TL;DR
A software product launch checklist works only if it treats launch as a controlled production change, not a marketing date. The highest-risk item across engineering, security, data, and go-to-market readiness should decide the launch tier and rollout method.
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Watch the Webinar → Key Takeaways A software launch is a production change, not a marketing milestone. Code-complete and launch-ready are different states, and conflating them is the single biggest cause of rollback-worthy launches. Not every release deserves the same process. Classifying a launch by risk tier (enterprise, major, standard, low-risk) prevents both under-preparation on big bets and process bloat on small ones. Rollout method (big-bang, phased, canary, dark launch, or shadow launch) should be chosen by rollback difficulty and blast radius, not by habit or deadline pressure. Engineering readiness (architecture review, capacity testing, backup and disaster recovery, feature flags, rollback plans) is usually the thinnest part of a launch checklist and the most common source of launch-day incidents. AI features and AI agents need a separate launch lane: evaluation before release, guardrails on autonomy, and drift monitoring after release, because model behavior can change even when the surrounding code doesn’t. A go/no-go framework with defined blocker categories turns launch approval into evidence review instead of a debate, and gives every functional owner a clear, defensible way to say “not yet.” Kanerika’s engineering teams have used disciplined release practices, including phased rollouts and API-first integration patterns, to help a project management platform team cut delivery friction and accelerate product releases by 30%. What a Launch Checklist Actually Protects Against A launch checklist rarely gets tested by big, obvious failures. It gets tested by the small ones: a feature flag left on, a rollback plan nobody wrote down, an AI agent behaving differently under real traffic than it did in staging. Those gaps are exactly what turn a routine release into a weekend incident.
This checklist is built around that reality. It covers classifying a launch by risk tier, picking a rollout model that matches the blast radius, and building phase-specific readiness for engineering, GTM, and the 30 days after release, the stretch where most unnoticed problems actually surface.
AI features get a dedicated lane, since model behavior can drift even when the underlying code hasn’t changed, and a go/no-go framework closes the loop by turning launch approval into evidence review rather than a last-minute debate among stakeholders.
What Is a Software Product Launch Checklist? A software product launch checklist is a structured record of the decisions, tests, approvals, dependencies, and response plans a team needs before releasing software to customers. It is not a to-do list. A task list tells you what to do; a launch checklist tells you what “done” means, who confirmed it, and what happens if it isn’t done in time.
Every usable checklist item needs more than a checkbox. It needs an owner, a due date, current status, evidence of completion, and a clear answer to one question: does this block the launch if it’s incomplete? Teams that skip the “evidence” and “blocker status” fields end up with checklists that look complete on a status call and fall apart during launch week, because nobody actually verified the underlying claim.
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A global expert-network platform deployed an AI compliance agent to screen risk signals before matching decision-makers with subject-matter experts, cutting backlog and event delays without loosening oversight on a regulated, high-autonomy workflow.
Read the Case Study → Launch Checklist vs. Release Checklist vs. Launch Plan These three documents get used interchangeably, which causes real confusion about who owns what. In practice, they serve different purposes and different audiences.
Document Main Purpose Primary Owners Typical Scope Product launch checklist Confirms cross-functional readiness Product, engineering, GTM, support Full launch lifecycle Software release checklist Controls the production deployment itself Engineering, DevOps, QA, security Code, infrastructure, deployment steps Product launch plan Sets strategy, timing, audience, and goals Product and marketing leaders Market positioning and execution plan Go/no-go scorecard Records the final launch decision Executive and functional leads Readiness evidence at decision time
This article focuses on the launch checklist, the cross-functional readiness gate that sits above the release checklist and below the launch plan. It assumes your product strategy, roadmap, and core build are already done; this is about the last mile between a finished build and a customer-facing release.
Why Enterprise Software Launches Need More Control Than Small Feature Releases A consumer app update that breaks can be rolled back with limited fallout. An enterprise software launch that breaks can touch existing customer workflows, contractual SLAs, data retention obligations, API consumers who built integrations on your endpoints, and support teams who now field tickets they weren’t trained on.
The blast radius is bigger, the rollback is slower, and the stakeholders are more numerous. That’s the reason enterprise launches need a formal tiering system instead of a single generic checklist, covered next.
Classify the Launch Before You Build the Checklist Not every release deserves the same level of process. Applying a full enterprise-grade checklist to a copy change wastes time; applying a lightweight checklist to a new product launch is how teams get burned. Start by classifying what you’re actually shipping.
New product launch. A new commercial product, platform, or service. Requires the fullest checklist: new infrastructure, new pricing, new support procedures, new contracts, new onboarding flows, and often a new sales motion.Major feature or platform release. A significant capability added to an existing product. Focus on backward compatibility, permission changes, data-model changes, and how existing customer behavior might shift.Minor feature release. Lower-risk, limited customer impact. Some checks can be lighter, but automated regression tests, monitoring, and a rollback path are never optional.Infrastructure or architecture launch. Platform migrations, database changes, cloud moves, or authentication overhauls. Little marketing activity, but often the highest operational risk of any launch type.AI feature or AI agent launch. A distinct class, because model behavior can drift after release even when the surrounding application code is completely stable. This deserves its own checklist lane, covered later in this guide.Choose a Launch Tier Instead of One-Size-Fits-All Process Once you know what kind of launch you’re running, assign it a risk tier. The tier determines how much testing, approval, and monitoring the launch requires, and who has to sign off before it ships.
Tier Examples Required Controls Tier 1: Enterprise or high-risk New platform, new billing system, major data migration, regulated workflow, an AI agent that takes autonomous action Executive sponsor, formal go/no-go meeting, security and compliance sign-off, disaster recovery validation, staged release, full rollback plan, launch command center Tier 2: Major product or feature Major workflow change, new integration, new mobile app, new API version Cross-functional readiness review, beta or controlled-access stage, feature flags, regression and performance tests, defined rollback criteria Tier 3: Standard feature Limited feature, existing workflow update, small integration improvement Product and engineering approval, automated test completion, monitoring, release notes, rollback mechanism Tier 4: Low-risk change Copy update, minor UI fix, internal admin change, configuration update Standard code review, monitoring, no launch-day ceremony required
The tiering decision should be scored, not guessed. Rate each launch against the riskiest dimension it touches: number of affected users, data impact, compliance exposure, revenue path, integration surface, rollback difficulty, and (increasingly) AI autonomy level. Whichever dimension scores highest sets the tier, even if every other dimension looks low-risk. A feature that touches almost no users but writes to a regulated data field is still a Tier 1 launch.
Pick a Rollout Model: Big-Bang, Phased, Beta, Canary, or Dark Launch Launch tier tells you how much control you need. Rollout model is how you apply it. The two decisions are related but not identical. A Tier 2 launch might still ship big-bang if the migration risk is genuinely low, and a Tier 1 launch almost always needs a staged rollout regardless of how confident the team feels.
Model What It Means Best Fit Big-bang Full release to the entire user base at once Low migration risk, strong test confidence, hard external deadline Phased rollout Released by percentage, region, or customer segment in sequence Uncertain blast radius, need to detect edge cases before full exposure Soft launch Limited promotion while testing operations and support demand New products validating pricing, onboarding, and support load Private / public beta Controlled release to selected users with active feedback loops Features needing real usage data before broad commitment Canary release Small production slice gets the new version while the rest stays on the old one Infrastructure and platform changes needing live health comparison Dark launch (feature flags) Code deployed but not exposed to customers until flipped on Separating deploy risk from release risk on any tier Shadow launch New logic processes real production input without controlling the live response AI models, recommendation engines, fraud detection, new APIs
Why Canary and Phased Releases Reduce Risk Google’s Site Reliability Engineering practice popularized canary releases specifically because they let a team compare error rates and latency between old and new versions on live traffic before committing, which catches the class of bug that never shows up in staging.1
Feature flags deserve special mention because they solve a problem most launch checklists get wrong: they let you separate deploying code from releasing a capability. As Martin Fowler’s widely cited engineering writing puts it, feature toggles let teams ship dormant code to production and activate it independently of deployment, which turns a risky release event into a low-risk configuration change.2
Assign Launch Ownership and Governance Launches fail administratively as often as they fail technically. As a result, a checklist with no single owner turns into a document nobody actually manages. Every functional lead assumes someone else is tracking the blockers.
Appoint One Launch Owner One person maintains the master checklist, tracks blockers, runs readiness meetings, and coordinates launch-day activity. This role doesn’t have to be senior, but it does have to be singular. Split ownership is functionally the same as no ownership.
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Kanerika Product Engineering Services
Kanerika’s product engineering team builds the release infrastructure, feature flags, staged rollout tooling, and observability, alongside the product itself.
Explore Product Engineering Define Functional Owners and a RACI Matrix Every workstream (product, engineering, QA, platform, security, data, legal, marketing, sales, customer success, support, and finance) needs a named accountable owner, not a team name. Build a RACI matrix with columns for workstream, task, responsible, accountable, consulted, informed, and approval evidence. The “approval evidence” column is what separates a real RACI from a wall decoration; without it, “approved” just means someone said yes in a meeting three weeks ago.
Set a Launch Decision Calendar Enterprise launches benefit from fixed checkpoints rather than ad hoc status meetings:
T-minus 8 to 12 weeks: launch scope reviewT-minus 6 weeks: architecture and dependency reviewT-minus 4 weeks: go-to-market and enablement reviewT-minus 2 weeks: production-readiness reviewT-minus 3 to 5 days: final go/no-go reviewT-plus 1 day: first health reviewT-plus 7 days: adoption and issue reviewT-plus 30 days: launch retrospectiveDefine what actually counts as a launch blocker ahead of time: a critical security weakness, a failed rollback test, an unresolved data-integrity defect, an error rate above an agreed threshold, or missing compliance sign-off. Without a pre-agreed blocker list, every go/no-go meeting relitigates the definition of “ready,” which is how launches slip by a week at a time.
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A look at how an AI scrum master agent keeps launch coordination, ownership, and status tracking moving without a human chasing every update.
Phase 1 Checklist: Product and Business Readiness Before engineering and GTM readiness even matter, the product and commercial basics need to be locked. This phase is where most generic launch checklists spend all their time. It matters, but it’s the floor, not the differentiator.
Area Checklist Items Audience and scope Target segment approved; primary use case documented; features included and explicitly excluded; supported regions, languages, and integrations defined Objectives and baselines Measurable launch objectives set across adoption, engineering health, and revenue; current-state baselines captured for error rates, latency, support volume, and conversion Pricing and entitlements Pricing approved; packaging mapped to tiers; feature entitlements configured; billing system tested end to end; usage-metering logic validated Legal, privacy, and contracts Terms of service and privacy notice updated; data processing terms reviewed; open-source license review complete; AI disclosure requirements checked where applicable Accessibility and localization Keyboard and screen-reader checks complete; color contrast verified against WCAG; translated content reviewed; regional legal copy in place
The baseline step is the one teams skip most often, and it’s the one that costs the most later. Without a documented “before” state for error rates, latency, support ticket volume, and conversion, nobody can prove the launch actually helped. Every post-launch metric becomes a guess about what changed.
Phase 2 Checklist: Engineering and Infrastructure Readiness This is the section most product-management-focused checklists compress into a single line item, “QA sign-off,” and it’s the section most likely to actually break a launch. Treat it with the same rigor as a production architecture review, because that’s what it is.
Production Architecture and Environment Architecture diagram is current and reviewed against what’s actually deployed Single points of failure identified and either mitigated or explicitly accepted Production environment configuration matches the approved design; secrets stored outside code; debug modes disabled Infrastructure-as-code is version-controlled, peer-reviewed, and production apply permissions are restricted Scalability, Availability, and Disaster Recovery Expected launch traffic, peak concurrency, and third-party rate limits are known and tested against, not estimated Availability-zone and service-dependency failure scenarios tested, with recorded failover timing Backup scope, frequency, encryption, and restore testing complete; recovery point and recovery time objectives documented and realistic Every third-party and vendor dependency has a named owner, a known SLA, and a documented fallback behavior if it goes down mid-launch The Reliability pillar of AWS’s Well-Architected Framework frames this well: the goal isn’t to prevent every failure, it’s to design systems that recover automatically from the failures you can predict and degrade gracefully from the ones you can’t.3
Feature Flags, API Versioning, and Data Migration Three specific engineering practices deserve their own line items because they’re the difference between a controlled release and a hope-it-works release:
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How AI-assisted quality engineering tightens test coverage and shortens the path from code-complete to launch-ready.
Watch the Webinar → Feature flags with an owner and an expiry date. Every flag needs someone responsible for turning it off or removing it. Orphaned flags are a leading cause of “it worked in staging” surprises months later.API versioning discipline. Breaking changes go on a new version path; existing consumers get a deprecation window, not a surprise. This matters even more when partners or customers have built integrations against your API.Reversible data migrations. Any schema or data change tied to the launch should have a tested rollback path. An irreversible migration paired with a rollback plan that says “we’ll figure it out” is not a rollback plan.Phase 3 Checklist: QA, Security, and Observability Readiness Quality assurance for a launch is broader than functional testing . Specifically, it includes performance under real load, a security review, and, critically, whether the team can actually see what’s happening once the release is live.
Area Checklist Items Functional and regression testing Acceptance criteria covered; positive, negative, and boundary cases tested; automated suite pass rate and flaky-test rate recorded, not assumed Performance and load testing Expected launch-day traffic simulated; database connection and queue-depth limits tested; autoscaling thresholds validated under load, not just in theory Security and compliance Penetration test or vulnerability scan complete on new attack surface; secrets and credentials audit clean; compliance sign-off obtained for regulated data flows Observability Dashboards exist for the specific metrics that matter to this launch; alert thresholds configured before launch day, not during it; on-call rotation confirmed and briefed Incident response Rollback procedure tested end to end, not just documented; incident commander assigned for launch week; communication templates ready for customer-facing incidents
The Open Web Application Security Project’s Application Security Verification Standard is a useful reference point for scoping the security checklist proportionally to risk tier. Tier 1 launches touching payment or regulated data warrant a higher verification level than a Tier 3 UI update.4
A rollback plan that has never actually been executed is a rollback hypothesis, not a rollback plan. Run it in staging against a production-like dataset before launch week, not for the first time during an incident.
Phase 4 Checklist: Go-to-Market and Sales Enablement Engineering readiness gets software to a state where it can ship. Go-to-market readiness determines whether anyone outside the building will notice, and whether the teams who talk to customers can actually support what just shipped.
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Planning a Launch You Can’t Afford to Get Wrong?
Kanerika scopes launch tier, rollout model, and the engineering gaps most likely to bite you, before you commit to a date.
Schedule a Working Session → Positioning and messaging finalized and reviewed against the actual shipped scope, not the original pitchMarketing assets : landing pages, launch emails, in-product announcements, built and QA’d against the live environment, not a staging mockSales enablement : updated pitch decks, demo environments, and objection-handling guidance delivered before launch day, not the morning ofCustomer success and support briefed on new workflows, common failure modes, and escalation paths; support macros and help-center articles published before, not after, the first ticket arrivesBilling and entitlement validation : a final end-to-end test of the exact commercial path a real customer will take, run by someone who isn’t the engineer who built itThe most common go-to-market gap isn’t messaging. Instead, it’s support readiness. A support team fielding tickets on a feature they’ve never seen creates a bad first impression that marketing spend can’t offset.
Launching AI Features and AI Agents Responsibly AI features break the assumptions built into most launch checklists, because the checklist items above mostly assume deterministic software: the same input produces the same output, and a passing test suite is a reasonably strong signal of readiness. Neither assumption holds for a model-backed feature.
Evaluate Before You Ship, Not Just Before You Demo A model that performs well on a curated demo set can behave differently on real, messy production input. Run evaluation against a representative sample of actual production-shaped data, including known edge cases and adversarial inputs, before launch, not just the happy-path examples used to build confidence internally.
Match Guardrails to Autonomy Level The right level of guardrail depends on what the AI feature is allowed to do on its own:
Recommendation only: the system suggests, a human decides. Lower risk, lighter guardrails.Human-approved action: the system proposes a specific action and a human confirms it before it executes.Autonomous action within bounds: the system acts on its own within a defined, narrow scope with hard limits and audit logging.Fully autonomous action: the system acts independently across a broad scope. This tier deserves Tier 1 launch controls by default, regardless of how confident the model evaluation looked.The U.S. NIST AI Risk Management Framework frames this as mapping risk to context of use rather than treating “AI” as a single risk category. An autonomous agent that can take real-world action warrants materially more governance than a model that only ranks search results.5
Monitor for Drift After Launch, Not Just Uptime Traditional monitoring watches whether the service is up. AI features need a second layer: is the model’s output distribution still what it was during evaluation? Model behavior can shift as real usage patterns diverge from training and evaluation data, quietly, with no error thrown. Build a dashboard for output-distribution drift and a defined threshold for when it triggers a re-evaluation or a rollback to a previous model version, the same way you’d define an error-rate threshold for a traditional service.
The Go/No-Go Framework: How to Decide Whether to Launch A go/no-go meeting works only when it’s a review of evidence, not a debate about opinions. Structure it as a scorecard, not a discussion.
Collect status from every functional owner against the Phase 1-4 checklists above, with evidence links, not verbal assurances.Flag anything in a pre-agreed blocker category : critical security findings, failed rollback tests, missing compliance sign-off, unresolved data-integrity defects.Score the launch tier’s required controls as complete, partial, or missing. A Tier 1 launch with any “missing” control is a no-go by definition, not a judgment call.Record the decision and the reasoning , including any accepted risks and who accepted them, so a launch-week incident has a clear paper trail back to the decision that allowed it.The goal of this structure is to make “not yet” an easy, low-drama answer. Teams that don’t have a formal go/no-go framework tend to let launch dates create their own gravity. The closer the date gets, the harder it becomes to say no, regardless of what the checklist actually shows.
Launch Day Checklist: Command Center Operations Launch day is an operations exercise, not a demo. For Tier 1 and Tier 2 launches, run a lightweight command center:
A single communication channel for the launch, staffed by every functional owner for the duration of the rollout window Live dashboards for the specific metrics that matter to this launch, not a generic uptime page Pre-agreed rollback triggers (error-rate threshold, latency threshold, specific customer-impact reports) that don’t require a fresh debate to invoke A named incident commander with the authority to pause or roll back the rollout without waiting for a committee A customer communication plan ready to go if the launch needs to be paused publicly, not drafted after the fact If you’re running a phased or canary rollout, launch day checklist items repeat at every stage gate: internal users, design partners, one percent, five percent, twenty-five percent, and so on, with an explicit go decision at each step, not just at the start.
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End-to-end delivery, from architecture through launch, for teams that need release discipline built in from day one rather than retrofitted.
Explore Custom Software Development Post-Launch Checklist: The First 30 Days The launch isn’t done when the release goes out; it’s done when the team has confirmed it worked and captured what to do differently next time.
Checkpoint Focus T+1 day First health review: error rates, latency, and support volume against the pre-launch baseline T+7 days Adoption and issue review: usage against launch objectives, open defects triaged by severity T+30 days Full launch retrospective: what worked, what nearly broke, and what becomes a standing process change
Compare every post-launch metric against the baseline captured in Phase 1. Without it, “engagement is up” or “tickets are down” are just impressions, not measurements. The retrospective should produce specific process changes, not a general “went well” summary; if nothing concrete changes for the next launch, the retrospective didn’t do its job.
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3 Wrong Assumptions About AI Agents
A quick gut check on the AI agent assumptions worth challenging before they turn into one of the launch mistakes below.
Common Software Launch Mistakes to Avoid Treating code-complete as launch-ready. Development finishing and a system being safe to expose to customers are two different milestones with two different checklists.Skipping the rollback rehearsal. A documented rollback plan that has never been executed usually fails the first time it’s actually needed.No pre-agreed blocker definition. Without one, every go/no-go meeting re-litigates what “ready” means, and launch dates win by default.Applying one process to every release. Full ceremony on a copy change wastes time; a lightweight process on a new platform launch is how outages happen.Under-resourcing support readiness. Marketing drives interest into a support team that’s never seen the feature, and the first impression is a bad support ticket.Launching AI features with traditional monitoring only. Uptime dashboards don’t catch model drift. That needs its own monitoring layer, built before launch, not after a customer notices.How Kanerika Builds Launch-Ready Software Kanerika’s product engineering teams treat launch readiness as an engineering discipline, not a checklist exercise bolted on at the end. The approach follows five stages that map directly onto the phases in this guide: assess the current architecture and risk surface, design the rollout model against actual blast radius, build with feature flags and API versioning from day one rather than retrofitting them, validate through load and failure testing before launch week starts, and operate with dashboards and rollback triggers defined before go-live, not during an incident.
Faster Releases Through Launch Discipline That discipline shows up directly in delivery speed. A project management platform team working with Kanerika needed to ship product updates faster without breaking the workflows existing customers depended on. Kanerika’s engineers rebuilt the release path around API-first integration patterns and staged rollout practices, the same principles covered in the engineering readiness section above, instead of the ad hoc, all-at-once releases the team had been running.
The result: a 42% productivity gain for the delivery team and a 30% acceleration in product release velocity, without an increase in customer-facing incidents. The gain came from process discipline as much as from code. Clearer rollback paths meant engineers spent less time firefighting and more time shipping.
Case Study
42% Productivity Gain, 30% Faster Product Releases
A project management platform team partnered with Kanerika to rebuild its release path around API-first integration and staged rollout, cutting delivery friction and accelerating release velocity without adding incidents.
Read the Case Study → Common Launch Pitfalls For teams that don’t have deep in-house release engineering capacity, this is usually where an outside partner adds the most value: not writing more features, but building the launch discipline (feature-flag infrastructure, staged rollout tooling, observability, and rollback automation) that makes every future launch faster and safer. Kanerika’s product engineering and custom software development teams build that release infrastructure alongside the product itself, so launch readiness isn’t a scramble in the final two weeks.
The pitfalls Kanerika’s delivery teams watch for most often on client launches are consistent: rollback plans that were written but never tested, feature flags left on indefinitely with no owner, and AI features evaluated against clean demo data instead of the messy production traffic they’ll actually see. Every one of those is preventable with the checklist structure in this guide, applied consistently rather than only on the launches that feel high-stakes.
Software Product Launch Checklist: The Full List at a Glance Use this as the condensed, printable version of everything above, a starting point to adapt into your own tracker, not a substitute for the ownership and evidence fields covered earlier.
Phase Key Checklist Items Governance Launch owner named; RACI matrix complete; decision calendar set; blocker definitions agreed Product & business Audience and scope locked; objectives and baselines captured; pricing and entitlements tested; legal and accessibility reviewed Engineering & infrastructure Architecture reviewed; capacity and failover tested; backup and DR validated; feature flags owned; API versioning and data migration reversible QA, security & observability Regression and load testing complete; security review done; dashboards and alerts live before launch; rollback rehearsed end to end Go-to-market Messaging finalized; sales and support enabled; billing path tested by someone outside engineering AI features (if applicable) Evaluated on production-shaped data; guardrails matched to autonomy level; drift monitoring live post-launch Go/no-go Evidence collected from every owner; tier-required controls scored complete, partial, or missing; decision and accepted risks recorded Launch day Command center staffed; rollback triggers pre-agreed; incident commander named Post-launch T+1, T+7, and T+30 reviews scheduled; metrics compared to baseline; retrospective produces concrete process changes
Frequently Asked Questions
What is a software product launch checklist? A software product launch checklist is a structured record of the decisions, tests, approvals, dependencies, and response plans a team needs before releasing software to customers. It covers engineering readiness, QA and security, go-to-market enablement, and post-launch review, with a named owner and evidence for every item, not just a list of tasks.
What is the difference between a launch checklist and a release checklist? A launch checklist confirms cross-functional readiness across product, engineering, GTM, and support for the full launch lifecycle. A release checklist is narrower and controls the production deployment itself, covering code, infrastructure, and deployment steps, and is usually owned by engineering and DevOps alone.
How do you decide which launch tier a release needs? Score the release against its riskiest dimension: number of affected users, data impact, compliance exposure, revenue path, integration surface, rollback difficulty, and AI autonomy level. Whichever dimension scores highest sets the tier, even if every other dimension looks low risk. A regulated data change touching few users is still a high tier launch.
What is the difference between a phased rollout and a canary release? A phased rollout releases to increasing percentages or segments of users in sequence, such as internal users, then one percent, then twenty five percent, and so on. A canary release is a specific pattern within that where a small production slice runs the new version while the rest of traffic stays on the old version, so the team can compare health metrics before committing further.
How do feature flags reduce software launch risk? Feature flags separate deploying code from releasing a capability to customers. A team can ship code to production dark, verify it works internally, then turn the feature on for specific users or all users independently of any new deployment. This turns a risky release event into a low risk configuration change, and makes rollback nearly instant.
What should a go/no-go meeting actually review? A go/no-go meeting should review evidence, not opinions: status from every functional owner against the readiness checklist, any items in a pre-agreed blocker category, and whether the launch tier’s required controls are complete, partial, or missing. The decision and any accepted risks should be recorded, not just discussed verbally.
How is launching an AI feature different from a normal software launch? AI features need evaluation against real production-shaped data before launch, not just curated demo data, because model behavior on messy real input can differ from a clean demo. They also need guardrails matched to how much autonomy the feature has, and a drift monitoring layer after launch, since model output distributions can shift even when the surrounding application code is stable.
How long should a post-launch review period last? Most teams run three checkpoints: a first health review at one day post-launch comparing error rates and latency to baseline, an adoption and issue review at seven days, and a full retrospective at thirty days that should produce concrete process changes for the next launch, not just a general summary of what went well.
