# AI Adoption Published articles for AI Adoption. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Australia's AI opportunity starts with data DevFeed: [Australia's AI opportunity starts with data]() Original publisher: [Read original article]() Author: Sean MacKirdy Published: 2026-09-17T01:00:00Z Content type: opinion Language: en Sources: [Elastic Blog - Elasticsearch, Kibana, and ELK Stack]() Topics: [data](), [Artificial Intelligence](), [AI Strategy]() Tags: [agentic-ai-government](), [ai](), [ai-adoption](), [australia](), [data](), [platform](), [public-sector-media-entertainment]() ### AI overview Elastic's participation in Australia's first Parliamentary AI Industry Showcase emphasized that data is a central constraint on AI performance and a prerequisite for deploying AI that is effective, safe, and trusted. ### Source excerpt Elastic, a Founding Partner at Australia's first Parliamentary AI Industry Showcase, highlights that data, not just models, is the key to deploying safe, effective, and trusted AI. ## Port vs. Jellyfish: Engineering Intelligence Compared DevFeed: [Port vs. Jellyfish: Engineering Intelligence Compared]() Original publisher: [Read original article]() Author: Tomasz Skora Published: 2026-09-15T12:02:54Z Content type: comparison Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [Software Engineering](), [Developer experience](), [dora metrics](), [AI Strategy](), [code productivity]() Tags: [ai-adoption](), [compare](), [devex](), [dora-metrics](), [engineering](), [software-engineering](), [workflows]() ### AI overview A comparison of Port and Jellyfish as Engineering Intelligence platforms. Jellyfish focuses on analytics and decision support for engineering performance, investment, developer experience, and AI impact, while Port connects findings to software context, owners, standards, and governed workflows so teams can act and measure outcomes. ### Source excerpt Compare Port and Jellyfish across Engineering Intelligence, AI impact, DevEx, software context, governed workflows, and measurable outcomes. ## Supporting independent journalism in Ukraine DevFeed: [Supporting independent journalism in Ukraine]() Original publisher: [Read original article]() Published: 2026-09-07T00:00:00Z Content type: news Language: en Sources: [OpenAI News]() Topics: [AI Strategy](), [AI Development](), [Responsibility & Safety]() Tags: [ai-adoption](), [api](), [global-affairs](), [openai](), [partnership]() ### AI overview OpenAI, WAN-IFRA, and AIRPPU announced a programme to help Ukrainian independent news organisations adopt AI through masterclasses, project support, implementation roadmaps, pilots, and OpenAI API credits. ### Source excerpt OpenAI, AIRPPU and WAN-IFRA launch an AI program to help Ukrainian news organizations strengthen innovation, resilience, and independent journalism. ## GitLab's internal playbook to foster AI-fluent technical teams DevFeed: [GitLab's internal playbook to foster AI-fluent technical teams]() Original publisher: [Read original article]() Author: Rob Allen Published: 2026-09-02T00:00:00Z Content type: article Language: en Sources: [GitLab]() Topics: [AI Strategy](), [GitLab]() Tags: [ai](), [ai-adoption](), [ai-strategy](), [collaboration](), [compliance](), [enablement](), [engineering](), [workflows]() ### AI overview GitLab shares an internal playbook for building AI fluency in technical teams. It describes a federated governance model that centrally manages foundational AI tools while allowing local experimentation and functional strategy. ### Source excerpt Give two engineering teams the same AI tool and you can end up with two very different outcomes. One team ships faster with fewer bugs, while the other gets burned by an agent that confidently generates the wrong output. At GitLab, our team had AI tools at their fingertips and some found real value fast, working faster and catching issues earlier. Meanwhile, others hadn't quite found an entry point yet to develop effective AI-native workflows. We learned that building AI fluency -- how our team members know what to delegate to AI, how to build the right AI-native processes, and how to judge what comes back -- was just as important as AI tool adoption and access. Building that fluency across GitLab was both an operational and technical challenge requiring close partnership between our Enterprise Technology and Talent Development teams. We're sharing our internal playbook so other technical leaders gain another perspective on how to encourage the right kinds of AI adoption across their own organizations. An AI strategy built for the pace of our work Part of building the right paths for our technical teams was predicated on establishing smart foundational infrastructure. Enterprise Technology considered a few different structures to our governance. The first was a fully centralized team, but we worried that with the speed of AI technology, shipping approvals from one group could end up as a bottleneck. As a result, team members could become impatient and try to circumvent governance infrastructure to experiment with AI. The second was a fully decentralized approach, but that could fragment efforts across the company, which adds complexity and makes guardrail consistency challenging. We landed on a hybrid model, building governance and enablement into a model that used the best aspects of centralized and decentralized strategies: Enterprise AI acts as a central governance and technology hub: Based out of our Enterprise Technology team, Enterprise AI operates as the platfo ## How AI-native companies turn workflows into operating capability DevFeed: [How AI-native companies turn workflows into operating capability]() Original publisher: [Read original article]() Published: 2026-09-01T17:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence]() Tags: [account-management](), [agents](), [ai](), [ai-adoption](), [ai-agents](), [codex](), [developer](), [experimentation](), [integration](), [onboarding](), [tools](), [workflows]() ### AI overview The article explains how Basis, Clay, and Exa Labs embed AI agents into onboarding, account management, and developer integrations. It presents a progression from teaching agents stable processes to giving them persistent context and enabling tested action, with emphasis on repeatability, measurement, trust, and continuous improvement. ### Source excerpt Basis, Clay, and Exa Labs use AI agents to improve onboarding, account management, and developer integrations. See what enterprise leaders can apply. ## How law firm Gilbert + Tobin governs and scales AI with OpenAI DevFeed: [How law firm Gilbert + Tobin governs and scales AI with OpenAI]() Original publisher: [Read original article]() Published: 2026-09-01T01:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [AI Chat](), [AI Strategy]() Tags: [ai-adoption](), [chatgpt](), [codex](), [enablement](), [enterprise](), [leadership](), [openai](), [operations](), [research](), [training](), [workflow]() ### AI overview Gilbert + Tobin describes scaling ChatGPT Enterprise and Codex through governance, leadership-led adoption, role-specific enablement, and continued human accountability. The firm uses these tools to support operational research, drafting, analysis, and defined workflow steps rather than replace legal judgment. ### Source excerpt See how Gilbert + Tobin combines CEO-led commitment, rigorous governance, and human accountability to scale ChatGPT Enterprise and Codex across the firm. ## Building the Future of Japanese Tech Talent DevFeed: [Building the Future of Japanese Tech Talent]() Original publisher: [Read original article]() Author: Hilary Carter Published: 2026-08-28T18:48:38Z Content type: article Language: en Sources: [Linux Foundation - Blog]() Topics: [Artificial Intelligence](), [linux foundation]() Tags: [ai-adoption](), [developers](), [japan](), [lf-education](), [lf-research](), [lf-survey](), [research](), [tech-talent]() ### AI overview The article examines Japan's technology talent ecosystem using findings from the 2026 State of Tech Talent Japan Report. It says AI is associated with projected net IT hiring growth of 54% in 2026 and 35% in 2027, while Japan has a strongly positive entry-level hiring effect of 46% compared with a global figure of -2%. ### Source excerpt At the Linux Foundation, our community thrives because of the dedicated work of developers, project maintainers, enterprise partners, platform engineers, and generous sponsors worldwide. On behalf of the team at LF Research, I want to extend our heartfelt thanks to all of our global sponsors and partners whose continued support makes our research, open source initiatives, and educational programs possible. ## Expanding OpenAI's presence in Brazil DevFeed: [Expanding OpenAI's presence in Brazil]() Original publisher: [Read original article]() Published: 2026-08-27T03:00:00Z Content type: news Language: en Sources: [OpenAI News]() Topics: [OpenAI](), [Artificial Intelligence](), [ChatGPT](), [Deployment](), [Code]() Tags: [ai](), [ai-adoption](), [brazil](), [chatgpt](), [company](), [deployment](), [developers](), [marketing](), [openai](), [production](), [writing-code]() ### AI overview OpenAI is launching commercial operations in Brazil, where ChatGPT adoption is growing rapidly. The local team will work with businesses, developers, researchers, and public institutions to support responsible AI use and broader economic and social benefits. ### Source excerpt OpenAI is expanding its presence in Brazil, deepening engagement with developers, businesses, and communities to support AI adoption across the country. ## How to start the AI-accelerated defense DevFeed: [How to start the AI-accelerated defense]() Original publisher: [Read original article]() Author: info@1password.com (Wade Wells) Published: 2026-08-27T00:00:00Z Content type: article Language: en Sources: [Blog on 1Password Blog]() Topics: [Artificial Intelligence](), [Detection engineering](), [Prompt Engineering](), [Large Language Model](), [log management](), [Security]() Tags: [ai](), [ai-adoption](), [documentation](), [engineering](), [llm](), [logging](), [onboarding](), [security](), [validation](), [workflow]() ### AI overview This article explains how a security team began adopting AI for detection engineering by supplying the documentation and workflow context that AI systems lack. It covers an AI Detection Engineering stack involving logging pipelines, log onboarding, detection validation, threat modeling, and detection logic, and shows why better context produces more reliable results than relying on prompts or increasingly powerful models. ### Source excerpt Early on in the AI adoption boom, I gained a reputation for just throwing everything at it to see what would stick. That wasn't the most effective strategy, and my token usage was crazy high. There are a ton of talks and posts on all the cool ways you can use AI for detection engineering, but I didn't see any that showed you where to begin. So, this isn't another blog about why you need to use AI in your defensive workflows. It seems most people understand why we need that. My focus is to show how our team got started and realized that providing AI with the necessary context is key to detection engineering successfully adopting AI. This is not just about building detection logic, but that is one of the goals. This foundation helps create the AI Detection Engineering stack: logging pipelines, log onboarding, detection validation, threat modeling, and more. An LLM does not know your stack, so out of the box it has limited value in a security review. In our experience, reliable results depend less on the fanciest model and more on the documentation and context around the workflow. If a human reads your log inventory and still has to ask three people what the ingestion method is, your agent does too. Why cold prompting fails When we first started using AI tooling, we realized prompts alone could get stuff done, but the output was inconsistent. Fields were missed, assumptions were made, and some detection logic was wrong. We saw it write queries that would not work in our SIEM. Usually these were around wildcards. The playbooks it wrote were generic, the tuning was poor, and some detections were just bad. With enough re-prompting, the output would improve, but it always required some massaging. The effort invested in the agent inputs had a noticeable impact on the quality of the outputs. TL;DR: garbage in, garbage out. Where our context came from At the start, this was just internal documentation we built to make our own lives easier. It started with new-hire materials a ## When AI adoption outpaces IT visibility DevFeed: [When AI adoption outpaces IT visibility]() Original publisher: [Read original article]() Author: info@1password.com (Stephanie Torto) Published: 2026-08-27T00:00:00Z Content type: article Language: en Sources: [Blog on 1Password Blog]() Topics: [Responsibility & Safety](), [dashboards]() Tags: [ai](), [ai-adoption](), [ai-governance](), [cost](), [dashboards](), [models](), [saas](), [saas-management](), [unified-access]() ### AI overview 1Password describes how fragmented vendor dashboards and consumption-based AI pricing made it difficult for IT to understand AI spending. It argues that IT should provide visibility and context for business and engineering leaders making budget and model decisions. ### Source excerpt At 1Password, we started expanding our use of AI with a familiar IT playbook. We identified the problems we wanted to solve and the tools that could help us achieve those goals. The plan was straightforward: enable teams, move quickly, learn what worked, and build the visibility needed to manage the cost. Then the operating model changed. AI vendors introduced consumption-based pricing faster than our processes could keep up, leaving us with a distributed system of vendor-specific dashboards to track and manage our AI use. For IT, that created a new kind of chaos when it came to understanding how much we were spending on AI and where that budget was being used throughout the company. We had data spread across systems, but we didn't yet have a clear, shared answer. How IT teams can govern AI use IT teams are close to the tools and access patterns that shape AI usage. That gives IT an important role in AI spend decisions, and is no small part of why AI governance can become framed as an IT mandate. Budget and model decisions belong with the leaders who set business and engineering priorities, while IT's role is to provide the context those leaders need. In the face of the changing nature of AI governance, IT teams should focus on finding ways to make AI spend explainable, to give the company a more useful basis for making decisions. Visibility changes the conversation Previously, 1Password's IT team could see activity in individual vendor consoles, but each view covered only part of the picture. We spent too much time moving between systems and interpreting different definitions. By the time we exported data from one tool and combined it with another, the result was already out of date. When we started using AI Spend and Consumption Management in 1Password SaaS Manager, it felt like a breath of fresh air. We now had a shared view of AI usage and spend across vendors and teams, with detailed insights on users and models, meaning that we could better understand our budg ## Bringing ChatGPT for Teachers to more U.S. school districts DevFeed: [Bringing ChatGPT for Teachers to more U.S. school districts]() Original publisher: [Read original article]() Published: 2026-08-26T10:00:00Z Content type: news Language: en Sources: [OpenAI News]() Topics: [ChatGPT](), [Artificial Intelligence](), [AI Strategy](), [Security, Privacy and Abuse Prevention](), [FIRST]() Tags: [ai](), [ai-adoption](), [chatgpt](), [education](), [openai](), [privacy](), [product](), [responsible-ai](), [support]() ### AI overview OpenAI is expanding ChatGPT for Teachers to 55 additional U.S. school systems across 20 states, providing free access, training, and support to more than 100,000 additional educators and staff. The announcement also introduces a 16-state data privacy agreement intended to simplify responsible adoption by school districts. ### Source excerpt ChatGPT for Teachers is expanding to 55 U.S. school systems, bringing secure AI tools, training, and support to over 100,000 more educators and staff. ## Introducing the Admin plugin for ChatGPT Work and Codex DevFeed: [Introducing the Admin plugin for ChatGPT Work and Codex]() Original publisher: [Read original article]() Published: 2026-08-25T00:00:00Z Content type: release Language: en Sources: [OpenAI News]() Topics: [ChatGPT](), [codex](), [Authorization](), [Monitoring](), [Slack]() Tags: [ai-adoption](), [analytics](), [chatgpt](), [codex](), [enablement](), [microsoft-teams](), [monitor](), [monitoring](), [onboarding](), [reduce](), [slack](), [tools](), [update](), [work](), [workflow]() ### AI overview OpenAI introduces an Admin plugin for ChatGPT Work and Codex that lets administrators analyze workspace usage, manage members and permissions, adjust limits, and handle requests in one conversation. It also supports recurring checks and workflows that route approvals to Slack or Microsoft Teams while keeping changes within existing roles and permissions. ### Source excerpt Use the Admin plugin for ChatGPT Work and Codex to analyze workspace usage, manage members and permissions, adjust limits, and act on admin requests. ## Responsible AI adoption needs developer workflow design DevFeed: [Responsible AI adoption needs developer workflow design]() Original publisher: [Read original article]() Author: Dr. Gleb Tsipursky Published: 2026-08-24T14:00:00Z Content type: article Language: en Sources: [Stack Overflow Blog]() Topics: [responsible-ai](), [shadow AI](), [AI Strategy](), [AI Platforms/Deployment](), [Tooling](), [Stack Overflow](), [Microsoft]() Tags: [ai](), [ai-adoption](), [article](), [cc-by-sa](), [compliance](), [developer](), [devex](), [experimentation](), [microsoft](), [platforms](), [responsible-ai](), [se-stackoverflow](), [se-tech](), [shadow-ai](), [tools](), [workflow]() ### AI overview The article argues that responsible AI adoption depends on designing developer workflows, not merely publishing policies. It presents shadow AI as a signal that approved tools or processes are too slow, vague, or disconnected from engineering work, and recommends investigating workflow friction while providing monitored gateways and approved AI platforms that support visibility and experimentation. ### Source excerpt Organizations cannot solve shadow AI with a document employees read once. They need to make responsible use easier than improvised use. ## AI Adoption Metrics: Connect Usage to Real Engineering Impact DevFeed: [AI Adoption Metrics: Connect Usage to Real Engineering Impact]() Original publisher: [Read original article]() Author: Tomasz Skora Published: 2026-08-10T11:32:12Z Content type: opinion Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [AI Strategy](), [code productivity](), [AI Development](), [data](), [Claude](), [cursor](), [GitHub Copilot]() Tags: [2025](), [ai](), [ai-adoption](), [ai-tools](), [claude](), [copilot](), [cursor](), [development](), [metrics](), [productivity](), [token](), [tokens]() ### AI overview The article argues that AI adoption metrics should connect tool usage with engineering context and outcomes. License activity, user engagement, token consumption, and prompt volume do not by themselves show whether teams are working more effectively. Meaningful evaluation should consider teams, services, processes, ownership, workflows, delivery stability, test coverage, incidents, and concrete results such as reduced latency. ### Source excerpt See why AI adoption metrics need engineering context: how connecting usage to teams, services, and outcomes reveals real impact. ## Explorers, exploiters, and the myth of the 100x engineer DevFeed: [Explorers, exploiters, and the myth of the 100x engineer]() Original publisher: [Read original article]() Author: Eira May Published: 2026-08-05T07:40:00Z Content type: article Language: en Sources: [Stack Overflow Blog]() Topics: [Artificial Intelligence](), [AI Strategy](), [Reinforcement learning](), [leaders-of-code]() Tags: [ai](), [ai-adoption](), [ai-tools](), [business](), [engineering](), [engineering-leadership](), [experiment](), [explore](), [leaders-of-code](), [learning](), [podcast](), [productivity]() ### AI overview The article challenges the myth that a small group of inherently exceptional engineers drives disproportionate AI gains. It argues that engineering leaders should treat exploration and exploitation as a continuum and help more engineers develop through curiosity, adaptability, experimentation, and learning, rather than focusing only on identifying and promoting existing "100x" performers. ### Source excerpt The "find the special ones and promote their traits" approach isn't the best or only way to drive AI adoption and productivity on an engineering team. ## AI adoption isn't the same as AI usage DevFeed: [AI adoption isn't the same as AI usage]() Original publisher: [Read original article]() Author: Harshal Shah Published: 2026-08-05T00:00:00Z Content type: opinion Language: en Sources: [Webflow Blog]() Topics: [Artificial Intelligence](), [AI Strategy](), [Code](), [pull-requests](), [Test coverage]() Tags: [ai](), [ai-adoption](), [dashboards](), [development](), [engineering](), [metrics](), [software](), [workflow]() ### AI overview The article argues that AI usage metrics--such as seat activations, token spend, pull request counts, and AI-generated code percentages--do not prove that an engineering team has changed how it builds software. It distinguishes temporary individual experimentation from durable adoption, where a recurring task is permanently delegated to an AI-supported workflow. ### Source excerpt Usage dashboards go up while nothing about how your team ships changes. Why that gap exists, and the one kind of AI adoption that actually lasts. ## How CFOs can manage AI costs and prove business value DevFeed: [How CFOs can manage AI costs and prove business value]() Original publisher: [Read original article]() Author: info@1password.com (Greg Henry) Published: 2026-08-04T00:00:00Z Content type: article Language: en Sources: [Blog on 1Password Blog]() Topics: [Artificial Intelligence](), [AI Strategy](), [Software as a service](), [Cloud](), [dashboards]() Tags: [ai](), [ai-adoption](), [billing](), [business](), [business-value](), [cloud](), [compliance](), [dashboards](), [finance](), [management](), [real-time](), [saas-management]() ### AI overview The article explains how CFOs and Finance teams can manage unpredictable AI spending by gaining earlier visibility into usage, forecasting budget risk, assigning ownership, and connecting AI investments to measurable business outcomes. It highlights consumption-based pricing, changing model costs, and delayed reporting from IT and vendor dashboards as key challenges. ### Source excerpt Earlier this year, a bill arrived from one of 1Password's AI vendors for 5x the value of the original contract. The initial agreement came in below a certain threshold, so it never reached the right approvers for review. By the time it did, we had a much clearer understanding of how quickly AI costs can add up. This unpleasant surprise revealed a structural gap between IT, Finance, and end users when it came to AI billing and consumption. Although Finance was accountable for the budget, it had no way to see what was being spent on AI until it was already spent. Other CFOs are seeing the same pattern of a bill arriving that no one can explain. Now, as leaders grapple with soaring and unpredictable token costs, what was considered a budget line item just a few months ago has become a board-level topic. Managing AI costs requires Finance and IT to share visibility into consumption before the invoice arrives. Organizations need a way to track usage, forecast budget risk, assign ownership, and connect AI investments to measurable business outcomes. Why AI costs are harder for Finance to forecast Effective Finance and IT are built on predictability: per-seat SaaS contracts, annual budget cycles, and predictable renewal dates. Contracts with AI vendors are fundamentally different. They're based on consumption pricing, which scales with usage, not headcount. As AI usage grows across a team or department, the bill can literally grow overnight. The closest comparison is cloud, which also uses consumption pricing. Cloud sprawl took years to bring under control, but Finance eventually learned to model it. With AI, there is no time for a learning curve. Pricing tiers change constantly, new models ship overnight, and AI adoption continues to accelerate. Why Finance sees AI overspend too late While Finance is responsible for AI spend management, the tools Finance relies on weren't designed to provide real-time visibility. Getting a complete picture requires going through the IT te ## Secure developer secrets with 1Password DevFeed: [Secure developer secrets with 1Password]() Original publisher: [Read original article]() Author: info@1password.com (Allie Dusome) Published: 2026-07-28T00:00:00Z Content type: article Language: en Sources: [Blog on 1Password Blog]() Topics: [Security](), [Code](), [App](), [Artificial Intelligence](), [GitHub]() Tags: [ai-adoption](), [breach](), [code](), [developer](), [developers](), [github](), [local](), [news](), [secrets](), [security](), [tools](), [unified-access](), [workflows]() ### AI overview This article introduces 1Password Environments and Developer Watchtower, two capabilities designed to reduce developer secret sprawl. Developer Watchtower detects plaintext credentials on local devices and guides users to import them into 1Password, while Environments provides a secure place to store, share, and use secrets for apps, services, automations, and AI-assisted workflows. ### Source excerpt No developer wants to be responsible for causing a catastrophic breach. But security best practices are often incompatible with the expectation that developers constantly write and ship code, and that velocity is massively accelerating thanks to AI adoption. Developers trying to work fast need convenience, but the easiest place to put a developer secret can sometimes be the riskiest place to leave it. For many developers, that place is a local .env file: fast, familiar, and supported by the tools they already use, but often stored in plaintext on disk. Even when developers are given discrete tools for managing secrets, they can be complex, time-consuming, and disconnected from their workflows. So when someone needs to get an app up and running, the fastest path can be the familiar path: put a credential in a plaintext file on your local disk. These habits create a visibility gap for IT and security leaders and contribute to secret sprawl. GitGuardian's 2026 State of Secrets Sprawl report found 28.65 million new secrets in public GitHub commits in 2025, up 34% from the previous year. The same report found the number of leaked secrets for AI services had grown by 81% in a single year. When credentials are stored in plaintext on a local device, IT and security teams may not know they exist, which means they cannot monitor them, revoke them, rotate them, or understand what else depends on them. That is the gap 1Password is closing with the launch of 1Password Environments and Developer Watchtower: helping teams find improperly stored developer credentials, secure them in the place employees already trust, and let developers keep using them without slowing down the work. Developer Watchtower identifies plaintext developer credentials on local devices and guides users to import them into 1Password. Developer credential visibility for admins provides a clearer view of credential risk across their organization, with reporting and remediation workflows to help employees secu ## No Dumb Questions: What is the AI bottleneck? How does context engineering fix it? DevFeed: [No Dumb Questions: What is the AI bottleneck? How does context engineering fix it?]() Original publisher: [Read original article]() Author: Phoebe Sajor Published: 2026-07-24T16:00:00Z Content type: article Language: en Sources: [Stack Overflow Blog]() Topics: [Artificial Intelligence](), [AI Strategy](), [Data Science]() Tags: [agentic-ai](), [ai](), [ai-adoption](), [autonomous-agents](), [data-science](), [no-dumb-questions]() ### AI overview Stack Overflow's No Dumb Questions series explores an AI adoption bottleneck: AI tools can perform tasks but often lack the surrounding context from email threads, Slack conversations, and meetings. Michael Foree explains how context engineering can help people provide relevant information so AI produces more useful responses. ### Source excerpt In this No Dumb Questions, Stack's Director of Data Science Michael Foree teaches Phoebe about AI context, context engineering, and what she can do to become a better context engineer. ## From pilot to production: The platform team's playbook for scaling AI coding agents in regulated industries DevFeed: [From pilot to production: The platform team's playbook for scaling AI coding agents in regulated industries]() Original publisher: [Read original article]() Author: Eric Paulsen Published: 2026-07-24T11:13:47Z Content type: article Language: en Sources: [Platform Engineering Blog]() Topics: [AI-assisted coding](), [ai-governance](), [observability](), [shadow AI](), [Deployment](), [coding]() Tags: [ai-adoption](), [ai-coding-agents](), [ai-governance](), [developers](), [financial-services](), [government](), [observability](), [shadow-ai]() ### AI overview This article presents a platform-team playbook for scaling AI coding agents from pilots to governed production in regulated industries. It emphasizes governance, observability, production infrastructure, and controls that provide visibility into agent actions, model usage, data access, token consumption, and costs. ### Source excerpt Scaling AI coding agents in regulated industries requires more than just successful pilots; it demands robust governance and production-ready infrastructure. Discover how platform teams can bridge the gap between experimentation and compliance by implementing observability, structured context engineering, and ephemeral workspace patterns to ensure safe, scalable AI adoption. ## Introducing the ChatGPT for small business program DevFeed: [Introducing the ChatGPT for small business program]() Original publisher: [Read original article]() Published: 2026-07-21T17:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [ChatGPT](), [Artificial Intelligence](), [OpenAI](), [OpenAI Academy](), [Prompt Engineering](), [Shopify](), [Slack]() Tags: [accounting](), [agents](), [ai](), [ai-adoption](), [business](), [chatgpt](), [content](), [customer-stories](), [ecommerce](), [events](), [guides](), [openai](), [openai-academy](), [partner](), [partners](), [product](), [resources](), [shopify](), [skills](), [small-business](), [work]() ### AI overview OpenAI launches the ChatGPT for small businesses program, offering training, AI academies, practical guides, workflows, and partner resources to help small businesses become more productive and scale. ### Source excerpt OpenAI launches the ChatGPT for Small Businesses program, helping entrepreneurs build AI skills, automate work, and grow with ChatGPT Work. ## How to manage AI investments in the agentic era DevFeed: [How to manage AI investments in the agentic era]() Original publisher: [Read original article]() Published: 2026-07-14T10:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [AI Chat](), [ai observability]() Tags: [agentic](), [ai](), [ai-adoption](), [analytics](), [chatgpt](), [cost](), [enterprise](), [how-to](), [model](), [models](), [performance](), [testing](), [work](), [workflow](), [workflows]() ### AI overview The article explains how enterprise leaders can assess AI spending by measuring useful work per dollar, monitoring usage and spend, and evaluating models against real tasks and total workflow cost. ### Source excerpt Learn how enterprises can manage AI investments in the agentic era by measuring useful work per dollar, improving efficiency, and scaling high-value workflows. ## 5 pitfalls to avoid when measuring DevEx in the AI era DevFeed: [5 pitfalls to avoid when measuring DevEx in the AI era]() Original publisher: [Read original article]() Author: Candace Shamieh; Teddy Gesbert Published: 2026-06-30T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [devex](), [code productivity](), [engineering-leadership](), [Artificial Intelligence](), [Code review]() Tags: [ai](), [ai-adoption](), [ai-impact](), [ci-cd](), [ci-visibility](), [collaboration](), [developer-experience](), [devex](), [devops](), [dora-metrics](), [engineering-management](), [internal-developer-portal](), [metrics](), [performance](), [productivity](), [review](), [software-delivery](), [test-optimization]() ### AI overview This article examines common pitfalls in measuring developer experience during the AI era. It argues that individual output metrics, including token consumption, lines of code, pull request counts, commits, and story points, can undermine trust and collaboration when treated as measures of personal productivity. ### Source excerpt Don't mistake AI adoption for productivity. Learn how to avoid 5 common pitfalls when measuring DevEx, with practices from Datadog engineering ## Reports on AI's Impact on Software Engineering and the Enshittification of Digital Products DevFeed: [Reports on AI's Impact on Software Engineering and the Enshittification of Digital Products]() Original publisher: [Read original article]() Author: Sergio Visinoni Published: 2026-06-28T13:07:11Z Content type: article Language: en Sources: [Sudo Make Me a CTO]() Topics: [Artificial Intelligence](), [Software Engineering](), [AI Development](), [Large Language Model](), [SIEM, Security, Observability]() Tags: [ai](), [ai-adoption](), [reports](), [software-engineering](), [something-big-is-happening]() ### AI overview This newsletter issue discusses new reports on AI's impact on software engineering, open-source projects pushing back on large language models, AI-driven enshittification of digital products, and the climate crisis affecting Europe. ### Source excerpt More reports on the impact of AI on software engineering, a look at the growing enshittification of digital products through AI, and how the tech industry is dealing with the climate crisis [Next page]()