article, we’ll look at the challenges developers can face when making a quality AI app.

Challenge Solution
1. Insufficient Data . Augment data

. Use transfer learning

. Utilize synthetic data

2. Overfitting . Regularization

. Use dropout

. Increase training data

3. Model Interpretability . Use explainable AI tools 

. Adopt simpler models if possible

4. Handling Bias in Data . Audit datasets

. Implement fairness

. Enhancing interventions

5. Scalability Issues . Optimize algorithms

. Use distributed training

6. Model Deployment . Containerization (e.g., Docker)

. Use deployment platforms like AWS SageMaker

7. Real-time Processing . Use edge computing

. Optimize model architecture

Technical Challenges to Developing Quality AI Apps

1. Data Complexity

Data complexity

Data is the cornerstone of any business. However, only 3% of companies produce data that meets their basic quality standards. Most companies’ poor-quality data costs them an average of $15 million annually. The importance of data is true for AI, too. It helps AI systems learn and decide things. But getting good and different data is hard. It’s not just about collecting data; it has to be right and clean. Data can have mistakes, be incomplete, or be biased. Real data from the world can be messy and need cleaning.

Consider Tay, Microsoft’s 2016 chatbot for Twitter. It was designed to learn and improve through user interactions. But shortly after its launch, Tay started posting unsuitable tweets and was quickly taken offline.

If data is not balanced, AI can give wrong results. For instance, If an AI learns mostly from one group’s data, it might not work well for a different group. So,what can be done about it? 

Fortunately, there’s no shortage of tools to help manage data. Some notable names are – Apache Hadoop, MongoDB, and Databricks Lakehouse. But how can you ensure effective data collection and management? Begin by diversifying your data sources; depending solely on one channel can be limiting. Incorporating data from various sources provides a broader, unbiased perspective.  

It’s equally vital to invest in data-cleaning utilities, with platforms like OpenRefine and Talend being instrumental in refining and preparing data for AI integration.

Ensure that your data adheres to prevailing regulations, such as the GDPR in Europe or CCPA in California. Staying updated with legal changes and routinely revisiting your data collection practices is crucial. With meticulous planning and management, your data can become an invaluable resource, simplifying the integration of AI into your software solutions.

3. The AI Model Limitations

The AI model limitations

Every AI system uses an algorithm, a set of instructions for making choices. But these algorithms aren’t perfect. In fact, they are extremely complex. This complexity is the biggest challenge in developing a near-perfect AI app. 

Consider image recognition as a case in point. Deep convolutional neural networks are typically the first pick for such tasks, and they perform excellently. Yet, for simpler image datasets, straightforward supervised learning methods like logistic regression or support vector machines (SVMs) might do the trick. They’re also more straightforward to set up.

Take sentiment analysis as another instance. Simple supervised techniques like Naive Bayes can match the performance of intricate deep-learning solutions. Selecting between advanced and basic AI models really hinges on your particular requirements.

For vast datasets, you’d naturally lean towards the sophisticated models. However, with smaller datasets, opting for a more straightforward model can be both efficient and cost-saving. A notable drawback of intricate models is their opacity. They can seem like “mysteries” – you get results without clear insight into the processes inside. This lack of transparency makes it challenging to grasp their decision-making rationale.

Would you embed something you can’t decipher into your product? Likely not. So, when bringing AI into your product, where should you begin? Starting with a basic model is a wise strategy. Gauge its effectiveness and then ponder the need for complexity.

Tools such as AutoML or H2O Driverless AI can assist in automating the AI model development, guiding you in algorithm selection and training. To address the opacity issue, techniques like LIME and SHAP come in handy. They aim to demystify the decisions of sophisticated AI models. For optimal AI deployment, striking a balance between model intricacy and real-world applicability is key. Always pick the most fitting solution for your specific task.

Related Read: 10 Best Programming Languages for AI Development

3. Computational Costs

Computational costs

Developing AI applications goes beyond the development of merely models and algorithms; it requires significant financial and infrastructural investments. Global expenditures on AI-driven systems are projected to surge to $154 billion in 2023, marking a notable 26.9% hike from the previous year. And by 2026, this figure is anticipated to double, crossing a staggering $300 billion. 

An illustrative case is DeepMind’s AlphaGo, an AI construct engineered to outclass world champions in the game of Go. It’s estimated that the training expenses for this singular model amounted to an eye-watering $35 million for DeepMind. This was solely due to the high computational costs. 

Like many tech problems, new solutions are coming up. To deal with the high computational costs, companies can think about making some changes.

  • Cloud Computing: Leveraging cloud platforms like AWS, Google Cloud, or Microsoft Azure allows developers to rent computational power as needed. These platforms provide scalable resources, enabling developers to access powerful GPU and TPU infrastructures without the need for hefty initial investments.
  • Optimized algorithms: Reinventing the wheel would do no good. Instead, developers can use optimized libraries and frameworks. Tools like TensorFlow Lite or ONNX offer optimized operations that can run deep learning models more efficiently, even on devices with limited resources.
  • Transfer learning: Developers can utilize pre-trained models and fine-tune them for specific tasks. This approach drastically reduces the computational power and time required, as a significant part of the model’s learning has already been accomplished.
  • Quantization: This involves reducing the precision of the numbers in computations, which can accelerate model inference times without a substantial loss in accuracy.
  • Hardware accelerators: Companies can invest in AI-specific hardware like Edge TPUs or NVIDIA’s Jetson series. These devices are designed to run AI computations efficiently, offloading the intensive tasks from primary processors.

4. Integration Issues 

Integration issues

Implementing AI goes beyond merely introducing new code into your software. It’s essential to ensure that it seamlessly meshes with your existing systems. This compatibility is often a significant challenge in AI integration. 

Consider the healthcare sector. Using AI for predictive medical diagnostics can enhance accuracy, which is pivotal for successful treatment, potentially saving countless lives. However, when trying to merge AI with electronic medical record (EMR) systems, difficulties arise. Many EMR systems are deeply entrenched and dated, leading to compatibility problems when hospitals attempt to bring in AI technologies.

To tackle this challenge, start by integrating AI in phases, instead of a full-fledged integration. This allows for any compatibility issues to be identified and addressed step by step without disrupting the whole system. Moreover, utilizes middleware platforms that act as an intermediary layer between the AI system and the existing system. These platforms can help in translating requests and data between the older system and the newer AI solution, ensuring seamless operation.   

Customer’s Perspective

1. User’s Trust

User’s trust

A lot of people are unsure about letting AI make decisions. They’re worried about how AI thinks, especially in important areas like healthcare and finance. Recent research revealed that over 47% of individuals say they’re really worried that AI might harm our world over time. They believe there are big risks in using AI every day. Not to forget that over 24% of these people are upset about AI and how it’s used. 

For example, if someone uses an AI app to check the symptoms of an illness, they might not trust the app’s advice over a real doctor’s. Or, imagine using an AI to manage your money. If it suggests buying a certain stock, people might wonder if it’s a good choice or if they should ask a human financial advisor instead. This lack of trust is a big challenge because people need to feel safe and sure when using these tools. 

However, companies can take several steps to build trust in AI systems. Firstly, they should prioritize transparency. If users understand how AI makes decisions, they’ll be more comfortable using it. This could mean offering simple explanations or visual representations of how the AI works. 

Secondly, AI systems should be designed to collaborate with humans. The idea here should be effective collaboration and not human replacement. In the medical field, for instance, an AI could be positioned as a tool for doctors, not a replacement.

Thirdly, companies should invest in rigorous testing and quality assurance. The more reliable an AI system proves to be, the more trust it will garner. Also, offering user education and training sessions can help. If users feel competent and informed, they’re likely to trust the system. 

Lastly, gathering and acting on user feedback can further align AI tools with users’ needs and concerns, thereby enhancing trust.

Related Read: How AI Assistants can Bring Value to Your Business

2. Privacy Concerns 

Privacy concerns

People are more conscious of their data use. They worry about who accesses their information and its purpose. Take a fitness app as an example. For health advice, the app uses AI to analyze exercise habits, food intake, and sleep patterns. However, this requires your personal details. Some might hesitate to share this information, fearing the company might sell it or hackers could breach it.

A McKinsey report indicates that 87% of customers avoid companies with questionable security practices, making them hesitant to allow AI systems to access their data. How can companies change this notion? For starters, they can take steps to make users feel safer. One way is by using something called “data anonymization.” 

This means that when the AI examines your data, it sees patterns, not personal details. For instance, it registers “a user ran 5 miles” rather than “John Smith ran 5 miles.” This keeps your personal information safe even if someone else accesses the data.

Another step is to let users choose what data they want to share. Companies should make this choice clear and easy. They can also explain why they need the data and what they’ll do with it. Being open and honest can help build trust.

3. Usability Issues  

Usability issues

Usability is key for AI apps. The app needs to be user-friendly, and AI should enhance, not hinder, its functionality. Even with powerful AI, if an app isn’t intuitive, it won’t be popular. BCG’s study revealed that 70% of user-focused digital transformation projects didn’t meet the desired outcomes. 

Developers must prioritize the end-user during development. For instance, a photo app using AI should sort photos logically. If users can’t easily find pictures, they’ll be disappointed, regardless of the AI’s sophistication. To overcome this, developers should engage with users, gathering feedback and conducting tests. This ensures the app is potent and user-centric. In essence, a successful AI app must prioritize its users.


AI app development elevates your product. It revolutionizes user experience, automates tasks, offers personalized insights, and unlocks new possibilities. It’s not a walk in the park, though. The complexities of AI require a deep understanding of both the domain and the technology. Data collection, preprocessing, model training, and fine-tuning are just the tip of the iceberg. Integrating AI can take up a lot of your time and resources. This is where ScrumDigital comes into play. With our vast experience and expertise in AI app development, we navigate the intricacies and provide tailored solutions that fit seamlessly into your product. Connect today to learn more.

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