[{"data":1,"prerenderedAt":646},["ShallowReactive",2],{"site-schema":3,"article-en-2026-ai-adoption-service-guide":85},{"@context":4,"@graph":5},"https://schema.org",[6,68,75],{"@type":7,"@id":8,"name":9,"alternateName":10,"legalName":9,"foundingDate":15,"url":16,"logo":17,"founder":18,"contactPoint":20,"address":34,"location":40,"sameAs":60},"Organization","https://joinx.co/#organization","哲煜科技股份有限公司",[11,12,13,14],"JoinX","TWJOIN","哲煜科技","JoinX 哲煜科技","2016","https://joinx.co","https://joinx.co/images/logo-with-name.png",{"@id":19},"https://joinx.co/#person-jack-lee",[21],{"@type":22,"contactType":23,"url":24,"telephone":25,"email":26,"areaServed":27,"availableLanguage":30},"ContactPoint","customer service","https://joinx.co/contact-us","+886-2-8771-9095","service@joinx.co",[28,29],"TW","JP",[31,32,33],"zh-Hant","en","ja",{"@type":35,"streetAddress":36,"postalCode":37,"addressLocality":38,"addressRegion":39,"addressCountry":28},"PostalAddress","民生東路二段170號8樓","104","台北市中山區","台灣",[41,48,54],{"@type":42,"name":43,"address":44},"Place","JoinX 台中辦公室",{"@type":35,"streetAddress":45,"postalCode":46,"addressLocality":47,"addressRegion":39,"addressCountry":28},"台灣大道二段360號21樓C室","40453","台中市北區",{"@type":42,"name":49,"address":50},"JoinX 高雄辦公室",{"@type":35,"streetAddress":51,"postalCode":52,"addressLocality":53,"addressRegion":39,"addressCountry":28},"民族一路80號2樓之一(B4)","807","高雄市三民區",{"@type":42,"name":55,"address":56},"JoinX 東京辦公室",{"@type":35,"streetAddress":57,"postalCode":58,"addressLocality":59,"addressCountry":29},"東五反田5-22-37 Office Circle N 五反田 9樓","141-0022","東京都品川區",[61,62,63,64,65,66,67],"https://www.facebook.com/JoinX.TW","https://www.linkedin.com/company/%E5%93%B2%E7%85%9C%E7%A7%91%E6%8A%80","https://www.youtube.com/c/joinstuido哲煜科技","https://medium.com/twjoin","https://www.104.com.tw/company/1a2x6bjomb","https://podcasts.apple.com/tw/podcast/jack%E5%8F%AD%E5%8F%AD/id1859867414","https://open.spotify.com/show/236PYieEt3gQ30bfk7iE3J",{"@type":69,"@id":19,"name":70,"alternateName":71,"jobTitle":72,"worksFor":73,"url":74},"Person","李秉哲","Jack Lee","創辦人暨執行長",{"@id":8},"https://joinx.co/about",{"@type":76,"@id":77,"url":16,"name":78,"alternateName":79,"publisher":80,"inLanguage":81},"WebSite","https://joinx.co/#website","JoinX哲煜科技",[11,13,78],{"@id":8},[82,83,84],"zh-Hant-TW","en-US","ja-JP",{"id":86,"title":87,"author":88,"authorUrl":88,"body":89,"category":88,"cover":633,"ctaFirstContent":88,"ctaFirstLinkText":88,"ctaFirstLinkUrl":88,"ctaLastContent":88,"ctaLastLinkText1":634,"ctaLastLinkText2":88,"ctaLastLinkUrl1":635,"ctaLastLinkUrl2":88,"ctaMiddleContent":88,"ctaMiddleLinkText":88,"ctaMiddleLinkUrl":88,"ctaServiceName":88,"dateModified":88,"description":636,"extension":637,"faq":88,"hasCoverTitle":638,"hasCtaFirst":638,"hasCtaLast":638,"isDescriptionFirst":638,"locale":32,"meta":639,"navigation":638,"path":640,"seo":641,"stem":642,"time":643,"type":644,"__hash__":645},"content/en/article/2026-ai-adoption-service-guide.md","What Is an AI Adoption Service? A Complete Guide to the Process, Deliverables, Roles, and Acceptance Criteria When Outsourcing AI Implementation",null,{"type":90,"value":91,"toc":616},"minimal",[92,96,101,104,214,217,224,230,233,237,240,243,248,251,254,257,270,273,276,279,282,286,289,292,295,306,309,312,315,318,322,325,328,331,342,345,348,351,355,358,361,367,373,376,379,382,386,389,392,395,398,401,404,408,411,472,475,479,482,485,511,514,517,521,524,544,547,550,554,559,562,565,570,573,575,580,583,585,590,593,595,600,603,605,610,613],[93,94,95],"p",{},"This article breaks down what \"outsourcing AI adoption\" actually involves: what work the service really covers, the five stages a project goes through from day one to launch, the deliverables and acceptance points at each stage, what your team has to contribute, and where each stage most often goes wrong. Once you've read it, you'll be able to look at any AI adoption quote and judge what it covers, what it's missing, and where the risk has been left sitting.",[97,98,100],"h2",{"id":99},"the-three-levels-of-ai-adoption-services-first-figure-out-which-one-youre-buying","The Three Levels of AI Adoption Services: First Figure Out Which One You're Buying",[93,102,103],{},"Every AI adoption service on the market, no matter how the name is packaged, falls into one of three levels.",[105,106,107,126],"table",{},[108,109,110],"thead",{},[111,112,113,117,120,123],"tr",{},[114,115],"th",{"align":116},"left",[114,118,119],{"align":116},"Level 1: Tool Adoption",[114,121,122],{"align":116},"Level 2: Scenario Adoption",[114,124,125],{"align":116},"Level 3: Process Adoption",[127,128,129,144,158,172,186,200],"tbody",{},[111,130,131,135,138,141],{},[132,133,134],"td",{"align":116},"What you buy",[132,136,137],{"align":116},"Tool selection, setup, and training",[132,139,140],{"align":116},"An AI application built for a single business scenario",[132,142,143],{"align":116},"Cross-system, cross-department process redesign and AI integration",[111,145,146,149,152,155],{},[132,147,148],{"align":116},"Typical examples",[132,150,151],{"align":116},"Rolling out enterprise generative AI, establishing usage guidelines and training",[132,153,154],{"align":116},"Automated classification and reply of inquiry emails, an internal knowledge base, automated report generation",[132,156,157],{"align":116},"End-to-end automation from order to shipment, integration overhaul of customer service and ticketing systems",[111,159,160,163,166,169],{},[132,161,162],{"align":116},"Scope of change",[132,164,165],{"align":116},"Individual productivity",[132,167,168],{"align":116},"The way one team works",[132,170,171],{"align":116},"The way departments collaborate",[111,173,174,177,180,183],{},[132,175,176],{"align":116},"Does it touch your systems?",[132,178,179],{"align":116},"Almost never",[132,181,182],{"align":116},"Requires integrating one or two systems",[132,184,185],{"align":116},"Requires multi-system integration, often including interface development for legacy systems",[111,187,188,191,194,197],{},[132,189,190],{"align":116},"What you invest",[132,192,193],{"align":116},"Employee learning time",[132,195,196],{"align":116},"One business point of contact, plus staff for data preparation",[132,198,199],{"align":116},"Cross-department decision-making, executive sponsorship",[111,201,202,205,208,211],{},[132,203,204],{"align":116},"Reasonable expectations",[132,206,207],{"align":116},"Efficiency gains, but dependent on individual usage habits",[132,209,210],{"align":116},"Freed-up labor for a specific process, with measurable benefits",[132,212,213],{"align":116},"A change in the operating model — highest benefit, and highest risk",[93,215,216],{},"None of the three levels is better than the others; it's only a matter of fit. The two most common mismatches:",[93,218,219,223],{},[220,221,222],"strong",{},"Mismatch one: buying a Level 1 service with Level 3 expectations."," The boss wants \"the whole company to go AI,\" but what gets purchased is tool accounts plus training. Three months later nobody is using the tool, and the conclusion becomes \"AI is useless.\" The problem isn't AI — it's that a personal productivity tool was never going to change how departments collaborate.",[93,225,226,229],{},[220,227,228],{},"Mismatch two: jumping straight to Level 3 before the process is stable."," A large integration project is launched while the process itself is still changing frequently and departmental responsibilities are still unclear. AI adoption \"locks\" a process into the system, and locking down a process that's still in flux amounts to institutionalizing the chaos.",[93,231,232],{},"The rule of thumb, in one sentence: Do you want to change individual productivity, the way one team works, or cross-department collaboration? The answer is the level you should be buying.",[97,234,236],{"id":235},"the-full-ai-adoption-process-five-stages-with-deliverables-and-acceptance-points-for-each","The Full AI Adoption Process: Five Stages, With Deliverables and Acceptance Points for Each",[93,238,239],{},"The following describes a complete Level 2 or Level 3 project.",[93,241,242],{},"Level 1 tool adoption doesn't need to go through every stage, but the assessment logic of Stage 1 still applies.",[244,245,247],"h3",{"id":246},"stage-1-feasibility-assessment-and-scenario-selection","Stage 1: Feasibility Assessment and Scenario Selection",[93,249,250],{},"What it does: Take stock of your business processes, current data, and system architecture; identify the scenarios suited to AI; and prioritize them by benefit, risk, and prerequisites.",[93,252,253],{},"The core of the assessment is not \"where can AI be used.\" With today's technology, almost every process \"can\" use AI.",[93,255,256],{},"What the assessment really has to answer is three harder questions:",[258,259,260,264,267],"ul",{},[261,262,263],"li",{},"Which scenario has the most measurable return on investment? (High volume, highly repetitive, currently consuming clearly identifiable labor.)",[261,265,266],{},"Which scenario's data conditions are already mature? (The data exists, the format is usable, permissions can be granted.)",[261,268,269],{},"Which scenario can afford to be wrong? (The first scenario should have plenty of tolerance for error, letting the organization build trust with low risk.)",[93,271,272],{},"Deliverables: A scenario-priority report, a prerequisite checklist for each scenario, and the items that are not recommended along with the reasons.",[93,274,275],{},"Acceptance point: Whether the report contains any \"not recommended\" items. This is the fastest way to tell assessment quality apart. Almost every company's wish list inevitably contains items whose ROI is unreasonable or whose conditions aren't ready; an assessment that recommends doing everything isn't an assessment — it's a sales document.",[93,277,278],{},"Where this stage most often goes wrong: skipping it. A project that starts straight from \"the boss heard a talk and decided to build an AI customer service bot\" has effectively handed the most important scenario decision over to intuition.",[93,280,281],{},"In JoinX (TWJOIN)'s hands-on experience, the most valuable output of the assessment stage is often not \"what to do,\" but the discovery that the scenario the company originally wanted isn't the most cost-effective place to start.",[244,283,285],{"id":284},"stage-2-data-and-system-preparation","Stage 2: Data and System Preparation",[93,287,288],{},"What it does: Consolidate, clean, and structure the data the target scenario needs, and take inventory of the systems' APIs and access permissions.",[93,290,291],{},"The quality of AI's output is determined by the quality of the data — every vendor says this, but its practical meaning is rarely spelled out.",[93,293,294],{},"Data problems come in three kinds, with completely different handling costs:",[258,296,297,300,303],{},[261,298,299],{},"Data exists and is structured: in a database or system, with clear fields. Preparation cost is low.",[261,301,302],{},"Data exists but is unstructured: scattered across PDFs, Word files, emails, and meeting notes. It needs cleaning, conversion, and deduplication — medium-to-high cost — and you must set up a \"retirement mechanism for outdated documents,\" because AI will very confidently cite a three-year-old version of a policy.",[261,304,305],{},"Data doesn't exist (it's in people's heads): the judgment logic of a veteran craftsman, a salesperson's pricing experience. This isn't a preparation problem, it's knowledge-extraction engineering — the highest cost and the longest timeline.",[93,307,308],{},"Deliverables: A dataset ready for development use, a data dictionary, a system interface list, and a permissions matrix.",[93,310,311],{},"Acceptance point: Sample-check the accuracy and freshness of the data. If the prepared data is wrong to begin with, every later stage is amplifying that error.",[93,313,314],{},"Where this stage most often goes wrong: cost disputes. Data preparation is the item most often omitted from a quote, then reappears mid-project in the form of a budget add-on.",[93,316,317],{},"Before commissioning, be sure to clarify: who assesses the volume of data-preparation work, which line item the cost falls under, and how it's handled if the actual situation turns out worse than expected.",[244,319,321],{"id":320},"stage-3-small-scale-validation-poc","Stage 3: Small-Scale Validation (POC)",[93,323,324],{},"What it does: Use real data and a narrowed scope to validate whether this scenario can actually be built.",[93,326,327],{},"The purpose of a POC is often misunderstood as \"making a demo to show the boss.\" A demo showcases possibility; a POC obtains the numbers for a decision.",[93,329,330],{},"A qualified POC has to answer three questions:",[258,332,333,336,339],{},[261,334,335],{},"Accuracy: What is AI's actual accuracy on this kind of task? What type of error is it concentrated in?",[261,337,338],{},"Per-unit cost: What is the actual cost of processing one task (including compute, including human review)?",[261,340,341],{},"Baseline comparison: Compared with the current manual process, how much faster, how much cheaper, and how much lower the error rate?",[93,343,344],{},"Deliverables: These three numbers, plus a pattern analysis of the error cases.",[93,346,347],{},"Acceptance point: A threshold must be agreed with the vendor in black and white beforehand — for example, \"classification accuracy must reach a certain percentage before entering formal development.\" The threshold is set in advance, not discussed after seeing the results.",[93,349,350],{},"Where this stage most often goes wrong: tying the POC and formal development into the same contract. A bundled contract means the project moves forward regardless of the validation results — your right to cut losses disappears the moment you sign. The POC must be an independent decision point: proceed only if the numbers hit the threshold, stop if they don't, and your maximum loss is the cost of the assessment plus the POC. This is the single most important risk-control design in the entire AI adoption process.",[244,352,354],{"id":353},"stage-4-development-integration-and-launch","Stage 4: Development, Integration, and Launch",[93,356,357],{},"What it does: Formally build the AI application, integrate it with existing systems, design the division of labor between humans and AI, and roll out in batches.",[93,359,360],{},"Two designs in this stage determine success or failure after launch, and neither is a purely technical question:",[93,362,363,366],{},[220,364,365],{},"The design of the human-confirmation mechanism."," AI makes mistakes; the problem isn't eliminating errors, it's designing how errors get intercepted. Which outputs AI can execute directly, which must be sent for human confirmation, who does the confirming, and how quickly it must be handled — these rules must be finalized before launch. The principle: anything involving large sums, external commitments, or regulatory sensitivity goes through human review without exception; only high-volume, internal, reversible items get executed directly by AI.",[93,368,369,372],{},[220,370,371],{},"The parallel-run arrangement."," The normal way to launch is to run AI and the manual process in parallel for a period — comparing results, building trust, verifying stability — and then gradually transition. Be on high alert when you hear advice to \"switch over directly, all at once\": this approach bets the entire organization's trust in AI on the first week's performance, and a single incident is enough to turn the whole organization against the project.",[93,374,375],{},"Deliverables: The launched system, operations and maintenance documentation, and the process design for the human-confirmation mechanism.",[93,377,378],{},"Acceptance point: Don't just test features — test the exceptions. The acceptance checklist must include exception paths such as \"what happens when AI can't answer\" and \"how the process runs when the system goes offline.\"",[93,380,381],{},"Where this stage most often goes wrong: treating launch as the finish line. Launch is the beginning of the tuning period, and every error case in the early days is tuning material. Both vendor and company must reserve effort for the first few months after launch; the speed of feedback during this shakedown period determines whether, six months later, the system is the team's everyday tool or an abandoned project nobody wants to touch.",[244,383,385],{"id":384},"stage-5-operations-and-expansion","Stage 5: Operations and Expansion",[93,387,388],{},"What it does: Continuous tuning, knowledge-base updates, model version management, feedback handling for exception cases, and — once the first scenario is stable — assessing expansion to a second scenario.",[93,390,391],{},"Operations is not optional. AI applications differ from traditional software in one key way: traditional software doesn't break if you don't change it, but the environment of an AI application is constantly changing (your business rules change, the data changes, the model gets updated), and if you ignore it, it will gradually drift off target. The common script for an adoption case with no operations arrangement is: after the first major change to business rules, the output starts becoming inaccurate; no one is responsible for fixing it; users start bypassing the system and going back to manual work; six months later the system is idle, and management's takeaway is \"we tried AI, it doesn't work.\"",[93,393,394],{},"The economics of expansion deserve a separate mention: the first scenario builds up the data foundation, the system integration, and the human-confirmation mechanism, and the second scenario can reuse most of that foundation at a markedly lower marginal cost. This is also why scenario prioritization (Stage 1) matters: get the first scenario right and what follows is compounding; get it wrong and what follows is starting over.",[93,396,397],{},"Deliverables: An agreement on the operations service level, a division of responsibility for knowledge-base updates, and assessment recommendations for expansion scenarios.",[93,399,400],{},"Acceptance point: Get clear answers on two things: Are the documentation and source code delivered? And if you later want to bring it in-house or switch vendors, how does the handover happen?",[93,402,403],{},"The answers to these two questions before signing determine whether, three years from now, you own an asset or are tied to a single vendor.",[97,405,407],{"id":406},"how-to-choose-the-technology-four-tools-for-four-kinds-of-problem","How to Choose the Technology: Four Tools for Four Kinds of Problem",[93,409,410],{},"AI adoption is not the same as \"deploying generative AI.\" Depending on the scenario's characteristics, the reasonable technology choice varies widely:",[105,412,413,426],{},[108,414,415],{},[111,416,417,420,423],{},[114,418,419],{"align":116},"Your scenario looks like this",[114,421,422],{"align":116},"The reasonable technical direction",[114,424,425],{"align":116},"Common misuse",[127,427,428,439,450,461],{},[111,429,430,433,436],{},[132,431,432],{"align":116},"Rules are 100% explicit, steps are fixed, no exception judgment",[132,434,435],{"align":116},"Traditional automation (RPA, scheduled scripts) is enough",[132,437,438],{"align":116},"Doing it with generative AI — high cost and introducing unnecessary uncertainty",[111,440,441,444,447],{},[132,442,443],{"align":116},"Need to search, aggregate, and answer from a large volume of company documents",[132,445,446],{"align":116},"A RAG knowledge-base architecture",[132,448,449],{"align":116},"Using a general chat tool directly, with answers not grounded in your documents",[111,451,452,455,458],{},[132,453,454],{"align":116},"Need to understand meaning and generate content (emails, reports, summaries)",[132,456,457],{"align":116},"A generative AI application",[132,459,460],{"align":116},"Expecting it to \"do everything,\" with no defined scope leading to unstable quality",[111,462,463,466,469],{},[132,464,465],{"align":116},"Need to autonomously execute multi-step tasks and operate across systems",[132,467,468],{"align":116},"An AI Agent architecture",[132,470,471],{"align":116},"Deploying before the process is clearly defined, automating the chaos",[93,473,474],{},"This table is also a tool for vetting vendors: a vendor who sells only one kind of technology will explain every one of your problems as a nail that the hammer in their hand can drive. When you hear the advice \"this one can just be done with traditional automation, no AI needed,\" the person in front of you is a genuine consultant.",[97,476,478],{"id":477},"the-division-of-labor-between-client-and-vendor-what-only-you-can-do","The Division of Labor Between Client and Vendor: What Only You Can Do",[93,480,481],{},"AI adoption is not a project that succeeds by outsourcing everything.",[93,483,484],{},"The vendor can take responsibility for the technology, process design, and project management, but there are four things only the company itself can provide:",[258,486,487,493,499,505],{},[261,488,489,492],{},[220,490,491],{},"A business point of contact with decision-making authority."," Not an IT liaison, but someone who understands the target process and can make the call on \"how this exception is handled.\" A project missing this role sees every small decision escalated up the chain, doubling the timeline outright.",[261,494,495,498],{},[220,496,497],{},"The real operational details of the process."," Including the exceptions and unwritten rules that never made it into the SOP. AI learns the real process, not the process on paper, and only your people know the gap between the two.",[261,500,501,504],{},[220,502,503],{},"User feedback in the early launch period."," The faster error cases are reported, the faster the system matures.",[261,506,507,510],{},[220,508,509],{},"Ongoing sponsorship from management."," AI adoption changes how some people work, and the organizational resistance it meets has to be handled by management — a vendor can't handle your internal politics.",[93,512,513],{},"A self-check before commissioning: Can you provide these four things right now?",[93,515,516],{},"If you can't, solve that first, then start the project.",[97,518,520],{"id":519},"the-final-check-before-commissioning-the-six-items-a-quote-should-have","The Final Check Before Commissioning: The Six Items a Quote Should Have",[93,522,523],{},"When you get a quote, check it against this list:",[258,525,526,529,532,535,538,541],{},[261,527,528],{},"Which level does the service map to (tool / scenario / process), and does it match your expectations?",[261,530,531],{},"Is there an independent assessment stage? Are the assessment conclusions tied to subsequent development?",[261,533,534],{},"Is the data-preparation work and cost clearly written out?",[261,536,537],{},"Does the POC have pre-agreed acceptance numbers and a stop-loss mechanism?",[261,539,540],{},"Are the scope, cost, and service level of operations written down?",[261,542,543],{},"Is delivery of documentation and source code, and the method of future handover, agreed upon?",[93,545,546],{},"Missing any one of the six doesn't mean the vendor is bad, but it means the risk for that piece currently sits with you.",[93,548,549],{},"At the very least, settle \"who bears the missing items\" before signing.",[97,551,553],{"id":552},"frequently-asked-questions-faq","Frequently Asked Questions (FAQ)",[93,555,556],{},[220,557,558],{},"Q1: What's the difference between AI adoption and digital transformation?",[93,560,561],{},"Digital transformation moves processes off paper and out of manual work and into systems; it solves \"whether data is being recorded and whether the process is systematized.\" AI adoption gives the system the ability to judge and generate; it solves \"which human judgments can be handed to a machine.\" The order matters: digitization comes first — a process whose data still lives on paper and in Excel has to be digitized before there's any foundation for AI to be adopted.",[563,564],"hr",{},[93,566,567],{},[220,568,569],{},"Q2: How long does an AI adoption project usually take?",[93,571,572],{},"It varies greatly by level: tool adoption is measured in weeks, scenario adoption in months, process adoption in quarters. In practice the biggest variable in the timeline is usually not the AI development itself, but the volume of data-preparation work and the speed of cross-department coordination — both of which depend on the company's own current state.",[563,574],{},[93,576,577],{},[220,578,579],{},"Q3: Will the adoption process affect current operations?",[93,581,582],{},"The proper approach is a parallel run: AI and humans operate in parallel for a period and transition gradually once stable, keeping disruption to operations very small. What to watch out for is the \"switch over directly\" proposal, which concentrates the risk in the first week of launch.",[563,584],{},[93,586,587],{},[220,588,589],{},"Q4: Our company isn't large — do we need to go through all five stages?",[93,591,592],{},"No stage gets skipped, but the scale of each stage shrinks proportionally: for a small company, assessing a single scenario might be a few interviews, and a POC might produce numbers in two weeks. The completeness of the process has nothing to do with company size and everything to do with risk control. What genuinely differs by size is the choice of starting point: the smaller you are, the more you should start from a single, high-volume, error-tolerant scenario.",[563,594],{},[93,596,597],{},[220,598,599],{},"Q5: We're already halfway through an adoption project but it's stuck — can it be taken over midway?",[93,601,602],{},"Yes, and this kind of need is more common than you'd think. A stuck adoption case usually needs a health check first: clarify whether it's stuck on data, technology, or organization, then decide whether to fix, narrow the scope, or cut losses. The logic of the health check is the same as the Stage 1 assessment — only the subject shifts from \"a project not yet started\" to \"a project in progress.\"",[563,604],{},[93,606,607],{},[220,608,609],{},"Q6: How does JoinX (TWJOIN)'s AI adoption service work?",[93,611,612],{},"It starts with a paid feasibility assessment. Assessment and development are two independent stages: the assessment report includes scenario prioritization, prerequisites, and stop-loss recommendations, and that report holds up when you take it to compare quotes with any vendor — because the assessment isn't tied to subsequent cooperation, our assessment conclusion can even be \"don't do it right now.\" When it's time to move forward, the same team carries it from diagnosis, development, and integration through to operations, with no handoff. Beyond new projects, we also take on health checks and takeovers of in-progress adoption cases.",[93,614,615],{},"Book a JoinX (TWJOIN) AI adoption feasibility assessment. What you'll get is: scenario prioritization, a prerequisite checklist for each scenario, an explanation of the cost structure, and clear stop-loss recommendations. This report isn't tied to any subsequent cooperation, and it holds up when used to vet any vendor's proposal.",{"title":617,"searchDepth":618,"depth":618,"links":619},"",2,[620,621,629,630,631,632],{"id":99,"depth":618,"text":100},{"id":235,"depth":618,"text":236,"children":622},[623,625,626,627,628],{"id":246,"depth":624,"text":247},3,{"id":284,"depth":624,"text":285},{"id":320,"depth":624,"text":321},{"id":353,"depth":624,"text":354},{"id":384,"depth":624,"text":385},{"id":406,"depth":618,"text":407},{"id":477,"depth":618,"text":478},{"id":519,"depth":618,"text":520},{"id":552,"depth":618,"text":553},"/images/blog/2026-ai-adoption-service-guide.webp","Explore our AI adoption consulting service","/en/development/software","The phrase \"AI adoption\" means something different on every vendor's quote. For some it means setting up tool accounts and running two training sessions; for others it means a full project that runs from process diagnosis and data preparation all the way through system integration and ongoing operations. The two differ by an order of magnitude in effort, yet in a proposal deck they often look like the same thing.","md",true,{},"/en/article/2026-ai-adoption-service-guide",{"title":87,"description":636},"en/article/2026-ai-adoption-service-guide","2026/07/17","blog","EfwDyDHac7uIg2-ltIb_Kv-hImGeoPIP78UICnTWioQ",1784898729356]