Building a good data strategy lays a strong foundation for handling data in the organization. Data strategy should be able to provide data culture, data quality, and data privacy, which is very crucial for scaling AI successfully in the organization. As an AI team, collaboration among data engineers, data analysts, data scientists, and machine learning engineers is very crucial.
In order to utilize the maximum potential of AI, the following steps can be undertaken:
1. Aligning your project with business priorities
AI can be utilized the most by defining a very clear business problem that is to be solved. The right contribution of AI to a project needs to be deeply understood before implementing the solution. The solution that matters the most for the maximum ROI is what needs to be picked rather than what is easier and often done.
2. Identify the right problem
The key business priorities and business strategy of the organization needs to be in perfect alignment with the AI solution. To make the most of the AI solution, the problem statement should be extremely well defined and clearly thought about. Not just any business priority, but the ones that can truly benefit from ML and AI solutions needs to be defined.
3. Finding the right leaders
Once the right solution is picked, the senior executives of the organization need to completely own that solution. The changes in an organization are usually top-down. Hence, a leader who actually accepts and implements better AI solution is equally crucial.
Building very efficient ML models only will not suffice. The models need to be explained in lay man’s terms with the help of clear dashboarding. The technique used to explain the model can vary based on each organization and the targeted audience. This makes picking a dashboarding mechanism is also crucial. Along with building ML models, a methodology to explain those models both locally and globally needs to be developed too. This data is fed into analytical dashboards, decision making statistics and dashboarding. The organization’s top level is usually concerned only with numbers rather than the actual model itself.
We are very well aware by now that the usage of one technology is very limited. A single technology is not entirely dependable. Upskilling is the most crucial step in any technology and organization. This applies here as well.