AI challenges

Artificial Intelligence is disrupting and innovating industries, business processes, and our lives. With this technology, it is now possible to augment human intelligence and use it in decision-making and customer interactions. However, the problem arises when it is difficult to decide whether the current resources would suffice to meet the high and evolving demands of AI technology. AI adoption is not easy as it sounds and this year we have a set of new challenges to overcome.

Data Storage and Limitations

The proliferation and wide adoption of AI have enabled the advancement of data and analytics. To run AI applications, companies will have to gather and store more data. Conventional systems might no be enough to store huge amounts of data and thus it poses a challenge to the industries. Analyzing and processing these data demands better and more scalable infrastructures. Recently, flash storage has become a popular method to store data.

Hiring Talents Skilled in AI

AI technology is being widely adopted today and the efficient application of the technology demands high-skilled talents. As the growth of AI is accelerated, organizations might fall short of AI talents and hence, this year companies should look forward to recruiting more developers and programmers in the field of AI. Democratization of AI is another way to spread awareness about the technology and enable all employees to perform the basic functions involving AI.

Ensuring Data Quality

Companies have been investing in AI but how many of them are gaining the desired results? They might be gathering the data in the wrong way and deriving value from it. Data is the fuel for better AI applications and results. Thus, it is necessary to ensure the quality of the data before feeding it into the algorithm. Prevalence of low-quality data will disable value from AI challenges investments and might also produce biased and unethical results. While checking the quality of data, it is imperative to remove the old data that might hinder the learning process of the system. We are generating tonnes of data and companies are collecting them from multiple sources. Thus, ensuring data quality will be a potential challenge.

Data Governance 

Businesses have essentially become customer-centric and this involves a huge amount of personal and sensitive data about the users. Not just businesses, but also the public sectors and the health sector is heavily dependent on people’s data. This highlights the issue of data governance and security. Customers are highly skeptical about the misuse of information and data leaks. The increased cyberattacks last year is proof of data vulnerability. Thus, this year companies need to address the issue by being transparent and consistently monitoring the use of data by their AI systems.

AI With Ethics and Without Bias

Even though AI is hailed as a gamechanger, it is being blamed for the bias and unethical approach. The AI systems are not responsible for these biases as it is not capable of self-learning. However, the people and the data used to train these algorithms might have biased views, which the AI will learn and project. People tend to fear the biases AI exhibits and this will reduce trust and transparency. Achieving ethical AI is a pressing concern and companies should protect the data used to train these algorithms from biases, and counter this issue. Developers should make sure that the algorithms do not lead to biased and unethical decisions.

Technology Compliance

Recently many countries introduced data and AI regulation policies. Complying with these regulations is a significant part of AI adoption. With the exponential growth of technology, an increased threat to privacy and human rights is being recognized. Thus, state and federal systems are keenly working towards regulating AI challenges and ensuring ethical functioning. AI companies should carefully comply with these changing regulations and ensure an ethical approach.

Computing Power

Disruptive technologies like machine learning, deep learning, and AI demands high computing power. This often becomes a challenge as supercomputers are not easily accessible and affordable. Thus companies need to invest in more scalable and powerful alternatives like cloud computing. Cloud computing enables developers to design AI systems by working efficiently.