AI Trailblazers: Tejeshwar Chowdhary on Transforming Non-Profits with AI at Learning Links Foundation

In our latest edition of AI Trailblazers, we dive deep into the practical application of AI within a leading non-profit organization, Learning Links Foundation.

Learning Links Foundation (LLF) is a 2500-person nonprofit organization dedicated to transforming education through technology and social innovation. By partnering with governments, universities, and private institutions, LLF enhances learning, fosters digital wellness, and drives skill development across India.

We spoke to Tejeshwar Chowdhary, Program Director at LLF. Tejeshwar is a pioneer in AI-driven education, helping integrate AI tools to personalize learning and improve teaching workflows. Through masterclasses and workshops, he empowers educators to utilize technology for better engagement and data-driven decision-making in the classroom. We discuss LLF’s journey embracing AI, the challenges they've faced, and the exciting future they envision.‍

Q. To kick things off, can you give us a high-level overview of what inspired Learning Links to embrace AI?

Tejeshwar: Technology has always been central to LLF's mission. Since our inception, we've focused on using technology to improve learning outcomes. We work with major tech companies like Google, Intel, and IBM, building students’ and teachers’ capacity in digital literacy across India. So, when AI emerged, particularly with the advent of tools like ChatGPT, there was immediate interest. We saw the potential to revolutionize education and skilling, the core areas we focus on. While we had an existing AI curriculum for students (focused on the technical aspects of AI, machine learning, etc.), the real shift came with the accessibility of generative AI tools for everyday use.

Q. What were those early learning experiences like? I imagine there was a lot of experimentation.

Tejeshwar: Absolutely. Like any organization, we had early adopters who were diving in headfirst, testing and experimenting daily. Others were more hesitant. I was one of those early adopters, using AI to generate reports, grant proposals, and communication documents. The key was demonstrating the tangible benefits. I started sharing my successes internally on our platform, Viva Engage, tagging key leaders and showing how much time I was saving. I also encouraged my team to use AI for their reports, creating guidelines and showcasing the improved quality.

Q. You mentioned some hesitancy initially. How did you overcome that?

Tejeshwar: There was a bit of a stigma, almost a feeling of "cheating" by using AI. Some people felt like the work wasn't truly "theirs" if AI was involved. I tackled this head-on by openly sharing that I was using ChatGPT extensively and showcasing the quality of the resulting work. I also presented live demos at internal meetings, showing how these tools could help various teams. This built momentum and created a "pull" effect, where people started requesting training rather than being forced to adopt. The demand also started coming from external stakeholders – schools, government bodies, and even funders like Dell, who wanted to see generative AI incorporated into our digital literacy programs.

Q. That's a great point about the "pull" effect. What best practices did you observe in terms of integrating AI into existing workflows and training the team?

Tejeshwar: Leadership buy-in is crucial. If senior leadership is visibly promoting and using the technology, it significantly accelerates adoption. The other key is to have that "pull" from end-users. This happens when you actively demonstrate the value and show them how AI can solve their specific problems. It's about applying design thinking principles – understanding the user's needs and showing how AI can address them.

Q. Are there any specific opportunities or adaptations you see for non-profit and educational organizations like Learning Links?

Tejeshwar: We're particularly excited about the potential of AI to support teachers. We envision creating AI-powered "teacher assistants" or bots that can help with lesson planning, personalized learning, and reducing administrative burdens. Imagine a bot that understands the learning levels of each student in a classroom (based on assessment data) and then helps the teacher tailor lesson plans for different groups. It's about freeing up teachers' time to focus on what matters most: engaging with students.

Q. What tools are you using to build these solutions?

Tejeshwar: We're exploring open-source solutions and working with technology partners. We're piloting a web-based chatbot platform called OCS (Open Chat Studio) from an organization called Dimagi. This allows us to develop custom bots and deploy them through APIs to platforms like WhatsApp or Telegram. We can upload our own content, create custom instructions, and even add voice recognition. We're also applying for grants, like the AVPN AI Opportunity Fund, to build AI literacy skills for those who will be most impacted by AI, such as women entrepreneurs, teachers, and young graduates.

Q. Many organizations struggle to quantify the ROI of AI. How have you approached that at LLF?

Tejeshwar: For internal productivity improvements, the ROI is relatively straightforward – it's about time saved. For example, grant writing used to take over a week, involving multiple team members and a lot of back-and-forth. Now, I can pull existing information from our knowledge base using Dashworks and complete new grant proposals in just three to four days, without needing to constantly reach out to different teams. The time savings and improved quality were immediately apparent.

Q. Looking ahead, what are some of the open questions or challenges that the industry, or organizations like yours, need to address?

Tejeshwar: I think a design thinking perspective is still often missing. People get excited about AI, but then they often revert to old habits. There's a gap in understanding how to best leverage these tools. Users need to understand the current capabilities and limitations of the technology. We need to make AI more intuitive and user-friendly. Ideally, we need AI agents that can truly understand an individual's workflow and proactively offer assistance – like a personalized executive assistant. Think Jarvis from Iron Man! That's the vision.

Q. Any final advice for organizations that are just starting their AI journey?

Tejeshwar: Curiosity and experimentation are key. You need to be willing to try new things, fail, and keep learning. Identify individuals within your organization who have that natural curiosity and empower them to lead the charge. The potential impact, especially for organizations in the social sector, is enormous. We've seen our productivity increase significantly, and that translates directly into a greater ability to make a positive difference.

Key Takeaways

1. Leadership Buy-in and End-User "Pull": Successful AI adoption requires both top-down support and bottom-up demand.
2. Demonstrate Tangible Value: Show, don't just tell, how AI can solve real problems and improve workflows.
3. Overcome the Stigma: Openly address any hesitancy or perception of "cheating" by showcasing the quality and efficiency gains.
4. Focus on User Needs: Apply design thinking principles to understand how AI can best serve the specific needs of different users.
5. Embrace Experimentation: Be willing to try new tools, learn from failures, and iterate.
6. The Future is Personalized Assistance: The ultimate goal is to create AI agents that can seamlessly integrate into individual workflows and provide proactive support.

This interview is part of our AI Trailblazer Series, highlighting organizations that are leading the way in practical AI adoption.