The AI Way ®

With our deep understanding of technology, business environment, human psychology and keeping the core purpose of a business transformation as a central theme, ATAI has come out with a systemic way for designing, architecting, process rehashing and implementing a solution for a given business problem. The step by step blue print is what we call as “The AI Way ®   ”.


Identify key business goals

This exercise needs to be done at a business level and clear purpose needs to be defined. This acts like a guiding star in the journey of AI adoption as well as helps set the direction for the cultural shift in the organization.


“As-Is” process & methodology

This helps us understand and appreciate the current status. This is one of the toughest parts of the entire exercise. Apart from understanding the as-is conditions, it also acts like a myth buster for many operations and procedures.


Heat map

This helps in creating the right prioritization and performing a true ROI model for this initiative. This drives the need for digitalization and understanding intelligence software.



This step helps the organization get a feel of the real benefits of AI. The pilot needs to be selected carefully as it provides (a) confidence to the management as well as various impacting stake holders on the technology (b) helps understand the unstated requirements from various stake holders.


Program execution

We strongly recommend against a big bang approach which can disrupt or confuse the larger teams and the current business. This stage should also be utilized to re-orient select teams and train them. They can bring in very good inputs to ensure quality of the product. The solution should be tested in real deployment scenario. This experience is also utilized to prepare rollout training material.



This is a solution rollout phase where the rubber hits the road. This needs to precede by the training program to all the impacting stake holders.


Continuous Learning

Unlike other technologies, AI based solutions undergo continuous evolution by self-learning from changing environment and conditions, it involves continuous data monitoring & finetuning of AI models.