Generative AI (GenAI) is reshaping enterprise architecture work in agile software organizations, yet evidence on its effects remains scattered. We report a systematic literature review (SLR), following established SLR protocols of Kitchenham and PRISMA, of 1,697 records, yielding 33 studies across enterprise, solution, domain, business, and IT architect roles. GenAI most consistently supports (i) design ideation and trade-off exploration; (ii) rapid creation and refinement of artifacts (e.g., code, models, documentation); and (iii) architectural decision support and knowledge retrieval. Reported risks include opacity and bias, contextually incorrect outputs leading to rework, privacy and compliance concerns, and social loafing. We also identify emerging skills and competencies, including prompt engineering, model evaluation, and professional oversight, and organizational enablers around readiness and adaptive governance. The review contributes with (1) a mapping of GenAI use cases and risks in agile architecting, (2) implications for capability building and governance, and (3) an initial research agenda on human-AI collaboration in architecture. Overall, the findings inform responsible adoption of GenAI that accelerates digital transformation while safeguarding architectural integrity.

In this paper, we propose a method for aligning models with their realization through the application of model-based systems engineering. Our approach is divided into three steps. (1) Firstly, we leverage domain expertise and the Unified Architecture Framework to establish a reference model that fundamentally describes some domain. (2) Subsequently, we instantiate the reference model as specific models tailored to different scenarios within the domain. (3) Finally, we incorporate corresponding run logic directly into both the reference model and the specific models. In total, we thus provide a practical means to ensure that every implementation result is justified by business demand. We demonstrate our approach using the example of maritime object detection as a specific application (specific model / implementation element) of automatic target recognition as a service reoccurring in various forms (reference model element). Our approach facilitates a more seamless integration of models and implementation, fostering enhanced Business-IT alignment.

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AI is becoming popular in real life. Many applications are using computer vision by implementing Convolutional Neural network algorithms. Agriculture apps are using CNN based techniques to analyze the crop images for crop's health and viability of seeds. Self driving cars are using them in moving car and other vehicle detection and classification. Video analysis software uses CNN for finding the automobiles, road blocks, and human beings on the road.

Primary business values of RPA are productivity, efficiency, and customer satisfaction. As part of my business architecture and designer roles, I architected and designed several business solutions using Robotics Process Automation (RPA) tools and processes for large business and government organisations. I also helped the startup companies and sole trading entrepreneurs to add this interesting and emerging technology to their growth agenda. In this post, my focus is on the business value of Robotics Process Automation. I excluded specific tools and service providers.

In this article, my aim is to briefly introduce what Robotics Process Automation (RPA) is, it's business value, the major use cases, how it works, how we can enhance it, and my experience on architecting solutions to meet the customer requirements. RPA is a rapidly growing technology offering which uses software robots and the capabilities of cognitive computing and artificial intelligence. RPA can be ideal for dealing with the legacy systems, modernising processes, and even can contribute to overall digital transformation goals for many business and government organisations striving to cut cost, increase profitability, and delight consumers. What problems do RPA offerings solve? At the most basic level (level 1), RPA is used to address repetitive and cumbersome work related problems; for example, tedious clerical tasks taking too long to complete and prone to human errors. At the second level, it can solve problems related to departmental activities especially for heavily administrative tasks being performed across multiple applications in the business departments.

Data is only as good as the insights it produces, the actions it influences, and the results it fosters. That is the secret recipe for data management. Big data promises business-changing insights, but technology management is still the largest user and benefactor. Learn how to manage and capitalize on big data as well as the latest developments and use cases of Hadoop, Apache Spark, MapReduce and NoSQL. You forgot to provide an Email Address.