What are the most significant challenges to developing AI-driven apps?

Have you ever thought of what’s so common in Siri, Cortana, Alexa, and Google Assistant? They are all AI-driven apps that make our lives easier and help us get through the day.

Nowadays, Artificial Intelligence is used in a number of different industries such as retail, health care, finance, real estate, gaming, and many more.

As reported by MarketsandMarkets, the global artificial intelligence market size was valued at USD 58.3 billion in 2020 and is expected to grow at USD 309.6 billion by 2026, at a CAGR of 39.7%. This clearly indicates that businesses are showing a keen interest in AI.

Although the advantages of AI and machine learning have become more prevalent, a set of disadvantages of AI cannot be ignored. These challenges are related to developing AI-driven apps that are becoming increasingly popular all around the globe.

Primary challenges in developing AI-driven apps

With AI empowering myriad devices at the core, the demand for apps that use Artificial Intelligence is also increasing. However, there are certain challenges with Artificial Intelligence that need to be addressed while developing an app.

Scarcity of data

One of the biggest challenges to developing AI-driven apps is the scarcity of data. This is because AI algorithms require a large amount of data to learn and improve.

It is difficult to train AI models and make them accurate without enough data that is legit and error-free. So, there is a need to develop and build a machine learning system and AI platforms that can work more on scarce data.

Unlabeled data

Another challenge related to data is that a lot of it is unlabeled, and this means that there is no information attached to the data that would help the AI algorithm to understand it.

This makes it more difficult for the algorithm to learn from the data and results in poorer performance. So, multiple approaches like programmatic labeling, synthetic labeling, and more should be used for data collection and data labeling.

Complexity of data

The complexity of data is another challenge that needs to be addressed while developing AI apps. As the data gathered from various sources is not structured and easily understandable by the system.

This data needs to be processed and converted into a form that can be easily understood by the AI system. Furthermore, the use of big data analytics, NLP, and machine learning in AI can help businesses address this challenge.

Privacy and ethical issues

Another set of challenges with Artificial Intelligence that are becoming more important as AI develops is privacy and ethical issues. As AI algorithms become more sophisticated, they can gather personal data about users, raising privacy concerns and ethical issues about how this data should be used.

You must know that AI has to represent your company values, so it’s necessary to create a responsible AI tool kit and AI frameworks that are fair and ethical. There is a need to involve employees in web and mobile AI app development so that they can learn about Artificial Intelligence and its implications.

Prone to data breaches

AI relies totally on the data, its comprehensiveness, and data science that guides on how well it is analyzed. AI-driven apps are also prone to data breaches because they often store a lot of personal data about users.

So, while migrating the data to the cloud or any other third-party storage, it is important to take care of data privacy and security. If this data falls into the wrong hands, it could be used to exploit or harm users. Some of the tech giants have already started working to minimize these barriers by building secure development environments, and data libraries to mitigate the data security risks.

Computing power

Another challenge to developing AI apps is the need for a lot of computing power. AI algorithms require a lot of processing power to run effectively.

Also, machine learning and deep learning require a great calculation speed, which is impossible with the standard CPU, and it requires a Graphics Processing Unit (GPU). This can make it expensive to develop AI-driven apps, as they need a lot of hardware.

Data collection and management

Another disadvantage of AI is the need to collect and manage a lot of data. AI app development often requires a large amount of data to function properly.

This can be one of the significant challenges in AI, as they need to ensure that the data is accurate and reliable. Although generating large amounts of data can provide better business opportunities, data breaches and cyberattacks can also leak data.

Finding the team

This can be difficult, as it requires finding individuals with the right skillset. A lack of knowledge can hinder web and mobile AI app development and further delay the process.

So, companies need to have a team of experts in AI, data science, machine learning, and software engineering. As they will help integrate, scale and deploy the AI democratization work model.

Also, to leverage the power of apps that use Artificial Intelligence, businesses should invest in AI training, and hire machine learning/model ops developers.

Integration challenges

The need to integrate AI-based apps with existing systems is a challenge because the data cannot be just integrated by using a few AI plugins or tools. AI requirements can vary for different apps and make it difficult to integrate the two of them.

It requires assistance from AI solution providers who are experts in the field and will help you in AI integration. Also after smooth transitioning, the team must be provided with proper training so that it’s easy for them to work while developing an app.

Infrastructure capabilities

Another disadvantage of AI to develop apps is the need for a robust infrastructure. AI solutions often require a lot of resources, such as storage and computing power.

This can make it difficult to deploy AI-driven apps on existing infrastructure because it may not be able to handle the additional load. However, the AI software development company that develops AI should be well-equipped in AI app development to replace outdated infrastructure.

Wrapping up

Despite these challenges in AI, businesses are still finding ways to develop AI-driven apps that are becoming more and more popular with users. As technology continues to evolve, companies will likely overcome these challenges.

Moreover, the advantages of AI and machine learning are the future of app development. Also, AI analytics, big data analytics, and automation are part of consolidated efforts that help increase the capability to monetize, create a data-driven structure, and minimize the risks coming a long way. So, businesses should focus on these to organize and stay ahead of the competition.