{"id":999979086,"date":"2025-11-03T11:29:19","date_gmt":"2025-11-03T04:29:19","guid":{"rendered":"https:\/\/bizzi.vn\/?p=999979086"},"modified":"2026-06-07T16:37:32","modified_gmt":"2026-06-07T09:37:32","slug":"difficult-to-deploy","status":"publish","type":"post","link":"https:\/\/bizzi.vn\/en\/difficult-to-deploy\/","title":{"rendered":"Are businesses having difficulty implementing AI? 8 barriers to applying artificial intelligence and solutions from Bizzi.vn"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">Most businesses today are having difficulty implementing AI, whether large or small. According to reports from Gartner, McKinsey and Deloitte, up to 70-80% AI projects globally do not meet expectations or fail in the early stages, and this situation is especially evident in developing markets like Vietnam. The 3 main groups of causes are: Unprepared data platform - Lack of strategy and specialized personnel - Unintegrated systems and processes.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This article will analyze in detail the barriers to applying artificial intelligence, and at the same time present comprehensive solutions that Bizzi provides, helping businesses overcome challenges in the journey of applying AI. Let&#039;s find out what problems businesses encounter with AI!\u00a0<\/span><\/p>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_80 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Index<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewbox=\"0 0 24 24\" version=\"1.2\" baseprofile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/bizzi.vn\/en\/difficult-to-deploy\/#Boi_canh_AI_%E2%80%93_Xu_huong_tat_yeu_nhung_day_thach_thuc\" >Background: AI \u2013 An inevitable but challenging trend<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/bizzi.vn\/en\/difficult-to-deploy\/#Nhung_kho_khan_khi_trien_khai_AI_cua_doanh_nghiep\" >Difficulties in implementing AI in enterprises<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/bizzi.vn\/en\/difficult-to-deploy\/#Thieu_du_lieu_chat_luong_va_kha_nang_quan_tri_du_lieu_Data_Governance\" >Lack of quality data and data governance<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/bizzi.vn\/en\/difficult-to-deploy\/#Ha_tang_cong_nghe_chua_san_sang\" >Technology infrastructure is not ready<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/bizzi.vn\/en\/difficult-to-deploy\/#Thieu_chuyen_gia_va_nang_luc_noi_bo_ve_du_lieu_%E2%80%93_AI\" >Lack of internal expertise and capacity in data \u2013 AI<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/bizzi.vn\/en\/difficult-to-deploy\/#Thieu_chien_luoc_AI_va_dinh_huong_ROI_ro_rang\" >Lack of clear AI strategy and ROI direction<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/bizzi.vn\/en\/difficult-to-deploy\/#Rao_can_khi_rao_can_ap_dung_tri_tue_nhan_tao_Tich_hop_giua_AI_va_he_thong_hien_co\" >Barriers to AI Adoption: Integration between AI and Existing Systems<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/bizzi.vn\/en\/difficult-to-deploy\/#Chi_phi_dau_tu_cao_va_hieu_qua_chua_tuong_xung\" >High investment costs and disproportionate efficiency<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/bizzi.vn\/en\/difficult-to-deploy\/#Van_de_dao_duc_phap_ly_va_an_toan_du_lieu\" >Ethical, legal and data security issues<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/bizzi.vn\/en\/difficult-to-deploy\/#Thieu_nen_van_hoa_du_lieu_Data-Driven_Culture\" >Lack of Data-Driven Culture<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/bizzi.vn\/en\/difficult-to-deploy\/#Bizzi_%E2%80%93_Giai_phap_toan_dien_cho_phong_ke_toan_%E2%80%93_tai_chinh_san_sang_ung_dung_AI\" >Bizzi \u2013 Comprehensive solution for accounting and finance departments ready to apply AI<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/bizzi.vn\/en\/difficult-to-deploy\/#1_Vai_tro_cua_phong_ke_toan_%E2%80%93_tai_chinh_trong_ung_dung_AI\" >1. The role of the accounting and finance department in AI application<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/bizzi.vn\/en\/difficult-to-deploy\/#Ket_luan\" >Conclude<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"Boi_canh_AI_%E2%80%93_Xu_huong_tat_yeu_nhung_day_thach_thuc\"><\/span><b>Background: AI \u2013 An inevitable but challenging trend<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Artificial Intelligence (AI) is gradually becoming a pillar in the digital transformation journey of enterprises, with a series of practical applications in finance, manufacturing, logistics, marketing and operations management. However, according to Gartner, more than 70% AI projects fail because they do not create measurable value (ROI) - mainly due to fragmented, unstandardized and improperly managed data.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In Vietnam, many businesses have begun experimenting with AI in activities such as automating financial reporting, invoice processing, or customer care chatbots, but most do not have a data platform that is \u201cclean\u201d and unified enough for AI to truly be effective.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u00a0The question is: <\/span><i><span style=\"font-weight: 400;\">What problems do businesses have with AI \u2013 and what is the right direction to turn AI into real value, not just a trend?<\/span><\/i><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Nhung_kho_khan_khi_trien_khai_AI_cua_doanh_nghiep\"><\/span><b>Difficulties in implementing AI in enterprises<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Although AI is an inevitable trend, most businesses still <\/span><b>not yet achieved real value<\/b><span style=\"font-weight: 400;\"> from the implementation projects. <\/span><b>difficulties in implementing AI<\/b><span style=\"font-weight: 400;\"> It&#039;s not the technology itself, but the <\/span><b>The governance, data and people platforms are not ready.<\/b><\/p>\n<table style=\"height: 408px; width: 100%;\" border=\"1\">\n<tbody>\n<tr style=\"height: 24px;\">\n<td style=\"width: 21.5845%; height: 24px;\"><b>Problem group<\/b><\/td>\n<td style=\"width: 39.4503%; height: 24px;\"><b>Root cause<\/b><\/td>\n<td style=\"width: 38.3185%; height: 24px;\"><b>Consequences<\/b><\/td>\n<\/tr>\n<tr style=\"height: 48px;\">\n<td style=\"width: 21.5845%; height: 48px;\"><b>1. Data<\/b><\/td>\n<td style=\"width: 39.4503%; height: 48px;\"><span style=\"font-weight: 400;\">Data lacks standardization and is not managed consistently across departments.<\/span><\/td>\n<td style=\"width: 38.3185%; height: 48px;\"><span style=\"font-weight: 400;\">AI \u201clearns wrongly\u201d, makes misleading predictions, and cannot create reliable insights<\/span><\/td>\n<\/tr>\n<tr style=\"height: 48px;\">\n<td style=\"width: 21.5845%; height: 48px;\"><b>2. Technology infrastructure<\/b><\/td>\n<td style=\"width: 39.4503%; height: 48px;\"><span style=\"font-weight: 400;\"><a href=\"https:\/\/bizzi.vn\/erp-la-gi-phan-mem-erp-mang-lai-loi-ich-gi-cho-doanh-nghiep\/\">ERP<\/a>\/Closed CRM, old technology does not support API integration<\/span><\/td>\n<td style=\"width: 38.3185%; height: 48px;\"><span style=\"font-weight: 400;\">AI cannot access or synchronize data, leading to \u201cstillborn projects\u201d<\/span><\/td>\n<\/tr>\n<tr style=\"height: 48px;\">\n<td style=\"width: 21.5845%; height: 48px;\"><b>3. Human resources<\/b><\/td>\n<td style=\"width: 39.4503%; height: 48px;\"><span style=\"font-weight: 400;\">Lack of AI experts, internal teams not yet capable of exploiting new technology, fear of change<\/span><\/td>\n<td style=\"width: 38.3185%; height: 48px;\"><span style=\"font-weight: 400;\">Project is delayed, dependent on external suppliers, cannot be expanded<\/span><\/td>\n<\/tr>\n<tr style=\"height: 48px;\">\n<td style=\"width: 21.5845%; height: 48px;\"><b>4. Strategy<\/b><\/td>\n<td style=\"width: 39.4503%; height: 48px;\"><span style=\"font-weight: 400;\">No clear AI roadmap, lack of specific KPIs and ROI goals<\/span><\/td>\n<td style=\"width: 38.3185%; height: 48px;\"><span style=\"font-weight: 400;\">Management loses confidence, projects are halted or redirected midway<\/span><\/td>\n<\/tr>\n<tr style=\"height: 48px;\">\n<td style=\"width: 21.5845%; height: 48px;\"><b>5. Cost<\/b><\/td>\n<td style=\"width: 39.4503%; height: 48px;\"><span style=\"font-weight: 400;\">High initial investment, costly model maintenance and update costs<\/span><\/td>\n<td style=\"width: 38.3185%; height: 48px;\"><span style=\"font-weight: 400;\">The project lacks sustainability and is difficult to maintain without quick results.<\/span><\/td>\n<\/tr>\n<tr style=\"height: 48px;\">\n<td style=\"width: 21.5845%; height: 48px;\"><b>6. Ethics &amp; Legality<\/b><\/td>\n<td style=\"width: 39.4503%; height: 48px;\"><span style=\"font-weight: 400;\">Lack of data control policies, risk of security breaches or AI bias<\/span><\/td>\n<td style=\"width: 38.3185%; height: 48px;\"><span style=\"font-weight: 400;\">Causing legal risks, loss of reputation and trust from customers and partners<\/span><\/td>\n<\/tr>\n<tr style=\"height: 24px;\">\n<td style=\"width: 21.5845%; height: 24px;\"><b>7. Corporate culture<\/b><\/td>\n<td style=\"width: 39.4503%; height: 24px;\"><span style=\"font-weight: 400;\">\u201cSilo\u201d thinking \u2013 departments do not share data, fear of being monitored<\/span><\/td>\n<td style=\"width: 38.3185%; height: 24px;\"><span style=\"font-weight: 400;\">AI does not have enough data to learn, the analysis results are not comprehensive<\/span><\/td>\n<\/tr>\n<tr style=\"height: 72px;\">\n<td style=\"width: 21.5845%; height: 72px;\"><b>8. Financial and data management<\/b><\/td>\n<td style=\"width: 39.4503%; height: 72px;\"><span style=\"font-weight: 400;\">Lack of FP&amp;A (Financial Planning &amp; Analysis) department and <a href=\"https:\/\/bizzi.vn\/epm-la-gi\/\">EPM<\/a> (Enterprise Performance Management) to control data quality<\/span><\/td>\n<td style=\"width: 38.3185%; height: 72px;\"><span style=\"font-weight: 400;\">Fragmented data does not create a solid foundation for AI to operate effectively.<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><span style=\"font-weight: 400;\">In short, most failures in AI projects do not come from algorithms, but from weak \u201cdata governance infrastructure\u201d. That is why EPM becomes a mandatory stepping stone \u2013 helping businesses standardize data, unify processes, and create a foundation for AI to learn correctly \u2013 predict correctly \u2013 and bring real value.<\/span><\/p>\n<figure id=\"attachment_999979087\" aria-describedby=\"caption-attachment-999979087\" style=\"width: 1171px\" class=\"wp-caption aligncenter\"><img fetchpriority=\"high\" decoding=\"async\" class=\"wp-image-999979087 size-full\" src=\"https:\/\/bizzi.vn\/wp-content\/uploads\/2025\/11\/kho-khan-khi-trien-khai-ai-1.png\" alt=\"AI 3 deployment difficulty\" width=\"1171\" height=\"792\" title=\"\" srcset=\"https:\/\/bizzi.vn\/wp-content\/uploads\/2025\/11\/kho-khan-khi-trien-khai-ai-1.png 1171w, https:\/\/bizzi.vn\/wp-content\/uploads\/2025\/11\/kho-khan-khi-trien-khai-ai-1-300x203.png 300w, https:\/\/bizzi.vn\/wp-content\/uploads\/2025\/11\/kho-khan-khi-trien-khai-ai-1-1024x693.png 1024w, https:\/\/bizzi.vn\/wp-content\/uploads\/2025\/11\/kho-khan-khi-trien-khai-ai-1-768x519.png 768w, https:\/\/bizzi.vn\/wp-content\/uploads\/2025\/11\/kho-khan-khi-trien-khai-ai-1-18x12.png 18w\" sizes=\"(max-width: 1171px) 100vw, 1171px\" \/><figcaption id=\"caption-attachment-999979087\" class=\"wp-caption-text\">The difficulties in implementing AI lie not only in the technology itself, but also in the lack of governance, data, and people.<\/figcaption><\/figure>\n<p><span style=\"font-weight: 400;\">Let&#039;s analyze typical reasons why businesses have problems with AI.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Thieu_du_lieu_chat_luong_va_kha_nang_quan_tri_du_lieu_Data_Governance\"><\/span><b>Lack of quality data and data governance<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">AI is only smart when the data is \u201cclean\u201d and \u201cstandard\u201d enough. But this is an inherent weakness of most Vietnamese businesses.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Currently, data in businesses is often divided by department, for example: accounting, ERP, marketing or operations departments are all stored and managed separately, without a seamless connection.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Metadata standards are also not unified \u2014 the same supplier, but the ERP system sets one code, but the invoice uses another code. As a result, the system cannot \u201crecognize\u201d identical data for AI to learn accurately.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">More importantly, many businesses do not have a Data Governance Framework:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Who is responsible for creating and maintaining the data?<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Who can edit?<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Who ensures that data is audited and updated in a timely manner?<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Without clear answers to those questions, data is prone to be biased, duplicated, or no longer useful.<\/span><\/p>\n<p><em><span style=\"font-weight: 400;\">Real-life example: A large FMCG company in Vietnam deployed an AI model to forecast sales, but the results were 25% off due to duplicate and inconsistent retail data between 3 systems (POS, ERP, CRM). The result was that the AI model &quot;learned wrongly&quot;, forecasted wrongly, and led to wrong business decisions.<\/span><\/em><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Ha_tang_cong_nghe_chua_san_sang\"><\/span><b>Technology infrastructure is not ready<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">What problems do businesses encounter with AI in terms of technology infrastructure? AI cannot operate effectively without a strong enough technology platform to \u201cfeed\u201d and \u201cconnect\u201d data. To avoid difficulties when deploying AI, businesses need two prerequisites:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Big Data Infrastructure \u2014 enables the collection, cleaning, and analysis of massive amounts of data in real time.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Integration Layer between AI and core systems such as ERP, CRM, EPM \u2014 helps data flow smoothly and synchronously.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">However, the reality in Vietnam shows that:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">About 60% small and medium-sized businesses are still saving data manually on Excel or Google Sheet.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Many ERP systems are outdated and lack open APIs, making it nearly impossible to connect to AI tools.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Real-life example: A logistics company deployed AI to optimize delivery routes, but repeatedly encountered \u201ctimeout\u201d errors and lack of GPS data because the old operating system was not compatible with the new AI platform. As a result, the project stalled, the AI model did not achieve the expected efficiency, causing a waste of investment resources.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Thieu_chuyen_gia_va_nang_luc_noi_bo_ve_du_lieu_%E2%80%93_AI\"><\/span><b>Lack of internal expertise and capacity in data \u2013 AI<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">One of the biggest challenges businesses face when implementing AI isn&#039;t the technology, but the <\/span><b>human<\/b><span style=\"font-weight: 400;\">So what problems do businesses have with AI?<\/span><\/p>\n<p>In Vietnam, human resources with expertise in data and AI are still extremely scarce \u2014 especially in key positions such as <i>Data Scientist<\/i>, <i>Machine Learning Engineer<\/i>, or <i>Data Analyst<\/i>Meanwhile, the internal team (especially the finance and accounting department) is mainly familiar with manual processes, depends on Excel, and has no experience in data modeling or reading and understanding results from AI.<\/p>\n<p><em><b>Real life example:<\/b><span style=\"font-weight: 400;\"> A retail group deployed AI to forecast cash flow, but the accounting team still entered data manually and did not standardize the format. The result: the AI model \u201clearned wrongly\u201d, produced erroneous forecasts, and forced the project to stop midway.<\/span><\/em><\/p>\n<p><span style=\"font-weight: 400;\">Besides, <\/span><b>culture of &quot;fear of change&quot;<\/b><span style=\"font-weight: 400;\"> AI implementation can also be difficult within an organization. When employees don\u2019t trust or understand the benefits of AI, projects can easily encounter hidden resistance, leading to delays or failure.<\/span><\/p>\n<figure id=\"attachment_999979091\" aria-describedby=\"caption-attachment-999979091\" style=\"width: 1424px\" class=\"wp-caption aligncenter\"><img decoding=\"async\" class=\"wp-image-999979091 size-full\" src=\"https:\/\/bizzi.vn\/wp-content\/uploads\/2025\/11\/kho-khan-khi-trien-khai-ai-4.jpg\" alt=\"difficulty in deploying AI \" width=\"1424\" height=\"1053\" title=\"\" srcset=\"https:\/\/bizzi.vn\/wp-content\/uploads\/2025\/11\/kho-khan-khi-trien-khai-ai-4.jpg 1424w, https:\/\/bizzi.vn\/wp-content\/uploads\/2025\/11\/kho-khan-khi-trien-khai-ai-4-300x222.jpg 300w, https:\/\/bizzi.vn\/wp-content\/uploads\/2025\/11\/kho-khan-khi-trien-khai-ai-4-1024x757.jpg 1024w, https:\/\/bizzi.vn\/wp-content\/uploads\/2025\/11\/kho-khan-khi-trien-khai-ai-4-768x568.jpg 768w, https:\/\/bizzi.vn\/wp-content\/uploads\/2025\/11\/kho-khan-khi-trien-khai-ai-4-16x12.jpg 16w\" sizes=\"(max-width: 1424px) 100vw, 1424px\" \/><figcaption id=\"caption-attachment-999979091\" class=\"wp-caption-text\">One of the biggest challenges businesses face when implementing AI isn&#039;t the technology, but the people.<\/figcaption><\/figure>\n<h3><span class=\"ez-toc-section\" id=\"Thieu_chien_luoc_AI_va_dinh_huong_ROI_ro_rang\"><\/span><b>Lack of clear AI strategy and ROI direction<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">One of the common reasons why AI projects fail is the lack of an overall strategy and clear quantitative goals.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Many businesses today deploy AI as a fad \u2014 seeing competitors doing it and following suit, or experimenting in fragmented ways in each department without linking it to the organization\u2019s long-term growth strategy. As a result, AI does not create real value, but just stops at the \u201ctry it out for the sake of it\u201d level.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In reality, most businesses do not have an AI Roadmap: they do not define specific KPIs, do not measure ROI (return on investment), and do not have a process for managing results.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Real-life example: A financial company deploys an AI chatbot to support customers, but does not set clear goals on how much % of customer service costs will be reduced or how much % of automatic response rate will be increased. As a result, without data to evaluate effectiveness, the project falls into a state of &quot;clinically dead&quot;.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The core problem is not AI technology, but the lack of a mechanism to connect strategy \u2013 data \u2013 performance. And that is the gap that EPM systems can fill, helping businesses turn strategic goals into plans, forecasts and measurable results.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Rao_can_khi_rao_can_ap_dung_tri_tue_nhan_tao_Tich_hop_giua_AI_va_he_thong_hien_co\"><\/span><b>Barriers to AI Adoption: T<\/b><b>integration between AI and existing systems<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">For AI to be effective, the prerequisite is <\/span><b>Seamless connectivity with core systems<\/b><span style=\"font-weight: 400;\"> such as ERP, CRM or accounting software. However, the reality in many Vietnamese businesses is completely opposite.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Most of <\/span><b>Legacy ERP or CRM lacking open API or SDK<\/b><span style=\"font-weight: 400;\">, making AI <\/span><b>historical data inaccessible<\/b><span style=\"font-weight: 400;\"> \u2014 which is an important \u201craw material\u201d for training predictive models. AI integration therefore becomes a complex problem, requiring <\/span><b>high IT costs<\/b><span style=\"font-weight: 400;\">, long implementation time and <\/span><b>risk of operational disruption<\/b><span style=\"font-weight: 400;\"> if not tightly managed.<\/span><\/p>\n<p><b>Real life example:<\/b><span style=\"font-weight: 400;\"> A manufacturing company with five plants had production planning data stored in the SAP system, while accounting data was stored in another internal software. When AI was deployed to forecast raw material demand, the system failed to \u201cconnect\u201d these two data sources \u2014 resulting in high errors and projects that did not achieve expected results.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This issue shows: <\/span><b>AI cannot operate in a fragmented data environment.<\/b><span style=\"font-weight: 400;\">. Businesses need an intermediary platform like <\/span><b>EPM or Bizz<\/b><span style=\"font-weight: 400;\"> Helps consolidate financial - operational - business data, creating a standardized data foundation before AI is applied at a strategic level.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Chi_phi_dau_tu_cao_va_hieu_qua_chua_tuong_xung\"><\/span><b>High investment costs and disproportionate efficiency<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">AI is not just a technology problem, but a long-term investment problem \u2013 requiring large costs for software, hardware, human resources and training.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For many small and medium-sized enterprises, the difficulty in implementing AI comes from the initial investment of up to billions of VND. In addition, AI is not a \u201cone-time purchase\u201d project \u2013 the system needs to be nurtured with continuous training data, leading to increased costs for maintenance and model optimization over time.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Real-life example: A transport company invested more than 1 billion VND to deploy AI to predict vehicle maintenance. However, after only one year, the project had to be suspended because the cost of cleaning and standardizing data took up more than 40% of the total budget, while the results were unclear.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This reflects a reality: without a standardized data platform and a clear ROI measurement mechanism, the difficulty of implementing AI is that it becomes a \u201cfinancial burden\u201d instead of a value creation tool. That is also the reason why many CFOs are starting to turn to EPM \u2013 a solution that can help standardize data, control costs, and simulate return on investment (ROI) before businesses invest in large-scale AI projects.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Van_de_dao_duc_phap_ly_va_an_toan_du_lieu\"><\/span><b>Ethical, legal and data security issues<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">AI opens up huge opportunities for businesses, but comes with barriers to adopting artificial intelligence,\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Ethical and legal risks if not strictly controlled. One of the difficulties in implementing AI is AI bias \u2013 when the model learns from unbalanced data, leading to biased or unfair results. At the same time, the problem of personal data infringement and violation of customer information security regulations is also increasingly worrying, especially in industries such as finance, insurance, and healthcare.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In Vietnam, the legal framework for using customer data for AI training is still limited. Many businesses do not have a clear process to ensure that data is anonymized, encrypted and used for the right purposes.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Real-life example: An insurance company applied AI to score customer risk, but faced backlash when it was discovered that it was using unanonymized personal data. This incident not only affected the brand reputation but also forced the company to pause the project to review the entire security process.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This issue raises urgent management requirements. <a href=\"https:\/\/bizzi.vn\/epm-minh-bach-du-lieu\/\">data transparency<\/a> and compliance (Data Governance &amp; Compliance) \u2014 elements that a system like EPM can help establish through data authorization, access control, and tracking the flow of data throughout the organization.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Thieu_nen_van_hoa_du_lieu_Data-Driven_Culture\"><\/span><b>Lack of Data-Driven Culture<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">AI only truly shines when businesses operate on data, not gut feelings. However, this is an inherent weakness of many organizations today.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In reality, employees and managers often make decisions based on personal experience, rather than on real data. In addition, the lack of a transparent data sharing process between departments causes information to be fragmented \u2013 data is considered the \u201cprivate property\u201d of each department. As a result, AI cannot learn from a comprehensive picture, leading to inaccurate forecasting models and loss of application value.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Real-world example: Sales keeps transaction and customer pipeline data separate from finance. When deploying AI to forecast revenue and cash flow, the system only learns from a portion of the data \u2014 resulting in inaccurate forecasts and misaligned budget strategies.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This shows that the difficulty in implementing AI is that businesses do not have a \u201cdata culture\u201d \u2014 where all decisions, processes and measurements are based on shared and verified data. This is also the reason why pioneering businesses choose EPM (Enterprise Performance Management) as the central platform: to help standardize, share and visualize performance data, thereby forming a sustainable culture of data-driven decision-making.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Bizzi_%E2%80%93_Giai_phap_toan_dien_cho_phong_ke_toan_%E2%80%93_tai_chinh_san_sang_ung_dung_AI\"><\/span><b>Bizzi \u2013 Comprehensive solution for accounting and finance departments ready to apply AI<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">The difficulties in implementing AI show that the decisive factor is not the algorithm, but <\/span><b>data quality, governance and analytical capabilities of the finance and accounting department<\/b><span style=\"font-weight: 400;\">. This is the \u201cbottleneck\u201d group in most digital transformation projects, where planning, forecasting and actual data are not synchronized or controlled uniformly.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In that context, <\/span><b>Bizzi<\/b><span style=\"font-weight: 400;\"> \u2013 in the role of <\/span><b>A pioneering unit in financial automation and the exclusive distributor of EPM solutions in Vietnam.<\/b><span style=\"font-weight: 400;\"> \u2013 providing a solution ecosystem to help accounting and finance departments overcome barriers to applying artificial intelligence:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Standardize data and performance management (EPM) processes<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Automate planning, forecasting, and analysis (FP&amp;A) tasks<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Create a high-quality data platform that is AI-ready.<\/span><\/li>\n<\/ul>\n<h3><span class=\"ez-toc-section\" id=\"1_Vai_tro_cua_phong_ke_toan_%E2%80%93_tai_chinh_trong_ung_dung_AI\"><\/span><b>1. The role of the accounting and finance department in AI application<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">The accounting and finance department concentrates on high-value data: plans, costs, revenues, cash flows, budgets. However:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">70% Unnormalized Financial Data<span style=\"font-weight: 400;\">, separated between ERP, accounting, Excel and operations.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Manual reporting<span style=\"font-weight: 400;\">, data is slow and unreliable.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Lack of data quality control mechanism<span style=\"font-weight: 400;\">, leaving AI without \u201cclean material\u201d to learn and predict.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Before implementing AI, businesses need to <\/span><b>Building an EPM data management infrastructure<\/b><span style=\"font-weight: 400;\">: Unify data sources, standardize planning \u2013 forecasting \u2013 analysis processes, and create a training ground for AI. This thorough preparation will help remove barriers to applying artificial intelligence during deployment.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Ket_luan\"><\/span><b>Conclude<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">The above article has summarized the barriers to applying artificial intelligence in businesses, the reasons why businesses have problems with AI. From fragmented data, unprepared technology infrastructure, to lack of expertise and strategy, failure does not lie in the AI algorithm, but comes from <\/span><b>difficulties in implementing AI<\/b><span style=\"font-weight: 400;\"> due to non-standard data platforms, lack of automation in FP&amp;A processes and unprepared teams.<\/span><\/p>","protected":false},"excerpt":{"rendered":"<p>Most businesses today are having difficulty implementing AI, whether large or small. According to\u2026<\/p>","protected":false},"author":56,"featured_media":999979089,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"categories":[369],"tags":[],"class_list":["post-999979086","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-chuyen-doi-tai-chinh"],"acf":[],"_links":{"self":[{"href":"https:\/\/bizzi.vn\/en\/wp-json\/wp\/v2\/posts\/999979086","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/bizzi.vn\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/bizzi.vn\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/bizzi.vn\/en\/wp-json\/wp\/v2\/users\/56"}],"replies":[{"embeddable":true,"href":"https:\/\/bizzi.vn\/en\/wp-json\/wp\/v2\/comments?post=999979086"}],"version-history":[{"count":4,"href":"https:\/\/bizzi.vn\/en\/wp-json\/wp\/v2\/posts\/999979086\/revisions"}],"predecessor-version":[{"id":999980421,"href":"https:\/\/bizzi.vn\/en\/wp-json\/wp\/v2\/posts\/999979086\/revisions\/999980421"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/bizzi.vn\/en\/wp-json\/wp\/v2\/media\/999979089"}],"wp:attachment":[{"href":"https:\/\/bizzi.vn\/en\/wp-json\/wp\/v2\/media?parent=999979086"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/bizzi.vn\/en\/wp-json\/wp\/v2\/categories?post=999979086"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/bizzi.vn\/en\/wp-json\/wp\/v2\/tags?post=999979086"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}