A team can ship steadily and still lack evidence that its work solves an important problem. Software engineering is not only a delivery challenge; it is also a discovery problem. Teams need to understand the problem, define the change they want to create, test possible approaches, and revise their plans as they learn—while still delivering reliable software.
Why shipped output is not the same as progress
Output is the work a team completes: features, fixes, releases, or other deliverables. An outcome is the change that work is intended to produce for users or the organization. Counting shipped work can help a team understand delivery, but the count alone cannot show whether the work addressed the right problem.
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A feature request is a proposed solution, not proof of the need behind it. Ask what user or organizational problem the request is meant to address, who experiences it, and what observable change would indicate that the response helped. DORA’s guidance puts the outcome or problem first: stories start from the business outcome or the problem they are trying to solve, after which teams decide what to do and test whether it works.
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Discovery is the work of reducing uncertainty before and during solution development. It brings together stakeholder perspectives, user needs, the existing landscape, constraints, and possible ways to address the problem. It is not a one-time phase that makes later learning unnecessary; new evidence during implementation or use can change what the team should build next.
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The Australian Government Digital Transformation Agency describes discovery as an initial exploration to determine what work might be needed to address a problem and plan for it. Its discovery guidance offers a practical sequence:
- Engage stakeholders. Understand the perspectives, goals, and constraints of the people affected by the problem and the people responsible for addressing it.
- Understand users and their needs. Seek direct evidence about what users are trying to do, where they encounter difficulty, and what they need. Separate observed needs from assumptions about a preferred feature.
- Examine the problem and its context. Distinguish symptoms from possible underlying causes, and review existing services, systems, processes, and constraints before proposing a replacement or addition.
- Define intended outcomes and measures. Decide what change would count as progress before solution development, so that later evaluation is not reduced to whether the feature shipped.
- Record findings and remaining uncertainty. Capture what the team learned, what it does not yet know, and what further investigation or test would help guide the next decision.
How discovery changes engineering decisions
Discovery-informed engineering does not mean delaying implementation until every uncertainty disappears. It means making a deliberate next move: test a risky assumption, prototype an approach, build a small increment, or proceed with a larger implementation when the evidence and constraints support it. The team then uses what it learns to confirm or revise its direction.
DORA describes team experimentation as part of lean product management. Teams need context about organizational outcomes and room to pursue ideas, write or change specifications, and revise stories without seeking outside permission for every adjustment. That autonomy has to work within the organization’s responsibilities and constraints; it is not a reason to ignore safety, security, or governance.
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Keep requirements clear as they evolve
Learning can change a requirement, but it should not make requirements vague or unverifiable. NASA’s Software Engineering Handbook says, “Software requirements analysis is a continuous activity performed on all software requirements and software requirement changes.” Its requirements-analysis guidance calls for checking requirements for clarity, correctness, consistency, completeness, feasibility, testability, traceability, and maintainability.
In practice, assess a proposed change both on its own and against the rest of the requirements. Make sure the updated requirement can be verified, identify what other requirements or tests it affects, and preserve the rationale for the change. In safety-critical work, traceability and controlled change are especially important: discovery does not remove the need to show that the system satisfies its obligations.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Measure outcomes without losing sight of delivery
Outcome measures help teams assess whether their work is making the intended difference; delivery and quality measures help them understand whether they can build and operate software effectively. These answer different questions and belong together. A delivery measure is not automatically a customer-value measure, and an outcome measure does not replace attention to reliability or safe operation.
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DORA’s Core Model organizes capabilities, metrics, and outcomes as a way to reason about performance. It is practitioner guidance informed by ongoing research, not a universal formula that identifies one best metric for every product. Select measures that fit the outcome and context, and revisit them as the team learns.
DORA and Google’s 2024 report record describes participation by more than 39,000 professionals across organizations of different sizes and industries globally; its findings highlight user-centricity and stable priorities. The figure describes the report’s reach, not a causal estimate that discovery alone improves performance. Likewise, the 2025 report record describes nearly 5,000 technology professionals worldwide and more than 100 hours of qualitative data, and characterizes AI as an amplifier of organizational strengths and dysfunctions. Those study-description figures are not proof that output optimization causes poor outcomes or that discovery by itself improves performance. The records are available from Google Research’s DORA 2024 report page and Google Research’s DORA 2025 report page.
A balanced way to plan the next increment
Before committing to a substantial piece of work, ask:
- What user or organizational problem are we addressing?
- What evidence supports that understanding, and whose perspective is missing?
- What change do we expect, and how will we recognize it?
- What is the most important uncertainty, and what is the smallest useful way to test it?
- Can the requirements be tested and traced, and do the relevant safety and quality constraints remain covered?
- What delivery or operational measures will help us understand how the work is being built and run?
As evidence accumulates, keep the outcome in view and adjust the stories or specifications when justified. The goal is not to choose between discovery and delivery: it is to deliver with a clearer reason for what the team is building and a way to learn whether it helped.
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