The Chief AI Officer Show

• SurveyMonkey’s Jing Huang on the Hidden Flaw in Synthetic Data for Enterprise AI Training

  As enterprises race to integrate generative AI, SurveyMonkey is taking a uniquely methodical approach: applying 20 years of survey methodology to enhance LLM capabilities beyond generic implementations. In this episode, Jing Huang, VP of Engineering & AI/ML/Personalization at SurveyMonkey, breaks down how her team evaluates AI opportunities through the lens of domain expertise, sharing a framework for distinguishing between market hype and genuine transformation potential.  Drawing from her experience witnessing the rise of deep learning since AlexNet's breakthrough in 2012, Jing provides a strategic framework for evaluating AI initiatives and emphasizes the critical role of human participation in shaping AI's evolution. The conversation offers unique insights into how enterprise leaders can thoughtfully approach AI adoption while maintaining competitive advantage through domain expertise. Topics discussed: How SurveyMonkey evaluated generative AI opportunities, choosing to focus on survey generation over content creation by applying their domain expertise to enhance LLM capabilities beyond what generic models could provide. The distinction between internal and product-focused AI implementations in enterprise, with internal operations benefiting from plug-and-play solutions while product integration requires deeper infrastructure investment. A strategic framework for modernizing technical infrastructure before AI adoption, including specific prerequisites for scalable data systems, MLOps capabilities, and real-time processing requirements. The transformation of survey creation from a months-long process to minutes through AI, while maintaining methodological rigor by embedding 20+ years of survey expertise into the generation process. The critical importance of quality human input data over quantity in AI development, with insights on why synthetic data and machine-generated content may not be the solution to current data limitations. How to evaluate new AI technologies through the lens of domain fit and implementation readiness rather than market hype, illustrated through SurveyMonkey's systematic assessment process. The role of human participation in shaping AI evolution, with specific recommendations for how organizations can contribute meaningful data to improve AI systems rather than just consuming them.

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• Schneider Electric's Sreedhar Sistu on Scaling AI for Energy Management

  From optimizing microgrids to managing peak energy loads, Sreedhar Sistu, VP of AI Offers, shares how Schneider Electric is harnessing AI to tackle critical energy challenges at global scale. Drawing from his experience deploying AI across a 150,000-person organization, he shares invaluable insights on building internal platforms, implementing stage-gate processes that prevent "POC purgatory," and creating frameworks for responsible innovation. The conversation spans practical deployment strategies, World Economic Forum governance initiatives, and why mastering fundamentals matters more than chasing technology headlines. Through concrete examples and honest discussion of challenges, Sreedhar demonstrates how enterprises can move beyond pilots to create lasting value with AI.   Topics discussed: Transforming energy management through AI-powered solutions that optimize microgrids, manage peak loads, and orchestrate renewable energy sources effectively. Building robust internal platforms and processes to scale AI deployment across a 150,000-person global organization. Creating stage-gate evaluation processes that prevent "POC purgatory" by focusing on clear business outcomes and value creation. Balancing in-house AI development for core products with strategic vendor partnerships for operational efficiency improvements. Managing uncertainty in AI systems through education, process design, and clear communication about probabilistic outcomes. Developing frameworks for responsible AI governance through collaboration with the World Economic Forum and regulatory bodies. Tackling climate challenges through AI applications that reduce energy footprint, optimize energy mix, and enable technology adoption. Implementing people-centric processes that combine technical expertise with business domain knowledge for successful AI deployment. Navigating the evolving regulatory landscape while maintaining focus on innovation and value creation across global markets. Building internal capabilities to master AI technology rather than relying solely on vendor solutions and external expertise. Listen to more episodes:  Apple  Spotify  YouTube  

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• Thoropass’ Sam Li on Why Compliance vs Innovation is a False Trade-off

  Thoropass Co-founder and CEO Sam Li joins Ben on Chief AI Officer to break down how AI is shaping the compliance and security landscape from two crucial angles: as a powerful tool for automation and as a source of new challenges requiring innovative solutions.    Sam shares how their First Pass AI feature is helping along the audit process by providing instant feedback, and also explores why back-office operations are the hidden frontier for AI transformation. The conversation explores everything from navigating state-level AI regulations to building effective testing frameworks for LLM-powered systems, offering a comprehensive look at how enterprises can maintain security while driving innovation in the AI era.   Topics discussed: The evolution of AI capabilities in compliance and security, from basic OCR technology to today's sophisticated LLM applications in audit automation. How companies are managing novel AI risks including hallucination, bias, and data privacy concerns in regulated environments. The transformation of back-office operations through AI agents, with predictions of 90% automation in traditional compliance work. Development of new testing frameworks for LLM-powered systems that go beyond traditional software testing approaches. Go-to-market strategies in the enterprise space, specifically shifting from direct sales to partner-driven approaches. The impact of AI integration on enterprise sales cycles and the importance of proactive stakeholder engagement. Emerging AI compliance standards, including ISO 42001 and HITRUST certification, preparing for increased regulatory scrutiny. Framework for evaluating POC success: distinguishing between use case fit, foundation model limitations, and implementation issues. The false dichotomy between compliance and innovation, and how companies can achieve both through strategic AI deployment.   Listen to more episodes:  Apple  Spotify  YouTube

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• ITV’s Sanjeevan Bala on Going Beyond AI Experiments to Unlock Enterprise Value

  Sanjeevan Bala, Former Group Chief Data & AI Officer at ITV and FTSE Non Executive Director's media value chain to content production and monetization. He reveals why starting with "last mile" business value led to better outcomes than following industry hype around creative AI.  Sanjeevan also provides a practical framework for moving from experimentation to enterprise-wide adoption. His conversation with Ben covers everything from increasing ad yields through AI-powered contextual targeting to building decentralized data teams that "go native" in business units.   Topics discussed: How AI has evolved from basic machine learning to today's generative capabilities, and why media companies should look beyond the creative AI hype to find real value. Breaking down how AI impacts each stage of media value chains: from reducing production costs and optimizing marketing spend to increasing viewer engagement and maximizing ad revenue. Why starting with "last mile" business value and proof-of-value experiments leads to better outcomes than traditional POCs, helping organizations avoid the trap of "POC purgatory." Creating successful AI teams by deploying them directly into business units, focusing on business literacy over technical skills, and ensuring they go native within departments. Developing AI systems that analyze content, subtitles, and audio to identify optimal ad placement moments, leading to premium advertising products with superior brand recall metrics. Understanding how agentic AI will transform media operations by automating complex business processes while maintaining the flexibility that rule-based automation couldn't achieve. How boards oscillate between value destruction fears and growth opportunities, and why successful AI governance requires balancing risk management with innovation potential. Evaluating build vs buy decisions based on core competencies, considering whether to partner with PE-backed startups or wait for big tech acquisition cycles. Challenging the narrative around AI productivity gains, exploring why enterprise OPEX costs often increase despite efficiency improvements as teams move to higher-value work. Connecting AI ethics frameworks to company purpose and values, moving beyond theoretical principles to create practical, behavioral guidelines for responsible AI deployment. Episode 16.

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• hackajob’s Mark Chaffey on Enhancing Talent Matching Through LLMs

  Mark Chaffey, Co-founder & CEO at hackajob talks about the impact of AI on the recruitment landscape, sharing insights into how leveraging LLMs can enhance talent matching by focusing on skills rather than traditional credentials.  He emphasizes the importance of maintaining a human touch in the hiring process, ensuring a positive candidate experience amidst increasing automation, while still leveraging those tools to create a more efficient and inclusive hiring experience. Additionally, Mark discusses the challenges posed by varying regulations across regions, highlighting the need for adaptability in the evolving recruitment space.   Topics discussed: The evolution of recruitment technology and how AI is reshaping the hiring landscape.   How skills-based assessments, rather than conventional credentials, allow companies to identify talent that may not fit traditional hiring molds.   Leveraging LLMs to enhance talent matching, enabling systems to understand context and reason beyond simple keyword searches.   The significance of maintaining a human touch in recruitment processes, ensuring candidates have a positive experience despite increasing automation in hiring.   Addressing the challenge of bias in AI-driven recruitment, emphasizing the need for transparency and fairness in automated decision-making systems.   The impact of varying regulations across regions on AI deployment in recruitment, highlighting the need for companies to adapt their strategies accordingly.   The role of internal experimentation and a culture of innovation in developing new recruitment technologies and solutions that meet evolving market needs.   Insights into the importance of building a strong data asset for training AI systems, which can significantly enhance the effectiveness of recruitment tools.   The balance between iterative improvements on core products and pursuing big bets in technology development to stay competitive in a rapidly changing market.   The potential for agentic AI systems to handle initial candidate interactions, streamlining the hiring process further.  (Episode 15)

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