{"id":18787,"date":"2025-11-24T14:26:04","date_gmt":"2025-11-24T14:26:04","guid":{"rendered":"https:\/\/goteech.io\/?p=18787"},"modified":"2025-12-14T17:45:23","modified_gmt":"2025-12-14T17:45:23","slug":"practical-steps-for-ai-business-wins","status":"publish","type":"post","link":"https:\/\/goteech.io\/zh-hk\/blog\/strategy\/practical-steps-for-ai-business-wins\/","title":{"rendered":"\u628a AI \u898f\u5283\u8f49\u5316\u70ba\u5be6\u969b\u696d\u52d9\u6210\u679c\u7684\u5177\u9ad4\u6b65\u9a5f"},"content":{"rendered":"<div data-elementor-type=\"wp-post\" data-elementor-id=\"18787\" class=\"elementor elementor-18787\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-cbfad5b elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"cbfad5b\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-85a16e6\" data-id=\"85a16e6\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-8d89dd7 elementor-toc--minimized-on-desktop elementor-widget elementor-widget-table-of-contents\" data-id=\"8d89dd7\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;exclude_headings_by_selector&quot;:&quot;post-recommend, post-recommend-grid&quot;,&quot;marker_view&quot;:&quot;bullets&quot;,&quot;icon&quot;:{&quot;value&quot;:&quot;far fa-circle&quot;,&quot;library&quot;:&quot;fa-regular&quot;},&quot;no_headings_message&quot;:&quot;No headings were found on this page.&quot;,&quot;_animation&quot;:&quot;none&quot;,&quot;minimized_on&quot;:&quot;desktop&quot;,&quot;headings_by_tags&quot;:[&quot;h4&quot;],&quot;minimize_box&quot;:&quot;yes&quot;,&quot;hierarchical_view&quot;:&quot;yes&quot;,&quot;min_height&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;min_height_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;min_height_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]}}\" data-widget_type=\"table-of-contents.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-toc__header\">\n\t\t\t\t\t\t<h4 class=\"elementor-toc__header-title\">\n\t\t\t\t\u5167\u5bb9\u76ee\u9304\t\t\t<\/h4>\n\t\t\t\t\t\t\t\t\t\t<div class=\"elementor-toc__toggle-button elementor-toc__toggle-button--expand\" role=\"button\" tabindex=\"0\" aria-controls=\"elementor-toc__8d89dd7\" aria-expanded=\"true\" aria-label=\"Open table of contents\"><i aria-hidden=\"true\" class=\"fas fa-chevron-down\"><\/i><\/div>\n\t\t\t\t<div class=\"elementor-toc__toggle-button elementor-toc__toggle-button--collapse\" role=\"button\" tabindex=\"0\" aria-controls=\"elementor-toc__8d89dd7\" aria-expanded=\"true\" aria-label=\"Close table of contents\"><i aria-hidden=\"true\" class=\"fas fa-chevron-up\"><\/i><\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<div id=\"elementor-toc__8d89dd7\" class=\"elementor-toc__body\">\n\t\t\t<div class=\"elementor-toc__spinner-container\">\n\t\t\t\t<i class=\"elementor-toc__spinner eicon-animation-spin eicon-loading\" aria-hidden=\"true\"><\/i>\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-adb21d2 elementor-widget elementor-widget-spacer\" data-id=\"adb21d2\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\n\t\t<div class=\"elementor-element elementor-element-c410a2a elementor-widget elementor-widget-wp-widget-text\" data-id=\"c410a2a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"wp-widget-text.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t<div class=\"textwidget\"><h4 style=\"margin-bottom: 12px\">Why so many AI plans stall before they win<\/h4>\n<p>AI is everywhere in slide decks\u2014and nowhere in actual workflows. Multiple studies have found that a very large share of analytics and AI initiatives fail to deliver measurable business value, often due to weak data foundations, poor integration, or a lack of clear ownership. Gartner has previously estimated failure rates of 80\u201385% for big data and analytics projects, and recent work citing <a href=\"https:\/\/www.datascience-pm.com\/project-failures\/\">MIT research<\/a> suggests that around 95% of generative AI pilots don\u2019t reach meaningful production impact.<\/p>\n<p>The good news: the small minority of organizations that do see outsized returns tend to follow a common pattern\u2014clear strategy, disciplined use-case selection, strong governance, and relentless focus on embedding AI into day-to-day work rather than one-off experiments. McKinsey, BCG, Deloitte and others describe this as moving from \u201cexperiments\u201d to \u201cAI at scale.\u201d<\/p>\n<p>This guide is about turning your AI plans into wins by following that pattern.<\/p>\n<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-df253e8 elementor-widget elementor-widget-wp-widget-text\" data-id=\"df253e8\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"wp-widget-text.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t<div class=\"textwidget\"><h4 style=\"margin-bottom: 12px\">Step 1: Tie AI to strategy, not demos<\/h4>\n<p>Before you touch a model or vendor, answer three questions:<\/p>\n<p><b>What problem are we solving?<\/b><br \/>\nBe specific: \u201cReduce average handle time in support by 20%\u201d beats \u201cuse chatbots.\u201d Industry playbooks repeatedly emphasize that top performers start from business outcomes, not from technology features.<\/p>\n<p><b>Where is AI uniquely suited to help?<\/b><br \/>\nLook for tasks that are data-rich, repetitive, and currently bottlenecked\u2014like classifying tickets, summarizing documents, forecasting demand, or extracting insights from text.<\/p>\n<p><b>How will we measure success?<\/b><br \/>\nDecide on 2\u20133 core metrics (e.g., time saved, revenue uplift, error rate reduction) and set baselines before implementation. NIST\u2019s AI Risk Management Framework encourages aligning metrics with broader organizational risk and value goals.<\/p>\n<p>Without these answers, you risk becoming another \u201c95% fail\u201d statistic.<\/p>\n<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f011ab7 elementor-widget elementor-widget-wp-widget-text\" data-id=\"f011ab7\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"wp-widget-text.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t<div class=\"textwidget\"><h4 style=\"margin-bottom: 12px\">Step 2: Choose high-signal, low-friction AI use cases<\/h4>\n<p>Not all AI ideas are created equal. Some deliver quick proof of value; others require major change. Early wins matter for trust, funding, and culture.<\/p>\n<p>Here\u2019s a simple way to score use cases:<\/p>\n<div class=\"table-scroll\">\n<table class=\"wide-table\" style=\"border-collapse: separate;border-spacing: 0;border-radius: 10px;width: 100%\">\n<tbody>\n<tr style=\"background-color: #0077cb;color: #ffffff\">\n<th style=\"width: 30%\">Criterion<\/th>\n<th style=\"width: 70%\">What \u201cgood\u201d looks like<\/th>\n<\/tr>\n<tr>\n<td><b>Business impact<\/b><\/td>\n<td>Clear link to revenue, cost, or risk; impact measurable within 3\u20136 months.<\/td>\n<\/tr>\n<tr>\n<td><b>Data readiness<\/b><\/td>\n<td>Reliable, accessible data with reasonable coverage and quality.<\/td>\n<\/tr>\n<tr>\n<td><b>Technical feasibility<\/b><\/td>\n<td>Well-understood patterns (classification, summarization, retrieval, forecasting).<\/td>\n<\/tr>\n<tr>\n<td><b>Process fit<\/b><\/td>\n<td>Owners identified; process can change without regulatory deadlock.<\/td>\n<\/tr>\n<tr>\n<td><b>Stakeholder appetite<\/b><\/td>\n<td>A business sponsor who <i>wants<\/i> this to work and will champion adoption.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-de06fd8 elementor-hidden-desktop elementor-widget elementor-widget-spacer\" data-id=\"de06fd8\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5260b4c elementor-widget elementor-widget-wp-widget-text\" data-id=\"5260b4c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"wp-widget-text.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t<div class=\"textwidget\"><p>Research on failed AI projects consistently points to poor scoping and weak data as root causes; using a simple scoring model up front dramatically improves your odds.<\/p>\n<p>Shortlist 3\u20135 use cases, then pick one or two that are high impact and tractable as your first pilots.<\/p>\n<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e46ac08 elementor-widget elementor-widget-wp-widget-text\" data-id=\"e46ac08\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"wp-widget-text.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t<div class=\"textwidget\"><h4 style=\"margin-bottom: 12px\"><strong>Step 3: Assemble the right AI \u201ctoolkit\u201d and architecture<\/strong><\/h4>\n<p>Think of implementation as building a house, not buying a gadget. You\u2019re choosing structural elements that must fit your existing foundations.<\/p>\n<p><b>Technical fit: can it plug into reality?<\/b><\/p>\n<p>When evaluating AI tooling and platforms, focus on:<\/p>\n<ul>\n<li><b>Integration:<\/b> Can it connect to your data warehouses, CRMs, ticketing systems, document stores, and identity systems via modern APIs and connectors? Cloud providers and consulting firms repeatedly identify integration as the deciding factor between pilots and scaled deployments.<\/li>\n<li><b>Scalability &amp; performance:<\/b> Will it handle real-world load, data volume, and latency needs as adoption grows?<\/li>\n<li><b>Security &amp; compliance:<\/b> Does it support encryption, access control, audit logs, and regional data residency where needed?<\/li>\n<li><b>Observability:<\/b> Can you log predictions, prompts, responses, features, and errors in a way that supports monitoring and debugging?<\/li>\n<\/ul>\n<p><b>Business fit: will people actually use it?<\/b><\/p>\n<p>Beyond tech, look at:<\/p>\n<ul>\n<li><b>Total cost of ownership:<\/b> License\/compute plus integration work, MLOps, monitoring, and ongoing support.<\/li>\n<li><b>Usability:<\/b> Non-technical users should be able to interact with the system through sensible UIs or conversational interfaces. Surveys on AI in the workplace emphasize that usability and workflow fit drive realized value as much as model quality.<\/li>\n<li><b>Vendor viability &amp; roadmap:<\/b> You\u2019re betting part of your operations on their reliability and long-term support.<\/li>\n<\/ul>\n<p>Shortlist 2\u20133 feasible tool stacks; run short technical spikes or proof-of-concepts to check integration friction before committing.<\/p>\n<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7b53294 elementor-widget elementor-widget-wp-widget-text\" data-id=\"7b53294\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"wp-widget-text.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t<div class=\"textwidget\"><h4 style=\"margin-bottom: 12px\"><strong>Step 4: Design the workflow, not just the model<\/strong><\/h4>\n<p>A common failure mode: teams build an impressive model, then realize nobody knows how it fits into the day-to-day work. Successful AI leaders reverse that order: design the workflow first.<\/p>\n<p><b>Key questions:<\/b><\/p>\n<ul>\n<li>Where does AI show up\u2014inside existing tools (CRM, helpdesk, ERP, IDE) or a new app?<\/li>\n<li>What does a \u201chappy path\u201d look like for a typical user?<\/li>\n<li>When should the system ask for more information, escalate to a human, or decline to act?<\/li>\n<li>How will feedback (corrections, rejections, approvals) flow back to improve the system?<\/li>\n<\/ul>\n<p>McKinsey and BCG case studies of AI \u201cleaders\u201d repeatedly stress end-to-end journey redesign\u2014embedding AI at key decision points instead of building stand-alone gadgets.<\/p>\n<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4ce106e elementor-widget elementor-widget-wp-widget-text\" data-id=\"4ce106e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"wp-widget-text.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t<div class=\"textwidget\"><h4 style=\"margin-bottom: 12px\"><strong>Step 5: Run AI pilots that actually prove value<\/strong><\/h4>\n<p>A good AI pilot is not a toy demo; it\u2019s a controlled experiment.<\/p>\n<p><b>Define a sharp hypothesis.<\/b><br \/>\nExample: \u201cIf we use an AI assistant to summarize customer tickets, we reduce average handle time by 15% without increasing escalations.\u201d<\/p>\n<p><b>Prepare your data and guardrails.<\/b><br \/>\nClean and label representative data; decide where AI suggestions are optional vs. enforced; anonymize or mask sensitive fields where possible. Gartner and NIST both highlight data quality and governance as non-negotiable foundations.<\/p>\n<p><b>Pick evaluation metrics and baselines.<\/b><br \/>\nCompare before\/after: productivity, quality, user satisfaction, and any risk indicators (error rates, bias measures, override rates).<\/p>\n<p><b>Limit scope but mimic real conditions.<\/b><br \/>\nPilot in one region, product, or team\u2014but avoid artificial \u201clab\u201d conditions. MIT\u2019s research on the 95% failure rate points to poor integration into actual workflows as a leading cause of pilot failure.<\/p>\n<p><b>Capture qualitative feedback.<\/b><br \/>\nTalk to frontline users about friction points, trust, and moments when they ignored or over-relied on AI.<\/p>\n<p>At the end of the pilot, you should be able to say \u201cthis works; here is the quantified value and risk profile\u201d or \u201cthis doesn\u2019t yet meet the bar and here\u2019s why.\u201d<\/p>\n<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-983069e elementor-widget elementor-widget-wp-widget-text\" data-id=\"983069e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"wp-widget-text.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t<div class=\"textwidget\"><h4 style=\"margin-bottom: 12px\"><strong>Step 6: Put governance and risk management in place<\/strong><\/h4>\n<p>As AI moves from slideware to operations, governance can\u2019t be an afterthought. The NIST AI Risk Management Framework and its companion Playbook provide a practical structure with four core functions: Govern, Map, Measure, Manage.<\/p>\n<p>In practice:<\/p>\n<ul>\n<li><b>Govern:<\/b> Define roles (sponsors, product owners, risk\/compliance, security, data owners). Publish internal policies on acceptable AI use, data retention, and escalation.<\/li>\n<li><b>Map:<\/b> Document context\u2014stakeholders, data sources, intended use, potential harms (e.g., discrimination, misinformation, security).<\/li>\n<li><b>Measure:<\/b> Monitor performance, drift, fairness, robustness, and security; set thresholds and alerts.<\/li>\n<li><b>Manage:<\/b> Respond to issues with documented incident playbooks, retraining, rollbacks, or changes to human oversight.<\/li>\n<\/ul>\n<p>External guidance emphasizes that aligning to a recognized framework like NIST AI RMF doesn\u2019t just reduce risk; it also builds trust with regulators, customers, and employees.<\/p>\n<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fbb0c6f elementor-widget elementor-widget-wp-widget-text\" data-id=\"fbb0c6f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"wp-widget-text.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t<div class=\"textwidget\"><h4 style=\"margin-bottom: 12px\"><strong>Step 7: Scale from pilot to portfolio<\/strong><\/h4>\n<p>Once an AI pilot proves value and passes governance checks, the goal is to scale:<\/p>\n<p><b>Industrialize your MLOps and LLMOps.<\/b><br \/>\nStandardize versioning, CI\/CD for models and prompts, monitoring, alerting, and rollback procedures.<\/p>\n<p><b>Embed AI into core workflows.<\/b><br \/>\nIntegrate into existing tools (CRM, ERP, productivity suites) rather than forcing users into yet another app. Leading companies treat AI as a horizontal capability woven into functions like sales, support, finance, and HR. <\/p>\n<p><b>Build a living AI roadmap.<\/b><br \/>\nUse learnings from early use cases to prioritize the next wave\u2014applying the same scoring lens (impact, feasibility, data readiness, sponsor ownership).<\/p>\n<p><b>Invest in people and culture.<\/b><br \/>\nMcKinsey\u2019s workplace AI research and multiple MIT-linked reports stress that the differentiator isn\u2019t just tech\u2014it\u2019s training, change management, and redesigning jobs so people and AI amplify each other. <\/p>\n<p><b>Continuously measure value.<\/b><br \/>\nRegularly review business KPIs, user adoption, risk incidents, and maintenance costs. Retire underperforming experiments, double down on proven engines.<\/p>\n<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8e65718 elementor-widget elementor-widget-wp-widget-text\" data-id=\"8e65718\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"wp-widget-text.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t<div class=\"textwidget\"><h4 style=\"margin-bottom: 12px\"><strong>Quick summary: turning plans into wins<\/strong><\/h4>\n<p>Most AI projects don\u2019t fail because the models are weak; they fail because they\u2019re disconnected from strategy, workflows, data reality, and governance. The path to wins looks very different:<\/p>\n<ul>\n<li>Start with business outcomes and high-signal use cases.<\/li>\n<li>Choose tools that fit your architecture and your people.<\/li>\n<li>Design end-to-end workflows and run pilots as serious experiments.<\/li>\n<li>Govern with a recognized framework like NIST AI RMF.<\/li>\n<li>Scale only when you have proven value and a repeatable playbook.<\/li>\n<\/ul>\n<p>Do that, and your AI plans stop being splashy slideware\u2014and start showing up in revenue, cost, risk, and employee experience metrics that actually matter.<\/p>\n<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d5b7f9c elementor-widget elementor-widget-spacer\" data-id=\"d5b7f9c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-150f1a1 e-grid-align-left elementor-shape-rounded elementor-grid-0 elementor-widget elementor-widget-social-icons\" data-id=\"150f1a1\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"social-icons.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-social-icons-wrapper elementor-grid\" role=\"list\">\n\t\t\t\t\t\t\t<span class=\"elementor-grid-item\" role=\"listitem\">\n\t\t\t\t\t<a class=\"elementor-icon elementor-social-icon elementor-social-icon-facebook-f elementor-repeater-item-bd158f5\" href=\"https:\/\/www.facebook.com\/\" target=\"_blank\">\n\t\t\t\t\t\t<span class=\"elementor-screen-only\">Facebook-f<\/span>\n\t\t\t\t\t\t<i aria-hidden=\"true\" class=\"fab fa-facebook-f\"><\/i>\t\t\t\t\t<\/a>\n\t\t\t\t<\/span>\n\t\t\t\t\t\t\t<span class=\"elementor-grid-item\" role=\"listitem\">\n\t\t\t\t\t<a class=\"elementor-icon elementor-social-icon elementor-social-icon-x-twitter elementor-repeater-item-c81668c\" href=\"http:\/\/x.com\/\" target=\"_blank\">\n\t\t\t\t\t\t<span class=\"elementor-screen-only\">X-twitter<\/span>\n\t\t\t\t\t\t<i aria-hidden=\"true\" class=\"fab fa-x-twitter\"><\/i>\t\t\t\t\t<\/a>\n\t\t\t\t<\/span>\n\t\t\t\t\t\t\t<span class=\"elementor-grid-item\" role=\"listitem\">\n\t\t\t\t\t<a class=\"elementor-icon elementor-social-icon elementor-social-icon-linkedin-in elementor-repeater-item-c1bfed6\" href=\"https:\/\/www.linkedin.com\" target=\"_blank\">\n\t\t\t\t\t\t<span class=\"elementor-screen-only\">Linkedin-in<\/span>\n\t\t\t\t\t\t<i aria-hidden=\"true\" class=\"fab fa-linkedin-in\"><\/i>\t\t\t\t\t<\/a>\n\t\t\t\t<\/span>\n\t\t\t\t\t\t\t<span class=\"elementor-grid-item\" role=\"listitem\">\n\t\t\t\t\t<a class=\"elementor-icon elementor-social-icon elementor-social-icon-whatsapp elementor-repeater-item-609b641\" href=\"https:\/\/web.whatsapp.com\/\" target=\"_blank\">\n\t\t\t\t\t\t<span class=\"elementor-screen-only\">Whatsapp<\/span>\n\t\t\t\t\t\t<i aria-hidden=\"true\" class=\"fab fa-whatsapp\"><\/i>\t\t\t\t\t<\/a>\n\t\t\t\t<\/span>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fb8fcab elementor-widget elementor-widget-spacer\" data-id=\"fb8fcab\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9482c13 align--mobileleft animated-fast align-left elementor-invisible elementor-widget elementor-widget-mae-link\" data-id=\"9482c13\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;_animation&quot;:&quot;fadeInRight&quot;,&quot;_animation_delay&quot;:200}\" data-widget_type=\"mae-link.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\n        <a class=\"master-link  icon-left\" href=\"https:\/\/goteech.io\/zh-hk\/resources\/\" >\n            <span class=\"icon unic unic-arrow-circle-left\"><\/span>            <span>\u8fd4\u56de\u60a8\u7684\u8cc7\u6e90<\/span>\n                    <\/a>\n\n        \t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-66725a6 elementor-widget elementor-widget-spacer\" data-id=\"66725a6\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"<p>\u900f\u904e\u9010\u6b65\u6307\u5f15\uff0c\u5f9e\u754c\u5b9a\u61c9\u7528\u5834\u666f\u3001\u9078\u64c7\u5408\u9069\u5de5\u5177\u3001\u7ba1\u7406\u98a8\u96aa\uff0c\u5230\u5c07 AI \u5d4c\u5165\u73fe\u6709\u5de5\u4f5c\u6d41\u7a0b\uff0c\u5354\u52a9\u4f60\u628a AI \u898f\u5283\u8f49\u5316\u70ba\u53ef\u91cf\u5ea6\u7684\u5be6\u969b\u696d\u52d9\u6210\u679c\u3002<\/p>","protected":false},"author":1,"featured_media":18790,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[101],"tags":[],"class_list":["post-18787","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-strategy"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/goteech.io\/zh-hk\/wp-json\/wp\/v2\/posts\/18787","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/goteech.io\/zh-hk\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/goteech.io\/zh-hk\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/goteech.io\/zh-hk\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/goteech.io\/zh-hk\/wp-json\/wp\/v2\/comments?post=18787"}],"version-history":[{"count":11,"href":"https:\/\/goteech.io\/zh-hk\/wp-json\/wp\/v2\/posts\/18787\/revisions"}],"predecessor-version":[{"id":19442,"href":"https:\/\/goteech.io\/zh-hk\/wp-json\/wp\/v2\/posts\/18787\/revisions\/19442"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/goteech.io\/zh-hk\/wp-json\/wp\/v2\/media\/18790"}],"wp:attachment":[{"href":"https:\/\/goteech.io\/zh-hk\/wp-json\/wp\/v2\/media?parent=18787"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/goteech.io\/zh-hk\/wp-json\/wp\/v2\/categories?post=18787"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/goteech.io\/zh-hk\/wp-json\/wp\/v2\/tags?post=18787"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}