Exploring the power of AI and ML
For most organizations, AI is far from a golden ticket. The rapid transformation it fuels has also brought new challenges. According to a recent study by McKinsey, more than 75% of enterprises have piloted some form of AI, yet less than 15% have realized a meaningful, scalable impact.
Even worse, many leaders often see data governance as a tax instead of a strategic initiative, leading to substandard risk management. As a result, teams often struggle to manage, access, understand, and – most importantly – trust their data.
And these issues only increase when data engineers layer black-box models on top of biased data. This approach leads to unintended consequences such as discrimination, prompting teams to waste time and resources retraining models.
For artificial intelligence and machine learning projects to succeed, it's crucial to have strong data governance and a well-defined framework for responsible innovation, such as machine learning operations (MLOps). By embracing industry best practices to manage complex ML models from the start, teams can operate efficiently, improving the speed, quality, and reliability of artificial intelligence in business.
What's more, through MLOps and a solid data foundation, enterprise leaders can effectively scale and replicate ML solutions across the business for a greater return on investment.