It’s clear that Agentic AI could be transformational for how organizations make better decisions. Organizations across every field are looking to put Agentic AI to work. What are the important steps you can take to ensure you’re building the data foundation to take advantage of Agentic AI when it flips from hype to reality? Doing the right things now can ensure you’re ready and won’t get left behind. Join Ted orme as he shares 3 critical steps to take today as you chart your course and 2 pitfalls to avoid.

Companies must modernize to stay competitive, but siloed, unreliable data makes digital transformation nearly impossible. Traditional solutions fail to address data fragmentation, lacking the ability to power business applications in real-time or deliver reliable data when and where it’s needed.

Join this roundtable to explore how to implement a comprehensive data unification framework to improve trust, reusability, and governance, ensure continuous data reliability for AI systems, and mitigate security risks in AI-driven processing and decision-making.

Join us for a discussion on how to optimize your on-prem AI technology stack to maximize the ROI of small domain-specific models. We'll explore HP AI solutions and best practices for infrastructure tuning, model development, and cost-performance optimization. Share challenges and insights with peers and learn strategies to innovate freely while balancing flexibility, efficiency and maintaining full sovereignty of your data and IP.

Data teams face pressure to deliver real-time business insights across finance, supply chain, HR, and beyond. They need apps that are fueled with AI recommendations and insights available in business terms. The key lies in adopting a business data fabric architecture that interoperates across multi-cloud landscapes. SAP’s data and analytics solutions serve as the foundation for this data fabric, delivering an integrated, semantically-rich data layer that ensures seamless and scalable access to data without duplication. Join us to learn how to build this crucial foundation for your AI applications.

The UK Ministry of Defence (MOD) oversees the lifecycle management of over 120,000 assets.

Previous reliance on Excel-based models and manual processing limited team collaboration and the development of predictive capabilities. Utilising SAS Viya, Defence Infrastructure Organisation (DIO) transitioned to AI-driven simulation models, enabling predictive and prescriptive analytics to optimise asset management. Now recognised as an advanced asset management model in government, hear how DIO have demonstrated data-driven decision-making, and advanced analytical capabilities across government.

AI-ready data is a key pillar for enabling AI-related ambitions. But how should you do this, and what are others doing? This session explores what we mean by AI-ready, what clients and vendors are doing, and what Gartner recommends you do.

Urgent Investments in data, analytics and AI use cases has put the spotlight once more on strong data management foundations. Is our Data even Ready for upcoming AI, analytics and data sharing initiatives is now top of mindshare for heads of data, CDAOs and their counterparts. Data Fabrics have emerged as a long term, foundational data management architecture that you should now pursue for sustained D&A success. This session will:
1. Help understand what data Fabrics are and what they mean for your data strategy and architecture
2. Help decide how to build and where to buy
3. Navigate the vendor landscape to assist in tech procurement decisions to aid your fabric journey

Dive into the symbiotic relationship between data and Artificial Intelligence in this comprehensive session. Explore how robust data foundations are critical for developing effective AI systems and how AI, in turn, refines and enhances data quality. Gain actionable insights into transforming raw data into intelligent solutions and leveraging AI to drive business innovation.

D&A and AI leaders including CDAOs continue to struggle to prove the value of D&A and AI. This session will provide a roadmap for gaining stakeholder buy-in on D&A initiatives, quantifying the value and building the business case, learning from the success of other organizations, and continuously measuring results and adjusting.

Analytics and Business Intelligence (ABI) Platforms are being transformed by AI. Data insights are more likely to be discovered by machine learning. Natural language generation will turn dashboards into data stories. Natural language query will make data more accessible. This session will show how ABI Platform vendors are incorporating AI and how analytic leaders can manage the additional cost and risk these trends will bring.

Communicating the reality of what can be achieved with AI against what people think can be achieved can be challenging, and risky, especially when it's your boss. Join this session to learn effective communication techniques and methods for navigating internal politics to help reset expectations and chart a realistic course for using AI in your organization.

Heads of analytics & AI face many challenges such as keeping afloat, staying the course or racing ahead in the race for AI superiority. This session will focus on the top fears, risks, objections and hurdles leaders of Analytics & AI face and most importantly, how to handle them.

D&A leaders and teams are at an AI-driven inflection point. They are crucial to enable business value through AI-readiness but must evolve their strategy and operating model to meet their organization’s AI ambitions. Reporting from the Gartner 2025 CDAO Agenda Survey, this session reveals actions successful D&A leaders have taken to deliver business outcomes from AI, data, and analytics, and what they need to do next year.

As we enter an increasingly AI-driven world, it is becoming increasingly clear that leveraging the power of LLMs presents considerable challenges. Hallucinations, trust issues, and governance risks cause considerable concerns. This session will demonstrate how comprehensive and relevant metadata forms the foundation of data understanding and governance. You'll see how Ab Initio’s platform enables the automation of trusted, well-documented, and end-to-end governed data products, ensuring AI models operate with greater reliability and confidence.

Three out of four companies are betting big on AI – but most are digging on shifting ground. In this $100 billion gold rush, none of these investments will pay off without data quality and strong governance – and that remains a challenge for many organizations. Not every enterprise has a solid data governance practice and maturity models vary widely. As a result, investments in innovation initiatives are at risk of failure. What are the most important data management issues to prioritize? See how your organization measures up and get ahead of the curve with Actian.

Enterprise-grade GenAI needs a unified data strategy for accurate, reliable results. Learn how knowledge graphs make structured and unstructured data AI-ready while enabling governance and transparency. See how GraphRAG (retrieval-augmented generation with knowledge graphs) drives real success: a major gaming company achieved 10x faster insights, while Data2 cut workloads by 50%. Discover how knowledge graphs and GraphRAG create a foundation for trustworthy agentic AI systems across retail, healthcare, finance, and more.

With high D&A ambitions and AI pilots becoming increasingly ubiquitous, focus is shifting toward consistent execution. We will share how to continue innovating and experimenting while scaling success. In this keynote, Gartner’s perspective will guide you on preparing for the expected and adapting to the unexpected. Learn to enhance your D&A and AI capabilities on the path to success.

Will generative artificial intelligence lead to nirvana or dystopia? Great question, which we don’t exactly answer in this week’s podcast, but we do weigh the most critical downstream effects of the technology based on recent research done by urban economists Frank Levy and Scott Abrahams. We assess how GenAI impacts the benefits of a college degree, the nation’s political dynamics, and which metro area economies will win (think Savannah) and lose (think San Francisco). Guests: Frank Levy, Visitor in the Strategy Group of the Fuqua School of Business, Duke University, and Scott Abrahams, Professor of Economics at Louisiana State University Read Frank and Scott's recent research on Gen AI here: From San Francisco to Savannah? The Downstream Effects of Generative AI (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4874104) Hosts: Mark Zandi – Chief Economist, Moody’s Analytics, Cris deRitis – Deputy Chief Economist, Moody’s Analytics, Marisa DiNatale – Senior Director - Head of Global Forecasting, Moody’s Analytics Follow Mark Zandi on 'X', BlueSky or LinkedIn @MarkZandi, Cris deRitis on LinkedIn, and Marisa DiNatale on LinkedIn

Questions or Comments, please email us at [email protected]. We would love to hear from you.    To stay informed and follow the insights of Moody's Analytics economists, visit Economic View.

Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The hard activity data continue to show resilient growth in the first four months of the year. We maintain our recession call owing to 1) front-loading that will impart a drag going forward, 2) material trade war drags that will further damp activity, and 3) sentiment that is falling sharply and risks becoming a drag on its own. Trade war news this week does not move the needle in our view. The Fed is equally balanced on rising risks to both sides of its dual mandate and unlikely to act absent a break in the data.

This podcast was recorded on May 9, 2025.

This communication is provided for information purposes only. Institutional clients please visit www.jpmm.com/research/disclosures for important disclosures. © 2025 JPMorgan Chase & Co. All rights reserved. This material or any portion hereof may not be reprinted, sold or redistributed without the written consent of J.P. Morgan. It is strictly prohibited to use or share without prior written consent from J.P. Morgan any research material received from J.P. Morgan or an authorized third-party (“J.P. Morgan Data”) in any third-party artificial intelligence (“AI”) systems or models when such J.P. Morgan Data is accessible by a third-party. It is permissible to use J.P. Morgan Data for internal business purposes only in an AI system or model that protects the confidentiality of J.P. Morgan Data so as to prevent any and all access to or use of such J.P. Morgan Data by any third-party.

In this podcast episode, we talked with Lavanya Gupta about Building a Strong Career in Data. About the Speaker: Lavanya is a Carnegie Mellon University (CMU) alumni of the Language Technologies Institute (LTI). She works as a Sr. AI/ML Applied Associate at JPMorgan Chase in their specialized Machine Learning Center of Excellence (MLCOE) vertical. Her latest research on long-context evaluation of LLMs was published in EMNLP 2024.

In addition to having a strong industrial research background of 5+ years, she is also an enthusiastic technical speaker. She has delivered talks at events such as Women in Data Science (WiDS) 2021, PyData, Illuminate AI 2021, TensorFlow User Group (TFUG), and MindHack! Summit. She also serves as a reviewer at top-tier NLP conferences (NeurIPS 2024, ICLR 2025, NAACL 2025). Additionally, through her collaborations with various prestigious organizations, like Anita BOrg and Women in Coding and Data Science (WiCDS), she is committed to mentoring aspiring machine learning enthusiasts.

In this episode, we talk about Lavanya Gupta’s journey from software engineer to AI researcher. She shares how hackathons sparked her passion for machine learning, her transition into NLP, and her current work benchmarking large language models in finance. Tune in for practical insights on building a strong data career and navigating the evolving AI landscape.

🕒 TIMECODES 00:00 Lavanya’s journey from software engineer to AI researcher 10:15 Benchmarking long context language models 12:36 Limitations of large context models in real domains 14:54 Handling large documents and publishing research in industry 19:45 Building a data science career: publications, motivation, and mentorship 25:01 Self-learning, hackathons, and networking 33:24 Community work and Kaggle projects 37:32 Mentorship and open-ended guidance 51:28 Building a strong data science portfolio 🔗 CONNECT WITH LAVANYALinkedIn -   / lgupta18  🔗 CONNECT WITH DataTalksClub Join the community - https://datatalks.club/slack.html Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/... Check other upcoming events - https://lu.ma/dtc-events LinkedIn -   / datatalks-club   Twitter -   / datatalksclub   Website - https://datatalks.club/