User acceptance testing that gives the business a real basis for saying yes
Acceptance is the business's decision. We prepare the scenarios, the data and the people, run the sessions, separate defects from change requests, and leave you with a record you can stand behind.
Overview
What it is. User acceptance testing (UAT) confirms that a system supports real business processes and meets agreed acceptance criteria, using the people who will depend on it, before it goes live.
Who it is for. Organisations rolling out a new platform, replacing a legacy system, or delivering a significant release where a business owner must formally accept the result.
Why it matters
The problem it addresses. UAT is often squeezed into a few days with borrowed staff, thin preparation and no clear exit criteria. The result is a sign-off that records optimism rather than evidence.
When it matters most. After system testing and before go-live, and again for major changes to processes the business relies on.
What happens without it. Defects surface after go-live, when users find that the system does not fit how work is actually done, and nobody can say what was accepted or why.
What we test
UAT planning
roles, entry and exit criteria, schedule and environments
Acceptance scenarios written from business processes and user stories
Test data preparation that resembles real cases
Facilitation of business testers, including briefing, support and observation
Defect capture, triage and the separation of defects, change requests and training gaps
Traceability from requirements to scenarios to results
Sign-off evidence
what was tested, what was found, what was accepted and by whom
How UpDoer approaches User Acceptance Testing
Understand
Requirements, tickets, code diffs and product context.
We read the requirements, process maps and release scope, and talk to the people who will actually use the system about how their work goes.
Risk Mapping
Where a failure would cost the most.
Processes are ranked by business consequence of failure, and users and roles are mapped to the scenarios that matter most to them.
Test Strategy
Coverage chosen, test cases drafted.
We agree entry and exit criteria, the scenario set, the test data and the schedule with the accountable business owner, and make roles explicit.
Execute
Manual and automated testing runs.
Business testers run the scenarios with support. Sessions are observed so unscripted behaviour and confusion are captured as well as pass or fail.
AI-Assisted Quality Engineering
Failures classified, release risk scored.
AI drafts candidate scenarios from user stories, groups similar feedback and checks that every requirement maps to at least one scenario.
Human Verification
A QA engineer checks every flagged result.
A QA facilitator reviews each reported issue with the tester, classifies it, and brings the business owner decisions that are not a defect.
Report & Improve
The verified quality signal, then better coverage.
We produce a sign-off pack and a list of accepted risks. Lessons feed the regression set and the next release's acceptance scenarios.
User acceptance testing: the business decides
01
Where it breaks
Borrowed testers, no planNo exit criteriaSign-off without evidence02
Test workflow
PlanScenariosSessionsTriage03
AI assists
Draft scenarios from storiesCheck requirement coverageGroup similar feedback04
Humans decide
Edit scenarios to real workDefect or change request?05
Release signal
Sign-off pack readyAccepted risks listedBusiness owner accepts
Illustrative workflow. Not a client result or a measured outcome.
UAT activities and ownership
| Activity | UpDoer's part | AI assistance | Business's part |
|---|---|---|---|
| Planning | Proposes roles, criteria, schedule | Drafts a plan outline from scope | Confirms owners and exit criteria |
| Scenario design | Drafts and structures scenarios with users | Suggests scenarios from user stories | Corrects scenarios to match real work |
| Test data | Prepares realistic, safe data | Suggests data variations | Approves what real cases look like |
| Execution | Briefs, supports and observes | None during sessions | Runs scenarios as real users |
| Issue triage | Classifies and reproduces issues | Groups similar feedback | Decides defect versus change request |
| Traceability | Maps requirements to results | Flags uncovered requirements | Reviews gaps and accepts or rejects them |
| Sign-off | Prepares the evidence pack | Summarises results | Decides to accept, accept with risks, or reject |
Activity: Planning
- UPDOER'S PART
- Proposes roles, criteria, schedule
- AI ASSISTANCE
- Drafts a plan outline from scope
- BUSINESS'S PART
- Confirms owners and exit criteria
Activity: Scenario design
- UPDOER'S PART
- Drafts and structures scenarios with users
- AI ASSISTANCE
- Suggests scenarios from user stories
- BUSINESS'S PART
- Corrects scenarios to match real work
Activity: Test data
- UPDOER'S PART
- Prepares realistic, safe data
- AI ASSISTANCE
- Suggests data variations
- BUSINESS'S PART
- Approves what real cases look like
Activity: Execution
- UPDOER'S PART
- Briefs, supports and observes
- AI ASSISTANCE
- None during sessions
- BUSINESS'S PART
- Runs scenarios as real users
Activity: Issue triage
- UPDOER'S PART
- Classifies and reproduces issues
- AI ASSISTANCE
- Groups similar feedback
- BUSINESS'S PART
- Decides defect versus change request
Activity: Traceability
- UPDOER'S PART
- Maps requirements to results
- AI ASSISTANCE
- Flags uncovered requirements
- BUSINESS'S PART
- Reviews gaps and accepts or rejects them
Activity: Sign-off
- UPDOER'S PART
- Prepares the evidence pack
- AI ASSISTANCE
- Summarises results
- BUSINESS'S PART
- Decides to accept, accept with risks, or reject
Where AI helps
- Drafting candidate acceptance scenarios from user stories and requirements for business owners to edit.
- Checking requirement-to-scenario traceability and flagging requirements that no scenario touches.
- Grouping similar tester feedback so the same confusion is not logged ten times.
- Summarising progress and open issues for the business owner.
How we use AI: AI can accelerate test design, analysis and repetitive QA work, but important outputs remain subject to human review and validation. It informs the release decision; it does not make it.
Where humans matter
- Deciding whether the system is acceptable. The business owner decides, not the test team and not an AI.
- Knowing which scenarios reflect real work and which are tidy fiction.
- Judging whether feedback is a defect, a change request or a training need.
- Observing testers and noticing hesitation and workarounds that no log captures.
- Facilitating honestly when the answer is 'not ready'.
Tools we build with
- Automation
- SeleniumPlaywrightAppiumKatalonTestComplete
- API & Performance
- PostmanSoapUISwaggerApache JMeter
- CI/CD & Reporting
- JenkinsTeamCityAllureMaven
Related services
Relevant industries
Frequently asked questions
Will UpDoer sign off the release?
No. Acceptance is the business owner's decision. We prepare the evidence, record the outcome and make the options and risks clear.
How much of our people's time is needed?
Enough to run the scenarios that matter most. We plan sessions around real work and agree the commitment with the business owner upfront.
How do you separate defects from change requests?
In triage with the business owner, against the agreed requirements and acceptance criteria. Anything outside them is recorded as a change request, not left to block release.
Can AI write our acceptance scenarios?
AI can draft candidates from your stories, which business owners then correct. Scenarios are never accepted without that review.