What it takes to deliver on data and AI strategy. Four years is a lifetime when it comes to artificial intelligence. Since the first edition of this study was published in 2021, AI's capabilities have been advancing at speed, and the advances have not slowed since generative AI's breakthrough. For example, multimodality-- the ability to process information not only as text but also as audio, video, and other unstructured formats--is becoming a common feature of AI models. AI's capacity to reason and act autonomously has also grown, and organizations are now starting to work with AI agents that can do just that. Amid all the change, there remains a constant: the quality of an AI model's outputs is only ever as good as the data that feeds it.

Nyalleng Moorosi is part of a movement aimed at involving more African voices in AI policymaking. The room is draped in white curtains, and a giant screen blinks with videos created with generative AI. A classic East African folk song by the Tanzanian singer Saida Karoli plays loudly on the speakers. Friends greet each other as waiters serve arrowroot crisps and sugary mocktails. A man and a woman wearing leopard skins atop their clothes sip beer and chat; many women are in handwoven Ethiopian garb with red, yellow, and green embroidery. "The best thing about the Indaba is always the parties," computer scientist Nyalleng Moorosi tells me.

The study assesses Hungary's National AI Strategy and its implementation through the analysis of strategic documents, publicly available financial records, and expert interviews with the Hungarian AI Coalition President and Chief Strategic Advisor to the Government Commissioner for AI. 22 goals from Hungary's strategy were evaluated through conceptual, governance, temporal, and financial dimensions before being benchmarked against Singapore's National AI Strategies (NAIS 1.0 and NAIS 2.0). Key findings include an estimated total of EUR 4.65 billion in AI-related public investment in Hungary. Openly available financial data was found for only half of the evaluated goals, and just three projects made up 98\% of all documented funding. The research also reveals Hungary's implementation challenges, including fragmented execution following ministerial reorganizations and the absence of designated biennial reviews since 2020. Furthermore, the paper provides targeted recommendations for Hungary's forthcoming AI strategy, drawing on Singapore's framework as a reference point. These include adapting to the era of large language models, restructuring the existing triple helix network to foster more effective dialogue and advocacy, and positioning the country as an East-West bridge for automotive AI experimentation.

Artificial intelligence (AI) has been one of the main drivers for the development of cutting-edge technologies that are impacting society at different levels [1-3]. To harness the benefits of AI, while mitigating the risks, governments are developing National Strategies, seeking geopolitical protagonism and leveraging economic, social and cultural progress [4]. Launched in 2017, the Pan-Canadian Artificial Intelligence Strategy [5] was the first national strategy with the goal of guiding the priorities of AI policy at the country level [6]. Finland also developed its national AI strategy in 2017, closely followed by Japan, France, Germany, and the United Kingdom in 2018.

Artificial Intelligence (AI) has the potential to revolutionize various sectors, yet its adoption is often hindered by concerns about data privacy, security, and the understanding of AI capabilities. This paper synthesizes AI governance approaches, strategic themes, and enablers and challenges for AI adoption by reviewing national AI strategies from leading nations. The key contribution is the development of an EPIC (Education, Partnership, Infrastructure, Community) framework, which maps AI implementation requirements to fully realize social impacts and public good from successful and sustained AI deployment. Through a multi-perspective content analysis of the latest AI strategy documents, this paper provides a structured comparison of AI governance strategies across nations. The findings offer valuable insights for governments, academics, industries, and communities to enable responsible and trustworthy AI deployments. Future work should focus on incorporating specific requirements for developing countries and applying the strategies to specific AI applications, industries, and the public sector.

As Artificial Intelligence (AI) becomes integral to business operations, integrating Responsible AI (RAI) within Environmental, Social, and Governance (ESG) frameworks is essential for ethical and sustainable AI deployment. This study examines how leading companies align RAI with their ESG goals. Through interviews with 28 industry leaders, we identified a strong link between RAI and ESG practices. However, a significant gap exists between internal RAI policies and public disclosures, highlighting the need for greater board-level expertise, robust governance, and employee engagement. We provide key recommendations to strengthen RAI strategies, focusing on transparency, cross-functional collaboration, and seamless integration into existing ESG frameworks.

While these prognostications may prove true, today's businesses are finding major hurdles when they seek to graduate from pilots and experiments to enterprise-wide AI deployment. Just 5.4% of US businesses, for example, were using AI to produce a product or service in 2024. Moving from initial forays into AI use, such as code generation and customer service, to firm-wide integration depends on strategic and organizational transitions in infrastructure, data governance, and supplier ecosystems. As well, organizations must weigh uncertainties about developments in AI performance and how to measure return on investment. If organizations seek to scale AI across the business in coming years, however, now is the time to act.

The deployment of artificial intelligence (AI) applications has accelerated rapidly. AI enabled technologies are facing the public in many ways including infrastructure, consumer products and home applications. Because many of these technologies present risks either in the form of physical injury, or bias, potentially yielding unfair outcomes, policy makers must consider the need for oversight. Most policymakers, however, lack the technical knowledge to judge whether an emerging AI technology is safe, effective, and requires oversight, therefore policy makers must depend on expert opinion. But policymakers are better served when, in addition to expert opinion, they have some general understanding of existing guidelines and regulations. This work provides an overview [the landscape] of AI legislation and directives at the international, U.S. state, city and federal levels. It also reviews relevant business standards, and technical society initiatives. Then an overlap and gap analysis are performed resulting in a reference guide that includes recommendations and guidance for future policy making.

Apple has a history of succeeding despite being late to market so many times before: the iPhone, the Apple Watch, AirPods, to name a few cases. Now the company hopes to show that the same approach will work with generative artificial intelligence, announcing today an Apple Intelligence initiative that bakes the technology into just about every device and application Apple offers. Apple unveiled its long-awaited AI strategy at the company's Worldwide Developer Conference (WWDC) today. "This is a moment we've been working towards for a long time," said Apple CEO Tim Cook at the event. "We're tremendously excited about the power of generating models."

We're putting together a few valuable insights for company executives in this episode, but it's so packed that, in the end, there's just something in it for everyone. This is because Afke Schouten, our special guest, has much to share with us! Paulo Nunes, your host and CEO at Two Impulse, knows this very well and lays down an open road to keep it all coming. Afke's mission is to help organizations generate true value with AI. She is the Head of Data & AI Strategy at Xebia Data, focusing on corporate training and consulting. Her previous background as a consultant, as well as a data scientist, AI strategist and analytics team lead at companies such as EY, Swiss Re, SwissQuant and AI Bridge allows Afke to help executives build AI strategies and become data-driven.