Data quality is pivotal for the efficacy and dependability of AI and ML models. In sectors where precision is paramount, like healthcare and finance, the calibre of data can make or break the predictive power of these technologies.

While AI and ML are transformative, they are not a replacement for established data quality best practices and tools. It is crucial to understand their capabilities, potential, and limitations, and to approach them as complementary tools rather than replacements. Using AI/ML as the only solution for data quality is akin to patching a boat's leak with tape – it helps but will not solve everything.