Product leadership extends beyond mere product management; it's about inspiring vision, fostering innovation, and strategically driving success. In this hands-on workshop, participants will discover how AI and ML are reshaping classical product leadership. Explore essential elements for thriving in an AI-led world and delivering exceptional products that meet evolving customer needs.

Key Learnings:

1. Expanded Vision of Product Leadership:
Gain a broader understanding of product leadership beyond traditional product management, discovering how it involves inspiring vision and driving strategic success.
2. Innovation Integration with AI and ML:
Learn how to integrate AI and ML into product leadership, fostering innovation to stay at the forefront of evolving market demands.
3. Strategic Mastery in an AI-Led World:
Explore essential elements and strategies for thriving in an AI-led landscape, equipping yourself with the tools to strategically drive product success.
4. Customer-Centric Excellence:
Understand how to deliver exceptional products that meet ever-changing customer needs, leveraging insights from the workshop to enhance customer-centric approaches in product leadership.

Finalists: Herkey, Utpan, Oracle, Edgeverve (FALM), Halliburton

The adoption of Generative AI, a revolutionary technology is now widespread and has started reshaping industries and functions by automating and augmenting human efforts to a large extent. Product Management traditionally leverages PM's competencies and skill sets like (not limited to) product thinking, product strategy, discovery, cross-functional collaborations, analytical skills, writing PRDs, PR-FAQs, delivery and product GTM and has never been about leveraging AI tools extensively. But, the time has come now.

Key Learnings:

The audience will be able to appreciate and understand how GenAI can augment their product management career.
1) Leverage ChatGPT and Google Bard to learn product discovery techniques.
2) Use GenAI tools to automate things like creating roadmap, generate wireframes through context and text inputs.
3) Use GenAI framework to create vision document, product requirement document from product strategy and consumer insights.
4) Use prompt engineering to create PRDs with user stories from PRFAQ, Strategy and design mocks as inputs
5) Create digital designs through GenAI tools (DALL-E) and covert them to code templates for engineering teams to consume
6) Gain efficiency and reduce cycle time in product GTM through quick insights by leveraging GenAI tools.

In this talk Dr Geetha will share her experience of building an innovative AI-based healthcare diagnosis product for India and beyond.  NIRAMAI, a deep tech start-up founded by the speaker has pioneered a new test for detecting early-stage breast cancer using machine learning as the core differentiator. Geetha will give an overview of this novel AI-based breast cancer detection test and will share her experience of taking an AI-based medical device from research idea to commercial product.

The term “product owner” was introduced with Scrum in the mid-1990’s when the product manager role had already been established widely across industries. When a software organization tries to implement Scrum or a similar agile approach, the understanding and cooperation of “product manager” and “product owner” is usually a big problem that can have a very negative impact on productivity. In this presentation, we analyze where the problem comes from, why it is so difficult to solve, and how solutions can look like.

AI/ML models are being extensively used in the financial industry for purposes ranging from Customer Sourcing, Credit Scoring, Campaign Management, Fraud Risk Management, Anti-Money Laundering, Rewards Management, Risk Management and Operations. Usage of ML models have made it possible to do tasks which otherwise would have been very cumbersome or humanly impossible. However, there is a flip slide for the usage of ML models as well. For example, Machine Learning based models could suffer from serious flaws such as  bias, and explainability, if the models are not constructed, used, and governed properly. 

This presentation intends to provide an overview of some of the best practices a Product Manager should adopt, throughout the lifecycle of AI Models, to mitigate AI Model Risk covering the following:

• AI Models: Usage in Fintech Products

• Key Functional & Technical Challenges for a Product Manager

• Key challenges of deploying models at scale: Indian context

• Key Regulatory Concerns: Bias and Explainability of AI Models

• Product Management across AI Model Lifecycle: Development |Deployment |Usage

• 3 Pillars for AI Models Product Management: Model Governance| Model Validation | Challenger Models

Finalists: Wibmo, SellerApp, Edgeverve (Finacle UPI Transaction), Zee entertainment, Vinculum

We begin by presenting the recent advances in the area of artificial intelligence, and the high level ideas underlying the progressively narrower domains of machine learning, deep learning, and foundation models, which have emerged over time as dominant paradigms for artificial intelligence. We describe the tremendous progress of these models on problems ranging from understanding, prediction and creativity on one hand, and open technical challenges like safety, fairness and transparency on the other hand. These challenges are further amplified as we seek to advance Inclusive AI to tackle problems for over a billion human beings in the context of India and the Global South. We present our work on multilingual models to democratize information access in a diverse set of Indian languages, on healthcare in environments where we lack data in digital form to begin with, and on analysis of satellite imagery to help transform agriculture and improve the lives of farmers. Through these examples, we hope to convey the excitement of the potential of AI to make a difference to the world, and also a fascinating set of open problems to tackle. 

This presentation explores the power of AI in enabling personalised experiences that cater to individual user needs and preferences.We will spotlight the synergy between the Intelligence of Things (InOT) and AI-powered personalisation, unlocking possibilities like Context-Aware Personalisation, Predictive Personalisation, and Enhanced User Engagement.The discussion traces the evolution of personalisation, from conventional methods to the AI-driven paradigm. Drawing from diverse case studies in industries such as e-commerce and streaming platforms, we'll showcase how AI revolutionises product experiences from a one-size-fits-all approach to a truly customised experience.

Key learnings:
1.The role of AI in uncovering hidden insights, enabling real-time personalisation, and ensuring continuous improvement
2.The importance of incorporating AI into product development strategies to create personalised experiences
3.Case studies showcasing the successful implementation of AI-powered personalisation across industries. What lessons can we learn from these examples?
4.Ethical considerations and responsible AI implementation practices to address data privacy, algorithmic bias, and user control
5.Insights into the future of AI-powered personalisation, including AI-powered design, personalisation, and AI-powered ecosystems
6.An illustration like Smart Homes like examples etc

Discover the potential of product leadership scalability with AI as your co-pilot in product management. Explore the impact of AI on managing diffusion of technology and product innovation cycles and how product leaders can adapt to AI driven strategies and execution. Join us for a plausible journey into hitherto uncharted territory of AI propelled People, Process, Products, Profits and Planet.