Artificial Intelligence (AI) has transitioned from a futuristic concept reserved for large corporations to a present-day, accessible, and essential growth lever for Small and Medium-sized Enterprises (SMEs). For entrepreneurs and business leaders, strategic AI adoption is no longer an option but an imperative for competitiveness, operational efficiency, and long-term survival. This report provides a comprehensive framework for SME leaders to navigate this technological shift, offering the foundational knowledge, business case, practical applications, and strategic guidance necessary to harness the power of AI. The quantitative evidence supporting AI adoption is compelling; 91% of SMEs using AI report that it directly boosts their revenue. Beyond top-line growth, AI drives profound operational efficiencies, with studies showing it can reduce operational costs by up to 30% and save businesses more than 20 hours of valuable time each month. This transformation is occurring within the context of a seismic economic shift; the global AI market is projected to surge from $233.46 Billion in 2024 to an astonishing $1.77 Trillion by 2032. This paper demystifies the core concepts of AI, presents a business case based on market data, details practical applications, and lays out a phased, actionable adoption strategy.

-- There is an exponential growth of connected Internet of Things (IoT) devices. These have given rise to applications that rely on real time data to make critical decisions quickly. Enterprises today are adopting cloud at a rapid pace. There is a critical need to develop secure and efficient strategy and architectures to best leverage capabilities of cloud and edge assets. This paper provides an end to end secure edge to cloud data and analytics strategy. To enable real life implementation, the paper provides reference architectures for device layer, edge layer and cloud layer. The industries across verticals are making a tectonic shift towards cloud migration.

Generative AI is transforming business applications by enabling natural language interfaces and intelligent automation. However, the underlying large language models (LLMs) are evolving rapidly and so prompting them consistently is a challenge. This leads to inconsistent and unpredictable application behavior, undermining the reliability that businesses require for mission-critical workflows. In this paper, we introduce the concept of prompt migration as a systematic approach to stabilizing GenAI applications amid changing LLMs. Using the Tursio enterprise search application as a case study, we analyze the impact of successive GPT model upgrades, detail our migration framework including prompt redesign and a migration testbed, and demonstrate how these techniques restore application consistency. Our results show that structured prompt migration can fully recover the application reliability that was lost due to model drift. We conclude with practical lessons learned, emphasizing the need for prompt lifecycle management and robust testing to ensure dependable GenAI-powered business applications.

ABSTRACT This study presents a novel benchmark for evaluating Large Language Models (LLMs) using challenges derived from the Financial Modeling W orld Cup (FMWC) Excel competitions. W e introduce a methodology for converting 113 existing FMWC challenges into programm atically evaluable JSON formats and use this dataset to compare the performance of several leading LLMs. Our findings demonstrate significant variations in performance across different challenge categories, with models showing specific strengths in pattern recognition tasks but struggling with complex numerical reasoning. The benchmark provides a standardized framework for assessing LLM capabilities in realistic business - oriented tasks rather than abstract academic problems. This resear ch contributes to the growing field of AI benchmarking by establishing proficiency among the 1.5 billion people who daily use Mic rosoft Exc el as a meaningful evaluation metric that bridges the gap between academic AI benchmarks and practical business applications. INTRODUCTION The recent rapid advancement of Large Language Models (LLMs) has sparked interest in developing specialized benchmarks to evaluate their capabilities across various domains. While existing benchmarks often focus on natural language understanding, programmi ng, or reasoning abilities in abstract contexts, there remains a notable gap in benchmarks that assess performance on practical business tasks (Brown et al., 2020). Microsoft Excel, being one of the most widely used business software tools globally, presen ts an opportunity to create tasks that simultaneously test multiple dimensions of LLM capabilities, including numerical reasoning, pattern recognition, rule comprehension, file conversion, and problem - solving strategies. The Financial Modeling W orld Cup (FMWC), established in 2020, has emerged as a premier global competition testing advanced Excel skills through creative challenges that range from financial modeling to game simulations implemented in spreadsheets (Grigolyu novich, 2022).

In large-scale software development, understanding the functionality and intent behind complex codebases is critical for effective development and maintenance. While code summarization has been widely studied, existing methods primarily focus on smaller code units, such as functions, and struggle with larger code artifacts like files and packages. Additionally, current summarization models tend to emphasize low-level implementation details, often overlooking the domain and business context that are crucial for real-world applications. This paper proposes a two-step hierarchical approach for repository-level code summarization, tailored to business applications. First, smaller code units such as functions and variables are identified using syntax analysis and summarized with local LLMs. These summaries are then aggregated to generate higher-level file and package summaries. To ensure the summaries are grounded in business context, we design custom prompts that capture the intended purpose of code artifacts based on the domain and problem context of the business application. We evaluate our approach on a business support system (BSS) for the telecommunications domain, showing that syntax analysis-based hierarchical summarization improves coverage, while business-context grounding enhances the relevance of the generated summaries.

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.

Generative AI has left its mark on computing, and it's only the beginning. The world of business applications will never be the same. For several years now, we have been iterating on and shipping copilot features in Microsoft Dynamics 365, and I'm pleased to announce our next wave of value. Today's customers demand tailored experiences that cater to their individual needs. They expect businesses to be responsive on all communication channels and to treat each individual with knowledge of their overall relationship and journey.

AI is fast becoming critical to business and IT applications and operations. Organizations have been investing in artificial intelligence capabilities for years to stay competitive, are hiring the best data scientist teams and are investing more and more in artificial intelligence and machine learning systems. However, implementing AI / ML models is not easy and the risk of failure is just around the corner. A solid methodology is needed to reduce this risk and enable companies to succeed. AI executives have been working toget more models in business for years now.

AI (Artificial Intelligence) is rapidly advancing, and it's going to change business forever. AI can give organizations a competitive edge in the marketplace by automating tasks and making better decisions. You've got to know about the implications of AI for business strategy, just like with any new technology. As well as the ethical and legal considerations organizations need to consider, this article will explore how AI could impact business operations and decision-making. Aside from that, it's about how companies can get an edge in the market by implementing AI and developing a strategy for it.

AI tech can make retail storefronts relevant and engaging to the modern consumer. With the continued dominance of artificial intelligence in business applications, we've started to see a dramatic shift in how people shop for and purchase products. At least 60% of the U.S. population have made mobile purchases; 82% of mobile phone users use their devices while in-store to help them make a product decision. As mobile commerce continues to grow, retail stores will need to adopt new technologies to stay afloat. Consumer reliance on smart devices will only become greater, so brick-and-mortar stores must act quickly if they don't want to become outdated.