Page 45 - AI for Good Innovate for Impact

AI for Good Innovate for Impact

adaptive platforms show promise, structured pilots, phased implementation, and teacher
feedback are key to building trust and enabling sustainable adoption.
8. In smart city and home environments, user awareness and usability are critical barriers.
Many users struggle to effectively interact with AI systems due to unfamiliarity or lack of
confidence. Without user-centric design and user awareness and capacity building efforts, 3-Learnings
even advanced features risk underutilization. Enhancing transparency, localization, and
user support is essential to maximize real-world impact.
9. AI accessibility use cases—ranging from mobile sign language translation to multimodal
assistive devices—demonstrate strong potential for inclusion. However, challenges
remain in hardware compatibility, regional data diversity, and user trust. Addressing
these requires lightweight model optimization, localized data, privacy safeguards, and
sustained deployment to ensure inclusive adoption.
10. AI in intelligent transport faces key gaps in large-scale deployment, real-time data
integration, and environmental impact. Yet, drone and UAV use cases are emerging
as agile, low-emission alternatives, and AI-powered energy solutions—such as traffic
optimization and smart EV charging—are proving vital for reducing fuel consumption
and supporting sustainable mobility.
11. Many smart agriculture AI use cases focus on rural settings, especially in Africa and
Southeast Asia. These applications tackle productivity and digital access challenges
through low-cost, offline-capable tools such as voice-enabled advisory platforms and
edge-based disease detection. In doing so, they not only support local farmers but also
enrich global agricultural AI systems with diverse, representative data.

3�2 Key Insights from 2025 Innovate for Impact analysis

1� Regional requirements and locally relevant data: The review of 160 AI use cases across
eleven domains reveals valuable lessons on how AI can move from concept to real-world impact.
A key takeaway is that successful implementation depends not only on technical innovation,
but on understanding regional requirements and locally relevant data. Many impactful projects
emerged from tailoring AI tools to the unique challenges, infrastructure, and linguistic contexts
of specific communities.

2� Capacity building: Another consistent pattern is the importance of capacity building,
especially through user training and knowledge sharing. Use cases demonstrate how providing
practical examples, reusable templates, and locally adapted tools can significantly improve
adoption and long-term sustainability.
3� Supportive ecosystem: In addition, the role of a supportive ecosystem—including access to
mentoring, cloud infrastructure, and financial assistance such as scholarships or pilot funding—
proves critical to scaling AI solutions. Many initiatives benefited from early-stage guidance and
resource access that allowed them to move beyond experimentation toward implementation.

Together, these learnings highlight that AI for Good is not simply about technology— it gives
us an opportunity for reaching out to regional innovators, creating the right conditions for
inclusive, effective, and context-sensitive deployment that delivers measurable benefits to
communities around the world.

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