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Build product thinking into engineering by starting with a user’s problem and a desired outcome, involving engineers in discovery, testing the smallest useful solution, and using user and delivery evidence to decide what to do next. A feature request is a starting point for discussion—not a fixed implementation order.

Start with the user’s problem, not the requested feature

Before accepting a ticket as a plan, make clear who is affected, what they are trying to accomplish, where they encounter friction, and what better outcome would look like. DORA puts it plainly: “Stories start from the business outcome that they are trying to achieve or the problem they are trying to solve.” DORA’s team experimentation guidance recommends testing whether the work will achieve that outcome.

Turn that idea into a short problem statement the team can use to guide decisions:

  • Who: Which users or customers are affected?
  • Need: What are they trying to do?
  • Evidence: What indicates that the current experience falls short?
  • Outcome: What should become easier, faster, safer, or more successful?

Keep the proposed feature separate from the problem statement. If the team learns that a different approach better addresses the need, the specification should be able to change.

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Bring engineering into discovery

Engineers can help discover what to build, not just determine how to build a predetermined solution. Involving them early can uncover technical constraints, dependencies, alternative approaches, and inexpensive ways to test assumptions before committing to a full implementation.

Use the evidence available for the question at hand: user conversations, product data, prototypes, technical investigation, or usability tests. Thoughtworks’ Product Thinking Playbook describes practices including research planning, technical research, prototyping, product testing, and validating a delivery backlog through discovery.

Discovery is useful when it changes a decision. For example, a prototype can test whether people understand a proposed workflow before a team builds its backend; technical research can establish whether an existing system supports the needed behavior; and a usability test can reveal that a seemingly small interface change does not help users finish the task.

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Give teams context and room to adapt

Share the product goals and organizational outcomes behind the work, then let the team use its judgment to shape the solution. DORA argues that teams should be empowered to experiment with real users and agreed-upon outcomes, rather than treating technical staff as order-takers. Its guidance supports changing stories and specifications during development as teams learn.

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That does not mean every team decision is unconstrained. Product, engineering, design, security, operations, and other stakeholders may need to agree on boundaries such as regulatory obligations, reliability requirements, or strategic priorities. Within those boundaries, teams need enough authority to respond when evidence challenges the original plan.

Compare options before committing

When there is more than one plausible solution, compare them against the same practical questions rather than choosing by feature count or technical novelty. This is a decision aid, not a standardized scoring model.

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Comparison lens Question to ask
Problem and outcome fit Does this option address a validated user problem and support the intended outcome?
Usability Can users understand the workflow and complete the task?
Effort, dependencies, and risk What must the team build or coordinate, and what could delay or complicate delivery?
Reliability and learning Can the change operate reliably, and can the team observe results and iterate after release?

A smaller implementation is not automatically better if it leaves the user’s core problem unsolved. The useful choice is the smallest option that can create meaningful user value or answer an important question without taking on unnecessary cost or risk.

Build a small, useful increment and release safely

Prefer a testable increment over a large, all-at-once build when the work can be divided without compromising the user journey. A prototype may be enough to test an uncertain idea; a production change may need implementation, operational safeguards, and a way to observe whether it works for users.

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DORA defines continuous delivery as “the ability to release changes of all kinds on demand quickly, safely, and sustainably.” Continuous delivery means software remains releasable on demand; it does not require automatically putting every change into production. Continuous deployment, by contrast, attempts to put every change into production as soon as possible. DORA’s continuous delivery guidance distinguishes these practices.

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More releases alone do not amount to product thinking. DORA cautions that raising deployment frequency without improving process and architecture can increase failure rates and burnout. Optimize the path to safe learning and delivery, not release counts in isolation.

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Measure the user experience and the delivery system

Ask both “Are we building a product people find valuable and easy to use?” and “How well and consistently can we deliver value to people who use our products?” The first question concerns user experience; the second concerns the team’s ability to deliver and respond. Neither is a substitute for the other.

Google Cloud’s overview of combining DORA and H.E.A.R.T. quotes Eric Maxwell, 10X Lead, delta Team, Google Cloud Consulting: “DORA tells you if you’re building it correctly. H.E.A.R.T. tells you if you’re building the right thing.” The article, published July 25, 2025, describes H.E.A.R.T. as a user-experience framework with five dimensions:

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  • Happiness: How users feel about the product.
  • Engagement: How users interact with it.
  • Adoption: Whether people begin using it.
  • Retention: Whether they continue to use it.
  • Task Success: Whether they can complete important tasks.

DORA’s delivery measures include change lead time, deployment frequency, change failure percentage, recovery time, and rework rate. Its platform guidance also lists developer satisfaction and platform adoption and retention. Choose measures that relate to the intended outcome; a movement in a metric does not, on its own, prove what caused it.

Use the signals to make a decision, not simply to populate a dashboard. If users struggle to finish a task or the intended outcome does not improve, revisit the problem, assumptions, or implementation. If releases are slow or risky, improve the delivery path so the team can learn and respond more safely. Tools and metrics cannot replace user conversations or the authority to act on what the team learns.

Apply product thinking to internal developer platforms

An internal platform is also a product, and developers are its customers. DORA recommends assigning product ownership focused on developer experience, mapping journeys such as starting a service or debugging a production issue, and addressing the most significant friction. Start with a minimum viable platform for a common workflow, then gather feedback and iterate rather than launching an all-encompassing system at once.

For a platform team, useful questions include whether developers can complete key tasks, where they get stuck, and whether the platform is adopted and retained. DORA warns that platforms built on assumptions may fail to solve developer problems, while rigid centrally imposed standards can encourage workarounds. In its page last updated January 12, 2026, DORA says its 2025 data found clear feedback on task outcomes to be the platform capability most correlated with positive user experience. DORA’s platform engineering guidance also reports that its 2024 research associated developer independence—the ability to perform tasks without relying on an enabling team—with a 5% productivity improvement at both team and individual levels. That is a reported association, not a guaranteed gain from any platform investment.

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A repeatable workflow for product-minded engineering

  1. Frame the problem: Identify affected users, their goal, evidence of friction, and the outcome the team wants to improve.
  2. Explore before committing: Involve engineering and relevant partners; investigate constraints and test important assumptions with research, data, prototypes, or users.
  3. Choose an option: Compare candidates for problem fit, usability, effort and risk, and ability to operate and learn after release.
  4. Build and release a useful increment: Keep the scope focused, preserve a safe path to production, and do not confuse continuous delivery with mandatory automatic deployment.
  5. Observe and adapt: Review user-experience and delivery signals, talk to users where appropriate, and change the problem framing or solution when the evidence calls for it.

DORA’s research archive highlights “User-centricity predicts 40% higher performance” as a 2023 finding. The archive landing page does not provide the underlying study methodology, so treat the figure as DORA’s reported headline rather than a guaranteed result for a particular team. DORA’s research archive is the source for that finding.

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