AI adoption is booming, but most organisations aren’t keeping pace with the risks. Our analysis of over 50 companies reveals that while two-thirds are already using AI, many lack essential safeguards like policies, oversight, and staff training. Learn the key trends, where businesses are most exposed, and practical steps to reduce AI risk before it becomes a costly problem.
Learn AI basics in plain English. Our AI 101 webinar covers fundamentals, real-world use cases, and a safe adoption framework for businesses.
Explore data sovereignty, compliance with the Privacy Act, risks of foreign laws like the U.S. CLOUD Act, and practical guidance using a traffic light system to safeguard sensitive information
WorkingMouse installs Mac Mini AI hub at Hydrobiology, enabling private, in-house AI with Local AI for secure, scalable, and tailored environmental data analysis.
David Burkett interviewed the WorkingMouse AI R&D team to prove a 32 GB Unified‑Memory Mac Mini can run fast, privacy‑safe open‑weight AI for five users, with concurrency solved via an O Lama backend, while the team plans a company AI survey, will tap new Open AI/Google models on HuggingFace, and will soon scale experiments with an arriving Mac Studio.
Curious about AI at work? Our AI Reference Guide breaks down essential terms and concepts behind Large Language Models (LLMs)—including training, inference, RAG, and hardware requirements—to help teams adopt AI safely, locally, and productively. Ideal for business and IT leaders evaluating AI in enterprise settings.
Renting AI is risky and expensive. Shadow AI, rising SaaS costs, and data exposure are real threats. Hosting your own AI gives you control, lowers long-term costs, and keeps your data safe.
The Journal of Software and Systems Modeling published paper offers organisations a path to escape the legacy trap.
IT consulting in 2025 is driven by trends like AI integration, edge computing, and quantum advancements, helping businesses streamline operations, enhance security, and adopt sustainable practices. By staying ahead of these innovations, consultants empower organisations to remain competitive, adapt to challenges, and leverage emerging technologies for growth.
By 2028, Queensland will advance digital transformation with digital IDs, cybersecurity, and inclusive services. Key goals include addressing diverse demographic needs, leveraging emerging technologies, and enhancing service delivery. Challenges involve managing legacy systems, data sovereignty, and attracting talent. Queensland's proactive approach aims to lead in digital innovation and set a high standard for government efficiency and engagement.
Abstraction in system development involves focusing on core aspects by removing unnecessary details, which can revolutionise how organisations modernise and streamline their processes. Instead of discarding diagrams and artefacts, reusing them for higher-level models can boost productivity, consistency, and quality while cutting costs. The Jidoka philosophy, prioritising automation and quality over speed, highlights that investing in systematic abstraction leads to more cost-effective and agile development. Embracing this approach helps organisations balance quality with cost management and accelerates their path to innovation.
In February 2024, WorkingMouse shared insights on "Composite AI & Models for Modernising Government Services" with the Queensland Government Customer and Digital Group. Composite AI involves combining various AI tools to leverage their strengths and address their weaknesses. Key updates include using AI tools like CoPilot for coding, optimizing problems, platform engineering, team topologies, and modeling for shared understanding. The session highlighted the importance of human oversight alongside AI to ensure accuracy and quality. For more details, see the presentation slides and case studies linked.
Over-reliance on generative AI for creating code can lead to issues such as poor understanding, challenging debugging, and potential quality and security risks. AI tools like ChatGPT and GitHub Copilot are useful for generating code snippets and suggestions, but they lack the contextual awareness of human developers and can produce errors. It’s crucial to use AI as a collaborative tool rather than a complete solution, ensuring that human developers review, validate, and integrate AI-generated code to maintain accuracy, security, and adherence to best practices.
Jörn Guy Süß, Head of Research and Development at Codebots, recently spoke at the WorkingMouse office about his presentation at the 2022 MODELS Conference in Montreal. MODELS is a leading conference on model-driven software and systems engineering. Jörn discussed his paper published in the International Journal on Software and Systems Modeling, which highlights the shift from unreliable manual model-driven workflows to modern, repeatable processes using advanced frameworks. His talk delved into the relevance of continuous integration (CI) in model-driven engineering, challenges in applying CI to models, and solutions such as reusing the Ant build system.
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