Artificial intelligence is an emerging technology which is going to be a big thing for the technology industry in the near future. The likely return on investment in machine learning and artificial intelligence is understandable to business leaders, but the actual acquisition of technology has been slower than expected. Yes, it is being used by some of the most well-known firms in a variety of industries to generate big money. A report related to AI shows 1 out of 3 work or projects across companies actually flourish.

AI has an expansive potential in the future. But just having a vast capability does not mean it doesn’t have any obstacles or challenges. Some challenges and problems can be small but some might be dynamic and large. Hence the important part is recognising and functioning towards resolution to challenges can assist further to move artificial intelligence’s emerging and fast growth. Below are some challenges that companies face when they adopt AI:

1. Data Privacy and Security :The availability of data and resources to train deep and machine learning models is the most important factor to consider. Yes, we have data, but because it is generated by millions of users around the world, there is a risk that it may be misused. For example, let’s assume there is a retail service provider which offers services to 10 million people in a city. Unfortunately, due to a cyber-attack, the data of the entire people lies in the hands of the attackers. This is a sign of data leakage. Some organisations have already started working to overcome these barriers.
2. The Bias Problem :The amount of data used to train an AI system determines whether it is good or terrible. As a result, in the future, the ability to obtain good data will be the key to developing strong AI systems. However, the data that the organisations collect on a daily basis is weak and has little meaning on its own. They can have a bias problem. Only by establishing relevant algorithms which can effectively track the problems can bring a genuine change.
3. Limited Knowledge :Although there are numerous areas in the industry where Artificial Intelligence can be used as a better alternative to traditional methods. The actual issue is Artificial Intelligence knowledge. Aside from technology enthusiasts, college students, and researchers, only a small percentage of the population is aware of AI’s potential.
4. Computing Power : In the past, the computer industry has encountered computational power shortages. However, the processing power required to handle huge amounts of data in order to develop an AI system, as well as to use techniques like deep learning, is unlike any other computer challenge previously encountered in the IT industry. For corporations, obtaining and funding that kind of processing capacity might be difficult.

Some of the ways to overcome the artificial intelligence challenges are given below:

1. Poor Data Quality :The most significant barrier of AI adoption is the presence of poor quality data. Many organisations gather large amounts of data which may lead to inconsistencies. Organisations should focus on data labelling, warehousing and cleansing. Hence in the near future, these changes and better software will provide organisations with high quality of data.
2. Data Governance Concerns :People are becoming more worried about how businesses access and use their personal data. Businesses that use customer-facing AI should take this into account in their future deployments. In the face of escalating cybercrime, responsible data governance is more important than ever. Visibility and categorization are crucial in this case. Companies must be able to observe and limit how their AI algorithms use data at all levels. Segmentation will lessen the impact of a data breach by making user data as secure as feasible. Customer concerns regarding AI can be eased by being clear about data collecting rules.
3. Data Storage Limitations :Companies must acquire and store more data as AI grows more popular. Traditional storage solutions are restricted and generally expensive, therefore this is becoming an issue. A solution has been found thanks to recent technical developments. Other developments have gained popularity as well, making flash storage more inexpensive and reliable than ever before. Businesses can now use flash storage for AI applications instead of hard discs, which are less scalable and more expensive.

The COVID-19 pandemic may have halted AI’s growth, but when it passes, it will very certainly have the opposite effect. AI will fuel economic recovery, and this rise in adoption will motivate both developers and users to overcome obstacles. As a result, 2021 may prove to be a new era in AI development and implementation.

M76 tackles the challenges of AI with strong data management and data quality frameworks, which focus on future integrations and scaling.