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What is changing in developer relations?
The familiar DevRel playbook combined a good developer experience, clear documentation, technical content, and a presence at developer events. DevRel.ai’s practitioner essay, “The Old DevRel Playbook”, argues that AI is changing both how developers work and how developer-tool companies reach and serve them. It is a useful framing, not a measured finding that every team should adopt the same strategy.
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Developers may increasingly work inside an IDE or ask an assistant to complete tasks that once sent them to a documentation site or product console. That creates a leadership question: what does a great developer experience look like when help and code generation are embedded in the workflow? The answer will vary by product. Map the journey from discovery through first successful use, production, and ongoing support, then validate assumptions with developer conversations and product usage data.
Map tasks, not trends
For each important task, identify where developers do it today and where they could reasonably do it next: documentation, console, IDE, assistant, agent, or a conversation with your team. Ask which tasks are moving and which still depend on a web interface, authoritative guidance, or human support. Treat possibilities as hypotheses rather than assuming that every developer is changing tools in the same way.
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How should a DevRel leader experiment?
Make deliberate, small experiments tied to real developer work. The goal is not to stage an impressive AI demonstration; it is to learn where a tool helps, where it fails, and what your product or team should do in response.
Demonstrate a complete, practical task
Choose a task developers genuinely need to complete and show it from start to finish. Make the steps reproducible, identify what the AI tool contributed, and show any errors or points where a person had to intervene. Angie Jones, Global VP of Developer Relations at Block, describes her team’s pivot to an AI product after its earlier product was sunset. In her July 2025 DevRelCon New York talk, “DevRel’s biggest stage yet”, she emphasizes practical use cases, openness about what the team did not yet know, adapting to AI-mediated discovery, and teaching beyond the usual technical audience.
Jones put the value of adaptability this way: “That ability though, to pivot without losing momentum, that is something that developer advocates already excel at.” The point is not to pivot toward AI for its own sake; it is to bring DevRel’s existing ability to learn and respond to a new product and workflow.
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When your team is still learning, say so. Distinguish what you observed in a demo or developer conversation from what you have verified at scale. That makes a demonstration more useful: developers can see not just a polished outcome, but the conditions, limitations, and workarounds that matter when they try it themselves.
How can documentation serve developers and AI assistants?
Documentation remains a source of product truth, but its usefulness depends on whether developers—and the tools they consult—can find accurate guidance. A July 2025 DevRelCon New York panel, “A 360 View of AI Developer Relations”, recommends structured, version-labeled documentation and educational material useful to both people and AI assistants. DevRel.ai also flags the risk that generated answers may reflect stale training knowledge or average examples.
Prioritize accuracy and retrieval
- Keep code examples aligned with supported product versions, and label the version they apply to.
- Make authoritative setup instructions, compatibility details, and migration guidance easy to locate and distinguish.
- Review examples and answers that are especially likely to be reused by assistants, including common setup and troubleshooting paths.
- Check whether developers can find the right information in the places they actually work; do not assume one documentation format guarantees retrieval by every AI tool.
Use observed questions and support friction to decide what to update. If an assistant produces an outdated or incompatible example, treat that as a signal to inspect the underlying product guidance and the paths developers use to reach it—not as proof that documentation alone caused the error.
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How should DevRel feed developer needs into product decisions?
DevRel works in two directions: helping developers understand and use a product, and helping the company understand developers. Preserve a clear route from recurring developer friction to product and engineering decisions. The DevRel Handbook and the DevRel Directory’s overview of Developer Relations describe this outward and inward-facing role.
- Capture the context. Record the task a developer was trying to complete, where they got stuck, and what workaround they used.
- Look for recurrence. Separate a repeated pattern from an isolated report, while preserving important edge cases.
- Route evidence to owners. Bring the problem and its developer context to the relevant product or engineering team.
- Close the loop. Tell developers what changed or, where it did not, explain the decision when you can.
This feedback function can be crowded out if the team is measured only on lead generation or content volume. Keep space for candid education and two-way relationships rather than treating every interaction as a promotional opportunity.
Who else should DevRel teach?
Developers remain central, but some organizations may benefit from DevRel helping non-technical teams understand AI products and their implications for developer experience. Jones describes broadening education in this way as part of her team’s response. Whether it makes sense depends on those teams’ needs and your DevRel mandate; her example does not establish that every DevRel team should own company-wide AI training.
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Before taking on that work, clarify who needs the education, what practical outcome it should support, and whether the team has the capacity and expertise to provide it. If the need is broader than DevRel’s remit, the team can still share what it knows and help connect the right internal owners.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How can a DevRel team measure its impact?
Choose indicators that fit the team’s actual mandate, then pair numbers with evidence of what changed for developers. The 11th Annual State of Developer Relations results, published by DevRel.Agency in September 2024, offer dated context—not a current benchmark for every team.
| Reported 2024 finding | What it tells a leader |
|---|---|
| 78% of DevRel professionals reported using AI in their toolkit. | AI was already part of many respondents’ work; this does not establish a 2026 adoption rate. |
| 66% cited awareness and adoption as a top DevRel goal. | Many respondents connected DevRel work with reaching developers and encouraging product use. |
| 44% cited active users as a primary success measure. | Usage is one reported measurement choice, not a complete account of DevRel’s contribution. |
| 61% said proving DevRel influence was difficult. | Attribution remains a challenge in the survey; it is worth pairing metrics with documented outcomes. |
| 42% selected content generation, 28.8% code suggestions and completions, and 15.2% documentation generation as AI uses. | The survey report identifies several kinds of AI use, rather than one universal DevRel workflow. |
| 60.7% named proving impact with data and metrics as a top challenge. | This separate figure in the 2024 survey report reinforces that measurement difficulty was a reported concern. |
The figures come from the publishers’ reported 2024 survey findings; the available sources do not establish that the sample represents all DevRel professionals or show updated 2026 rates. The announcement is available from DevRel.Agency, and the survey figures are presented by State of Developer Relations.
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For your own team, select a small set of measures tied to its responsibilities. Depending on the mandate, these could include successful onboarding or product usage, the quality and resolution of developer issues, recurring friction addressed by product, or meaningful community participation. These are options, not a universal scorecard. Document concrete examples of developer outcomes and product changes alongside the quantitative indicators.
Where should DevRel sit in the organization?
There is no single org chart established as best for DevRel. Teams may sit within marketing, product, open source, or another function, and their access, incentives, and measures can differ. The DevRel Directory’s guide to roles and responsibilities describes the range of work; Bear Douglas’s 2024 DevRelCon talk, “Successful DevRel no matter your org chart”, addresses how the reporting line shapes the role.
Assess a proposed structure against four practical questions:
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- Decision access: Can the team bring developer evidence to product and engineering decision-makers?
- Mandate and measures: Do assigned goals fit the team’s organizational home, and are expectations clear?
- Community trust: Is there room for candid education and two-way relationships, not just promotional output?
- Resourcing and sponsorship: Is a leader able to protect the team’s capacity and connect its work to company priorities?
Clarify the team’s audience, scope, feedback route, decision access, and executive sponsor. Whichever structure you choose, preserve a practical connection between developer needs and product truth.
What does “stop playing it safe” mean in practice?
It means testing assumptions instead of relying on inherited routines—not replacing events, abandoning search-oriented content, or turning every DevRel team into an AI team. Practitioner accounts and recommendations support experimentation and adaptation, but they do not show that one activity mix works for every company.
Start with a developer task that matters, observe how people currently complete it, and test whether AI changes the path or outcome. Improve the instructions and examples developers rely on, route repeated friction to product owners, and share what your team learned with the right internal audience. Keep, change, or stop the experiment based on evidence from your developers.
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