ibl.ai

IBM: The CEO's Guide to Generative AI – 2nd Edition

Summary of https://www.ibm.com/downloads/documents/us-en/107a02e9bec8fbd9
This document from the IBM Institute for Business Value explores how CEOs can leverage generative AI to transform their businesses. It examines key areas including digital product engineering, IT automation, AI model optimization, platform development, open innovation, application modernization, responsible AI, tech spending, operating model transformation, and talent management.
The report emphasizes the importance of a human-centered approach, ethical considerations, and the need for strategic investment to maximize the return on generative AI initiatives. Practical advice and research data, gathered from numerous surveys of executives globally, are provided to guide CEOs in navigating this rapidly evolving technological landscape.
The ultimate goal is to equip CEOs with the knowledge and tools to successfully integrate generative AI into their organizations while mitigating risks.

World Economic Forum: 2025 Future of Jobs Report

Summary of https://reports.weforum.org/docs/WEF_Future_of_Jobs_Report_2025.pdf
The World Economic Forum's 2025 Future of Jobs Report analyzes how macrotrends and technological advancements will reshape global labor markets through 2030. It examines five key macrotrends—technological change, the green transition, geoeconomic fragmentation, economic uncertainty, and demographic shifts—and their impact on job creation and displacement.
The report projects significant job growth overall but also highlights emerging skills gaps and the need for substantial workforce reskilling and upskilling.
It further explores employer strategies for adapting to these changes, including investments in training and wage adjustments, and offers regional, economic, and industry-specific insights.
Finally, it emphasizes the continued importance of human-centered skills alongside technological proficiency.

MIT Technology Review: A Playbook for Crafting AI Strategy

Summary of https://wp.technologyreview.com/wp-content/uploads/2024/07/MITTR-x-Boomi_final_19jul2024.pdf
This MIT Technology Review Insights report examines enterprise AI adoption. A global survey of C-suite executives reveals widespread AI ambition but limited scaling beyond pilot projects.
The report explores challenges like high costs, data quality issues, and regulatory concerns. It also offers strategies for building a robust data foundation, selecting appropriate vendors, and measuring return on investment.
Ultimately, the report provides a playbook for developing a successful enterprise AI strategy.

IST: Implications of AI in Cybersecurity – Shifting the Offense-Defense Balance

Summary of https://securityandtechnology.org/wp-content/uploads/2024/10/The-Implications-of-Artificial-Intelligence-in-Cybersecurity.pdf
This report from the Institute for Security and Technology examines the implications of artificial intelligence (AI) on cybersecurity, exploring how AI is revolutionizing both offensive and defensive capabilities. The authors analyze AI's impact on various aspects of cybersecurity, including content analysis, authentication, software security, and security operations.
They identify key premises about AI's effects and offer recommendations for mitigating risks and leveraging AI's potential benefits. The report also examines emerging threats, such as AI-generated deepfakes and polymorphic malware, and suggests strategies for minimizing attack surfaces and improving overall cybersecurity posture.
Finally, the report emphasizes the importance of a balanced approach, integrating AI effectively while maintaining human oversight and addressing potential vulnerabilities in AI systems themselves.

George Mason University: Artificial Intelligence Policy Framework for Institutions

Summary of https://arxiv.org/pdf/2412.02834v1
This paper proposes an AI policy framework for institutions, focusing on the ethical and practical considerations of integrating artificial intelligence, especially generative AI. The framework addresses key issues such as data privacy, bias mitigation, and energy efficiency.
It emphasizes the importance of interpretability and explainability in AI systems to foster trust and ensure fairness. Case studies illustrate how the framework can be applied in various institutional settings, from academic to medical contexts. The authors also discuss the unique challenges presented by AI in educational environments.

IBM: Enterprise AI Development – Obstacles and Opportunities

Summary of https://filecache.mediaroom.com/mr5mr_ibmnewsroom/198591/Enterprise%20AI%20Development%20Survey.pdf
The Morning Consult survey of 1,063 US enterprise AI developers reveals key challenges and opportunities in the field. Significant skill gaps exist, particularly in generative AI, and developers cite a lack of standardized processes and trusted tools as major obstacles.
The survey highlights the importance of ease of use and integration in AI development tools, despite these qualities being rare. Finally, while AI agents are widely explored, concerns remain about trustworthiness and compliance.

O'Reilly: Technology Trends for 2025

Summary of https://ae.oreilly.com/l/1009792/2024-12-06/332nf/1009792/1733515474UOvDN6IM/OReilly_Technology_Trends_for_2025.pdf
Analyzes O'Reilly's online learning platform usage data from January 1, 2024, to September 30, 2024, to identify key technology trends for 2025. Artificial intelligence (AI), especially its applications and related skills like prompt engineering, dominates the findings, showing significant growth despite potential disillusionment.
Software development trends reveal a shift away from microservices and towards AI-integrated tools. Security emerges as another major area of growth, with increasing interest in security governance and certifications.
Finally, the report also examines the impact of new platform initiatives, such as badging and a generative AI-powered Q&A tool, and offers predictions for the coming year.

U.S. Congressional Budget Office: AI and Its Potential Effects on the Economy and the Federal Budget

Summary of https://www.cbo.gov/system/files/2024-12/60774-AI-fed-budget.pdf
This Congressional Budget Office report analyzes the potential economic and budgetary impacts of artificial intelligence (AI). It examines how AI's increased efficiency and new product development could boost economic growth, potentially affecting federal revenues and spending.
The report also explores AI's uncertain effects on employment and wages, and how its government use could influence tax collection and program spending.
While acknowledging the current limited adoption of AI by businesses, the report highlights the technology's potential for long-term transformative effects on the economy and the federal budget. However, significant uncertainties remain regarding the timing and magnitude of these impacts.

Australian Government: Voluntary AI Safety Standard

Summary of https://www.industry.gov.au/sites/default/files/2024-09/voluntary-ai-safety-standard.pdf
Outlines a Voluntary AI Safety Standard developed by the Australian Government. It provides ten voluntary guardrails for Australian organizations to implement safe and responsible AI practices, covering areas like accountability, risk management, data governance, testing, transparency, and stakeholder engagement.
The standard aims to establish consistent practices, anticipate future legislation, and promote best practices within the AI supply chain. It emphasizes a human-centered approach, aligning with Australia's AI Ethics Principles and international standards.
Examples illustrate the application of the guardrails in various AI use cases.

NVIDIA: Cosmos World Foundation Model Platform for Physical AI

Summary of https://d1qx31qr3h6wln.cloudfront.net/publications/NVIDIA%20Cosmos_3.pdf
Introduces NVIDIA's Cosmos World Foundation Model (WFM) platform for Physical AI. Cosmos uses a pre-training and post-training paradigm, employing both diffusion and autoregressive models trained on a massive, curated video dataset (20M hours) to create generalist WFMs.
These are then fine-tuned for specialized Physical AI tasks like robotic manipulation and autonomous driving. The platform includes a novel video tokenizer for efficient processing and a guardrail system for safety.
Results demonstrate state-of-the-art performance across various benchmarks and applications.