https://engineering-leadership.hinshelwood.com/categories/artificial-intelligence/ Recent content in Artificial Intelligence on Engineering Leadership in AI & Software Hugo en Wed, 24 Jun 2026 10:41:27 +0000 https://engineering-leadership.hinshelwood.com/guides/kendall-guide/ Wed, 17 Sep 2025 00:00:00 +0000 https://engineering-leadership.hinshelwood.com/guides/kendall-guide/ A practical framework guiding organisations to adopt AI by prioritising real problems, clarifying context, and enabling adaptive, evidence-based decision-making and collaboration. https://engineering-leadership.hinshelwood.com/engineering-notes/how-i-used-generative-ai-to-transform-site-tagging-and-categories/ Thu, 15 May 2025 09:00:00 +0000 https://engineering-leadership.hinshelwood.com/engineering-notes/how-i-used-generative-ai-to-transform-site-tagging-and-categories/ Migrating a large, legacy blog to Hugo enabled the use of generative AI for automated tagging and categorisation, significantly improving content discoverability and editorial consistency while reducing manual effort. The system combines AI-driven suggestions with human oversight, using multi-factor scoring, penalty logic, and transparent reasoning to ensure quality and accountability. Development managers considering similar automation should maintain human control over final decisions and leverage AI to streamline, not replace, editorial processes. https://engineering-leadership.hinshelwood.com/articles/robots-and-ai-are-not-taking-our-jobs-they-are-giving-us-our-dignity-back/ Mon, 19 May 2025 09:00:00 +0000 https://engineering-leadership.hinshelwood.com/articles/robots-and-ai-are-not-taking-our-jobs-they-are-giving-us-our-dignity-back/ AI and automation are not threats to jobs but are removing repetitive, dehumanising work, allowing people to focus on creative and meaningful tasks. Organisations that cling to outdated management practices like bonuses, rigid hierarchies, and output-based measures risk becoming obsolete, while those that foster autonomy and purpose will thrive. Development managers should embrace AI to elevate human potential and shift their teams toward work that requires critical thinking and innovation. https://engineering-leadership.hinshelwood.com/engineering-notes/leveraging-ai-embeddings-for-related-content-classification/ Wed, 04 Jun 2025 09:00:00 +0000 https://engineering-leadership.hinshelwood.com/engineering-notes/leveraging-ai-embeddings-for-related-content-classification/ Switching from Hugo’s basic related content feature to an AI embeddings-based approach enabled much more accurate and meaningful content recommendations, improving both user navigation and AI discoverability. The solution was cost-effective, scalable, and reduced computational overhead by caching similarity scores and storing embeddings for reuse. Development managers can consider using AI embeddings for smarter content linking, which can enhance user experience and support future automation or classification needs. https://engineering-leadership.hinshelwood.com/signals/ai-won-t-replace-humans/ Fri, 23 May 2025 15:30:36 +0100 https://engineering-leadership.hinshelwood.com/signals/ai-won-t-replace-humans/ AI will not replace people but will automate repetitive and mechanical tasks, freeing humans to focus on creative and strategic work. Companies that use AI to eliminate low-value tasks and empower employees will be more successful. Leaders should assess whether their processes rely too much on dehumanising work and use AI to improve job quality. https://engineering-leadership.hinshelwood.com/videos/how-ai-is-revolutionising-our-work-embrace-the-future-of-productivity-and-creativity/ Wed, 05 Jul 2023 14:49:20 +0000 https://engineering-leadership.hinshelwood.com/videos/how-ai-is-revolutionising-our-work-embrace-the-future-of-productivity-and-creativity/ AI is rapidly transforming work by boosting productivity and creativity, acting as a supportive tool for idea generation, content creation, and automating repetitive tasks. Teams that adopt AI can work more efficiently and gain a competitive advantage. Development managers should start exploring and integrating AI tools now to stay ahead in a changing landscape. https://engineering-leadership.hinshelwood.com/tags/ai-product-operating-model/ Mon, 24 Nov 2025 13:21:23 +0000 https://engineering-leadership.hinshelwood.com/tags/ai-product-operating-model/ An AI Product Operating Model is an operating model that guides how organizations design, develop, deploy, and manage artificial intelligence (AI) products within their business context. As a specialization of the Adaptive Operating Model, it addresses the unique requirements and challenges of AI-powered product delivery, including data management, model lifecycle, ethical considerations, and continuous learning. It originates from the need to align AI initiatives with organizational goals, ensuring that AI solutions are integrated into existing processes, teams, and value streams rather than developed in isolation. This model outlines roles, responsibilities, workflows, governance structures, and feedback mechanisms specific to AI product development. An AI Product Operating Model provides clarity and repeatability, enabling cross-functional teams to collaborate effectively, manage risks, and deliver AI-driven value. It supports organizations in scaling AI capabilities, maintaining compliance, and adapting to rapid technological changes, thereby fostering innovation while ensuring responsible and sustainable AI adoption. Organizations may implement AI Product Operating Models using various delivery approaches, though iterative methods are generally recommended for managing AI uncertainty. https://engineering-leadership.hinshelwood.com/tags/agentic-engineering/ Tue, 22 Jul 2025 15:58:00 +0000 https://engineering-leadership.hinshelwood.com/tags/agentic-engineering/ Agentic Engineering is the deliberate design and practice of software development that maximises the agency of both humans and intelligent systems. It integrates engineering excellence, DevOps ethos, and ethical autonomy to create environments where decisions are decentralised, feedback is fast, and value delivery is continuous. It’s characterised by Developer Agency, Systemic Observability, DevOps-Infused Craft, Ethical AI Integration, and Feedback-Driven Adaptation. Agentic Engineering is not a job title, role, or method, it’s a philosophy of engineering in which the ability to act with clarity, intent, and impact is engineered into the way we build, learn, and evolve. https://engineering-leadership.hinshelwood.com/tags/agentic-agility/ Mon, 07 Apr 2025 12:39:49 +0000 https://engineering-leadership.hinshelwood.com/tags/agentic-agility/ Agentic Agility is the capacity, human or AI, to take intentional, adaptive action within socio-technical environments to improve outcomes and align with evolving goals. It is grounded in agency: the power to act with autonomy, accountability, and purpose. Without agency, Agile devolves into hollow rituals; with it, people and systems can deliberately shape value delivery. Agentic Agility manifests through human judgement and learning or AI-driven optimisation within constraints, enabling continuous evolution of both what is delivered and how it is delivered. It is the critical lever that sustains agility as a living, resilient capability rather than a hollow label. https://engineering-leadership.hinshelwood.com/categories/artificial-intelligence/ Tue, 11 Feb 2025 10:17:24 +0000 https://engineering-leadership.hinshelwood.com/categories/artificial-intelligence/ Artificial Intelligence (AI) is the capability of machines to perform tasks that typically require human intelligence, such as learning, reasoning, and problem-solving. In organisational settings, AI enhances decision-making by providing data-driven insights, automating repetitive tasks, and fostering innovation. This is particularly important in Agile, DevOps, and modern product development, as it enables teams to respond quickly to changing market demands and customer needs. By streamlining workflows, optimising resource allocation, and improving forecasting accuracy, AI allows organisations to deliver value predictably and sustainably. Integrating AI into practices not only supports immediate project goals but also promotes a culture of continuous improvement and learning. The long-term advantages of AI include the ability to adapt to complex environments and make informed decisions based on real-time data analysis, empowering teams to concentrate on high-value activities. This ultimately leads to better products and services that align with customer expectations. Furthermore, AI plays a crucial role in enhancing collaboration and team performance, aligning with Agile and Lean methodologies, and driving organisations towards greater agility and resilience in a rapidly evolving landscape. https://engineering-leadership.hinshelwood.com/tags/collective-intelligence/ Thu, 23 Jan 2025 10:17:24 +0000 https://engineering-leadership.hinshelwood.com/tags/collective-intelligence/ Collective Intelligence represents the enhanced problem-solving and innovation capabilities that emerge when humans with agency effectively collaborate with AI agents as team members. This concept goes beyond traditional human collaboration to encompass human-AI partnerships where both parties contribute complementary strengths, human creativity, judgment, and contextual understanding combined with AI processing power, pattern recognition, and consistent execution. Unlike passive tool usage, Collective Intelligence requires humans to have genuine agency and AI systems to operate with designed autonomy within appropriate constraints. The resulting synergy enables teams to navigate complex socio-technical environments, make more informed decisions, and deliver superior outcomes that neither humans nor AI could achieve independently. This form of agentic agility is essential for modern product development, where the volume and complexity of information, rapid change cycles, and need for continuous adaptation exceed purely human cognitive capabilities. By cultivating Collective Intelligence, organisations can harness the full potential of human-AI collaboration, transforming how value is created and delivered in digital product development. https://engineering-leadership.hinshelwood.com/tags/agentic-software-delivery/ Tue, 21 Jan 2025 10:00:00 +0000 https://engineering-leadership.hinshelwood.com/tags/agentic-software-delivery/ Agentic Software Delivery is a strategy for continuously achieving business outcomes through the deliberate integration of autonomous AI agents, human expertise, and organisational context. It is not about automation for automation’s sake, but about enabling teams to move faster and smarter by embedding proactive, context-aware intelligence into their systems of work. The term ‘agentic’ implies more than assistance, it implies agency. These agents operate autonomously within defined boundaries, learning from data, adapting to patterns, and making context-informed decisions. They contribute meaningfully to outcomes across discovery, development, delivery, and operations. This approach relies on the synergy between domain experts and AI agents, requiring lean, empirical systems of work, strong product strategy, and modern engineering practices such as CI/CD, observability, infrastructure as code, and automated testing.