Attending Bengaluru Tech Summit 2025 (BTS) from November 18th to 20th was a great experience not just for the sessions, but for the people and the ideas. As someone focused on enterprise data and integration, I approached the summit with a clear lens: where does the hype around AI, quantum, and digitization intersect with practical, scalable data infrastructure?
This recap highlights a mix of learnings from sessions, real conversations during networking, and key takeaways that felt particularly relevant to our work at CData especially in making data more accessible, secure, and usable across platforms.
What I learned: Key highlights from sessions
1. AI is nothing without clean, connected data
In multiple sessions, from "AI for Fintech" to "AI for Governance", a common theme emerged was data fragmentation and it remains a major challenge.
Abhishek Singh (IAS) spoke about ML models built on vast government datasets and how India has launched thousands of solutions to tackle this.
Deepak Shenoy discussed data driven decision making in investments and mutual funds.
Shrehith Karkera shared the journey of building Ditto Insurance, emphasizing structured data as a growth enabler.
The focus on Aadhaar-linked AI use cases also revealed how data and digital identity will shape services, fraud prevention, and customer engagement.
2. AI impact summit: 7 pillars and people first design
Day 2 explored the strategic vision behind the AI Impact Summit, organized around three pillars: People, Planet, Progress, and seven working themes focused on real-world outcomes. Global programs like AI by Her, AI for All, and YuvAI were spotlighted.
3. Foundation models need Indian context and safe data
In a standout session on "Foundation Models for India", panellists like Vivek Raghavan, Sashikumar Ganesan, and Ananth Nagaraj highlighted:
Why India needs its own multilingual, culturally aware foundation models
The risks of "poisoned" datasets
How domain specific use cases must define model benchmarks
As a company focused on real-time data access, this validated the importance of trustworthy pipelines. Without reliable connectors to structured sources, foundational models risk becoming black boxes built on bad data.
4. Generative AI & complexity in enterprise
The buzz around Generative AI was not just theoretical. Sessions emphasized simplifying complexity using GenAI to solve engineering and system problems, not just generate content.
5. AI for social good & inclusion
Speakers like Soumyadipta Acharya and Meenakshi Gopinath gave practical examples:
These use cases are only possible when data from fragmented systems is securely and meaningfully integrated.
Who I met: Real conversations, real relevance
Beyond the sessions, BTS gave me a chance to meet teams from companies like Qualysec, Trade360 AI, IntelliDB, Codehall, Neotouch, and others each building or applying tech in areas adjacent to data integration.
These conversations were not just surface level we discussed what they were building, how they approached data or AI challenges, and explored collaboration opportunities where CData could support their efforts. Whether it is accelerating access to operational data or enabling analytics across systems, we saw firsthand how our tools could fill integration gaps they had already encountered.
Emerging focus areas across sectors
Governments need secure, compliant access models for AI deployments.
Enterprise AI is bottlenecked not by models, but by data silos.
There’s a growing demand for domain-aware foundation models and that demands high-quality input pipelines.
Digital identity and fraud prevention will need data agility across systems (e.g., Aadhaar, financial infra, healthcare).
Final thoughts
BTS 2025 was more than a tech event it was a useful reflection point for how India is thinking about data, intelligence, and inclusion. As someone working at CData, it was encouraging to see that what we build maps directly to where the ecosystem is heading.
The next frontier isn’t more dashboards it’s giving users, AI, and systems access to the right data at the right time, in the right way. And that is exactly what we are enabling.
Ready to see how CData can help your organization build a solid foundation for AI? Check out CData Connect AI.
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