AI must be applied effectively and widely to create significant value. It requires business engagement, best ML and AI practices alongside innovation, scaling and accelerating automation and operationalization practices. AI engineering helps bring in operational efficiency and economies of scale by ensuring reliability, reproducibility, and reusability toward building an augmented digital workspace.

Data science teams fail to adopt agile methods and struggle to deliver the AI-based systems required in an innovative and dynamic environment. Delivering data science solutions iteratively and incrementally requires the data engineers, data scientists, and ML engineers to work closely and fully understand their roles and responsibilities as part of operationalizing the platform.