- Poor quality of data
The prevalence of poor-quality data is perhaps one of the biggest barriers to profitable AI adoption. Although most people have a lot of praise for this technology, more than 65 percent of executives say their recent AI investments have not added value to their businesses. The leading problem here is the poor quality of data. AI applications will only be of great value to a company if the right data is available to enable them to make informed choices. Although many organizations gather large amounts of data, most of the data have inconsistencies and redundancies. This means that the quality of data must be enhanced to help in decision-making.
- Scarcity of data
Major companies such as Google, Facebook, and Amazon are facing backlash over the use of data. They are blamed for the unethical use of personal data in their marketing programs. This has led to the adoption of new rules such as the GDPR, restricting access and distribution of data by companies and individuals. These rules are likely to affect AI projects in small and big companies and the training of machines in making predictions. Without adequate training data, AI models could become flawed or biased, and the AI systems will be of little to no value to organizations.
- Ethical issues
As AI systems continue making way into our everyday life and businesses, ethics has become a leading concern that must be addressed promptly. Artificial intelligence can acquire biases from humans casting doubt on the ability of AI applications to make independent decisions without racial, gender, or any other form of prejudice. This is the conversation that has been going on in AI circles, including the leading AI companies such as Google. Despite this challenge, the rising awareness of ethical concerns is a good sign. It is a crucial step towards acknowledging the possibility of human bias making way into AI algorithms and systems. Organizations and researchers must work closely to identify the potential bias and eliminate prejudiced data that may affect AI algorithms.
- Data governance issues
People are highly concerned about how businesses access and use personally identifiable information (PII). Companies, therefore, need to account for all the data they access and use in their AI deployments and enhance transparency so that they can gain trust from their customers. As an organization, responsible data governance is now critical than ever, especially with the rising cybercrimes. Ensure there is adequate visibility and segmentation of data, and ensure you see what your AI algorithms do at every stage. Keep the user data as safe as possible and establish transparent data collection and access policies to help address the customers' concerns with regard to the security of their data.
While the coronavirus pandemic may have affected businesses in many ways, it has, without doubt, hastened the adoption of technology such as AI. This technology will drive economic recovery and reduce the cost of operations, now and in the future. Due to these advantages, 2021 could be a turning point for the future of your business if you embrace AI and implement the project correctly.