DX

Since we released our Guide to AI-Assisted Engineering early last year, we’ve updated it with new 2026 survey data on how teams are using AI assistants in practice, including updates to the top 10 AI use cases engineers rank highest for time savings. The guide is built for engineering leaders to share with their teams, and it covers: prompting techniques used by advanced AI users to improve output quality, a ranked set of high time-saving use cases, and clear actions leaders can take to support AI adoption. Read the updated guide here: https://lnkd.in/gKZB3KUE

DX now ingests data from Harness. By integrating Harness with DX, you can analyze pipeline execution data, calculate deployment metrics, and monitor build performance across your CI/CD workflows.

We recently launched Linear-specific Allocation, CapEx, and Lifecycle by Phase reports. These reports mirror the functionality our customers rely on across Jira and Azure DevOps, while accounting for the structural differences in Linear’s data model. The result is consistent, trustworthy reporting across tools without forcing teams to change how they operate.

At DX Annual 2026, Eleanor Millman and Mina Tawadrous will share how SiriusXM’s Platform Engineering organization turned prioritization into a product discipline—and then into “Prioritization as Code.” Supporting ~700 internal developers, they built a consistent, data-informed framework that has guided 200+ platform investments by tying the roadmap directly to company strategy, user research, and measurable impact across dimensions like security, cost, speed, and reliability. They’ll walk through how they: - Translate developer pain, reliability risks, and executive concerns into a shared scoring model that makes tradeoffs transparent - Use AI to synthesize feedback from multiple sources and surface the hidden assumptions behind decisions - Codify those validated assumptions into a living, machine-readable artifact (“Prioritization as Code”) that evolves over time and keeps teams aligned The session will offer the full framework, impact rubric, and concrete lessons learned so other platform and product teams can adapt the approach to resolve roadmap conflicts and build alignment when priorities compete.

If you’re traveling to Amsterdam for KubeCon EU, join us the evening of March 23 for a private DX Panel. Topic: The Future of the AI-Native SDLC Speakers confirmed: - Micaela Stump, Adyen - Pedro Silva, OLX - Justin Reock, DX - More to be announced Attendance is limited to maintain a senior, candid environment. If you’re leading engineering, platform, or developer productivity and will be in Amsterdam, send us (DX) a LinkedIn message to RSVP.

I've got another blog series out with my friends at Google! I've collaborated with Darren Evans and #GoogleCloud again, and this time we're tackling the "platform usage trap" - that tempting assumption that high adoption means you're doing a good job. Spoiler: it doesn't. The two posts walk through how we evolved measurement for the johnlewis.com Digital Platform - from proving the platform's value through lead times and DORA metrics, to eventually focusing on whether teams are actually getting the value they should from it. Measuring adoption is easy. Measuring whether your platform is actually helping teams do the right things is harder - but more important. We've built lights along the edges of our paved road, to help teams stay on it. Part 1: https://cloud.google.com/blog/products/application-development/at-john-lewis-partnership-measuring-developer-platform-value Part 2: https://cloud.google.com/blog/products/application-development/how-john-lewis-partnership-chose-its-monitoring-metrics Thanks especially to Rob Hornby, who was the leading thinker behind the creation of these techniques! #JohnLewisPartnership #PlatformEngineering #DevOps #Kubernetes

“It’s not a single metric that we are trying to move… We are trying to improve productivity as a system, not just in one dimension.” — Uma Namasivayam New on the Engineering Enablement podcast: Dropbox’s Senior Director of Engineering Productivity, Uma Namasivayam, shares the operating model behind how they measure and operationalize productivity across 1,000+ engineers. This conversation covers: - How Dropbox runs their developer platform like a product - Using balanced metrics as guardrails for decisions - Allocating VP-level engineering capacity to productivity work Listen to the full conversation: https://lnkd.in/gzevqzZn

New in DX: multi-language Snapshot surveys for global teams. You can now send a single DX Snapshot to your whole organization and let developers choose their preferred language, right in the survey experience.  Today, respondents can select from Japanese, Portuguese, Spanish, or German, with more languages coming soon. This makes it easier for global teams to run consistent developer experience assessments and quickly get the data they need to improve DX across every region.

Update: SLAs in DX are now customizable. DX SLAs let you turn “how we want to work” into measurable thresholds. You define targets (for example, “PRs reviewed within 1 business day” or “Recover from failed deployments within 30 minutes for Tier 1 services”), and DX tracks how your group is performing against those targets over time. Optionally, you can receive alerts in Slack or Teams when thresholds aren’t met. Previously, you could use DX’s out-of-the-box SLAs. Now, you can create SLAs based on any metric.

DX’s Deputy CTO Justin Reock asked analysts from Accenture and RedMonk to share the biggest changes they’re seeing in how engineering organizations are structured in response to AI. A few patterns they’re seeing:  - Hiring priorities are emphasizing AI fluency and code quality - Organizations are consolidating AI responsibilities into centralized platforms to enforce consistent engineering standards - Platform teams are absorbing the responsibility of LLMOps Read more about the patterns these analysts are seeing in the latest Engineering Enablement newsletter: https://lnkd.in/gSnv-UVw