This story was originally published on CIO Dive. To receive daily news and insights, subscribe to our free daily CIO Dive newsletter. Dive Brief: Enterprises have increased hiring for people with AI governance expertise as adoption of the technology unfolds, according to data from enterprise talent intelligence firm Draup. The company reviewed Fortune 500 hiring posts alongside its proprietary data for the Tuesday report.  Hiring for AI governance and model risk skills increased 81% year over year, the r ...

Core Insights - Trustible and Leidos are collaborating to redefine AI governance through automation, aiming to reduce the time required for governance processes from weeks to hours or even minutes while ensuring oversight and control [1][5][6] AI Governance Overview - AI governance involves establishing guardrails for AI usage, ensuring systems are reviewed, approved, and monitored to understand their functionality, risks, and readiness for deployment [2] - The goal is to ensure responsible AI deployment with transparency, accountability, and alignment to legal and ethical standards [2] Collaboration Details - The partnership leverages Leidos' extensive experience in AI deployment and Trustible's automated governance platform to facilitate AI adoption while managing associated risks [3][4] - The collaboration aims to operationalize governance through automation, allowing agencies to transition from policy to practice more efficiently [4] Automation Impact - In a proof-of-concept, the collaboration demonstrated that automated governance can significantly reduce barriers to AI deployment, compressing the initial governance intake process from weeks to hours or minutes based on system complexity [5] - This automation is expected to streamline governance workflows while maintaining necessary rigor, accountability, and transparency in mission-critical environments [5][6] Future Outlook - With ongoing development of advanced agentic capabilities, the collaboration anticipates further compression of governance timelines, enabling a focus on outcomes rather than processes [6] - The joint approach is designed to support AI adoption across various sectors, including civilian, defense, and intelligence, by embedding governance directly into AI workflows [7] Integration and Commitment - Leidos has integrated Trustible's platform into its enterprise governance framework, reinforcing its commitment to delivering secure and accountable AI systems at scale [8]

Summary of Key Points from the Conference Call Industry Overview - **Industry**: AI-driven new economy in China - **Context**: The new economy is rapidly catching up with global standards, particularly in technology sectors, leading to a tech-heavy equity rally in the market [1][4][7]. Core Insights and Arguments - **Economic Impact**: The new economy's growth is now macro-relevant, potentially offsetting the negative impact of the property sector on GDP [1][4][34]. - **Job Displacement Risk**: Approximately 31% of jobs in China are highly exposed to AI, with 9.6% (around 70 million jobs) facing direct displacement risk and 21.4% experiencing productivity augmentation but increased competition [1][5][62]. - **Sector Vulnerability**: The services sector and young workers are particularly vulnerable to AI disruptions, with youth unemployment at 16.5% as of the end of 2025 [5][64][70]. - **Policy Recommendations**: There is a need for policies that prioritize augmentation over substitution to avoid the "Turing Trap," which could exacerbate inequality and job loss [6][82]. Additional Important Insights - **Consumer Sentiment**: Despite positive macroeconomic indicators, consumer confidence remains low, indicating a disconnect between macro growth and micro sentiment [39][40]. - **Investment in AI**: AI capital expenditure (capex) is estimated to reach approximately RMB 3.3 trillion from 2025 to 2030, highlighting the significant investment in this sector [34][35]. - **Technological Advancements**: China's performance in large language models (LLMs) is improving, narrowing the gap with the US, and the country is becoming a leader in AI talent and patent production [15][22][23]. - **Work-Life Balance**: Improving work-life balance is seen as essential for translating AI productivity gains into increased domestic consumption [82][87]. Conclusion The AI-driven new economy in China presents both significant opportunities and risks. While it has the potential to drive macroeconomic growth and technological advancement, it also poses challenges in terms of job displacement and consumer sentiment. Policymakers are urged to focus on strategies that enhance job augmentation and ensure equitable distribution of AI's benefits.

Core Insights - IBM has launched IBM Sovereign Core, a software solution aimed at enterprises, government bodies, and service providers to manage AI workloads in secure environments, addressing the need for operational authority as regulatory requirements evolve [1][2] - The solution allows customers to build and oversee AI-ready environments under their jurisdiction, focusing on data control and system governance [2][5] Product Features - IBM Sovereign Core is based on Red Hat's open-source software, providing a customer-controlled operational model where authentication, encryption keys, and access management are maintained within jurisdictional boundaries [3] - The product generates operational data, system telemetry, and audit trails to support compliance requirements [3] - It enables local AI model deployment and operations, ensuring data does not need to be exported externally [4] Market Strategy - IBM is collaborating with IT service providers for the initial rollout in Europe, starting with Cegeka in Belgium and the Netherlands, and Computacenter in Germany, to provide local operational control for large-scale AI workloads [5][6] - A tech preview of IBM Sovereign Core is expected to be available from February, with full general availability anticipated by mid-2026 [6]

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AI Governance in Fortune 500 Companies - AI governance has become a top priority for boards at Fortune 500 companies as they work to deploy AI at scale [1] - A Sedgwick report indicates that 70% of Fortune 500 executives have AI risk committees, 67% report progress on AI infrastructure, and 41% have dedicated AI governance teams, but only 14% feel fully ready for AI deployment, highlighting a gap between governance structures and actual readiness [2][3] - The report surveyed 300 senior leaders, including C-suite executives, revealing that while oversight has been formalized, foundational elements like processes and skills have not kept pace with AI advancements [3] Implementation Challenges - The leading challenge in AI implementation is the rapid pace of AI change, followed by difficulties in governance execution and data privacy management [4] - Regulatory uncertainty and change management are also significant hurdles, indicating that success requires alignment of people, policy, and technology [4] Gaps in AI Governance - Visibility is a major gap, as many organizations lack a comprehensive view of AI usage, leading to unmanaged risks from shadow AI and unsanctioned tools [6] - There is a conceptual gap where governance is often mistaken for regulation, whereas it should encompass risk understanding, product quality, and alignment with organizational values [7] - AI literacy is another critical gap; organizations cannot govern effectively if only a small team understands AI technology while the rest deploys AI tools [8] Evolving AI Landscape - The AI landscape is evolving from predictive models to generative AI and now to agentic systems capable of autonomous actions [9] - Good governance is contextual and should align with what organizations value most, as illustrated by PepsiCo's focus on reputation and responsible AI [9][10] - Effective governance practices must move beyond theoretical frameworks to operational practices that ensure AI is safe, trustworthy, and fit for purpose [10] Economic Outlook - KKR's 2026 Global Macro Outlook predicts better-than-expected GDP and earnings growth across major regions, suggesting a favorable environment for investment [13] - The report emphasizes the importance of "high grading" portfolios in a mature cycle, with an implied 10-year forward CAGR for the S&P 500 close to 16% [14]

Core Insights - ModelOp has joined the NVIDIA Inception Program, enhancing its collaboration with NVIDIA and expanding its AI governance capabilities for enterprises [1][4][10] Company Developments - The NVIDIA Inception Program provides ModelOp with access to GPU resources, technical expertise, and go-to-market support, which will accelerate the development of agentic governance capabilities [4][5] - ModelOp's integration with NVIDIA technologies allows real-time monitoring of AI portfolios, automatic detection of drift and anomalies, and rapid compliance insights [6][10] Technological Advancements - ModelOp is developing conversational and agentic AI governance capabilities, enabling users to interact with governance systems through natural-language queries [7][8] - New features include AI governance agents for task automation, instant insight retrieval on governance risks, and automated compliance reporting [8][9] Market Positioning - ModelOp is evolving from a system of record to an intelligent governance platform, facilitating proactive risk management and continuous insight generation [9][10] - The company is recognized as a leading vendor in AI governance and lifecycle automation, serving complex and regulated institutions globally [12]

Summary of Key Points from the Citi 2025 China Conference Industry and Company Involvement - **Industry**: US-China relations, International Trade, AI Governance, De-globalization - **Company**: Citigroup Global Markets Inc. Core Insights and Arguments 1. **US-China Trade Deal**: The recent US-China deal is viewed as a partial rollback of trade actions, with uncertainty surrounding its implementation. The US's share in China's trade has significantly declined, influencing China's strategic approach [5][6][8] 2. **China's External Environment**: There is an observed improvement in China's external environment, with countries balancing economic reliance on China against security reliance on the US. The geopolitical landscape has shifted towards a dual-polar status between the US and China [6][8] 3. **Diplomatic Focus Shift**: China's diplomatic focus has shifted from major powers to regional countries, indicating a change in priorities in international relations [6][8] 4. **AI Governance Collaboration**: AI governance is identified as a critical area for US-China collaboration, with the need for global rules on AI production due to its potential impact [7][8] 5. **De-globalization Trend**: The trend of de-globalization is expected to persist, with rising protectionism and market fragmentation. The global economy is moving towards the establishment of product-specific clubs rather than geographic clusters [8][9] 6. **Global Overcapacity Issues**: Overcapacity is highlighted as a significant global issue, leading to economic tensions and competition for markets. An OPEC-style mechanism is suggested as a potential solution for global pricing amid production competition [9][8] Additional Important Insights 1. **Sustainability of US-China Truce**: The sustainability of the US-China truce is uncertain, with potential piecemeal implementation and possible violations [5][6] 2. **Rare Earth Trade Dynamics**: The decline of the US's share in China's trade to below 10% is noted as a significant factor in the current trade dynamics, particularly concerning rare earth materials [5][6] 3. **Long-term Relationship Outlook**: Competition is deemed more likely than cooperation in the medium-to-long-term bilateral relationship between the US and China [5][6] This summary encapsulates the key takeaways from the Citi 2025 China Conference, focusing on the evolving dynamics of US-China relations and the implications for global trade and governance.

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Core Insights - The Big Four accountancy firms are now offering audits to ensure AI products are compliant and effective, indicating the maturation of AI and the growing prevalence of customer-facing use cases [1] - Insurance companies are providing AI liability coverage to protect organizations from risks associated with AI, reflecting a desire for organizations to safeguard themselves amid regulatory changes and reputational concerns [1] Group 1: AI Compliance and Risk Management - Audits and insurance are seen as safety nets but do not address the underlying issues of data and infrastructure that hinder safe and effective AI use [2] - Large organizations face challenges in managing vast amounts of sensitive data, which complicates oversight and governance as AI adoption increases [3] - The distributed nature of AI risks makes it difficult to monitor usage, accuracy, and outputs, leading to potential serious consequences if visibility is lost [4] Group 2: Data Security and Regulatory Pressure - There are risks of data leaks through public AI models and unauthorized access to sensitive information, which can result in biased outputs [5] - Organizations face dual risks: loss of customer trust if they cannot demonstrate AI safety and growing regulatory pressure, such as the EU AI Act, which can impose significant fines [6] - AI liability insurance may mitigate financial losses from AI errors, but it cannot recover lost customers or rectify past mistakes, highlighting the need for proper governance [7]