Page 16 - AI Ready – Analysis Towards a Standardized Readiness Framework

AI Ready – Analysis Towards a Standardized Readiness Framework

Combining AI technologies with government policies enables various stakeholders to make
informed decisions to achieve sustainable development goals. With this goal in mind, the
use case uses data from such as the Indian Agricultural Statistics Research Institute (IASRI),
Indian Institute of Soil Science (IISS), National Bureau of Soil Survey and Land Use Planning
(NBSS&LUP), Indian Meteorological Department (IMD) to collect information regarding the
Indian agriculture, land use, soil information, climate data and so on.

Unified data from various agencies and machine learning models can be used to predict the
best plans, policies, and strategies for stakeholders to make informed decisions and implement
effective interventions for sustainable agriculture and development. We refer to a pilot study
from the World Economic Forum [37] which shows that agriculture-related AI technology on
7 000 farmers in the Khammam district of Telangana (India) showed promising results, where
the net income of the farmers using the AI technology had doubled ($800 per acre) from the
average income in 6 months.

Figure 3: Instances of Readiness Factors in Case Study-2

3.3 Case Study-3: Collaborative Multi-agent Systems

This case study includes use cases which use multi-agent systems hosted on end-user devices
such as drones, collaborating on specific missions such as disaster response. The devices may
be equipped with multiple data inputs such as visual cameras and networking capabilities
such as ad hoc networking. The agents may be integrated with models such as reinforcement
learning and route optimization algorithms.

Use case provided by Istanbul Technical University and Turkcell that aims to harness the
advancements in reinforcement learning (RL) to enhance the deployment, route selection,
and coordination of unmanned aerial vehicles (UAV) in disaster scenarios [52], especially for
scenarios that require immediate response such as earthquakes and floods. This case study
emphasizes its use of ad hoc networks among drones.

Enhancing the efficiency of response efforts increases resilience and accelerates recovery in
communities affected by disasters. Delays, resource limitations, and logistical challenges often

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