{"id":220733,"date":"2026-08-02T19:02:41","date_gmt":"2026-08-02T17:02:41","guid":{"rendered":"https:\/\/caroli.org\/en\/?page_id=220733"},"modified":"2026-08-07T18:51:03","modified_gmt":"2026-08-07T16:51:03","slug":"artificial-intelligence-experiments","status":"publish","type":"page","link":"https:\/\/caroli.org\/en\/artificial-intelligence-experiments\/","title":{"rendered":"AI Experiments"},"content":{"rendered":"<p>[et_pb_section fb_built=&#8221;1&#8243; admin_label=&#8221;Contact hero&#8221; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; use_background_color_gradient=&#8221;on&#8221; background_color_gradient_direction=&#8221;45deg&#8221; background_color_gradient_stops=&#8221;#034a63 0%|#028ab1 100%&#8221; background_color_gradient_start=&#8221;#474ab6&#8243; background_color_gradient_end=&#8221;#9271f6&#8243; background_enable_image=&#8221;off&#8221; background_blend=&#8221;overlay&#8221; custom_margin=&#8221;|10px||10px|false|false&#8221; custom_padding=&#8221;||100px||false|false&#8221; 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global_colors_info=&#8221;{}&#8221;][\/et_pb_image][\/et_pb_column][et_pb_column type=&#8221;3_5&#8243; _builder_version=&#8221;4.18.0&#8243; _module_preset=&#8221;default&#8221; custom_padding=&#8221;60px|40px|60px|40px&#8221; custom_padding_tablet=&#8221;60px|40px|60px|40px&#8221; custom_padding_phone=&#8221;30px||||false|false&#8221; custom_padding_last_edited=&#8221;on|phone&#8221; global_colors_info=&#8221;{}&#8221; custom_padding__hover=&#8221;|||&#8221;][et_pb_text _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; header_text_align=&#8221;center&#8221; header_2_text_align=&#8221;center&#8221; custom_margin=&#8221;20px||0px||false|false&#8221; custom_margin_tablet=&#8221;0px||||false|false&#8221; custom_margin_phone=&#8221;0px||||false|false&#8221; custom_margin_last_edited=&#8221;on|phone&#8221; custom_padding=&#8221;0px||0px||false|false&#8221; header_2_font_size_tablet=&#8221;&#8221; header_2_font_size_phone=&#8221;24px&#8221; header_2_font_size_last_edited=&#8221;on|phone&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<h2>Artificial Intelligence Experiments<\/h2>\n<p>[\/et_pb_text][et_pb_divider color=&#8221;#FFCC02&#8243; divider_weight=&#8221;10px&#8221; _builder_version=&#8221;4.17.4&#8243; _module_preset=&#8221;default&#8221; width=&#8221;150px&#8221; width_tablet=&#8221;120px&#8221; width_phone=&#8221;100px&#8221; width_last_edited=&#8221;on|phone&#8221; module_alignment=&#8221;center&#8221; locked=&#8221;off&#8221; global_colors_info=&#8221;{}&#8221;][\/et_pb_divider][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=&#8221;1&#8243; admin_label=&#8221;Section&#8221; _builder_version=&#8221;4.22.2&#8243; _module_preset=&#8221;default&#8221; da_disable_devices=&#8221;off|off|off&#8221; global_colors_info=&#8221;{}&#8221; da_is_popup=&#8221;off&#8221; da_exit_intent=&#8221;off&#8221; da_has_close=&#8221;on&#8221; da_alt_close=&#8221;off&#8221; da_dark_close=&#8221;off&#8221; da_not_modal=&#8221;on&#8221; da_is_singular=&#8221;off&#8221; da_with_loader=&#8221;off&#8221; da_has_shadow=&#8221;on&#8221;][et_pb_row _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; width=&#8221;90%&#8221; max_width=&#8221;90rem&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_text _builder_version=&#8221;4.27.4&#8243; _module_preset=&#8221;_initial&#8221; text_text_color=&#8221;#0E272D&#8221; link_font=&#8221;|700|||||||&#8221; link_text_color=&#8221;#EA7502&#8243; header_text_color=&#8221;#0E272D&#8221; header_4_font=&#8221;|300|||||||&#8221; header_4_text_color=&#8221;#0E272D&#8221; header_4_font_size=&#8221;30px&#8221; hover_enabled=&#8221;0&#8243; global_colors_info=&#8221;{}&#8221; sticky_enabled=&#8221;0&#8243;]<\/p>\n<h2>Experimental tools and practical resources to support OKRs, MVPs, Lean Inception, and retrospectives<\/h2>\n<p>Artificial intelligence can accelerate analysis, structure ideas, and expand possibilities. But it does not define what matters or replace the conversations needed to build alignment.<\/p>\n<p>Paulo Caroli creates these experiments to explore, in practice, how AI can support strategy, product, and teamwork. They also make some of the ideas presented in his new book, <a href=\"https:\/\/caroli.org\/en\/livro\/teams-still-matter\/\"><em>Teams Still Matter<\/em><\/a>, more tangible.<\/p>\n<p>A well-written answer does not necessarily lead to a good decision. These tools and resources are designed to support better questions, reveal inconsistencies, challenge assumptions, and help leaders and teams move important conversations forward.<\/p>\n<nav aria-label=\"Table of contents\" class=\"ai-experiments-toc\">\n<p class=\"ai-experiments-toc-title\"><strong>Explore the experiments<\/strong><\/p>\n<ul>\n<li><a href=\"#chatokr\">ChatOKR.ai<\/a><\/li>\n<li><a href=\"#chatmvp\">ChatMVP.ai<\/a><\/li>\n<li><a href=\"#lean-inception-ai\">Lean Inception in the Age of AI<\/a><\/li>\n<li><a href=\"#lean-inception-prompts\">Lean Inception Prompts<\/a><\/li>\n<li><a href=\"#funretrospectives-copilot\">FunRetrospectives Co-pilot<\/a><\/li>\n<li><a href=\"#ai-accelerates-people-provide-direction\">AI accelerates. People provide direction.<\/a><\/li>\n<li><a href=\"#teams-still-matter\">Teams Still Matter<\/a><\/li>\n<\/ul>\n<\/nav>\n<hr \/>\n<h2 id=\"chatokr\">ChatOKR.ai<\/h2>\n<h3>Review your team\u2019s OKR<\/h3>\n<p>Identify generic objectives, operational metrics, and deliverables disguised as Key Results.<\/p>\n<p>ChatOKR.ai helps analyze:<\/p>\n<ul>\n<li>the clarity of the objective;<\/li>\n<li>the quality of the Key Results;<\/li>\n<li>the connection between direction, ownership, and outcomes.<\/li>\n<\/ul>\n<p>The analysis uses the Team OKR approach developed by Paulo Caroli. The goal is not simply to improve the wording of an OKR, but to support a deeper conversation about responsibility, focus, and outcomes.<\/p>\n<p><a href=\"https:\/\/www.chatokr.ai\/\" rel=\"noopener\" target=\"_blank\"><strong>Try ChatOKR.ai<\/strong><\/a><\/p>\n<hr \/>\n<h2 id=\"chatmvp\">ChatMVP.ai<\/h2>\n<h3>Turn an idea into a learning experiment<\/h3>\n<p>Structure the problem, explore hypotheses, and move toward defining the smallest experiment capable of reducing uncertainty.<\/p>\n<p>ChatMVP.ai helps you reflect on:<\/p>\n<ul>\n<li>the problem and the people affected by it;<\/li>\n<li>the most important hypotheses;<\/li>\n<li>the learning required;<\/li>\n<li>the smallest experiment capable of testing those hypotheses.<\/li>\n<\/ul>\n<p>The tool is based on the <a href=\"https:\/\/caroli.org\/en\/the-mvp-canvas\/\">MVP Canvas<\/a> and Paulo Caroli\u2019s experience with Lean Inception. The goal is not to turn every idea into a list of features, but to help you learn before investing too much in a solution.<\/p>\n<p><a href=\"https:\/\/www.chatmvp.ai\/\" rel=\"noopener\" target=\"_blank\"><strong>Try ChatMVP.ai<\/strong><\/a><\/p>\n<hr \/>\n<h2 id=\"lean-inception-ai\">Lean Inception in the Age of AI<\/h2>\n<h3>Use AI to enrich the conversation, not to eliminate the conversation<\/h3>\n<p>Artificial intelligence is dramatically reducing the time between an intention and something concrete. Product visions, user journeys, prototypes, code, and solution alternatives can be produced increasingly fast.<\/p>\n<p>But deciding what to build, why to build it, what to leave out, and what we need to learn first still requires context, conversation, and judgment.<\/p>\n<p><strong>Lean Inception in the Age of AI<\/strong> is a practical guide to using AI before, during, and after a Lean Inception without replacing what makes the workshop valuable: alignment, explicit disagreements, shared decisions, and collective learning.<\/p>\n<p>The guide includes:<\/p>\n<ul>\n<li>ways to use AI before, during, and after a Lean Inception;<\/li>\n<li>examples for Product Vision, Personas, User Journeys, Features Brainstorming, Feature Sequencer, and MVP Canvas;<\/li>\n<li>prompts designed to enrich team conversations;<\/li>\n<li>an AI-assisted Parking Lot and a glossary connected to Domain-Driven Design;<\/li>\n<li>a checklist to assess whether AI is improving or avoiding the conversation;<\/li>\n<li>extensions for products that include AI agents.<\/li>\n<\/ul>\n<blockquote><p><strong>AI accelerates building. Lean Inception helps the team decide what deserves to be built.<\/strong><\/p><\/blockquote>\n<p><a href=\"https:\/\/caroli.org\/en\/lean-inception-ai\/\"><strong>Read Lean Inception in the Age of AI<\/strong><\/a><\/p>\n<hr \/>\n<h2 id=\"lean-inception-prompts\">Lean Inception Prompts<\/h2>\n<h3>Use AI as a thought partner during a Lean Inception<\/h3>\n<p>Paulo Caroli has created a collection of prompts to support different Lean Inception activities with AI.<\/p>\n<p>The prompts can help you:<\/p>\n<ul>\n<li>organize information and context;<\/li>\n<li>explore hypotheses and alternatives;<\/li>\n<li>identify ambiguities and disagreements;<\/li>\n<li>support MVP definition and review;<\/li>\n<li>provide additional support for solopreneurs and teams.<\/li>\n<\/ul>\n<p>Use AI responses as drafts, alternatives, or material for discussion \u2014 not as ready-made decisions.<\/p>\n<p>These prompts were created to support Lean Inception, not to replace collaboration, critical thinking, judgment, or the conversations needed to build alignment.<\/p>\n<blockquote><p><strong>Use AI to enrich the conversation, not to eliminate the conversation.<\/strong><\/p><\/blockquote>\n<p><a href=\"https:\/\/caroli.org\/lp\/lean-inception-prompts-en\/\"><strong>Explore the prompts and download the PDF<\/strong><\/a><\/p>\n<p>To understand the thinking behind this approach, also read <a href=\"https:\/\/caroli.org\/en\/lean-inception-ai\/\"><strong>Lean Inception in the Age of AI<\/strong><\/a>.<\/p>\n<hr \/>\n<h2 id=\"funretrospectives-copilot\">FunRetrospectives Co-pilot<\/h2>\n<h3>Prepare retrospectives that better fit your team\u2019s context<\/h3>\n<p>Find activities, organize the retrospective flow, and explore questions that can support a meaningful conversation.<\/p>\n<p>FunRetrospectives Co-pilot helps you consider:<\/p>\n<ul>\n<li>the team\u2019s current moment;<\/li>\n<li>the purpose of the retrospective;<\/li>\n<li>the activities best suited to the context;<\/li>\n<li>the sequence and facilitation questions.<\/li>\n<\/ul>\n<p>AI expands the facilitator\u2019s repertoire. The facilitator remains responsible for understanding the group, observing interactions, and guiding the conversation with care.<\/p>\n<p><a href=\"https:\/\/www.funretrospectives.com\/co-pilot\/\" rel=\"noopener\" target=\"_blank\"><strong>Open FunRetrospectives Co-pilot<\/strong><\/a><\/p>\n<hr \/>\n<h2 id=\"ai-accelerates-people-provide-direction\">AI accelerates. People provide direction.<\/h2>\n<p>Artificial intelligence can organize information, identify patterns, explore alternatives, and produce first drafts.<\/p>\n<p>Leaders and teams remain responsible for deciding what matters, understanding the context, making difficult choices, aligning different perspectives, and taking responsibility for outcomes.<\/p>\n<blockquote><p><strong>AI supports the work. It does not replace direction, judgment, collaboration, or ownership.<\/strong><\/p><\/blockquote>\n<hr \/>\n<h2 id=\"teams-still-matter\">Teams Still Matter<\/h2>\n<p>These experiments are part of the reflection presented in Paulo Caroli\u2019s new book about human direction, AI acceleration, and the future of product work.<\/p>\n<blockquote><p>The more AI accelerates production, the more important direction, judgment, collaboration, and ownership become.<\/p><\/blockquote>\n<p><a href=\"https:\/\/caroli.org\/en\/livro\/teams-still-matter\/\"><strong>Discover the book Teams Still Matter<\/strong><\/a><\/p>\n<p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Artificial Intelligence ExperimentsExperimental tools and practical resources to support OKRs, MVPs, Lean Inception, and retrospectives Artificial intelligence can accelerate analysis, structure ideas, and expand possibilities. But it does not define what matters or replace the conversations needed to build alignment. Paulo Caroli creates these experiments to explore, in practice, how AI can support strategy, product, [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"class_list":["post-220733","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/caroli.org\/en\/wp-json\/wp\/v2\/pages\/220733","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/caroli.org\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/caroli.org\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/caroli.org\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/caroli.org\/en\/wp-json\/wp\/v2\/comments?post=220733"}],"version-history":[{"count":4,"href":"https:\/\/caroli.org\/en\/wp-json\/wp\/v2\/pages\/220733\/revisions"}],"predecessor-version":[{"id":220757,"href":"https:\/\/caroli.org\/en\/wp-json\/wp\/v2\/pages\/220733\/revisions\/220757"}],"wp:attachment":[{"href":"https:\/\/caroli.org\/en\/wp-json\/wp\/v2\/media?parent=220733"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}