Bright insights from the people doing the work

Adopting AI coding tools has increased the velocity of most companies' software development teams, sometimes by tens of percent, sometimes several-fold. The team is faster than ever, but, surprisingly, often less productive than before. Not in lines of code, but in validated value shipped. They build more, yet less of it counts: features ship that nobody asked for, and every one of them quietly joins the maintenance roster for years. Turns out, speed without direction is very expensive. AI is an amplifier: it speeds up execution, but it has no opinion about whether you're executing the right thing. Where the vision is clear, that speed creates leverage. Where it's hazy, it just creates waste, faster. The true competitive advantage was never coding speed but knowing what to build. Before a line gets written comes the (often) harder work of figuring out what's worth building at all. And AI is just as useful there as it is once you're building. Yet most teams fully utilize AI only when implementing: the coding gets the acceleration, while discovery still runs at its pre-AI pace.

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Every team is shipping AI agents now, and most have been handed the same docx file of the AI Governance Policy. Not one of those agents can follow it, because an agent doesn't read your policy, it just calls tools. Tool calls (an API, an MCP server, a function) are where data leaves the building and attacks land. They are where your governance isn't. Attackers are already there, from a zero-click leak in Microsoft 365 Copilot to a worm that hijacked AI coding agents outright. No better document would have stopped either one. The fix is to move the control onto every tool call and data retrieval, and make every team inherit it by default. Here is how to turn a policy no agent reads into a control every agent carries.

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Effective AI-assisted development depends less on clever prompts and more on the engineering practices around the agent. Clear context, a repeatable spec → grill → plan → implement → verify workflow, and strong review gates help teams catch avoidable failures before they become rework. Agents tend to fail in recognizable ways, so teams need current documentation, reusable skills, and deliberate safeguards around planning, implementation, and review. The real leverage comes from building the scaffolding around AI: context curation, verification, documentation, and human judgment.

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AI coding agents have not removed the need for software developers, but they have changed where developer judgment matters most. Less effort goes into typing implementation code, and more effort moves into writing clear specifications, steering agent work, and reviewing the results critically. This shift makes product clarity, technical intent, review culture, and verification more important than ever. The teams that benefit most from AI agents are not the ones generating the most code, but the ones most disciplined about intent, context, and quality.

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Helen is transitioning from combustion-based energy production to a carbon-neutral system by 2030 and aims to end all combustion by 2040. To support this shift, Helen partnered with Brightly to develop a robust, scalable data platform that enables real-time monitoring, forecasting, and optimisation across their energy infrastructure. This blog post describes our partnership and the solution we have built together.

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If you’re attending the Data Innovation Summit this year, I’d love to see you there. Whether you’re exploring your next move in data and AI or just want to trade ideas, please come by and say hi. You can meet our team at booth A2, join our session with Helen on district heating, or book a 1:1 meeting to talk more in-depth about your goals.

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We've built autonomous AI agents since 2022 —here's what we've learned about clear instructions, tool design, memory management, and choosing the right LLM. Practical insights, real examples, zero fluff.

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Explore how agentic AI differs from basic AI assistants, delivering 3-4x ROI through autonomous operation. Practical insights and implementation strategies inside.

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Are you curious about the AI landscape, its real-world applications, and where things are headed? This article breaks down key trends and insights, focusing on practical AI tools rather than just theory.

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At Santen Pharmaceutical, Brightly designed and implemented ChatAIRI, a generative AI tool that enables quick analysis of a vast amount of regulatory data. Read more about how we used AI to tackle a real-world problem efficiently.

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Brightly joins the LifeFactFuture research program to lead the Technology Excellence Work Package in a consortium of university entities, leading life science manufacturers as well as data and technology companies.

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Microsoft Fabric is the new kid on the data block, holding a lot of promise. But where is it currenty, and where is it headed? We attended Fabric Community Conference Europe 2024 to give you the latest.

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Running Amazon Web Services workloads can prove tricky. We'd like to share our method that reduces costs, speeds things up, and allows for more customization.

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Wir sind stolz, bekannt geben zu können, dass Brightly in die Fachgruppe Software und Digitalisierung des Maschinen- und Anlagenbauverbands VDMA aufgenommen wurde.

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OP Financial Group, one of Finland's largest financial services companies, teamed up with Brightly to scale up their data and analytics capabilities, fostering a robust data community within the organization. The project demonstrated that fostering a culture of data literacy and innovation requires the right tools, infrastructure and a data-oriented, problem-solving mindset.

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Brightly created a PoC of an AI-powered tool that enables Normet personnel to make natural language queries into IoT data, making data more accessible and valuable throughout the organization.

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Software testing is the unsung hero of the various development disciplines. Often the testing is seen as ‘Complex!’ ‘Time-consuming!’ and ‘Tedious!’ But as a systematic part of your development processes, it makes developers’ work smoother, easier, and cost-effective while making your product solid, bug-free and future-proof. Here are some ways to leverage testing to improve your product and help your developers do their best work.

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Gemäß unseren Plänen zur Expansion in die DACH-Märkte haben wir im März 2024 ein Büro im Herzen Mitteleuropas eröffnet.

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The hype is strong with AI. As we're bombarded with AI-related topics, it might seem imperative to move quickly, but it's equally important to ensure that we're solving the right problems. Read more to learn how use a problem-solving mindset to make your AI project successful.

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Modern data solution development has surprisingly many similarities with the innovation process. In this blog, I describe how to recognize innovation phases when developing data and analytics cases.

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This blog post delves into the application of modern AI for extracting meaningful insights from the turmoil of social media discourse. We showcase this technique by examining the social media conversations about the ongoing labor market reforms and political strikes in Finland.

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This article will address the impact of unit testing on projects, define testable units, explore the benefits of disciplined practices, and provide guidance on getting started.

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This article distills Brightly’s hands-on experience into essential dos and don'ts when developing GenAI applications for production use.

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Data monetization is a process of converting organizations’ raw data into sellable data products. This article covers two real-life examples of data monetization, explains the key benefits of data monetization, and describes how to monetize your data in practice.

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Generative AI has the potential to contribute $2.6 trillion to $4.4 trillion annually to the global economy, making this the right moment to seize market share. The question is, how can you determine GenAI's real value for your business?

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This case study explains the solution we built in partnership with a prominent car dealer to enhance their business efficiency by leveraging a predictive model to estimate used car sales cycles.

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Explore the essential guide to streamline Git workflows for efficient software development and quality assurance. From understanding core Git workflows to implementing release management, Semantic Versioning, and embracing automation, this article offers practical strategies to enhance collaboration, code quality, and overall development efficiency.

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In this blog post, we'll break down the essential components of today's GenAI applications, from the AI models that drive content generation to the user interfaces that connect users with these applications.

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Explore key insights and architecture tips in our latest blog post on Azure Data Explorer (ADX). Learn from my 4-month journey of mastering ADX for IoT data analysis with Power BI, and discover how to optimise your data processing with real-time performance, efficient pricing, and practical advice.

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The process industry and machinery sector have always been at the forefront of adopting innovative technologies. Two recent research papers propose approaches to using large language models (LLMs) in an industrial context that might inspire professionals working in this field. Eureka and GenSim, two advanced AI systems, shed light on LLMs' potential capabilities, and the implications for industrial companies are potentially profound. Eureka uses LLM's to motor control tasks and GenSim creates rich simulation environments for robotic machinery.

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In pursuit of our growth and internationalization strategy, we're thrilled to share a significant milestone in our story: The opening of our Amsterdam office. We bring our unique blend of cloud data, smart IIoT, and design-driven digital solutions expertise to the heart of the Netherlands.

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In this article, we’ll take a closer look at how we can use dynamic templating to simplify and streamline the management of Databricks deployments.

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Avoid costly mistakes and invest your time and money wisely with Proof of Concept (PoC) projects. The guidelines we introduce here for PoCs have been verified by successful customer projects, and we cover topics like setting goals, involving customers throughout the project, and delivering valuable evidence of feasibility.

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