Over the next several years, both AI and machine learning (ML) software will be essential for businesses to stay competitive, enabling targeted customer interactions in both B2B and B2C settings while bolstering operational efficiencies.
Market research firm Trifactica predicts the global artificial intelligence software market will experience massive growth in the coming year, increasing revenues from around $9.5 billion in 2018 to a projected $118.6 billion by 2025.
One major factor contributing to this explosive market growth is the increase in research and development of AI and ML technologies. In fact, R&D is so widespread that’s is hard for companies to keep up with key disciplines that provide the necessary underpinnings of AI and ML use cases – namely data quality, data integration, and governance.
Data Quality: AI and ML initiatives, and the critical business and customer-facing decisions they support, are only as strong as the quality of the data feeding the algorithm. High-quality data—generally considered to be data that is consistent, trustworthy, accurate and complete—is essential in an era of automated decision-making.
Data Integration: AI algorithms work best when they are based on the richest, most comprehensive information. Consider popular mapping applications where the more high-quality, accurate data is represented (street names, traffic light locations, buildings, and landmarks), the easier and more intuitive it becomes to reach the destination.
Governance: One of the great things about AI algorithms is that once a proof of concept has been established in one department, the algorithm can be used elsewhere by making modifications to meet that department’s unique needs. What is often harder to achieve, and tends to not be so uniform, is enterprise-wide AI governance—that is, ensuring that AI is only used in certain ways that align with the ethical principles and values of the organization.