Delivering AI Projects

Before You Start

Quick Answer

Learning Objectives

What Does Delivering an AI Project Mean?

Delivering an AI project is more than sending files. It is the structured process of confirming that the project meets the agreed scope, has been tested for the agreed use, includes appropriate data controls and can be supported after handover.

A strong delivery explains what was completed, how it was tested, where the files are stored, what the client must do next and what support remains available.

The Six Pillars of Professional Delivery

1. Review the Approved Scope

Begin by comparing the completed work with the approved proposal, contract and change requests.

Use the approved baseline and change-request versions. Record each deliverable’s ID, required format, acceptance criteria, reviewer, evidence and status. Mark omissions, open defects and dependencies explicitly. Delivery, acceptance and permission to launch a live workflow are separate decisions.

2. Create a Final Delivery Checklist

Scale the checklist to the agreed service. A writing assignment may need editable files and source notes; a production automation needs operational tests and a named owner. Do not imply training, deployment or ongoing maintenance is included unless agreed. A completed checklist reduces risk but does not certify absolute security or error-free output.

3. Complete an Accuracy Review

AI-generated and AI-assisted work must be verified before delivery.

• Check names, dates and numbers.
• Verify quotations and statistics.
• Confirm prices, policies and deadlines.
• Test calculations.
• Open every cited source.
• Remove unsupported claims.

Confirm that each source actually supports the associated claim, not merely that its URL opens. Record the policy/source version and check date. Mark unresolved facts for review; do not guess to make the package look complete. High-stakes claims need a suitably qualified reviewer when the task requires one.

4. Run a Quality-Assurance Review

5. Test AI Automations Before Handover

Record the environment, workflow/model version, test ID, synthetic input, expected result, actual result, reviewer and pass/fail decision. Use a sandbox with sending and payment actions disabled. Include retries, duplicate protection, permission failures and untrusted instructions inside source material. One successful run does not prove reliability; repeat representative cases and disclose the test limits.

Before any separately authorized launch, agree monitoring, a named operator, stop conditions and a tested recovery procedure. Failed critical tests block deployment. A beginner should hand over a prototype as a prototype rather than presenting an unvalidated live system as production-ready.

6. Complete a Security and Privacy Review

Before delivery, confirm that client data and access are protected.

• Remove temporary test data.
• Delete unnecessary personal information.
• Restrict access permissions.
• Replace shared access with named roles; rotate exposed or temporary credentials through an authorized plan.
• Confirm multi-factor authentication.
• Review AI tool data-retention settings.
• Remove unused integrations.

Inventory accounts, API keys, integrations, share links and service accounts before changing them. Use least-privilege roles and MFA, preferably phishing-resistant where supported. Verify client access and recovery arrangements before removing a provider account; rotating a key or deleting an integration can break the live workflow. Keep secrets out of the handover document, screenshots and recordings.

7. Organize Final Delivery Files

Include a manifest with filename, version/date, purpose, format and location. Verify required licenses and transfer rights; distinguish client work from pre-existing templates and third-party assets. Keep credentials in an approved secret manager, not the “Access and Setup” folder. Test restricted links using the intended recipient’s permissions.

8. Use Clear Final File Names

Remove unnecessary drafts from the client’s final folder. Keep internal working files separately when required.

A “FINAL” label alone can become ambiguous after a correction. Record the accepted version in the manifest and change log, preserve approved history under the retention plan and provide editable source files when included in the agreement.

9. Create a Delivery Summary Document

The delivery summary should explain:

• Project goal.
• Completed deliverables.
• Final file locations.
• Testing completed.
• Known limitations.
• Client responsibilities.
• Support period.
• Recommended next steps.

Also state excluded work, unresolved issues with severity/owner, approved versions, test environment and any separate launch decision. List prerequisites, tool subscription/usage costs, license restrictions and who owns ongoing monitoring. Do not hide material limitations in an archive the client is unlikely to read.

10. Document the AI-Assisted Process

Document approved inputs and tool/model versions where available, the review points and the operator’s authority. Record representative test evidence without unnecessary personal data. A workflow diagram or saved prompt does not guarantee identical model behavior after a tool update.

11. Deliver an Approved Prompt Library

When prompts are part of the project, organize them clearly.

Transfer prompts only when included and permitted; separate reusable provider material and third-party restrictions. Add version/date, approved tool, synthetic example, test limits and stop/escalation rules. Remove secrets and private client details from examples and prompt history. A prompt library is a starting procedure, not a guarantee of accurate outputs.

12. Create a Standard Operating Procedure

A standard operating procedure explains how to repeat the workflow consistently.

Include the owner, version, last review date, prerequisites, approved data, stopping rule and recovery contact. Explain which external actions require authorization. Have the intended user follow the instructions in a safe test; update gaps they discover before handover.

13. Train the Client

Training helps the client use the delivered system safely and confidently.

Agree the audience, duration, format and accessibility needs. Use synthetic examples and obtain permission before recording. Ask the client to complete one normal task and one exception case; attending a demonstration alone does not establish operational readiness.

14. Use a Client Training Agenda

Record the practice outcome, unanswered questions and any remaining training needs. If the client cannot complete a critical step, resolve it or document an agreed restriction before launch. Keep the training evidence separate from a general claim that the whole system is safe.

15. Run a Formal Handover Meeting

The handover meeting should confirm:

• All deliverables are present.
• The client can access the files.
• The system works in the client environment.
• Training is complete.
• Known limitations are understood.
• Support terms are clear.
• Final approval is ready.

Use the meeting to record receipt, access, review status, open issues, owner and next action. Do not present an acceptance request as automatic approval. If launch is outside the original scope, it requires a separate agreement and authorization.

16. Transfer Access Safely

Access transfer should follow secure procedures.

• Use the client’s own accounts where possible.
• Avoid sending passwords through insecure messages.
• Transfer ownership of files and automations.
• Remove your access when no longer required.
• Confirm the client can sign in successfully.
• Record who owns each account.

Confirm whether each platform permits ownership transfer; some subscriptions or personal accounts cannot be transferred. Use supported invitations or client-created accounts, move billing and recovery contacts as agreed, and verify scheduled jobs and integrations under the new owner. Revoke obsolete sessions, keys and access only after authorized checks. Never hand over your personal login or MFA recovery codes.

17. Explain Known Limitations

Professional delivery includes honest limitations.

State the tested context and what has not been tested. Separate a demonstrated result from a hoped-for benefit; client acceptance does not prove universal accuracy, copyright protection or regulatory compliance. Identify when use must stop and who can approve a fix.

18. Define Post-Project Support

These durations are examples, not a default policy. Agree start/end dates and time zone, covered channels/hours, response targets, usage limits, exclusions and any fees. Distinguish acknowledging a request from resolving it. Keep correction of agreed defects separate from new features and paid maintenance, consistent with the agreement and applicable rights.

19. Create a Maintenance Plan

AI-supported systems may require ongoing updates.

• Review prompts and templates.
• Update policies and source material.
• Test integrations.
• Check error logs.
• Review user permissions.
• Measure output quality.
• Remove outdated tools or data.

Name the monitoring owner and review interval, update triggers, tool costs and incident contact. Re-test after material model, policy or integration changes. Document who can disable the workflow and how to restore an approved version. Do not imply ongoing monitoring is included if no one has accepted that responsibility.

20. Obtain Formal Client Acceptance

Client acceptance records the client’s decision against the agreed criteria and version.

Use the acceptance method required by your contract or platform.

This example is a discussion aid, not a universal legal release. Identify the deliverables/version, reviewer, review date, criteria and any listed exceptions or unresolved defects. Use the applicable agreement or platform process; do not add an unagreed silence-as-acceptance rule or imply that signing removes all rights. Keep receipt, acceptance, launch authorization and payment status distinct.

21. Send the Final Invoice

Invoice and deliver according to the agreed payment and platform terms; there is no universal rule that payment must precede every handover.

• Use the correct client and project details.
• Reference the agreed milestone.
• State the amount and due date.
• Include approved payment instructions.
• Record the payment status.

Check currency, taxes, prior payments and approved changes so the balance reconciles. Keep invoice status separate from cash received. Verify any changed payment instructions through an established contact route, and do not move marketplace payment off-platform contrary to its rules.

22. Send a Professional Delivery Email

Adapt this fictional email to the actual manifest, acceptance process and support agreement. Include exact review/support dates and time zone, a secure link and unresolved issues. Do not claim approval or completion of testing that has not occurred. Sending a delivery email is not itself client acceptance.

23. Present Results Clearly

A final presentation may include:

If results are measured, show the baseline, sample, period, method and review/rework time. For an illustrative comparison of 120 minutes before and 90 minutes after, the reduction is 30 minutes, or 25% of the baseline. This does not establish cash savings, causation or a future guarantee. If no outcome was measured, state that clearly.

24. Archive the Project Properly

Keep only records needed for an agreed purpose or applicable obligations. Record owner, access permissions, retention/review dates and authorized disposal. An archive is not permission to keep all client data indefinitely; separate required business records from unnecessary working copies.

25. Delete Data That Is No Longer Needed

After the project and required retention period:

• Delete temporary client files.
• Remove test records.
• Close unused accounts.
• Revoke unnecessary access.
• Delete private prompts containing client details.
• Confirm deletion when required.

Check retention duties, documented holds and client instructions before disposal. Apply the plan to local copies, cloud storage, histories and backups as appropriate; backups may expire on a schedule. Use the provider’s deletion controls and record scope, date, exceptions and evidence. Do not certify immediate deletion from every backup or AI system unless you can verify it.

26. Complete an Internal Project Review

Review the project after delivery.

Separate agreed fees, cash collected, direct expenses and the internal value assigned to your labor. A project margin estimate is not necessarily net business profit. Record defects and lessons honestly rather than counting all rework as client-requested revisions.

27. Prepare the Project for Your Portfolio

With client permission, create a case study containing:

• The client problem.
• The agreed solution.
• Your AI-assisted process.
• The human-review framework.
• The final deliverables.
• The measured result.
• The client testimonial.

Doing the work does not automatically grant display permission. Agree what may be shared, remove identifying and confidential material, check asset rights and label your contribution. Results and testimonials are optional: do not invent them when unavailable. Editing or AI rewriting alone does not establish originality or exclusive rights.

28. Choose the Right Time to Request Feedback

Request feedback after the client has had a reasonable opportunity to use and evaluate the agreed work.

Do not pressure the client before they have confirmed that the project works.

Make the request optional and neutral. Do not condition support, defect correction or a promised benefit on a positive review. Use genuine experience, preserve the reviewer’s meaning, obtain display permission and follow applicable disclosure and platform requirements.

Professional AI Delivery vs Incomplete Delivery

Common AI Project Delivery Mistakes

Mini Case Studies

These are fictional teaching scenarios, not documented client outcomes.

Finish the Project With Confidence

A polished handover helps the client understand the value of the work and use it successfully.

Internal Links and Recommended Resources

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Official External Resources

Your Weekly Challenge

Practical Project: Prepare a Complete Handover Package

Exercise 3: Write Usable Instructions

Prepare a one-page guide naming the approved inputs, AI tool/version where known, human source checks, ambiguity escalation and recordkeeping. Add a synthetic example and a normal task plus an exception task for the client to practise. Record remaining questions instead of marking training complete solely because a video exists.

Prompt: “Using only this approved fictional brief, manifest and test record, draft a delivery summary, short user guide and handover email. Mark missing owners, dates, results and permissions. Preserve open failures. Do not invent acceptance, business outcomes or authorization for live actions. Return drafts for human review.”

Exercise 5: Check Results and Closeout

Recalculate the fictional time example: 120 − 90 = 30 minutes, and 30/120 × 100 = 25%. State the measurement conditions and limits; do not turn the reduction into guaranteed financial savings.

Draft a version-specific acceptance request, identify open issues and prepare an unsent delivery email. Keep invoice/payment status separate. Save the manifest, checks, guide, training evidence, access/support plan and closeout notes. A checked fictional package is sufficient; sending it to a real client is optional and outside this exercise.

Knowledge Check

Answer first, then open the explanation.

Reflection Questions

Lesson 38 Workbook: Delivering AI Projects