DataScience&AITrends Asia Summit
Data and AI are front and center in optimizing business decisions and driving the next wave of enterprise growth. While investment into AI is accelerating in the race to realize new capabilities and use cases, potential risks and regulations are also emerging.
As Data and AI leaders embark on the multi-year journey to realize business outcomes from AI initiatives, they are shifting to more focused and strategic approaches. Having a robust foundation in data and analytics, adequate infrastructure, and efficient governance are keys to progressing from experimentation to achieving meaningful ROI.
The second annual DataScience&AITrends Asia Summit aims to provide a platform for data, AI, digital, and IT leaders to explore challenges and opportunities to implement data-centric AI. The Summit, which will feature a blend of insightful presentations and panel discussions, aims to solve the business problems of data and AI while exploring the latest tech stacks that accelerate the unlocking of data value.
This Summit is for all professionals involved in digital, data, cybersecurity, transformation, and IT, including:
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Chief Data Officers
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Chief Analytic Officers
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Chief AI Officers
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Chief Digital Officers
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Chief Information Officers
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Chief Technology Officers
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Chief Transformation Officers
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Chief Innovation Officers
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Chief Customer Officers
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Heads of AI
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Heads of Data Science and Analytics
AGENDA
The democratization of Generative AI has ignited a fervor around AI in enterprises. Yet to progress from experimentation to durable usage with widespread business impacts necessitates overcoming of challenges and demystifying against risks. A leading consultant will address the potentials and pitfalls of AI and how to accelerate their adoptions to drive real business growth.
After years of innovation, there are still gaps to address in how data is used for decision-making. Good decision makers often lack technical access to data, while data scientists struggle to communicate insights in business-friendly terms. This session will reveal how business leaders, analysts, and data scientists can better collaborate to enable end-to-end decision-making: from data to insight to action. Learn how AI can automate, visualize, and accelerate decision-making processes for smarter business outcomes.
Generative AI has been in the spotlight. Behind its vast potential, it has been plagued with security, privacy, and accuracy concerns. This panel will explore:
- Early enterprise use cases of GenAI: the low hanging fruits
- Ensuring trust and reliability of GenAI
- Localizing GenAI with proprietary first-party data
- CAIO role change: considerations for AI solutions as AI developers vs AI users of open source and SaaS solutions
In the dynamic landscape of Gen-AI, enterprises face numerous complexities, from navigating point solutions to justifying financial investments and measuring business impact. This panel will explore how to accelerate Gen-AI adoption and deployment efficiently. Key questions addressed in this discussion:
- Why would enterprises adopt Gen-AI?
- How do they get started?
- When can organisations enjoy the business benefits?
- What are the complexities around ongoing operation including updates and upgrades?
- Who can help companies through all these?
Moderator:
Delivering effective AI initiatives for any organisation starts with data strategy. In this talk, Sachin will share the service model approach of designing and implementing impactful data strategy for advanced analytics and AI use cases. He will also share real life use cases and metrics to measure success of data strategy and AI programs.
To produce robust, reusable AI systems, at scale and efficiently, enterprises are shifting from a model- and code-centric approach to being data-centric. This panel will discuss:
- Synthetizing data to trained machine learning models effectively
- Solving data accessibility, volume, and quality challenges
- Overcoming the complexity of producing and maintaining robust AIs
- Producing scalable, multi-objective, and practical AI
- Realigning IT infrastructure to enable data-centric AI
AI is transformative. Companies of all sizes are exploring AI to power transformation and drive the next wave of digital growth. But, Gartner predicted 30% of GenAI projects would be canceled in 2025, and poor data quality is one of the top reasons.
Data quality is imperative to the performance of any AI model. The models must be based on accurate data to produce reliable and trusted AI results.
Yet, the excitement of building AI models often takes priority over data management—creating confusion, mistrust, and escalating costs in AI adoption.
This roundtable, hosted in partnership with Informatica, will discuss scaling AI adoption with reliable data governance practices. Through peer-to-peer discussions, we will explore the power of AI-assisted data governance.
SPEAKERS
