DORA | User-centric focus

All software is made for human users. The true worth of any technology, from a highly visible front-end interface to an unseen back-end process, is determined by its usefulness to the individual at the conclusion of the value chain. However, complex technology stacks and intricate supporting systems can often obscure the essential connection between the act of creating software and the benefit it eventually provides.

A user-centric focus is not just a design philosophy; it is a measurable capability that drives organizational performance. It is defined by the extent to which teams understand user needs, prioritize user experience, and leverage feedback to continuously reprioritize their work. When organizations know and understand users’ needs, they increase the likelihood of building features that address real problems, rather than just shipping “shiny but hardly used” features.

Teams that focus on the user have 40% higher organizational performance.

The AI angle

The rise of AI has made a user-centric focus more critical than ever. In the 2025 State of AI-assisted Software Development report, DORA found that AI acts as an amplifier: it magnifies the strengths of high-performing organizations and the dysfunctions of struggling ones. Without a user-centric compass, AI’s ability to accelerate code generation can simply propel a team faster in the wrong direction.

Our research reveals a stark contrast in how AI impacts teams based on their focus:

Amplified performance: When teams with a strong user focus adopt AI tools, their effectiveness and team performance grow. The user-centricity acts as a “North Star,” guiding AI-assisted developers toward appropriate goals.
The “Feature factory” trap: Conversely, adopting AI without a user-centric focus can actually harm team performance. If a team is optimizing for output (features shipped) rather than outcomes (user value), AI can accelerate the production of low-value software, leading to high activity but low impact.

If a team’s priorities are not aligned to the North Star of user needs, amplification with AI can propel them even further in the wrong direction.

How to implement a user-centric focus

Building a user-centric focus requires a cultural shift that moves user stories, feedback, and analytics from the periphery to the core of the development process.

Integrate user feedback loops

Teams must actively solicit and make use of direct user feedback. This involves creating low-latency channels for feedback, such as in-app surveys or direct observation sessions. Critically, this feedback must be available to the team immediately and used to refine development priorities. For AI-assisted teams, this feedback is essential for refining prompts and validating that the AI’s output aligns with user needs.

Make user metrics visible

A team’s focus follows what it measures. If dashboards only show metrics like velocity and deployment frequency, the user is easily forgotten. To shift focus, teams should display user experience metrics, such as customer satisfaction (CSAT), task completion rates, or the H.E.A.R.T. framework metrics, prominently alongside technical metrics. By making the “why” and “for whom” visible, teams remain grounded in the impact of their work.

Involve engineering in user research

Product managers and UX researchers play a vital role, but they should facilitate connection rather than enforce distance between engineers and users. Distilled findings often strip away nuance. Invite developers to observe user testing sessions directly. Seeing a user struggle firsthand creates a lasting empathy that informs how developers design solutions and write tests.

Leverage “Spec-driven Development”

An emerging paradigm for aligning AI with user needs is spec-driven development (SDD). In this approach, developers refine user needs and constraints into detailed documentation (specs) before writing code. This documentation becomes the source of truth for AI agents, ensuring that generated code is constrained by actual user requirements rather than just generic patterns.

Common pitfalls

Even teams with good intentions can lose sight of the user. Watch out for these common antipatterns:

The feature factory mindset: This occurs when teams focus on measuring output (velocity, features shipped) rather than outcomes (user value). AI can exacerbate this by making it easier to produce more code that solves fewer problems.
Resume-driven development: Teams may fall into the trap of “solutionism,” adopting new technologies—including specific AI models—for their own sake rather than to solve a specific user problem. This adds complexity and pulls focus away from the user.
Organizational silos: Policies or team structures that systematically disconnect developers from end users create a “gatekeeper” model. This robs developers of the deep context required to build valuable solutions and verify AI outputs effectively.

Measuring impact

The ultimate measure of a user-centric focus is reflected in product metrics like adoption, retention, and customer satisfaction. However, you can also evaluate your team’s orientation using the following signals:

Product performance: Track metrics that indicate value, such as adoption rates, retention rates, and customer satisfaction scores.
Feedback integration: Measure how often user feedback leads to a reprioritization of features or changes in specifications.
Team alignment: Assess the degree to which the team agrees on statements such as “Creating value for our users is our focus” and “We have a clear understanding of what our users want to accomplish.”

Prioritizing user-centricity isn’t just a design choice; it’s a performance multiplier. DORA research shows that teams who focus on the user have 40% higher organizational performance and significantly higher job satisfaction. In an era where AI allows us to build faster than ever, a user-centric focus acts as our steering wheel. Without it, we risk simply crashing faster. By investing in user feedback loops and connecting our engineers directly to user problems, we ensure that our increased velocity translates directly into business value.

What’s next?

Read the blog post: Unlocking product success by combining DORA and H.E.A.R.T.
Take the DORA quick check to get a baseline of your team’s software delivery performance.

User-centric focus AI