Reflections on Dev Jirga at Quaid-i-Azam University: The AI Journey
My recent experience at Dev Jirga at Quaid-i-Azam University was inspiring. Engaging with students as they discussed and validated their Final Year Project (FYP) ideas reminded me of my own journey in tech. It was encouraging to see that they weren’t just incorporating AI for the sake of AI; instead, they were deeply focused on making it purposeful and scalable.
The session gave me a chance to reflect on my experiences, particularly two projects I worked on a decade apart: one relying on traditional OCR and algorithms, and the other powered by Large Language Models (LLMs). This comparison highlighted some key reasons why many AI projects struggle to gain traction in the industry. As we discussed these insights, it became evident that certain core elements can make or break an AI product’s success.

The Key Elements of a Successful AI Product
A successful AI product is more than just a powerful algorithm—it’s a balance of technical robustness, user-focused design, and ethical responsibility. Here are the elements I shared with the students that are crucial for any AI solution to stand the test of time:
Technical Aspects
- Data Quality: Access to a rich and diverse dataset is essential to train a reliable AI model. High-quality, unbiased data ensures that the model can generalize well and make accurate predictions.
- Reliability: Consistent performance is crucial for industry acceptance. The model must maintain accuracy under various conditions and use cases.
- Scalability: As data and user demand increase, the AI model should scale seamlessly to handle the load without compromising performance.
- Security: Protecting sensitive data and ensuring user privacy are critical. Strong data protection safeguards must be in place to prevent breaches.
- Manual Override: In critical or error-prone scenarios, AI systems should allow for human intervention. Having a “human-in-the-loop” is key for sensitive decision-making.
User-Centric and Operational Aspects
- Value Addition: AI should deliver measurable improvements, whether in productivity, efficiency, or user experience.
- Effort Reduction: A good AI system simplifies complex processes and minimizes manual work for the end user.
- User-Friendly Interface: A simple and intuitive interface encourages adoption, making the technology accessible and practical.
Ethical and Social Aspects
- Human-in-the-Loop: Human oversight is essential to manage edge cases and ensure quality, allowing AI to complement human intelligence.
- Ethics and Compliance: The AI product must adhere to data privacy regulations and uphold ethical standards.
- Trust & Transparency: Transparent processes build trust with users, making them more comfortable with the AI’s role.
- Accountability: Defined accountability is necessary to ensure fair handling of AI-driven decisions, especially in cases where errors could impact users.
The AI Journey: From Ideation to Scalable Solution
We discussed that building AI solutions involves much more than algorithms and models—it’s about creating a meaningful impact. As I shared these principles, I could see students resonate with the importance of focusing on AI’s practical benefits rather than its technical novelty. This mindset shift is what separates a prototype from a real-world solution that brings value.
Dev Jirga Motivation and Goals
Dev Jirga 2024 was designed to bridge the gap between academia and industry, empowering students with the practical skills and industry insights needed to thrive in today’s rapidly changing tech landscape. The event offered an engaging lineup of sessions, including expert talks, interactive networking opportunities, and two impactful panel discussions that connected students and professionals around shared goals in technology.
I am incredibly grateful to Ali Mumtaz, Lead of MLSA QAU, as well as the dedicated leads from other MLSA chapters (whose names I regret not having on hand), along with all volunteers and sponsors, who made this event a success. Together, they transformed an idea into an opportunity-rich environment for both students and industry experts.
Special thanks to Mudassar and the students from the University of Haripur, who joined us under his supervision. Mudassar and I crossed paths at QAU, sharing many co-curricular experiences despite being in different programs. It was inspiring to see students from IIUI and NUML also participating with enthusiasm—they were full of energy, eager to gain valuable insights, and committed to making the most of this event.
Looking Forward
It was fulfilling to see the next generation of tech talent so deeply engaged with the challenges of AI development. I’m excited to see how these genius minds will refine AI-based products and create solutions that drive real value in the industry.