Key takeaways: The "demo versus product" gap: An AI demo is judged on its best day, but an institutional-grade product is judged on its worst. Building reliable financial tools requires rigorous, auditable grounding frameworks to avoid hallucinations and ensure every output can be traced back to a verified source. Don't bet against the model: Developers should avoid wasting resources on complex workarounds for temporary constraints on models that are evolving rapidly. Instead, they should focus on elements that models cannot natively learn: details specific to the firm, such as proprietary data or how data sets are connected to each other. Tackling legacy constraints: Successful innovation lies in redesigning workflows from first principles rather than simply automating legacy bottlenecks to do the same tasks faster. In this episode of Goldman Sachs Exchanges, Chris Churchman, head of Marquee, Goldman Sachs’ digital platform for institutional and corporate clients, talks about building effective artificial intelligence (AI) products for institutional investors. Churchman, who is also co-chair of the Global Banking & Markets AI Working Group, describes an ongoing shift from a world where users must learn software to one where software learns the user. He tells hosts Allison Nathan of Goldman Sachs Research and George Lee, co-head of the Goldman Sachs Global Institute, about the challenges of grounding generative AI in hard facts, and emphasizes the need to design systems that enhance human reasoning rather than outsourcing critical thinking to machines. The opinions and views expressed herein are as of the date of publication, subject to change without notice, and may not necessarily reflect the institutional views of Goldman Sachs or its affiliates. The material provided is intended for informational purposes only, and does not constitute investment, legal, or tax advice, a recommendation from any Goldman Sachs entity to take any particular action or be used as a basis for any other investment decision, or an offer or solicitation to purchase or sell any securities or financial products. Any forward-looking statements, case studies, computations or examples set forth herein are for illustrative purposes only. Past performance is not indicative of future results. Neither Goldman Sachs nor any of its affiliates make any representations or warranties, express or implied, as to the accuracy or completeness of the statements or information contained herein and disclaim any liability whatsoever for reliance on such information for any purpose. Each name of a third-party organization mentioned is the property of the company to which it relates, is used here strictly for informational and identification purposes only and is not used to imply any sponsorship, affiliation, endorsement, ownership or license rights between any such company and Goldman Sachs. This material should not be copied, distributed, published, or reproduced in whole or in part or disclosed by any recipient to any other person without the express written consent of Goldman Sachs. Disclosures applicable to information relating to Goldman Sachs Global Banking & Markets, if any, mentioned herein, are available: https://www.goldmansachs.com/disclaimer/salesandtrading Disclosures applicable to research with respect to issuers, if any, mentioned herein are available through your Goldman Sachs representative or at http://www.gs.com/research/hedge.html. A transcript is provided for convenience and may differ from the original video or audio content. Goldman Sachs is not responsible for any errors in the transcript. Date of Recording August 11, 2026 © 2026 Goldman Sachs. All rights reserved. Learn more about your ad choices. Visit megaphone.fm/adchoices

Building AI Systems for Capital Markets

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Key takeaways:     The "demo versus product" gap:  An AI demo is judged on its best day, but an institutional-grade product is judged on its worst. Building reliable financial tools requires rigorous, auditable grounding frameworks to avoid hallucinations and ensure every output can be traced back to a verified source.    Don't bet against the model:  Developers should avoid wasting resources on complex workarounds for temporary constraints on models that are evolving rapidly. Instead, they should focus on elements that models cannot natively learn: details specific to the firm, such as proprietary data or how data sets are connected to each other.    Tackling legacy constraints: Successful innovation lies in redesigning workflows from first principles rather than simply automating legacy bottlenecks to do the same tasks faster.   In this episode of Goldman Sachs Exchanges, Chris Churchman, head of Marquee, Goldman Sachs’ digital platform for institutional and corporate clients, talks about building effective artificial intelligence (AI) products for institutional investors.  Churchman, who is also co-chair of the Global Banking & Markets AI Working Group, describes an ongoing shift from a world where users must learn software to one where software learns the user. He tells hosts Allison Nathan of Goldman Sachs Research and George Lee, co-head of the Goldman Sachs Global Institute, about the challenges of grounding generative AI in hard facts, and emphasizes the need to design systems that enhance human reasoning rather than outsourcing critical thinking to machines.    The opinions and views expressed herein are as of the date of publication, subject to change without notice, and may not necessarily reflect the institutional views of Goldman Sachs or its affiliates. The material provided is intended for informational purposes only, and does not constitute investment, legal, or tax advice, a recommendation from any Goldman Sachs entity to take any particular action or be used as a basis for any other investment decision, or an offer or solicitation to purchase or sell any securities or financial products. Any forward-looking statements, case studies, computations or examples set forth herein are for illustrative purposes only. Past performance is not indicative of future results. Neither Goldman Sachs nor any of its affiliates make any representations or warranties, express or implied, as to the accuracy or completeness of the statements or information contained herein and disclaim any liability whatsoever for reliance on such information for any purpose. Each name of a third-party organization mentioned is the property of the company to which it relates, is used here strictly for informational and identification purposes only and is not used to imply any sponsorship, affiliation, endorsement, ownership or license rights between any such company and Goldman Sachs. This material should not be copied, distributed, published, or reproduced in whole or in part or disclosed by any recipient to any other person without the express written consent of Goldman Sachs.     Disclosures applicable to information relating to Goldman Sachs Global Banking & Markets, if any, mentioned herein, are available: https://www.goldmansachs.com/disclaimer/salesandtrading     Disclosures applicable to research with respect to issuers, if any, mentioned herein are available through your Goldman Sachs representative or at http://www.gs.com/research/hedge.html.     A transcript is provided for convenience and may differ from the original video or audio content. Goldman Sachs is not responsible for any errors in the transcript.     Date of Recording August 11, 2026  © 2026 Goldman Sachs.  All rights reserved.      Learn more about your ad choices. Visit megaphone.fm/adchoices

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