Rethinking Engineering for the AI Era Sahaj Software

Topic 1: Engineering AI-Ready Enterprise Data

As enterprises move from AI experimentation to production, the limiting factor is increasingly not model capability, but the readiness of the data those models depend on. Most enterprises already have vast amounts of data, but it is often fragmented across databases, APIs, documents, and applications, with gaps in semantics, governance, connectivity, and discoverability. Building reliable AI systems therefore requires more than selecting the right model or adding an agent layer, it requires an enterprise data foundation designed for AI to query, learn, reason, and act on.

The talk presents a practical blueprint for building an AI-ready enterprise data foundation, with a focus on how data, knowledge, governance, and AI applications come together as an integrated ecosystem. It explores the architectural choices and mindset shifts needed to build this foundation incrementally, rather than treating AI readiness as another layer on top of existing systems. It also examines two complementary directions: Data for AI, which prepares enterprise data to support AI, and AI for Data, where AI helps prepare, enrich, and continuously improve the data foundation itself.

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Topic 2: The Hourglass-Shaped SDLC

Most software teams still organise delivery around implementation. Estimates are often driven by the expected complexity of the implementation, progress is measured through the development stage, and testing and review wait for the implementation to finish.

That arrangement made sense. Implementation consumed most of the effort needed to turn an idea into working software. Building also forced the design to meet the codebase and change under pressure.

AI changes the balance by reducing the human attention spent on implementation. Work around implementation continues to consume substantial human attention. Teams still define the problem and choose a direction before code is written. After code exists, somebody has to decide whether the result is correct, useful, and safe to release.

If the width of each stage represents its share of human attention, the SDLC starts to look like an hourglass. Adding a faster coding tool to the old workflow accelerates the part already taking less human time, while decisions and evidence continue to set the pace for software delivery.

Software output can then rise faster than confidence, which is why this talk argues for an SDLC organised around decisions before implementation, evidence after implementation, and learning that carries production feedback into the next change.

Agenda

10:00 AM – 10:15 AM: Entry & Networking
10:15 AM – 11:15 AM: Talk 1 – A Green Harness Is Not a Healthy Harness
11:15 AM – 11:30 AM: Coffee Break & Networking
11:30 AM – 12:30 PM: Talk 2 – The Hourglass-Shaped SDLC
12:30 PM onwards: Networking

Entry to the event will close at 10:30 AM.

Karthika Vijayan

Solution Consultant

Dr. Karthika Vijayan has been conducting research in the field of conversational AI with voice and text data for almost a decade. Her research has been published in several journals and presented at various international conferences. Prior to joining Sahaj, Dr. Vijayan worked as a research fellow in the Department of Electrical and Computer Engineering at the National University of Singapore. She also worked as research associate in the Department of Electrical Engineering at the Indian Institute of Science, Bangalore. She obtained her PhD in Speech Signal Processing from the Department of Electrical Engineering at the Indian Institute of Technology Hyderabad in 2016. She is an active member of IEEE, ACM and APSIPA.

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About the Event

Meet our Speaker(s)

Karthika Vijayan

Solution Consultant

Sujit Kamthe

Solution Consultant