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From automation to intelligent management and personal assistants to autonomous vehicles, we are seeing a shift in how artificial intelligence (AI) is used. According to the Fortune Business Insights report, the global AI market will grow from US$ 387.45 billion in 2022 to US$ 1394.30 billion in 2029. While it has had a lasting influence on our lives and the economy, there are still many challenges that AI users are trying to overcome.

AI challenges to consider

Despite its astounding growth, AI faces several obstacles. Enterprises seeking to keep AI utilization at the top of the industry trends must devise ways to tackle AI challenges.

The data acquisition challenge

Data acquisition is the most pressing AI challenge that companies face. Various branches of AI, such as machine learning and deep learning, require the training of models. This model training phase requires a significant amount of first-party real-life data. Often it becomes difficult to gauge the amount of data a company needs to develop the model accurately or leverage the AI algorithm. This requirement often depends on conversion goals and rates, project goals, perfection requirements, and analytics precision.

Early startups and multi-billion dollar companies strive to accomplish a correct data acquisition strategy to resolve this AI challenge. Companies should understand what data they are collecting and from what sources before acquiring them for AI use. In situations where training datasets are not available (such as accidents), companies should develop or leverage algorithms to create dummy datasets for model training.

Data privacy control

Data is an essential commodity whose value keeps increasing as it fits into modelling and training AI algorithms. As previously mentioned, massive amounts of real-world data go into machine learning and deep learning training. It also gives rise to the possibility that the data may get stolen or used for illicit purposes. Any massive cyber attack or insider data leakage can cause problems to millions, if not billions, of users. In the worst-case scenario, anyone can sell them on the dark web for monetary benefit.

Enterprises handling real-world data (not dummy ones) must follow strict regulations like GDPR and other data-privacy-controlled worldwide policies. Awareness among customers of data privacy is also essential. Policy-makers and security firms should empower users to influence regulatory debate around data privacy.

AI has automated cyber fraud

Cybercriminals and fraudsters have also leveraged AI and artificial bots to generate traffic, attack a system (DDoS), harvest fake subscribers, and perform other bot farming schemes. If they are detected, the bot-farming fraudsters quickly devise new mechanisms to trick the system and continue the fraudulent process for monetary benefit. Digital advertisement fraud has also gained momentum with the advent of AI. According to a report, the total cost of ad fraud in 2022 is US$ 81 billion. Their prediction says it will increase to US$ 100 billion by 2023.

One way to prevent such fraudulent actions is to implement bot-detection tools and fraud-detection algorithms. They can use behavioural analysis and patterns to identify and eliminate such threats. These third-party tools filter traffic that is auto-generated or has anomalies.

Trust deficiency

Another AI challenge many AI-based projects face is how deep learning models predict the output. The first issue is that the data used in training the model can be biased. This makes it more difficult for users to understand how a specific set of inputs can improvise a solution for distinct scenarios. There have been instances of companies indulging in malpractice by training AI with biased datasets, leading to trust issues. This incidence of malpractice has created a trust deficit among users.

To tackle such AI challenges, enterprises and researchers should promote more knowledge of how AI works. They must also enlighten users on the difference between supervised and unsupervised learning. Researchers and AI engineers must also follow standard policies while leveraging datasets for training AI algorithms. These company policies should clearly state that there is no bias in the datasets used during the training.

Conclusion

The AI challenges are endless and ever-changing. Although AI and its subsidiaries, like machine learning and deep learning, are in their infancy, researchers and engineers must work toward ethically harnessing its true potential. However, such a goal is possible only when they successfully tackle the existing AI challenges. This article highlighted four AI challenges and how enterprises and engineers can address them.