Thinqe has developed a methodology for designing a bespoke strategy for businesses to become AI mature. This involves analyzing current state, understanding the expectations of the client in terms of AI maturity, assessing the gap and developing a detailed map with measurable steps. The main steps of AI strategy design at a high level are –
Discovery
Create Process Pipeline
Develop Data Strategy
Developing a Technical Pipeline -
Implementation (Initial, Mature, Optimized)
Implementation - Thinqe implementation methodology recommends to break the roll out into three phases; Initial, Managed and Optimized Initial phase is about developing a proof of concept, setting up the environment, setting up the data ingestion pipeline and developing a minimum viable product (MVP). This will unearth challenges unique to the client environment and allow us to showcase the potential of AI in one or two chosen business processes to start with Managed phase is about scaling and rolling out the technical delivery across other processes. We will also develop advance telemetry and business intelligence to give the insights from the functioning of the model. We will also address performance related challenges arising out of scaling of the model. At the end of managed phase, we will have a robust technical implementation meeting all critical business objectives across the business units Optimized phase is where we enable our clients to run the show on their own with lot of built in efficiencies and automated processes. We will provide all the trainings and IP transfer required. Also, we will enhance the solution by implementing auto-discovery, auto-healing and automated quality. The end result of this phase is an optimized AI solution which an empowered and experienced client can either manage on their own or get it managed with minimal cost. We will also develop a detailed process for future enhancements