Celebrate a major AI fail.
An AI experiment can produce no usable output and still leave your company better off. Celebrate the people who tried it and everything they learned. Make it visible that exploration matters here.
Try it now
Copy the prompt into your AI tool of choice, then answer its questions as they come.
Paste it into any AI chat to start. For most people, this is all you need.
Everything on this page in one paste: the prompt plus the full plan, every step and what to check. Use it for longer moves, to plan ahead, to share with a colleague, or if your AI can't open links.
How to run it
Identify an AI initiative that cost a meaningful amount of money, time and effort and did not produce usable output. Choose a real attempt whose failure people will recognize.
Identify all the things you learned from it. Ask the people involved what surprised them, what they now understand and what they would do differently. Include small discoveries as well as big ones. What can the company now see, judge or attempt that it could not before?
Prepare a public celebration of the attempt within your company. Name the people who did the work, thank them for exploring, describe what they learned and highlight the specific ways the company is better off because of this failure. Be clear about the failed result; let the real learning carry the celebration.
Make the larger point explicit: AI is moving so quickly, and the transformation is so surprising, that it is only through experiments that are quite likely to fail that we will meet the future. This is the kind of exploration you want people to undertake.
A celebratory email or message can be enough. If it was a really big failure, or you really want to make the point, have a little party. Get some cake. Invite everyone to share their biggest AI fail, starting with you.
Optional: use the prompt below if you would like an AI to guide you through choosing the failure, drawing out the lessons and preparing the email or celebration. Bring whatever you already have; it should pick up from there.
These are general instructions, not a description of this user's particular AI system, tools, access, or circumstances. Adapt the method to the user's goal and the capabilities you can actually use, while preserving the activity's essential sequence, review points, and intended result. ### Start with the activity and the user's context Read the complete activity and guide the user through it. Use the activity prompt yourself; the user does not need to paste it again. Keep the responsibilities of the user, the AI, and any colleague clear. Begin with what the user has already supplied. Establish the intended result and resolve missing context through focused questions when needed. Work within the activity's stated scope, permissions, required confirmations, and stopping points, including any deliberate order for introducing information. Make progress with the available information and reasonable, clearly identified assumptions where the activity allows them. ### Check the capabilities needed for this work Inspect relevant available tools, connectors, and data. Use small, relevant, non-destructive checks to establish whether necessary access or a capability actually works. Naming a product does not establish access to it. Let this activity determine what needs checking; do not impose a universal preliminary questionnaire or a full system audit. When useful access is missing: - Explain what you cannot reach and how that material would help. - Investigate practical setup, connection, export, or other access alternatives before asking the user to gather material or troubleshoot. - If progress depends on access, request only the specific user action or permission needed to enable it or provide the material another way. Continue useful independent work while awaiting the response. - Offer to walk the user through an export or upload. Welcome files, links, copied text, rough notes, or an interview as supported by your system and the activity, and use the best available route. If the missing access is optional, continue with what is available. ### Ask focused questions and respect the answers Ask questions only when necessary to complete the work well or satisfy the activity's required review points. Build on answers already supplied. Ask one question at a time, or a small group of closely related questions. Wait on work that depends on the answer, and continue useful work that does not. Explain unfamiliar language with examples where helpful. Confirm answers or decisions you supply on the user's behalf before treating them as the user's final answers or decisions. This does not require separate approval of every sourced finding; follow the activity's specific review requirements. ### Keep evidence and independent contributions distinct Distinguish evidence, inference, and missing information. Check your work against the source material. Use independent agents when your system actually supports delegation and independent investigation or review would improve the work: - Give each agent a bounded brief and the context appropriate to its role. - Keep discovery independent until synthesis where the activity requires it. Preserve any requirement for the user or colleagues to contribute before seeing other explanations. - Follow the activity's stated fallback and any required confirmation when independent agents or fresh contexts are unavailable. Preserve reviewer isolation where required; describe shared-context or sequential passes accurately. ### Produce, check, and revise the result Do the substantive investigation and production. Carry the activity through to the most complete, usable result within its scope, rather than ending with a plan or handing the main work back to the user. When missing information does not prevent useful work, proceed with clearly identified assumptions where the activity permits them. Check the result against the activity's completion criteria. At each substantive delivery, state what is complete and any material unfinished work, assumptions, helpful missing information, and remaining uncertainty. Explain how these affect what the user can use, and help the user revise the result. Preserve the activity's stopping point. Do not turn a rehearsal, investigation, or test-design activity into execution that the user has not authorized. ### Help me celebrate an AI failure Guide me through celebrating an AI initiative that cost meaningful money, time and effort but did not produce usable output. The purpose is to recognize the people who explored it, make everything we learned visible, and show how the company is better off because they tried. AI is changing so quickly, and the transformation is so surprising, that meeting the future requires experiments that are quite likely to fail. This is optional help with the human activity. Start from anything I have already done; do not restart completed steps. Ask one focused question at a time when my answer matters, and wait. Rough notes or a spoken account are enough to begin. ### Establish what happened and what we learned Identify the initiative, its intended result, the people involved and the audience for the celebration. If I have not chosen an initiative, ask me about one that required a real investment and produced no usable output. Try relevant connected project documents, experiment results, budgets, emails, team chats and meeting transcripts. Use the initiative's name, participants and dates to find the original goal, investment, attempts, outcome and lessons. Use correspondence and the team's own accounts to understand contributors' roles and what the intended audience already knows. Keep the search focused on this initiative. If those records are unavailable, help me reconstruct it conversationally and label details that need confirmation. Build an expansive list of what we learned. Explore what the experiment revealed about the technology, our data, users, workflows, assumptions, organizational habits and people's capabilities; these are starting points, not required categories. For each lesson, establish what happened that taught it to us and what it lets us understand or do differently now. Invite additions from the people involved through questions I can take to them; do not contact anyone yourself. Show me a concise account of the attempt, contributors, lessons and specific ways the company is better off. Distinguish demonstrated benefits from possible future benefits. Do not invent learning, pretend the output was usable, or claim the financial cost has been recovered. Ask me to correct and confirm the account before writing the celebration. ### Choose the celebration Offer two concrete versions, adapted to this initiative and audience: - A celebratory email or message that tells the story, names and thanks the people involved, shares the lessons and explains why the company is better off. - A small party for a bigger failure, or when I want to make a stronger cultural point: get some cake and invite everyone to share their biggest AI fail, starting with me. Recommend a proportionate version and ask which I want. Wait for my choice. Keep the emphasis on exploration, people and learning. The invitation to experiment should be unmistakable: with AI moving this quickly and changing work in surprising ways, we will meet the future only by trying things that are quite likely to fail. ### Prepare everything I need For the email or message, write the complete ready-to-review text, including a subject or opening line. For the party, write the invitation, my opening remarks about my own failure, a short running order, prompts that invite others' stories and a simple reminder to arrange cake. Use the confirmed facts and preserve the contributors' credit. If separate agents are available, give one reviewer the proposed message or remarks and the confirmed facts and audience context, without the drafting conversation. Ask whether a reader would feel the company is celebrating exploration and learning, and whether every claimed benefit is supported. Revise material problems. If a separate reviewer is unavailable, do a clearly identified second reading yourself; do not call it independent review. Show me the complete material and revise it from my feedback. I will send the message or host the event. Do not send, post, schedule or invite people. Finish when I have a faithful celebration ready to use, and make clear that the human activity is completed when I actually share it or hold it. These instructions were written in September 2026 using the frontier models available at the time. You may now have better tools or models, or restrictions that make some tasks harder. Your AI should adapt to the tools and access you actually have and find the best available way to help. Everything here is a starting suggestion for you and your AI, not a proven best practice for every setup. Feel free to rewrite any of it to better meet your needs.
How it goes
What the AI will do
- Optional: help uncover the lessons and prepare a celebratory message or a small party.
What to have ready
- An AI initiative that consumed real resources and produced no usable output; rough notes or a spoken account are enough for optional AI help.
Where you come in
- Identify the lessons and the people to recognize.
- Share a celebratory email or message, or host a little party with cake and failure stories, starting with yours.
What you'll get
A public celebration inside your company of the attempt, the people and what the failure taught you.
You're done when
You have shared the celebratory message or held the celebration, recognized the people involved and made the lessons and value of experimentation visible.
View full AI instructions
Celebrate a major AI fail.
An AI experiment can produce no usable output and still leave your company better off. Celebrate the people who tried it and everything they learned. Make it visible that exploration matters here.
Step 1 — For the user
Identify an AI initiative that cost a meaningful amount of money, time and effort and did not produce usable output. Choose a real attempt whose failure people will recognize.
Step 2 — For the user
Identify all the things you learned from it. Ask the people involved what surprised them, what they now understand and what they would do differently. Include small discoveries as well as big ones. What can the company now see, judge or attempt that it could not before?
Step 3 — For the user
Prepare a public celebration of the attempt within your company. Name the people who did the work, thank them for exploring, describe what they learned and highlight the specific ways the company is better off because of this failure. Be clear about the failed result; let the real learning carry the celebration.
Step 4 — For the user
Make the larger point explicit: AI is moving so quickly, and the transformation is so surprising, that it is only through experiments that are quite likely to fail that we will meet the future. This is the kind of exploration you want people to undertake.
Step 5 — For the user
A celebratory email or message can be enough. If it was a really big failure, or you really want to make the point, have a little party. Get some cake. Invite everyone to share their biggest AI fail, starting with you.
Step 6 — For the user
Optional: use the prompt below if you would like an AI to guide you through choosing the failure, drawing out the lessons and preparing the email or celebration. Bring whatever you already have; it should pick up from there.
Done when
You have shared the celebratory message or held the celebration, recognized the people involved and made the lessons and value of experimentation visible.
Guidance for the AI
These are general instructions, not a description of this user's particular AI system, tools, access, or circumstances. Adapt the method to the user's goal and the capabilities you can actually use, while preserving the activity's essential sequence, review points, and intended result.
Start with the activity and the user's context
Read the complete activity and guide the user through it. Use the activity prompt yourself; the user does not need to paste it again. Keep the responsibilities of the user, the AI, and any colleague clear.
Begin with what the user has already supplied. Establish the intended result and resolve missing context through focused questions when needed. Work within the activity's stated scope, permissions, required confirmations, and stopping points, including any deliberate order for introducing information. Make progress with the available information and reasonable, clearly identified assumptions where the activity allows them.
Check the capabilities needed for this work
Inspect relevant available tools, connectors, and data. Use small, relevant, non-destructive checks to establish whether necessary access or a capability actually works. Naming a product does not establish access to it. Let this activity determine what needs checking; do not impose a universal preliminary questionnaire or a full system audit.
When useful access is missing:
- Explain what you cannot reach and how that material would help.
- Investigate practical setup, connection, export, or other access alternatives before asking the user to gather material or troubleshoot.
- If progress depends on access, request only the specific user action or permission needed to enable it or provide the material another way. Continue useful independent work while awaiting the response.
- Offer to walk the user through an export or upload. Welcome files, links, copied text, rough notes, or an interview as supported by your system and the activity, and use the best available route. If the missing access is optional, continue with what is available.
Ask focused questions and respect the answers
Ask questions only when necessary to complete the work well or satisfy the activity's required review points. Build on answers already supplied. Ask one question at a time, or a small group of closely related questions. Wait on work that depends on the answer, and continue useful work that does not. Explain unfamiliar language with examples where helpful.
Confirm answers or decisions you supply on the user's behalf before treating them as the user's final answers or decisions. This does not require separate approval of every sourced finding; follow the activity's specific review requirements.
Keep evidence and independent contributions distinct
Distinguish evidence, inference, and missing information. Check your work against the source material.
Use independent agents when your system actually supports delegation and independent investigation or review would improve the work:
- Give each agent a bounded brief and the context appropriate to its role.
- Keep discovery independent until synthesis where the activity requires it. Preserve any requirement for the user or colleagues to contribute before seeing other explanations.
- Follow the activity's stated fallback and any required confirmation when independent agents or fresh contexts are unavailable. Preserve reviewer isolation where required; describe shared-context or sequential passes accurately.
Produce, check, and revise the result
Do the substantive investigation and production. Carry the activity through to the most complete, usable result within its scope, rather than ending with a plan or handing the main work back to the user. When missing information does not prevent useful work, proceed with clearly identified assumptions where the activity permits them.
Check the result against the activity's completion criteria. At each substantive delivery, state what is complete and any material unfinished work, assumptions, helpful missing information, and remaining uncertainty. Explain how these affect what the user can use, and help the user revise the result.
Preserve the activity's stopping point. Do not turn a rehearsal, investigation, or test-design activity into execution that the user has not authorized.
Your AI prompt
Help me celebrate an AI failure
Guide me through celebrating an AI initiative that cost meaningful money, time and effort but did not produce usable output. The purpose is to recognize the people who explored it, make everything we learned visible, and show how the company is better off because they tried. AI is changing so quickly, and the transformation is so surprising, that meeting the future requires experiments that are quite likely to fail.
This is optional help with the human activity. Start from anything I have already done; do not restart completed steps. Ask one focused question at a time when my answer matters, and wait. Rough notes or a spoken account are enough to begin.
Establish what happened and what we learned
Identify the initiative, its intended result, the people involved and the audience for the celebration. If I have not chosen an initiative, ask me about one that required a real investment and produced no usable output.
Try relevant connected project documents, experiment results, budgets, emails, team chats and meeting transcripts. Use the initiative's name, participants and dates to find the original goal, investment, attempts, outcome and lessons. Use correspondence and the team's own accounts to understand contributors' roles and what the intended audience already knows. Keep the search focused on this initiative. If those records are unavailable, help me reconstruct it conversationally and label details that need confirmation.
Build an expansive list of what we learned. Explore what the experiment revealed about the technology, our data, users, workflows, assumptions, organizational habits and people's capabilities; these are starting points, not required categories. For each lesson, establish what happened that taught it to us and what it lets us understand or do differently now. Invite additions from the people involved through questions I can take to them; do not contact anyone yourself.
Show me a concise account of the attempt, contributors, lessons and specific ways the company is better off. Distinguish demonstrated benefits from possible future benefits. Do not invent learning, pretend the output was usable, or claim the financial cost has been recovered. Ask me to correct and confirm the account before writing the celebration.
Choose the celebration
Offer two concrete versions, adapted to this initiative and audience:
- A celebratory email or message that tells the story, names and thanks the people involved, shares the lessons and explains why the company is better off.
- A small party for a bigger failure, or when I want to make a stronger cultural point: get some cake and invite everyone to share their biggest AI fail, starting with me.
Recommend a proportionate version and ask which I want. Wait for my choice. Keep the emphasis on exploration, people and learning. The invitation to experiment should be unmistakable: with AI moving this quickly and changing work in surprising ways, we will meet the future only by trying things that are quite likely to fail.
Prepare everything I need
For the email or message, write the complete ready-to-review text, including a subject or opening line. For the party, write the invitation, my opening remarks about my own failure, a short running order, prompts that invite others' stories and a simple reminder to arrange cake. Use the confirmed facts and preserve the contributors' credit.
If separate agents are available, give one reviewer the proposed message or remarks and the confirmed facts and audience context, without the drafting conversation. Ask whether a reader would feel the company is celebrating exploration and learning, and whether every claimed benefit is supported. Revise material problems. If a separate reviewer is unavailable, do a clearly identified second reading yourself; do not call it independent review.
Show me the complete material and revise it from my feedback. I will send the message or host the event. Do not send, post, schedule or invite people. Finish when I have a faithful celebration ready to use, and make clear that the human activity is completed when I actually share it or hold it.
These instructions were written in September 2026 using the frontier models available at the time. You may now have better tools or models, or restrictions that make some tasks harder. Your AI should adapt to the tools and access you actually have and find the best available way to help. Everything here is a starting suggestion for you and your AI, not a proven best practice for every setup. Feel free to rewrite any of it to better meet your needs.
These instructions were written in September 2026 using the frontier models available at the time. You may now have better tools or models, or restrictions that make some tasks harder. Your AI should adapt to the tools and access you actually have and find the best available way to help. Everything here is a starting suggestion for you and your AI, not a proven best practice for every setup. Feel free to rewrite any of it to better meet your needs.