Page 15 - AI Ready – Analysis Towards a Standardized Readiness Framework

AI Ready – Analysis Towards a Standardized Readiness Framework

Sensor data collected in real-time as part of the use case is stored in an integrated cloud server
where all members can access and analyze the data, enabling an ecosystem for all developers,
analysts, and designers to learn and reuse.

Figure 2: Instances of Readiness Factors in Case Study-1

3.2 Case Study-2: AI-based Frontend with Multimodal Backend Data
Aggregation

This case study aggregates multiple types of data from varied sources and maps them together
to form actionable insights for potential users. These insights may be offered as question/
answers over chat interface. Context sensitivity and local customization are important for these
types of use cases. Dynamic update of data in the backend should result in corresponding
updated insights to users. Cloud APIs for extraction and exposition of data are important
interoperability consideration in such use cases.

"AI-Based Chat Box for Farmers" [49] aims to optimize the agriculture sector by unifying multiple
government agencies' data using Artificial intelligence models.

The productivity of agriculture and its allied sectors is influenced by numerous factors [34],
including climate conditions; soil fertility; crop breeding; water management; seed quality,
pesticides, fertilisers, and machinery; environmental sustainability; farmer's training and
education; market access, and government policies. Many of these factors are managed using
traditional methods and practices till now, which often limit farmer’s livelihoods and grain
productivity.

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