From Pilot to Production: Applying Generative AI in Business Workflows
A working demo proves that a model can respond. Production requires a business owner, controlled data, repeatable evaluations, security, permissions, monitoring, support, and a workflow that creates measurable value.
Generative AI pilots are easy to start. A team selects a model, connects a few documents, writes a prompt, and demonstrates a result within days.
The hard part begins when the system must handle real users, incomplete data, unusual requests, changing policies, permissions, outages, privacy obligations, security attacks, model updates, budget limits, and decisions that affect customers or employees.
The production problem is not simply “How do we improve the prompt?” It is “How do we operate this as a reliable business system?”
Quick Answer: What Moves a Generative AI Pilot Into Production?
A generative AI pilot becomes production-ready when the organization has defined the business outcome, workflow owner, inputs, outputs, permissions, evaluation set, acceptance thresholds, human review, security controls, privacy requirements, fallback process, observability, cost limits, incident response, and retirement plan.
Do not scale because the demonstration looks impressive. Scale after the workflow performs reliably against representative cases and the business can support it when the model is wrong.
The Real Bottleneck Is Usually Organizational
Generative AI often exposes problems that were already present:
- No clear owner for the workflow
- Several versions of the same policy
- Approvals that depend on informal relationships
- Data copied between spreadsheets, email, CRM, and line-of-business software
- Exceptions handled through personal knowledge
- Metrics that count activity instead of outcomes
- Departments that disagree about the source of truth
- No documented fallback when a person or system is unavailable
A model cannot repair these conditions by itself. It may summarize conflicting documents more quickly or automate an inconsistent process at greater scale.
NIST’s AI Risk Management Framework treats AI as a socio-technical system. Its core functions—Govern, Map, Measure, and Manage—place organizational context, accountability, measurement, and risk response around the technology.
AI does not remove the need to design the operation. It makes weak ownership, weak data, and weak controls more expensive.
A Pilot and a Production System Are Different Products
| Pilot | Production |
|---|---|
| Uses a small and selected dataset | Handles representative, incomplete, outdated, conflicting, and adversarial inputs |
| Tested by the project team | Used by people with different roles, skill levels, incentives, and access |
| Success demonstrated through examples | Success measured through repeatable evaluations and operating metrics |
| Errors can be explained during a demo | Errors create support tickets, rework, customer harm, financial loss, or legal risk |
| Manual intervention is hidden | Human review, escalation, and exception handling are documented and staffed |
| One model and prompt may be sufficient | The system includes data retrieval, permissions, validation, logging, monitoring, security, and fallback |
| Cost is based on a small test | Cost includes volume, tokens, retrieval, storage, integrations, support, review, and incidents |
| Downtime is inconvenient | Downtime requires service targets, alerts, recovery, and continuity procedures |
| Model changes are welcome | Model changes require regression testing and controlled deployment |
| The pilot can be abandoned | The production system needs ownership, support, change control, and retirement planning |
Cloud and model providers publish production guidance because generative AI applications inherit the normal requirements of software systems and add non-determinism, grounding, model, evaluation, and safety concerns.
Start With the Workflow, Not the Model
Before selecting a model, map the current workflow.
Document the present state
- Trigger
- Users and stakeholders
- Inputs and sources
- Current steps
- Decisions
- Approvals
- Systems
- Exceptions
- Outputs
- Time and cost
- Failure modes
- Compliance requirements
- Current performance baseline
Remove avoidable work first
Many apparent AI opportunities are better solved with:
- A required field
- A rules engine
- An API integration
- A standard template
- A searchable database
- A notification
- A queue with ownership
- A deterministic calculation
- A clearer policy
- Removal of an obsolete approval
Use generative AI when the workflow contains unstructured language, classification, extraction, summarization, drafting, retrieval, or bounded reasoning that rules alone cannot handle economically.
The smallest reliable solution is usually the best first production release. A workflow may combine rules, conventional software, search, and generative AI rather than forcing the model to do everything.
Select a Use Case That Can Survive Production
A strong first use case has enough value to matter and enough control to test safely.
| Dimension | Stronger first use case | Weaker first use case |
|---|---|---|
| Business pain | High-volume delay, rework, missed follow-up, or measurable labour | General desire to appear innovative |
| Output | Draft, classification, summary, extraction, recommendation, or task | Open-ended authority over important decisions |
| Review | A qualified person can verify the result quickly | The result cannot be checked before harm occurs |
| Data | Approved, accessible, current, and reasonably complete | Unknown ownership, weak quality, or highly sensitive without controls |
| Errors | Reversible and contained | Legal, medical, financial, employment, security, or safety consequences |
| Volume | Enough cases to evaluate and justify integration | Rare cases with no representative test set |
| Integration | Clear source and destination systems | Depends on many systems with no API or stable identifier |
| Ownership | One accountable business owner | Shared enthusiasm with no decision authority |
| Baseline | Current time, cost, quality, or conversion is known | No way to compare the new workflow |
Practical first-use cases
Document Intake
Extract approved fields, classify the document, flag missing information, and route it for verification.
Internal Knowledge Assistant
Retrieve approved policies or procedures and produce a cited answer with access controls and escalation.
Customer-Service Drafting
Prepare responses from approved account and knowledge data for staff review.
Sales and Lead Triage
Summarize inquiries, classify intent, identify missing information, and create the correct follow-up task.
Meeting and Case Summaries
Convert approved notes or transcripts into structured decisions, actions, risks, and owners.
Report Preparation
Draft recurring reports from governed data while preserving calculations, sources, and reviewer approval.
Production Requires Clear Ownership and Decision Rights
Every production workflow needs one accountable business owner. IT, data, security, legal, privacy, and vendors can support the system, but they should not be expected to define the business outcome alone.
Minimum ownership model
| Role | Primary responsibility |
|---|---|
| Executive sponsor | Approves the business priority, risk tolerance, funding, and organizational change |
| Business owner | Owns the workflow, outcome, users, policy, acceptance criteria, and benefit realization |
| Product owner | Maintains requirements, backlog, releases, feedback, and adoption |
| Technical owner | Owns architecture, integrations, reliability, deployment, and technical support |
| Data owner | Approves source data, quality, access, retention, and use |
| Security and privacy | Reviews threats, controls, personal information, vendors, incidents, and legal obligations |
| Risk or domain reviewer | Defines human-review rules for high-impact or specialized outputs |
| Operations owner | Owns alerts, incidents, service procedures, support, and continuity after launch |
| End-user representatives | Validate the actual workflow, edge cases, usability, and burden |
Production approval should name who can change prompts, models, retrieval sources, permissions, thresholds, and tools. Those are operational controls, not informal developer choices.
Data Readiness Is a Workstream, Not a Checkbox
Organizations often say they “have the data” because files exist. Production requires more.
Questions to resolve
- Which source is authoritative?
- Who owns it?
- Is it current?
- Which records are incomplete or duplicated?
- Are access permissions preserved?
- Does the content contain personal, confidential, privileged, or regulated information?
- Can it be used for this purpose?
- How is it updated or withdrawn?
- How will the system cite or trace it?
- What happens when sources conflict?
- Which information must never be sent to the model provider?
- How will deletion and retention rules propagate?
Retrieval-augmented generation is not automatic truth
Retrieval-augmented generation can provide the model with selected business information at request time. It can improve relevance and source attribution, but it introduces its own failure modes:
- The correct document is not retrieved
- An outdated document ranks higher
- Access controls are lost during indexing
- The document contains malicious or misleading instructions
- The model misinterprets the retrieved passage
- The answer combines sources incorrectly
- The citation does not support the claim
Grounding data needs ingestion controls, metadata, access filters, freshness rules, versioning, deletion, retrieval evaluation, and source display.
A Practical Production Architecture
Employee application, customer portal, CRM, service desk, email, voice, API, or another controlled interface.
Authentication, role, tenant, record-level access, consent, and approved user context.
Model routing, authentication, quotas, logging, policy enforcement, cost limits, and provider isolation.
Validation, file scanning, content limits, prompt-injection screening, sensitive-data handling, and normalization.
Workflow state, task sequence, retries, timeouts, model selection, deterministic rules, and approval checkpoints.
Approved documents, databases, APIs, search, metadata, access control, freshness, and citations.
Selected model or models based on quality, latency, cost, context, privacy, hosting, and availability.
CRM, ERP, ticketing, email, calendar, document, payment, or internal APIs with narrow permissions.
Schema validation, policy checks, citation checks, calculations, prohibited actions, and safe rendering.
Approval, correction, escalation, exception handling, and high-impact decision authority.
Traces, prompts, model versions, retrieval results, tool calls, latency, cost, errors, and user feedback.
Test sets, regression evaluation, red-team cases, thresholds, staged deployment, rollback, and change records.
Microsoft, AWS, Google Cloud, and OpenAI all publish production guidance emphasizing that AI workloads need the same reliability, security, cost, deployment, and operational disciplines as other software—plus controls for model variability, data, grounding, evaluation, and responsible use.
Evaluations Are the Bridge Between a Demo and a Service
Generative AI is non-deterministic. The same input may produce different wording or reasoning paths. Traditional unit tests remain necessary for ordinary code, but they are not enough for model behaviour.
OpenAI’s evaluation guidance recommends evaluating early and continuously, using task-specific tests, logging production data, automating scoring where possible, and combining automated measures with human judgement.
Build a representative evaluation set
The evaluation set should include:
- Normal cases
- Rare but important cases
- Incomplete inputs
- Conflicting sources
- Outdated data
- Ambiguous requests
- Unsupported requests
- Long documents
- Different languages or writing styles where supported
- Prompt injection attempts
- Sensitive information
- Tool failures and timeouts
- Requests requiring escalation
- Known historical errors
Measure the actual task
| Use case | Useful evaluation measures |
|---|---|
| Extraction | Field accuracy, missing-field detection, unsupported values, schema validity |
| Classification | Precision, recall, confusion by category, escalation accuracy |
| Knowledge assistant | Retrieval relevance, groundedness, citation support, access compliance, refusal quality |
| Drafting | Factual accuracy, policy compliance, tone, required edits, reviewer acceptance |
| Summarization | Important fact coverage, unsupported additions, attribution, action accuracy |
| Agent or tool use | Correct tool, correct arguments, permission compliance, action success, unnecessary actions |
| Customer workflow | Resolution, transfer, complaint, opt-out, conversion, and downstream rework |
Use production feedback carefully
Thumbs-up and thumbs-down controls can help, but they are not complete evaluations. Users may approve a fluent but incorrect answer or reject a correct answer because it is inconvenient.
Combine user feedback with reviewed samples, operational outcomes, incidents, corrections, and regression tests.
No model, prompt, retrieval change, or new tool should enter production without regression evaluation against the approved test set.
Human Review and Agency Must Match the Consequence
Human oversight should be designed around the risk of the action, not added as a vague statement that “a human is in the loop.”
| Pattern | Appropriate example | Control |
|---|---|---|
| AI drafts, human approves | Customer email, report, proposal section, case summary | No external action until an authorized person approves |
| AI recommends, human decides | Lead priority, exception category, supplier review, operational response | Decision owner receives evidence and may override |
| AI acts within a narrow rule | Create a task, add a tag, request missing information | Reversible action, limited fields, complete logging |
| AI acts, human reviews exceptions | High-volume low-risk classification with confidence threshold | Sampling, exception queue, rollback, and error monitoring |
| Human-only decision | Legal, medical, financial, employment, safety, or security decision | AI may organize evidence but cannot make the final determination |
Limit tool permissions
Agents should not receive unrestricted access to CRM, email, finance, HR, production, or customer systems.
Use:
- Read-only access first
- Separate credentials
- Least privilege
- Record- and tenant-level access
- Approved action lists
- Transaction and amount limits
- Human approval for sensitive actions
- Idempotency and duplicate protection
- Complete tool-call logging
- Immediate revocation and kill switches
Security Changes When Natural Language Can Control the System
Generative AI applications introduce security concerns beyond normal web and API security.
The OWASP Top 10 for LLM and generative AI applications identifies risks including prompt injection, sensitive information disclosure, supply-chain vulnerabilities, improper output handling, excessive agency, and unbounded consumption.
Prompt injection
Prompt injection occurs when untrusted text changes model behaviour. The malicious instruction may come directly from a user or indirectly from a document, webpage, email, ticket, or tool result.
System prompts alone cannot enforce strict security. Controls must exist outside the model.
Production security controls
- Treat user and retrieved content as untrusted
- Separate instructions from data where possible
- Do not place secrets in prompts
- Scan files and restrict supported formats
- Preserve access control during retrieval
- Validate tool arguments outside the model
- Allowlist tools and actions
- Require approval for sensitive operations
- Validate and safely render model output
- Limit request size, tool loops, tokens, retries, and spend
- Log model and tool activity
- Red-team realistic attacks
- Monitor model, library, plugin, and provider supply chains
Secure output handling
Model output may contain code, links, markup, commands, SQL, or instructions. Do not execute or render it as trusted content without validation and encoding.
The model should never be the final security control for its own permissions or output.
Privacy and Information Governance Cannot Wait Until Launch
Generative AI applications may process customer, employee, supplier, financial, operational, confidential, copyrighted, privileged, or regulated information.
Canadian privacy regulators’ principles for generative AI emphasize legal authority or consent, appropriate purposes, necessity and proportionality, openness, accountability, safeguards, limiting collection, accuracy, and individual rights.
Map the information lifecycle
- What enters the prompt?
- What is retrieved?
- What is logged?
- What is sent to each provider?
- Where is it processed?
- Can the provider retain or train on it?
- Which subprocessors receive it?
- How long are prompts, files, embeddings, traces, and outputs stored?
- How are access, correction, deletion, and legal holds handled?
- What appears in support and incident records?
Do not confuse enterprise settings with complete compliance
A vendor’s contractual privacy setting, data region, encryption, or certification can support the organization’s controls. It does not prove that the workflow has an appropriate purpose, valid authority, accurate output, correct retention, or lawful high-impact use.
Minimize data
Send only the information required for the task. Redact or tokenize sensitive fields where practical. Separate production, evaluation, development, and support data. Do not use real personal information for casual prompt development.
Production AI Must Be Operated Like a Production Workload
Microsoft’s Well-Architected guidance states that operations teams need to support AI workloads as they would other production systems. Google Cloud describes adapting DevOps and MLOps processes for deployment and operation. AWS provides generative and agentic AI lenses for secure, reliable, cost-effective operation.
Minimum operating capabilities
Observability
Trace requests, retrieval, model versions, tool calls, errors, latency, cost, decisions, and reviewer changes.
Service Monitoring
Monitor availability, quotas, provider errors, integration failures, queues, and fallback use.
Quality Monitoring
Sample outputs, measure regressions, track corrections, and review high-risk cases.
Cost Controls
Use budgets, quotas, model routing, caching, context limits, batch processing, and spend alerts.
Incident Response
Define severity, containment, user communication, model or tool shutdown, investigation, and remediation.
Change Management
Version prompts, models, retrieval sources, tools, policies, and evaluation sets. Use staged release and rollback.
Fallback
Provide a deterministic, manual, alternate-provider, queue, or safe-unavailable path.
Support
Train users and support staff to recognize wrong answers, report issues, recover work, and escalate.
Model updates are software changes
A provider may change a model, retire a version, alter quotas, or modify behaviour. The organization should maintain version records, deprecation monitoring, regression tests, and migration plans.
A Practical Pilot-to-Production Roadmap
Map the current workflow, pain, users, systems, data, controls, volume, baseline, and expected business outcome.
Score use cases by value, frequency, data readiness, reviewability, integration effort, risk, and reversibility. Select one.
Name the owner. Define intended use, prohibited use, risk tolerance, permissions, privacy, security, human review, and approval authority.
Identify approved sources, clean critical fields, preserve permissions, define freshness, create metadata, and establish deletion and update procedures.
Create representative test cases, human reference answers, security cases, scoring methods, acceptance thresholds, and failure categories before optimizing the model.
Build the smallest end-to-end workflow with test data or read-only access. Compare prompts, models, retrieval, rules, and non-AI alternatives.
Use a limited group, real workflow, approved data, complete logging, and human approval. Record every failure and manual workaround.
Add identity, access, validation, prompt-injection controls, tool limits, output checks, monitoring, retries, fallback, cost controls, and incident procedures.
Run regression, load, security, privacy, usability, failover, cost, and operational acceptance tests. Confirm support readiness.
Use staged deployment, feature flags, limited volume, change records, clear communications, and rollback criteria.
Monitor quality, incidents, latency, cost, adoption, data freshness, model changes, human overrides, and business outcomes.
Increase volume, permissions, actions, departments, or use cases only after the current workflow meets its production thresholds.
Production Readiness Gates
A project should pass explicit gates rather than moving to production because the pilot period ended.
| Gate | Evidence required |
|---|---|
| Business | Named owner, baseline, target, benefit model, users, scope, and funding |
| Workflow | Current and future process, roles, exceptions, approvals, and fallback |
| Data | Approved sources, access, quality, freshness, lineage, retention, and deletion |
| Model | Selection rationale, supported use, limitations, version, and provider terms |
| Evaluation | Representative test set, scoring, thresholds, regression result, and known failures |
| Security | Threat model, prompt-injection tests, permissions, output validation, secrets, and incident controls |
| Privacy and legal | Purpose, authority, notices, contracts, data flows, rights, retention, and professional review |
| Human oversight | Decision authority, review queue, staffing, escalation, override, and contest process |
| Reliability | Capacity, quotas, retries, timeouts, fallback, recovery, support, and service targets |
| Observability | Logs, traces, model and prompt versions, quality alerts, cost alerts, and dashboards |
| Change control | Release process, evaluation before change, feature flags, rollback, and deprecation plan |
| Adoption | Training, user instructions, feedback, workload impact, and support materials |
A failed gate does not always mean cancelling the project. It may mean reducing scope, removing an action, improving data, adding review, or returning to a read-only pilot.
Measure the Workflow, Not Just the Model
| Category | Example metrics |
|---|---|
| Business outcome | Cycle time, conversion, resolution, backlog, revenue recovered, error cost, or service level |
| Workflow | Touches, handoffs, review time, exceptions, duplicate work, and completion rate |
| Quality | Accuracy, completeness, groundedness, citation support, policy compliance, and required edits |
| Human review | Approval, correction, rejection, override, escalation, and review time |
| Retrieval | Relevant-source retrieval, freshness, permission compliance, missing source, and citation quality |
| Tool use | Correct tool, valid arguments, successful action, duplicate action, and unauthorized attempt |
| User experience | Completion, abandonment, satisfaction, trust, complaint, and support demand |
| Reliability | Availability, latency, error, timeout, provider failure, retry, and fallback |
| Security and privacy | Injection attempts, sensitive-data events, access violations, incidents, and retention exceptions |
| Adoption | Eligible users, active use, repeat use, bypass, shadow process, and feature-specific use |
| Cost | Model, tokens, retrieval, storage, tools, integration, review, support, and incident cost |
| Change | Regression after model, prompt, data, policy, or tool updates |
Illustrative production value formula
Net value = business benefit + labour capacity recovered + avoided rework − model and infrastructure − integration − human review − support − governance − incidents − new downstream workTime saved is not automatically value. Confirm what happens to the recovered capacity and whether the workflow creates new work elsewhere.
Common Risks and Recommended Controls
| Risk | Example | Recommended control |
|---|---|---|
| Undefined outcome | The project is approved to “use AI” | Named workflow, baseline, owner, metric, and acceptance threshold |
| Bad source data | The assistant cites an outdated policy | Source ownership, versioning, freshness, metadata, retirement, and retrieval tests |
| Hallucinated content | The model invents a term, fact, calculation, or commitment | Grounding, structured outputs, deterministic calculations, validation, and human approval |
| Prompt injection | A retrieved document tells the model to reveal data or call a tool | Untrusted-content isolation, external policy controls, tool allowlists, and adversarial tests |
| Excessive agency | An agent sends, deletes, pays, or changes records without appropriate authority | Read-only first, least privilege, approval, action limits, logging, and kill switch |
| Sensitive disclosure | Customer or employee information appears in an unauthorized answer | Access-aware retrieval, data minimization, output controls, testing, and monitoring |
| Insecure output | Generated code or markup is executed as trusted content | Schema validation, encoding, sandboxing, allowlists, and ordinary application security |
| Model regression | A provider update changes output quality or tool behaviour | Version control, regression evals, staged release, monitoring, and rollback |
| Cost escalation | Long prompts, loops, or abuse increase usage unexpectedly | Budgets, quotas, token limits, caching, routing, loop limits, and alerts |
| Burden shifting | Staff spend more time correcting outputs than the original task required | Whole-workflow measurement, usability review, scope reduction, and stop threshold |
| Vendor lock-in | Prompts, embeddings, evaluations, or workflow logic cannot move | Abstraction where valuable, export rights, documentation, standard data, and exit testing |
| No fallback | The workflow stops when the model or provider is unavailable | Manual route, alternate provider, queue, retry, or safe unavailable state |
| False ROI | Pilot enthusiasm is counted as business value | Baseline, control or comparison where practical, full cost, and conservative attribution |
Build, Buy, or Integrate?
Most organizations should not build a large AI platform for the first workflow.
| Approach | Best fit | Main trade-off |
|---|---|---|
| Use an existing SaaS product | Common workflow with acceptable features, integrations, security, and terms | Less control over workflow, model, roadmap, and data handling |
| Configure a low-code platform | Moderate customization, standard connectors, human approval, and fast pilot | Platform limits, usage cost, and hidden complexity as volume grows |
| Build a focused integration | Existing systems are good but need AI extraction, drafting, retrieval, or routing | Requires technical ownership and maintenance |
| Build a custom application | Workflow is differentiating, integrations are specific, and control is commercially important | Higher delivery, governance, support, and product-management burden |
| Build a shared internal platform | Several proven use cases require common identity, data, gateway, evaluation, and observability | High scope and risk if built before individual workflows are proven |
Prove the workflow before building the platform. Shared infrastructure should emerge from repeated requirements, not architecture ambition.
FAQs About Moving Generative AI Into Production
How long should a generative AI pilot run?
Long enough to cover representative volume, users, exceptions, data conditions, failures, security tests, and operating cycles. A short technical proof can validate connectivity, but it may not establish business value or production readiness.
What is the best first generative AI workflow?
Select a high-volume task involving unstructured information, clear inputs and outputs, fast human verification, reversible errors, approved data, and a measurable baseline. Document intake, knowledge retrieval, drafting, summaries, and classification are common starting points.
Do we need retrieval-augmented generation?
Use retrieval when the answer depends on current or private business information that should be supplied at request time. A simple drafting or classification task may not need it. Retrieval adds data, access, indexing, evaluation, and security requirements.
Should we fine-tune a model?
Not automatically. Improve the workflow, prompt, examples, structured output, retrieval, and evaluation first. Fine-tuning may help consistent style or task behaviour when there is sufficient approved training data and a clear evaluation advantage.
Can an AI agent update our CRM automatically?
It can, but start with read-only access or draft changes. Add narrowly scoped write actions after validating identity, permissions, fields, duplicate protection, review rules, rollback, and logging.
How do we prevent hallucinations?
They cannot be eliminated universally. Reduce risk with narrow tasks, approved grounding, citations, structured outputs, deterministic calculations, validation, refusal rules, human review, evaluations, and monitoring.
What should be monitored after launch?
Monitor business outcomes, quality, retrieval, tool use, human overrides, latency, availability, cost, data freshness, security events, privacy exceptions, user complaints, model changes, and downstream rework.
When should a project not move to production?
Do not launch when there is no owner, no measurable outcome, no representative evaluation, uncontrolled sensitive data, unresolved high-impact risk, excessive manual correction, unsafe tool permissions, no fallback, or no team prepared to operate the system.
Sources
- NIST: Artificial Intelligence Risk Management Framework
- NIST AI Resource Center: AI RMF Core — Govern, Map, Measure, and Manage
- NIST AI RMF Playbook
- NIST: Generative Artificial Intelligence Profile
- OpenAI: Evaluation best practices
- OpenAI: Production best practices
- OWASP GenAI Security Project: Top 10 risks for LLM and generative AI applications
- OWASP: Prompt injection risk
- OWASP: LLM prompt injection prevention cheat sheet
- AWS Well-Architected Framework: Generative AI Lens
- AWS Well-Architected Framework: Agentic AI Lens
- Google Cloud: Deploy and operate generative AI applications
- Google Cloud: Generative AI security best practices
- Microsoft Azure Well-Architected Framework: AI workloads
- Microsoft: MLOps and GenAIOps for AI workloads
- Microsoft: Production operations for AI workloads
- Office of the Privacy Commissioner of Canada: Principles for responsible and privacy-protective generative AI
- Office of the Privacy Commissioner of Canada: AI, privacy, and your business
Move One Business Workflow From Experiment to Production
Web Inventix AI can assess the current workflow, business case, data, systems, risks, integrations, evaluation requirements, human approvals, architecture, operating model, and production gates. The first release should prove one measurable workflow before permissions, volume, and autonomy expand.
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