{"id":679,"date":"2026-08-13T22:17:55","date_gmt":"2026-08-13T14:17:55","guid":{"rendered":"https:\/\/yenhui.co\/?p=679"},"modified":"2026-08-13T22:17:55","modified_gmt":"2026-08-13T14:17:55","slug":"ai-poc-topic-selection","status":"publish","type":"post","link":"https:\/\/yenhui.co\/en\/insights\/ai-poc-topic-selection\/","title":{"rendered":"How to Choose AI PoC Topics: From Problem Value and Data to Validation Metrics"},"content":{"rendered":"<p class=\"wp-block-paragraph\">The purpose of an AI PoC is not to prove that AI can generate a piece of text or recognize an image, but to reduce the uncertainty of whether a company is worth investing in the next stage. A good AI PoC starts from a specific problem and sets a baseline for the current state, data and permissions, comparable metrics, human review, and stopping criteria.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">When selecting an AI PoC, problem value, data availability, technical feasibility, risks, and adoption difficulty must be evaluated together. A more eye-catching demonstration is not necessarily the most suitable choice for the first project; a topic that can yield real decision-making evidence within a small scope is usually more valuable.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">First, the conclusion: AI PoC must validate business hypotheses, not just the model.<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">A complete hypothesis must include at least: which role encounters what problem in which process, which metric is expected to improve after AI intervention, which errors are unacceptable, and who reviews the results. Writing only \"introduce generative AI to improve efficiency\" makes it impossible to design an effective PoC.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Problem Assumption:<\/strong>Do bottlenecks truly exist, and is the impact worth addressing?<\/li>\n\n\n<li><strong>Data assumptions:<\/strong>Is the legally obtainable data sufficient to support verification?<\/li>\n\n\n<li><strong>Capability assumptions:<\/strong>Whether AI output is more useful than the current baseline, rather than just looking at the demo.<\/li>\n\n\n<li><strong>Adoption assumption:<\/strong>Are users willing to adjust their processes and able to understand their responsibilities?<\/li>\n\n\n<li><strong>Risk hypothesis:<\/strong>Can errors, biases, data leaks, and unexplained results be controlled?<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">What is an AI PoC? Deciding the next step using small-scale evidence<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">PoC stands for Proof of Concept. It is used to verify whether key assumptions hold true within a limited scope, and its output is evidence to support decision-making, rather than a complete product ready for full-scale operations. A PoC may involve testing models, data, processes, and personnel.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Before beginning, companies should clearly define what decision needs to be made at the conclusion of the PoC\u2014for example, whether to halt the project, adjust the scope, proceed to a pilot phase, or invest in a full-scale system. A PoC without a clear decision point can easily turn into a demonstration project that drags on indefinitely.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">What is the difference between an AI PoC, Prototype, Pilot, and Production System?<\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><th>Stairs<\/th><th>Main Purpose<\/th><th>Users and Data<\/th><th>The decision at the end<\/th><\/tr><\/thead><tbody><tr><td>Prototype<\/td><td>Validate interactions, workflows, or conceptual representations<\/td><td>Simulated content can be used; complete reliability is not necessary.<\/td><td>Is the design worth continuing<\/td><\/tr><tr><td>PoC (Proof of Concept)<\/td><td>Validate critical business, data, or technical assumptions<\/td><td>Limited real data and controlled users<\/td><td>Is the hypothesis valid?<\/td><\/tr><tr><td>pilot program<\/td><td>Verify authentic operations, adoption, and governance<\/td><td>Real workflow for a specific department or scenario<\/td><td>Can it be scaled up?<\/td><\/tr><tr><td>Production system<\/td><td>Provide stable and sustainable services<\/td><td>Official documentation, permissions, monitoring, and support<\/td><td>How to operate and continuously improve<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">The names may vary slightly across teams, but the key is that reliability and accountability cannot be conflated. A successful PoC does not mean having the security, performance, monitoring, support, and exception handling required for a production launch.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">What kind of topic is suitable for the first AI PoC?<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The frequency and impact of the problem can be explained, not just as an interesting idea.<\/li>\n\n\n<li>There is currently a non-AI baseline for comparison of time, quality, cost, or risk.<\/li>\n\n\n<li>The data is legally obtainable, and its quality and representativeness can be preliminarily evaluated.<\/li>\n\n\n<li>The scope can be limited to controllable characters, processes, and datasets.<\/li>\n\n\n<li>The consequences of the error can be manually reviewed or rolled back, and will not directly cause major harm.<\/li>\n\n\n<li>Process owners and users are willing to participate in testing and provide feedback.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">High-impact scenarios in healthcare, law, finance, recruitment, or automated decision-making should not be treated as the first use case simply because the data appears to be complete. These scenarios require more rigorous reviews of domain-specific considerations, compliance, bias, and human rights implications.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">AI PoC Selection Matrix: Value, Data, Technology, Risk, and Adoption<\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><th>Facing<\/th><th>Question to be answered<\/th><th>More robust evidence<\/th><th>Red Light<\/th><\/tr><\/thead><tbody><tr><td>Business Value<\/td><td>Whose problem and which problem to improve<\/td><td>Current Status Criteria and Responsible Person<\/td><td>For technical demonstration purposes only<\/td><\/tr><tr><td>Data<\/td><td>What about the source, legality, quality, and representativeness<\/td><td>Data list, samples, and owners<\/td><td>Unknown source or unavailable<\/td><\/tr><tr><td>Technology<\/td><td>Can it reach the usability threshold under constraints?<\/td><td>Benchmarking and High-Risk Verification<\/td><td>Use supplier demo only<\/td><\/tr><tr><td>Risk<\/td><td>How to handle errors, biases, and leaks<\/td><td>Manual review, permissions and recovery<\/td><td>The error had a direct and significant impact<\/td><\/tr><tr><td>Hiring<\/td><td>Can the users and processes take this over?<\/td><td>Real Character Participation and Educational Programs<\/td><td>No process owner<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Decisions based on the matrix are not made automatically based on the total score. Any high-risk &quot;red light&quot; may constitute a stop condition, even if the business value is very high. Companies should document the rationale for their assessments and the responsibilities involved, rather than simply recording the model scores.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">What data and governance requirements need to be in place before launching an AI PoC?<\/h2>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Issues, Current Status, Assumptions, and Expected Decisions.<\/li>\n\n\n<li>Source, legal basis for use, quality, sensitivity, and retention.<\/li>\n\n\n<li>The method for separating training, retrieval, testing, and production data.<\/li>\n\n\n<li>Models, tools, suppliers, and data flows.<\/li>\n\n\n<li>Access permissions, logging, manual review, and exception handling.<\/li>\n\n\n<li>Quality, Efficiency, Adoption, Risk, and Discontinuation Metrics.<\/li>\n\n\n<li>Product, Process, Data, Information Security, Compliance, and Domain Owners.<\/li>\n\n\n<li>Handling of data, accounts, outputs, and environments after the PoC ends.<\/li>\n<\/ol>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.nist.gov\/itl\/ai-risk-management-framework\" target=\"_blank\" rel=\"noopener\">NIST AI Risk Management Framework<\/a>Provides a voluntary framework for governing, assessing, measuring, and managing AI risks;<a href=\"https:\/\/www.nist.gov\/publications\/artificial-intelligence-risk-management-framework-generative-artificial-intelligence\" target=\"_blank\" rel=\"noopener\">Generative AI Profile<\/a>This addresses the specific risks associated with generative AI. Actual obligations should still be determined based on the industry, the data, and applicable regulations.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">How Should an AI PoC Be Conducted and Validated?<\/h2>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Establish a non-AI baseline and a representative test set.<\/li>\n\n\n<li>Define the PoC assumptions, scope, data version, and termination criteria.<\/li>\n\n\n<li>First, validate the data and high-risk technical assumptions, then proceed with the complete process.<\/li>\n\n\n<li>Operated by real users in a controlled environment, with manual review retained.<\/li>\n\n\n<li>Compare quality, efficiency, adoption, risk, and labor burden simultaneously.<\/li>\n\n\n<li>Record failure samples, biases, exceptions, and vendor limitations.<\/li>\n\n\n<li>Determined by decision-makers based on predetermined thresholds to stop, adjust, or conduct a pilot run.<\/li>\n<\/ol>\n\n\n\n<p class=\"wp-block-paragraph\">Transition only when moving to formal development or API integration after a successful PoC<a href=\"https:\/\/site-now.co\/contact\/\" target=\"_blank\" rel=\"noopener\">Ji Zhan Li submitted a system development requirement<\/a>Before that, through<a href=\"https:\/\/yenhui.co\/en\/insights\/ai-adoption-readiness-checklist\/\">AI Implementation Preparation Checklist<\/a>Confirm the business fundamentals.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Common failure in AI PoC: Treating a demo as a product<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Without a baseline, success is judged solely by \"it looks good.\"<\/li>\n\n\n<li>The test set is too small or only selects simple samples, failing to represent real-world scenarios.<\/li>\n\n\n<li>Handing over confidential information directly to unverified tools or accounts.<\/li>\n\n\n<li>We only measure accuracy and ignore risk, manual review, latency, and adoption.<\/li>\n\n\n<li>Without stopping conditions, features are continuously expanded despite poor results.<\/li>\n\n\n<li>After the PoC team left, no one took over the processes, data, and governance.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">AI PoC FAQ<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">What is the difference between an AI PoC and a general prototype?<\/h3>\n\n\n<p class=\"wp-block-paragraph\">Prototypes focus on interaction or conceptual presentation, while PoCs focus on whether key assumptions hold true. The two can be combined, but reliability, data, and exit criteria must be kept distinct.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">How long should an AI PoC take?<\/h3>\n\n\n<p class=\"wp-block-paragraph\">There is no universal fixed cycle. Scope, data, integration, risks, and decision speed all affect the timeline. Completion should be determined by whether the hypothesis can be answered rather than the calendar.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">How much data does an AI PoC need?<\/h3>\n\n\n<p class=\"wp-block-paragraph\">It depends on the problem, method, and risks. More fundamental than the data volume is verifying whether the legality, representativeness, quality, labeling, and test set can reflect real-world scenarios.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Does high accuracy mean the PoC is successful?<\/h3>\n\n\n<p class=\"wp-block-paragraph\">Not necessarily. It also depends on the error type, risk, manual burden, speed, cost, adoption, and process outcomes. A single average can mask important failures.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">When should an AI PoC be stopped?<\/h3>\n\n\n<p class=\"wp-block-paragraph\">If the core assumption fails, the data cannot be legally used, the risk is unacceptable, or the adoption cost exceeds the supportable value, the project should be stopped or reset based on prior conditions.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Can we go live directly after a successful PoC?<\/h3>\n\n\n<p class=\"wp-block-paragraph\">Usually, they cannot be directly equated to a finished product. It is also necessary to add reliability, safety, monitoring, permissions, support, exception handling, education, and governance, and to verify the risks after scaling.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Text Summary<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>PoC for Decision Service:<\/strong>First, write down what needs to be decided at the end.<\/li>\n\n\n<li><strong>When choosing a topic, look at five dimensions:<\/strong>Evaluate value, data, technology, risk, and adoption together.<\/li>\n\n\n<li><strong>Establish a non-AI baseline:<\/strong>Without a baseline, improvement cannot be proven.<\/li>\n\n\n<li><strong>Keep manual review:<\/strong>The controlled scope, failure samples, and stopping conditions must all be designed in advance.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Yanhui is available for requirements discussions regarding corporate issues and AI PoC concepts. The actual scope of consulting, deliverables, and timelines will be confirmed on a case-by-case basis. This document does not promise a fixed accuracy rate or return on investment.<\/p>\n\n\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/yenhui.co\/en\/contact\/\">Submit AI PoC evaluation request<\/a><\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>AI PoC \u7684\u76ee\u7684\uff0c\u4e0d\u662f\u8b49\u660e AI \u53ef\u4ee5\u7522\u751f\u4e00\u6bb5\u6587\u5b57\u6216\u8fa8\u8b58\u4e00\u5f35\u5716\u7247\uff0c\u800c\u662f\u964d\u4f4e\u4f01\u696d\u662f\u5426\u503c\u5f97\u6295\u8cc7\u4e0b\u4e00\u968e\u6bb5\u7684\u4e0d\u78ba\u5b9a [&hellip;]<\/p>\n","protected":false},"author":0,"featured_media":698,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_gspb_post_css":"","footnotes":""},"categories":[19],"tags":[],"class_list":["post-679","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-consulting"],"blocksy_meta":{"styles_descriptor":{"styles":{"desktop":"","tablet":"","mobile":""},"google_fonts":[],"version":7}},"_links":{"self":[{"href":"https:\/\/yenhui.co\/en\/wp-json\/wp\/v2\/posts\/679","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/yenhui.co\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/yenhui.co\/en\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/yenhui.co\/en\/wp-json\/wp\/v2\/comments?post=679"}],"version-history":[{"count":1,"href":"https:\/\/yenhui.co\/en\/wp-json\/wp\/v2\/posts\/679\/revisions"}],"predecessor-version":[{"id":689,"href":"https:\/\/yenhui.co\/en\/wp-json\/wp\/v2\/posts\/679\/revisions\/689"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/yenhui.co\/en\/wp-json\/wp\/v2\/media\/698"}],"wp:attachment":[{"href":"https:\/\/yenhui.co\/en\/wp-json\/wp\/v2\/media?parent=679"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/yenhui.co\/en\/wp-json\/wp\/v2\/categories?post=679"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/yenhui.co\/en\/wp-json\/wp\/v2\/tags?post=679"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}