As artificial intelligence (AI) continues to rapidly advance, there is a growing demand to integrate AI capabilities into existing business applications. However, a significant gap exists between the rapid progress in AI and how slowly AI is being embedded into business environments. Deploying well-performing lab models into production settings, especially in on-premise environments, often entails specialized expertise and imposes a heavy burden of model management, creating significant barriers to implementing AI models in real-world applications. KModels leverages proven libraries and platforms (Kubeflow Pipelines, KServe) to streamline AI adoption by supporting both AI developers and consumers. It allows model developers to focus solely on model development and share models as transportable units (Templates), abstracting away complex production deployment concerns. KModels enables AI consumers to eliminate the need for a dedicated data scientist, as the templates encapsulate most data science considerations while providing business-oriented control. This paper presents the architecture of KModels and the key decisions that shape it. We outline KModels' main components as well as its interfaces. Furthermore, we explain how KModels is highly suited for on-premise deployment but can also be used in cloud environments. The efficacy of KModels is demonstrated through the successful deployment of three AI models within an existing Work Order Management system. These models operate in a client's data center and are trained on local data, without data scientist intervention. One model improved the accuracy of Failure Code specification for work orders from 46% to 83%, showcasing the substantial benefit of accessible and localized AI solutions.

OneDrive is investing in improved sharing and organizational features for business users, but consumers are being left behind: Microsoft will add media-centric search and AI capabilities, but that won't occur until next year. That will include the ability to search photos through facial recognition, a feature Google debuted about seven years ago. Microsoft executives referred to the updates as "OneDrive 3.0," a salt shaker of granular features being added to Microsoft's cloud storage. For now, the biggest changes are in organization: You'll be able to search for OneDrive files in a people view, as Microsoft has tipped before. OneDrive is also using some of the recent Windows 11 22H2/23H2 updates to File Explorer as a model to place recommended files in a carousel view at the top of the OneDrive page.

Artificial intelligence is not only increasingly used in business and administration contexts, but a race for its regulation is also underway, with the EU spearheading the efforts. Contrary to existing literature, this article suggests, however, that the most far-reaching and effective EU rules for AI applications in the digital economy will not be contained in the proposed AI Act - but have just been enacted in the Digital Markets Act. We analyze the impact of the DMA and related EU acts on AI models and their underlying data across four key areas: disclosure requirements; the regulation of AI training data; access rules; and the regime for fair rankings. The paper demonstrates that fairness, in the sense of the DMA, goes beyond traditionally protected categories of non-discrimination law on which scholarship at the intersection of AI and law has so far largely focused on. Rather, we draw on competition law and the FRAND criteria known from intellectual property law to interpret and refine the DMA provisions on fair rankings. Moreover, we show how, based on CJEU jurisprudence, a coherent interpretation of the concept of non-discrimination in both traditional non-discrimination and competition law may be found. The final part sketches specific proposals for a comprehensive framework of transparency, access, and fairness under the DMA and beyond.

As AI technology advances rapidly, concerns over the risks of bigness in digital markets are also growing. The EU's Digital Markets Act (DMA) aims to address these risks. Still, the current framework may not adequately cover generative AI systems that could become gateways for AI-based services. This paper argues for integrating certain AI software as core platform services and classifying certain developers as gatekeepers under the DMA. We also propose an assessment of gatekeeper obligations to ensure they cover generative AI services. As the EU considers generative AI-specific rules and possible DMA amendments, this paper provides insights towards diversity and openness in generative AI services.

Low-code and no-code platforms are used to build applications, websites, mobile apps, forms, dashboards, data pipelines, and integrations. No-code platforms help business users, sometimes termed citizen developers, to migrate from spreadsheets, extend beyond email collaborations, and transition from manual task execution to using tools and automations across departments. Low-code platforms are usually for technologists and provide ways to deliver and support software with little or no coding. "You have to remember low code is just a fancy term for abstraction. We are abstracting away non-essential elements in order to simplify the user experience," says Gordon Allott, President and CEO of K3.

Generative AI can seem like magic. Image generators such as Stable Diffusion, Midjourney, or DALL·E 2 can produce remarkable visuals in styles from aged photographs and water colors to pencil drawings and Pointillism. The resulting products can be fascinating -- both quality and speed of creation are elevated compared to average human performance. The Museum of Modern Art in New York hosted an AI-generated installation generated from the museum's own collection, and the Mauritshuis in The Hague hung an AI variant of Vermeer's Girl with a Pearl Earring while the original was away on loan. The capabilities of text generators are perhaps even more striking, as they write essays, poems, and summaries, and are proving adept mimics of style and form (though they can take creative license with facts).

Ever since ChatGPT captured our imaginations, people have been contemplating its pending impact on the business world. This week these thoughts became a reality, with Google and Microsoft embedding artificial reality (AI) features into their business productivity suites. Microsoft took another major step by releasing AI Copilot for Power Apps, Microsoft's low-code platform. Power Apps can connect far and beyond the Microsoft ecosystem, with almost 1,000 built-in connectors to everything from Salesforce to on-prem and Amazon Web Services. With one swift move, AI has been integrated into the day-to-day workflows of the world's largest organizations. This is an amazing achievement, and other low-code/no-code platforms will surely try to catch up quickly.

Informatica, an enterprise cloud data management specialist, has launched the industry's only free cloud data loading, integration and ETL/ELT service – Informatica Cloud Data Integration-Free and PayGo. The new offering targets data practitioners and non-technical users such as in marketing, sales, and revenue operations teams to build data pipelines within minutes. For example, it provides operations teams with a fast, free, and frictionless way to load, integrate and analyze high-quality campaign, pipeline, forecast, and revenue data. In addition, data analysts and data engineers benefit from increased productivity and rapid development. This is the second in a series of releases that began with the Informatica Data Loader launch in May 2022.

ChatGPT promises to democratize artificial intelligence, and is already making it relatively easy for non-data-scientist types to partake in the wonders of machine-generated wisdom. Google and Microsoft are following suit and souping up their search engines. Does this portend reduced demand for AI talent? Relief from AI talent shortages isn't likely anytime soon, but democratized AI may expand the meaning of business intelligence. "We are trying to teach business users to speak AI instead of teaching AI to speak business," says Arijit Sengupta, CEO and Founder of Aible.

When we started AI Expo Africa here in South Africa back in 2018, it would be fair to say the atmosphere was one of excitement with a fair degree of hype mixed with solid doses of reality. There was still talk of "AI Winters" and that adoption would be slow. Well, 5 years on, the landscape has radically changed. Tools and techniques that were once the exclusive domain of "the developer" are now freely accessible via zero cost platforms / apps / APIs allowing business users to leverage all kinds of AI related tech, be that AI generated presentations or logos, to art, videos, music and animations to name but a few. Even in the time we have been running the show, the creativity and use cases have exploded and it would be fair to say, we are now well into the AI Spring!