Organizations are rapidly adopting Large Language Models (LLMs) to transform their operations, yet they lack clear guidance on key decisions for adoption and implementation. While LLMs offer powerful capabilities in content generation, assisted coding, and process automation, businesses face critical challenges in data security, LLM solution development approach, infrastructure requirements, and deployment strategies. Healthcare providers must protect patient data while leveraging LLMs for medical analysis, financial institutions need to balance automated customer service with regulatory compliance, and software companies seek to enhance development productivity while maintaining code security. This article presents a systematic six-step decision framework for LLM adoption, helping organizations navigate from initial application selection to final deployment. Based on extensive interviews and analysis of successful and failed implementations, our framework provides practical guidance for business leaders to align technological capabilities with business objectives. Through key decision points and real-world examples from both B2B and B2C contexts, organizations can make informed decisions about LLM adoption while ensuring secure and efficient integration across various use cases, from customer service automation to content creation and advanced analytics.

This paper details the machine learning (ML) journey of a group of people focused on software testing. It tells the story of how this group progressed through a ML workflow (similar to the CRISP-DM process). This workflow consists of the following steps and can be used by anyone applying ML techniques to a project: gather the data; clean the data; perform feature engineering on the data; splitting the data into two sets, one for training and one for testing; choosing a machine learning model; training the model; testing the model and evaluating the model performance. By following this workflow, anyone can effectively apply ML to any project that they are doing.

[Context] The increasing adoption of machine learning (ML) in software systems demands specialized ideation approaches that address ML-specific challenges, including data dependencies, technical feasibility, and alignment between business objectives and probabilistic system behavior. Traditional ideation methods like Lean Inception lack structured support for these ML considerations, which can result in misaligned product visions and unrealistic expectations. [Goal] This paper presents Define-ML, a framework that extends Lean Inception with tailored activities - Data Source Mapping, Feature-to-Data Source Mapping, and ML Mapping - to systematically integrate data and technical constraints into early-stage ML product ideation. [Method] We developed and validated Define-ML following the Technology Transfer Model, conducting both static validation (with a toy problem) and dynamic validation (in a real-world industrial case study). The analysis combined quantitative surveys with qualitative feedback, assessing utility, ease of use, and intent of adoption. [Results] Participants found Define-ML effective for clarifying data concerns, aligning ML capabilities with business goals, and fostering cross-functional collaboration. The approach's structured activities reduced ideation ambiguity, though some noted a learning curve for ML-specific components, which can be mitigated by expert facilitation. All participants expressed the intention to adopt Define-ML. [Conclusion] Define-ML provides an openly available, validated approach for ML product ideation, building on Lean Inception's agility while aligning features with available data and increasing awareness of technical feasibility.

Throughout the history from pre-industry 4.0 to post-industry 4.0, cybersecurity at banks has undergone significant changes. Pre-industry 4.0 cyber security at banks relied on individual security methods that were highly manual and had low accuracy. When moving to post-industry 4.0, cybersecurity at banks had a major turning point with security methods that combined different technologies such as Artificial Intelligence (AI), Blockchain, IoT, automating necessary processes and significantly increasing the defence layer for banks. However, along with the development of new technologies, the current challenge of cybersecurity at banks lies in scalability, high costs and resources in both money and time for R&D of defence methods along with the threat of high-tech cybercriminals growing and expanding. This report goes from introducing the importance of cybersecurity at banks, analyzing their management, operational and business objectives, evaluating pre-industry 4.0 technologies used for cybersecurity at banks to assessing post-industry 4.0 technologies focusing on Artificial Intelligence and Blockchain, discussing current policies and practices and ending with discussing key advantages and challenges for 4.0 technologies and recommendations for further developing cybersecurity at banks.

Emerging technologies, such as artificial intelligence (AI) and machine learning, will play a leading role in influencing operational efficiency and business decision-making in 2023. The reliance on these technologies is such that according to GartnerOpens a new window, the worldwide AI software market will reach $62 billion in 2022. In fact in a poll by SpiceworksOpens a new window, 42% of tech professionals attested that artificial intelligence will be the biggest technology trend for 2022. Let's hear from experts on ways AI will evolve in 2023 to enable new use cases for businesses of all sizes. Myles Gilsenan, the vice president of data, analytics, and AI at Apps Associates, believes that AI will continue to evolve and transform industries, businesses, and our day-to-day lives.

"The best way to have a good idea is to have a lot of ideas." Let's assume we are running an e-commerce search engine that uses machine learning on user-issued queries to identify the intended product category. Say the model in production incurs a 20ms prediction latency and has 90% accuracy. A natural next goal from a modeling perspective would be to drive the accuracy higher, say to 95% or beyond. However, we know that improving the accuracy almost always requires the consumption of more computational resources for training models and may also increase the inference latency.

In 2019, Venturebeat reported that almost 87% of data science projects do not get into production. Redapt, an end-to-end technology solution provider, also reported a similar number of 90% ML models not making it to production. However, there has been an improvement. In 2020, enterprises realized the need for AI in their business. Due to COVID-19, most companies have scaled up their AI adoption and increased their AI investment.

Businesses continue transforming their operations to increase productivity and deliver memorable consumer experiences. This digital transition accelerates timeframes for interactions, transactions, and decisions. Additionally, it generates reams of data with brand-new insights into operations, clients, and competition. Machine learning helps companies in harnessing this data to gain a competitive advantage. ML (Machine Learning) models can detect patterns in massive amounts of data, allowing them to make faster, more accurate decisions on a larger scale than humans could.

Comparing data to oil, has become almost a cliché, and although this image correctly translates the potential value of data it doesn't translate the challenges when it comes to enable that value. Comparing data to water may be a more accurate way to describe the challenges most organizations face when trying to get actionable insights from their data - Despite being in a sea of data, they can't benefit from it. Just like potable water, actionable data is also a scarce resource. We've witnessed, in these last few years, to organizations investing heavily on new IT infrastructure, on new digital channels, leading to volumes of data flowing into their systems increasing exponentially. And now business leaders want to see value being drawn from all this data – and started investing breaking down the data silos or creating data lakes – to enable them to retrieve those insights and generate business value from their data.

Earlier this year, Aible, the only enterprise artificial intelligence (AI) solution that guarantees impact in one month, announced the initial results of the Intel and Aible Benchmark Study, a collaboration that is helping enterprises fast-track benefits from advanced analytics and AI, while also evaluating server vs. serverless architecture. According to MIT-BCG, only "a mere 10% of organizations achieve significant financial benefits with AI." The Gartner report, A CTO's Guide to Top Artificial Intelligence Engineering Practices, published 29 October 2021 states, "AI projects are characterized by high failure rates and take a long time to move from pilot to production. In this same market, Aible has delivered significant results for every customer in this benchmark study in 30 days or less. The detailed report with case studies can be downloaded here. "Intel is helping us change the art of the possible in AI.