The world is familiar with artificial intelligence models such at ChatGPT. These “foundation
models” have triggered global excitement and interest in the promise of AI. A next step in this
journey is sector-specific AI models that can be tailored for specific industries.
Such models can also be used to address development challenges such as climate change.
For example, Agrepreneur developed an AI-powered agri-fintech platform that provides real-
time advice to smallholder farmers on farm management, including how they could optimize
available resources and prevent crop disease. It also uses machine learning algorithms for
creditworthiness assessments.
Machine learning is also used to forecast the amount of farm inputs farmers will need for their
crops so they can streamline their procurement processes.
Viamo is another AI-driven solution. It is available via voice calls, allowing farmers even from
areas with limited or no Internet connection to get guidance on sustainable agriculture practices.
Natural language understanding techniques and a pre-trained large language model, along with
speech-to-text and text-to-speech features, are used to enable the farmers to get critical
information from the app.
ClimateGPT is another example. It was trained on interdisciplinary research to provide users
with a holistic understanding of climate change.
Sector-specific AI models can be particularly useful for Asia and the Pacific, which is highly
vulnerable to extreme weather events due to climate change. Users can also go small by
creating a model that informs them of the chances of flooding or drought in a specific
neighborhood at a given time.
So how can this technology be leveraged to improve planetary resilience? The advantage
offered by sector-specific models is that they have relatively minimal requirements, unlike other
tools that require millions of dollars of initial funding, human resources, and government
support.
It is possible to build a sector-specific model regardless of whether you are going to develop it
as an individual or as an institutional representative. The requirements may be higher
depending on the size of your dataset and how complex your model or application may be, but
you can build a simple one using a laptop and a small dataset to train the model.
An AI model can be built through a series of steps that include defining its scope and purpose to
establish clear objectives and parameters, and then preparing the training data and breaking it
into smaller units. It can then be customized using platforms and frameworks, like GitHub and
Hugging Face, for customization, rather than doing it from scratch.
Following customization, the model can be trained on the data, with continuous evaluating and
fine-tuning using feedback mechanisms and metrics to ensure accuracy and coherence.
Multiple iterations may be necessary to optimize the model’s responses. A beta testing phase
engaging diverse user groups can be used to validate the model's functionality and check for
biases, which enhances its reliability before wider deployment.

Depending on the complexity of the model, your available resources and data, and your
familiarity with the programming language, it can take anywhere from a few hours to weeks
before you have a functioning climate model ready for deployment.
AI, like other forms of technology, has downsides and risks. Responsible AI frameworks, which
are now being developed and mainstreamed, need to be adhered to. In the next year or two,
smaller models will come into play under these responsible AI frameworks, similar to what we
saw with guardrails around e-commerce transactions, social networking, and the Internet with
respect to the dark web.
Users who develop their own sector-specific model need to be aware that AI models are heavily
dependent on data. Poor-quality data will result in poor-quality analyses. In addition, biases in
the data may be reflected in what the AI model will churn out. For example, using gender-blind
data to train AI models can be detrimental to women, who face unique challenges during times
of crisis.
AI can serve as a force multiplier that development institutions can use in their work. In the
context of climate change, sector-specific AI models can be used to accelerate progress on
climate action, adapt to the changing climate to resolve contemporary issues in adaptation, and
reflect the overall impact of climate change across the planet.
Depending on the model, the technology can be used to show global trends, or customized so
that one developed for one country can be adjusted for use in other countries. Having these
models could be the difference between climate resilience and vulnerability.
As we harness AI’s potential, specialized models for sectors like climate change offer a
promising path forward. If developed, these models could be a critical aspect of the AI solutions
that set a new course for planetary sustainability and resilience.
Ozzeir Khan is Director, Digital Innovation and Architecture, at the Asian Development Bank’s
Information Technology Department. The views expressed are those of the authors and do not
necessarily reflect the views of ADB, its management, its Board of Directors, or its members.

By Ozzeir Khan