AI scaling is hindered by fragmented enterprise infrastructure in a constantly shifting technology ecosystem. A new architectural paradigm of composable, sovereign AI can help enterprises move past pilot purgatory. Despite billions invested in generative AI, only 5% of integrated pilots deliver measurable business value and nearly one in two companies abandons AI initiatives before reaching production. The bottleneck is not the models themselves. What's holding enterprises back is the surrounding infrastructure: Limited data accessibility, rigid integration, and fragile deployment pathways prevent AI initiatives from scaling beyond early LLM and RAG experiments. In response, enterprises are moving toward composable and sovereign AI architectures that lower costs, preserve data ownership, and adapt to the rapid, unpredictable evolution of AI--a shift IDC expects 75% of global businesses to make by 2027.

Google announced Tuesday that it is overhauling the principles governing how it uses artificial intelligence and other advanced technology. The company removed language promising not to pursue "technologies that cause or are likely to cause overall harm," "weapons or other technologies whose principal purpose or implementation is to cause or directly facilitate injury to people," "technologies that gather or use information for surveillance violating internationally accepted norms," and "technologies whose purpose contravenes widely accepted principles of international law and human rights." The changes were disclosed in a note appended to the top of a 2018 blog post unveiling the guidelines. "We've made updates to our AI Principles. Visit AI.Google for the latest," the note reads.

The video game industry has been in a troubled place for the past year, with studio closures and job security at the forefront of developer concerns. Increasing layoffs with seemingly no end paint a bleak picture for devs, while companies are busy pumping money into AI initiatives. According to a new report from the organizers of the Game Developers Conference, 52 percent of devs surveyed said they worked at companies that were using generative AI on their games. Of the 3,000 people surveyed, roughly half said they were concerned about the technology's impact on the industry and an increasing number reported they felt negatively about AI overall. The "State of the Game Industry" report, released Tuesday, is one of a series of surveys conducted each year by GDC organizers prior to their annual conference.

There is no shortage of AI use cases across sectors. Retailers are tailoring shopping experiences to individual preferences by leveraging customer behavior data and advanced machine learning models. Traditional AI models can deliver personalized offerings. However, with generative AI, these personalized offerings are elevated by incorporating tailored communication that considers the customer's persona, behavior, and past interactions. In insurance, by leveraging generative AI, companies can identify subrogation recovery opportunities that a manual handler might overlook, enhancing efficiency and maximizing recovery potential.

As the artificial intelligence (AI) supply chain grows more complex, AI systems and models are increasingly likely to incorporate externally-sourced ingredients such as datasets and other models. In such cases, determining whether or not an AI system or model complies with the EU AI Act will require gathering compliance-related metadata about both the AI system or model at-large as well as those externally-supplied ingredients. There must then be an analysis that looks across all of this metadata to render a prediction about the compliance of the overall AI system or model. Up until now, this process has not been automated. Thus, it has not been possible to make real-time compliance determinations in scenarios where doing so would be advantageous, such as the iterative workflows of today's AI developers, search and acquisition of AI ingredients on communities like Hugging Face, federated and continuous learning, and more. To address this shortcoming, we introduce a highly automated system for AI Act compliance analysis. This system has two key elements. First is an interlocking set of computational artifacts that capture compliance-related metadata about both: (1) the AI system or model at-large; (2) any constituent ingredients such as datasets and models. Second is an automated analysis algorithm that operates across those computational artifacts to render a run-time prediction about whether or not the overall AI system or model complies with the AI Act. Working together, these elements promise to enhance and accelerate AI Act compliance assessments.

The integration of Artificial Intelligence (AI) into corporate strategy has become a pivotal focus for organizations aiming to maintain a competitive advantage in the digital age. As AI reshapes business operations and drives innovation, the need for specialized leadership to effectively manage these changes becomes increasingly apparent. In this paper, I explore the role of the Chief AI Officer (CAIO) within the C-suite, emphasizing the necessity of this position for successful AI strategy, integration, and governance. I analyze future scenarios based on current trends in three key areas: the AI Economy, AI Organization, and Competition in the Age of AI. These explorations lay the foundation for identifying the antecedents (environmental, structural, and strategic factors) that justify the inclusion of a CAIO in top management teams. This sets the stage for a comprehensive examination of the CAIO's role and the broader implications of AI leadership. This paper advances the discussion on AI leadership by providing a rationale for the strategic integration of AI at the executive level and examining the role of the Chief AI Officer within organizations.

Although AI was recognized as strategically important before generative AI became prominent, our 2022 survey found CIOs' ambitions limited: while 94% of organizations were using AI in some way, only 14% were aiming to achieve "enterprise-wide" AI by 2025. By contrast, the power of generative AI tools to democratize AI--to spread it through every function of the enterprise, to support every employee, and to engage every customer --heralds an inflection point where AI can grow from a technology employed for particular use cases to one that truly defines the modern enterprise. As such, chief information officers and technical leaders will have to act decisively: embracing generative AI to seize its opportunities and avoid ceding competitive ground, while also making strategic decisions about data infrastructure, model ownership, workforce structure, and AI governance that will have long-term consequences for organizational success. This report explores the latest thinking of chief information officers at some of the world's largest and best-known companies, as well as experts from the public, private, and academic sectors. It presents their thoughts about AI against the backdrop of our global survey of 600 senior data and technology executives.

According to the USCO: "This initiative is in direct response to the recent striking advances in generative AI technologies and their rapidly growing use by individuals and businesses." It is also a response to requests from Congress and the public. A summary of this guidance is here. The Guide provides a convenient collection of relevant materials in one document for your convenience. We are also planning a webinar on legal issues with Generative AI, generating employee guidance on the use of AI and dealing with contractors that produce content for you.

The benefits of AI are undeniable -- but so are the risks of getting it wrong. In this post, you'll learn the 12 biggest AI mistakes organizations make and get practical ways to avoid these common missteps so you can effectively harness the power of AI. AI is the most powerful technology humans have ever had access to -- and now, every organization can put it to good use and create value for customers. To fully realize the potential of AI, though, organizations must commit to its implementation and integration. It's crucial to invest in the right infrastructure, personnel, and training to ensure successful AI adoption and avoid half-hearted attempts that can lead to wasted resources and suboptimal results.

In the 1990s, business process reengineering was all the rage: Companies used budding technologies such as enterprise resource planning (ERP) systems and the internet to enact radical changes to broad, end-to-end business processes. Buoyed by reengineering's academic and consulting proponents, companies anticipated transformative changes to broad processes like order-to-cash and conception to commercialization of new products. But while technology did bring major updates, implementations often failed to live up to the sky-high expectations. For example, large-scale ERP systems like SAP or Oracle provided a useful IT backbone to exchange data, yet also created very rigid processes that were hard to change past the IT implementation. Since then, process management typically involved only incremental change to local processes -- Lean and Six Sigma for repetitive processes, and Agile Lean Startup methods for development -- all without any assistance from technology.