Continuing data shortage obstructs progress in Artificial Intelligence

usinesses are ready for Artificial Intelligence (AI), but a lack of data is holding them back. A recent survey revealed that only 24% of organizations consider themselves data-driven and only 21% have a “data culture”. Additionally, only 24% of companies are taking the necessary steps to ensure responsible and ethical use of data within their organizations and industry.

Mona Chadha, Director of Category Management at Amazon Web Services, believes that the data gap is the biggest challenge facing AI success. Poor data quality, unfair bias, and lack of security are just a few of the issues that companies need to address. The quality of AI model predictions is directly tied to the data used to train them, and if the data is of poor quality, the results will be inaccurate and inconsistent, leading to a lack of trust from customers and internal stakeholders.

To ensure fair and unbiased results, businesses must also address data bias and security. AI models can make decisions that are more objective than humans, but if the data used to train the models is biased, it can result in discriminatory behavior that puts businesses at risk. Additionally, attackers are constantly trying to exploit vulnerabilities in AI, so businesses must ensure their AI systems are protected.

To overcome this data gap, businesses must take control of their data assets. This involves cleaning, labeling, and removing duplicates, outliers, and irrelevant data points. Once the data gap is closed, businesses can start building their business cases for advancing AI. AI is already showing results in industries such as healthcare, transportation, and manufacturing, and can drive product innovation, reduce risk, and improve overall efficiency and safety.