While ethical arguments for fairness in healthcare AI are well-established, the economic and strategic value of inclusive design remains underexplored. This perspective introduces the ``inclusive innovation dividend'' -- the counterintuitive principle that solutions engineered for diverse, constrained use cases generate superior economic returns in broader markets. Drawing from assistive technologies that evolved into billion-dollar mainstream industries, we demonstrate how inclusive healthcare AI development creates business value beyond compliance requirements. We identify four mechanisms through which inclusive innovation drives returns: (1) market expansion via geographic scalability and trust acceleration; (2) risk mitigation through reduced remediation costs and litigation exposure; (3) performance dividends from superior generalization and reduced technical debt, and (4) competitive advantages in talent acquisition and clinical adoption. We present the Healthcare AI Inclusive Innovation Framework (HAIIF), a practical scoring system that enables organizations to evaluate AI investments based on their potential to capture these benefits. HAIIF provides structured guidance for resource allocation, transforming fairness and inclusivity from regulatory checkboxes into sources of strategic differentiation. Our findings suggest that organizations investing incrementally in inclusive design can achieve expanded market reach and sustained competitive advantages, while those treating these considerations as overhead face compounding disadvantages as network effects and data advantages accrue to early movers.

This paper presents a comprehensive analysis of the strategic imperative for healthcare organizations to develop proprietary foundation models rather than relying exclusively on commercial alternatives. We examine four fundamental considerations driving this imperative: the domain-specific requirements of healthcare data representation, critical data sovereignty and governance considerations unique to healthcare, strategic competitive advantages afforded by proprietary AI infrastructure, and the transformative potential of healthcare-specific foundation models for patient care and organizational operations. Through analysis of empirical evidence, economic frameworks, and organizational case studies, we demonstrate that proprietary multimodal foundation models enable healthcare organizations to achieve superior clinical performance, maintain robust data governance, create sustainable competitive advantages, and accelerate innovation pathways. While acknowledging implementation challenges, we present evidence showing organizations with proprietary AI capabilities demonstrate measurably improved outcomes, faster innovation cycles, and stronger strategic positioning in the evolving healthcare ecosystem. This analysis provides healthcare leaders with a comprehensive framework for evaluating build-versus-buy decisions regarding foundation model implementation, positioning proprietary foundation model development as a cornerstone capability for forward-thinking healthcare organizations.

Can human decision-makers learn from AI feedback? Using data on 52,000 decision-makers from a large online chess platform, we investigate how their AI use affects three interrelated long-term outcomes: Learning, skill gap, and diversity of decision strategies. First, we show that individuals are far more likely to seek AI feedback in situations in which they experienced success rather than failure. This AI feedback seeking strategy turns out to be detrimental to learning: Feedback on successes decreases future performance, while feedback on failures increases it. Second, higher-skilled decision-makers seek AI feedback more often and are far more likely to seek AI feedback after a failure, and benefit more from AI feedback than lower-skilled individuals. As a result, access to AI feedback increases, rather than decreases, the skill gap between high- and low-skilled individuals. Finally, we leverage 42 major platform updates as natural experiments to show that access to AI feedback causes a decrease in intellectual diversity of the population as individuals tend to specialize in the same areas. Together, those results indicate that learning from AI feedback is not automatic and using AI correctly seems to be a skill itself. Furthermore, despite its individual-level benefits, access to AI feedback can have significant population-level downsides including loss of intellectual diversity and an increasing skill gap.

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.

Until now, brands and publishers have been unable to benchmark total revenue on mobile. The mobile app economy is worth $500 billion, with 66% from ad revenue and 33% from in-app purchases, according to data.ai's Customers can now break down revenue streams and anticipate market expansion opportunities based on shifts in consumer behavior. "Total App Revenue provides a clear picture of data we've been missing in our business development and model creation. Carlos Salvado, Senior Market Analyst at Rovio Entertainment"Total App Revenue is the ultimate mobile performance metric.

Do large language models create a moat? In this given hype, what type of businesses should you invest your time and money in? While the technology of Large Language Models (LLMs) is new, the approach to analyzing the business moat is still the same. AI-driven businesses have a combination of either a model moat, a data moat, or the brand moat. The new LLMs, like OpenAI's model, can give an advantage in all areas, but it is by no means a given.

Since its launch in November 2022, ChatGPT, the chatbot developed by OpenAI, has taken the business world by storm. Following this success, Microsoft has increased its investment in OpenAI and has launched a new version of its search engine Bing that provides users with generated answers in response to searches, as opposed to providing them with thousands of links to choose from. Not surprisingly, Google, as the incumbent in the search engine market, quickly reacted and is launching Bard, its own attempt to create an AI chatbot leveraging the power of large language models and integrate it into the search process. Moving beyond search, both Google and Microsoft are now making their chatbots available through an API (application programming interface, a form of a protocol), thereby enabling software developers from other firms to integrate their systems with these new chatbots. From finance to healthcare and from education to travel, industry observers expect an explosion of service innovations and new digital user experiences. Leveraging the capabilities of large language models, chatbots have developed amazing capabilities to generate human-like responses, and to speak in different languages and styles.

Artificial Intelligence (AI) is becoming increasingly important in powering websites in the digital transformation era. AI has enabled websites to become more efficient and effective while providing users with better experiences. This article explores 8 new websites powered by AI that will get you massive ahead in 2023. The power of AI technology makes it possible for website developers to leverage artificial intelligence capabilities such as natural language processing, machine learning, deep learning, predictive analytics, and computer vision. These technologies have been used to develop powerful tools which enable websites to perform complex tasks quickly and accurately.

Artificial intelligence (AI) has become an increasingly important tool for businesses of all sizes. While AI was once seen as a technology reserved for large corporations with deep pockets, it has now become accessible even to small businesses. In fact, AI tools can be particularly beneficial for small businesses, helping them to automate routine tasks, improve efficiency, and provide better customer experiences. In this article, we'll explore why AI tools are essential for small businesses and how they can help businesses of all types and sizes. One of the primary benefits of AI tools is their ability to automate routine tasks.

In today's data-driven world, predictive analytics has become an integral part of businesses to anticipate future trends and gain a competitive advantage. Machine learning models have made it easier to analyze and interpret data and make informed decisions. This article will discuss the 10 powerful machine learning models for predictive analytics that businesses can utilize to improve their operations. Predictive analytics is the process of analyzing historical data to make predictions about future events. Machine learning models have made it possible to predict these events accurately by analyzing large volumes of data.