Demonstrated the results<\/li>\n<\/ul>\nFrom a development perspective, the project may feel almost complete.<\/p>\n
Production introduces a completely different set of questions.<\/p>\n
Where does new data come from?<\/p>\n
How does the application send data to the model?<\/p>\n
Where is the prediction stored?<\/p>\n
What happens when the model is unavailable?<\/p>\n
How is incorrect output handled?<\/p>\n
Who can access the system?<\/p>\n
How is model performance monitored?<\/p>\n
What happens when the data changes?<\/p>\n
How does the prediction affect the customer’s workflow?<\/p>\n
These questions may not matter much during a controlled proof of concept.<\/p>\n
They become critical when the system is expected to operate every day.<\/p>\n
The Last 20% Is Where Complexity Appears<\/h2>\n
The first part of an AI project is usually about proving that something can work.<\/p>\n
The final stage is about proving that it can keep working.<\/p>\n
A development environment can tolerate manual processes. Production cannot.<\/p>\n
A developer might manually upload a dataset during experimentation. A production system may need to process thousands of records automatically.<\/p>\n
A developer can manually inspect model results. Production requires monitoring, alerts, logging, and defined responses to failures.<\/p>\n
A prototype may work for a handful of users. Production needs to handle real traffic, unexpected inputs, system failures, and changing requirements.<\/p>\n
The model may be ready.<\/p>\n
The system around the model is not.
\n![\"controlled](\"https:\/\/www.journai.us\/blog\/wp-content\/uploads\/2026\/10\/controlled-development-environment-1024x572.png\")
\n<\/strong><\/p>\nIntegration Becomes the Hidden Bottleneck<\/h2>\n
One of the biggest reasons AI projects slow down near production is integration.<\/p>\n
Most businesses already depend on multiple systems:<\/p>\n
\n- CRM platforms<\/li>\n
- ERP systems<\/li>\n
- Databases<\/li>\n
- Payment systems<\/li>\n
- Customer applications<\/li>\n
- Internal tools<\/li>\n
- Analytics platforms<\/li>\n
- Ticketing systems<\/li>\n
- Data warehouses<\/li>\n<\/ul>\n
AI needs to work with these systems rather than operate in isolation.<\/p>\n
Imagine an AI model that predicts which customers are most likely to purchase a product.<\/p>\n
The model generates a prediction, but that prediction has limited value if it remains inside the machine learning environment.<\/p>\n
It needs to reach the CRM.<\/p>\n
The sales team needs to see it.<\/p>\n
The right customer needs to be identified.<\/p>\n
The recommendation may need to trigger a task.<\/p>\n
The sales activity needs to be recorded.<\/p>\n
And the organization needs to measure whether the recommendation actually improves conversion.<\/p>\n
The model may take days to build.<\/p>\n
Making all these systems work together can take months.<\/p>\n
This is why AI integration can become a larger engineering challenge than model development.<\/b><\/p>\nData Makes Production More Complicated<\/h2>\n
During a proof of concept, teams often work with a prepared dataset.<\/p>\n
Production data is different.<\/p>\n
It arrives continuously. It may be incomplete, inconsistent, delayed, or structured differently from the original training data.<\/p>\n
A production AI system therefore needs reliable data pipelines that can collect, validate, transform, and deliver information to the model.<\/p>\n
Those pipelines also need mechanisms to detect when something goes wrong.<\/p>\n
A model cannot consistently produce reliable results from unreliable inputs.<\/p>\n
Production AI therefore depends on several important data capabilities:<\/p>\n
\n- Data validation<\/li>\n
- Data transformation<\/li>\n
- Data quality monitoring<\/li>\n
- Automated data pipelines<\/li>\n
- Error handling<\/li>\n
- Data consistency checks<\/li>\n<\/ul>\n
Production AI is only as reliable as the data pipeline supporting it.<\/b><\/p>\nSecurity Cannot Be an Afterthought<\/h2>\n
Security is another reason the final stage can take longer than expected.<\/p>\n
A prototype might use sample data.<\/p>\n
A production AI system could process customer information, financial information, internal business data, or other sensitive information.<\/p>\n
That changes the requirements.<\/p>\n
Organizations may need:<\/p>\n
\n- Authentication<\/li>\n
- Authorization<\/li>\n
- Data encryption<\/li>\n
- Access controls<\/li>\n
- Audit logging<\/li>\n
- Secure API communication<\/li>\n
- Data retention policies<\/li>\n
- Environment separation<\/li>\n<\/ul>\n
The question is no longer simply:<\/p>\n
\u201cCan the model process this data?\u201d<\/b><\/p>\n
It becomes:<\/p>\n
\u201cCan the organization safely allow this system to process this data?\u201d<\/b><\/p>\n
That is a much larger production requirement.<\/p>\n
Monitoring Keeps AI Useful<\/h2>\n
Deploying an AI system is not the end of the project.<\/p>\n
AI systems can change in performance even when the underlying model has not been modified.<\/p>\n
Customer behavior changes. Markets change. Products change. Business processes change. Input data changes.<\/p>\n
A model can therefore remain online while becoming less useful.<\/p>\n
Production monitoring needs to answer two questions:<\/p>\n
Is the system running?<\/b><\/p>\n
and<\/p>\n
Is the AI still performing as expected?<\/b><\/p>\n
Teams may need to monitor:<\/p>\n
\n- Infrastructure health<\/li>\n
- Response time<\/li>\n
- Data quality<\/li>\n
- Model performance<\/li>\n
- Prediction patterns<\/li>\n
- System errors<\/li>\n
- Changes in real-world outcomes<\/li>\n<\/ul>\n
Without this visibility, organizations may discover problems only after business performance has already been affected.<\/p>\n
Testing AI Is Different<\/h2>\n
AI systems also require a broader approach to testing.<\/p>\n
Traditional software often follows predictable rules. AI systems can be probabilistic and may behave differently across different inputs.<\/p>\n
A production AI system may need to be evaluated for:<\/p>\n
\n- Model performance<\/li>\n
- Edge cases<\/li>\n
- Unexpected inputs<\/li>\n
- Failure scenarios<\/li>\n
- Response consistency<\/li>\n
- Security<\/li>\n
- Integration behavior<\/li>\n
- Human review processes<\/li>\n<\/ul>\n
The goal is not simply to prove that the model works.<\/p>\n
It is to understand how the entire AI system behaves when things do not go according to plan.<\/b><\/p>\nThe Business Workflow Still Matters<\/h2>\n
Even after the technical infrastructure is ready, one major question remains:<\/p>\n
What should people actually do with the AI output?<\/b><\/p>\n
Suppose an AI system identifies a customer as high risk for churn.<\/p>\n
The prediction is generated successfully.<\/p>\n
Now what?<\/p>\n
Should the customer success manager receive an alert?<\/p>\n
Should the CRM automatically create a task?<\/p>\n
Should a human review the prediction?<\/p>\n
Should different risk levels trigger different actions?<\/p>\n
These are business decisions.<\/p>\n
Technology alone cannot answer them.<\/p>\n
An AI system creates value when its output leads to a meaningful action.<\/p>\n
Without that connection, the organization may simply have another dashboard displaying another prediction.
\n![\"ai-Recommendation](\"https:\/\/www.journai.us\/blog\/wp-content\/uploads\/2026\/10\/ai-recommendation-integrated-into-a-business-workflow-1024x572.png\")
<\/p>\n
Human Oversight Can Be Part of the Solution<\/h2>\n
Not every AI system needs to operate completely autonomously.<\/p>\n
In many business environments, a better approach is to combine AI with human judgment.<\/p>\n
AI can:<\/p>\n
Analyze \u2192 Predict \u2192 Recommend<\/b><\/p>\n
while the employee:<\/p>\n
Reviews \u2192 Decides \u2192 Acts<\/b><\/p>\n
This approach allows organizations to benefit from AI while maintaining human accountability, particularly when incorrect decisions can have significant consequences.<\/p>\n
The goal is not always to remove people from the process.<\/p>\n
It can be to help people make better decisions faster.<\/p>\n
Think About Production From Day One<\/h2>\n
The biggest mistake is waiting until the model is finished before thinking about production.<\/p>\n
Teams should consider production requirements during the initial AI design.<\/p>\n
Important questions include:<\/p>\n
\n- Where will the model run?<\/li>\n
- What systems will it connect with?<\/li>\n
- What data will it receive?<\/li>\n
- How will the system be secured?<\/li>\n
- How will performance be monitored?<\/li>\n
- Who will use the output?<\/li>\n
- What happens when the model is wrong?<\/li>\n
- What business outcome should improve?<\/li>\n<\/ul>\n
These questions reduce the gap between experimentation and production.<\/p>\n
Instead of treating production readiness as a final checklist, organizations can make it part of the AI architecture from the beginning.<\/p>\n
From AI Model to Production Capability<\/h2>\n
The AI journey should not look like:<\/p>\n
Build Model \u2192 Finish Model \u2192 Think About Production<\/b><\/p>\n
A stronger approach is:<\/p>\n
Business Problem \u2192 Data \u2192 AI Experiment \u2192 Production Architecture \u2192 Integration \u2192 Security \u2192 Testing \u2192 Monitoring \u2192 Workflow \u2192 Adoption \u2192 Measurement<\/b><\/p>\n
Each stage prepares the next.<\/p>\n
The model remains important, but it becomes one component of a larger business capability.<\/p>\n
That is why project progress should not be measured only by how much of the model has been completed.<\/p>\n
The more important question is:<\/p>\n
\u201cHow close are we to a production-ready AI capability?\u201d<\/b><\/p>\nThe Last 20% Is Where AI Becomes Real<\/h2>\n
The frustrating part about the final 20% is that it often contains the work that determines whether the entire project succeeds.<\/p>\n
Building the model proves technical feasibility.<\/p>\n
Production readiness proves operational feasibility.<\/p>\n
Integration connects intelligence with business systems.<\/p>\n
Workflow design connects predictions with actions.<\/p>\n
Monitoring keeps the system useful.<\/p>\n
Human oversight keeps decisions accountable.<\/p>\n
Measurement proves whether the investment is creating value.<\/p>\n
That is why the final 20% can take six months.<\/p>\n
It is not simply the remaining development work.<\/p>\n
It is the work of turning an AI experiment into something the business can actually depend on.<\/p>\n
The first 80% may prove that the idea is possible.<\/p>\n
The last 20% proves that the idea is useful.<\/b><\/p>\n
And in production AI, that final distinction is everything.<\/p>\n","protected":false},"excerpt":{"rendered":"
An AI project can move from an idea to a working model surprisingly quickly. The data is prepared. The model is trained. The proof of concept works. The accuracy looks promising. The business stakeholders are impressed. Then someone asks: \u201cWhen can we put this into production?\u201d Suddenly, the project that looked 80% complete still has […]<\/p>\n","protected":false},"author":1,"featured_media":1120,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[251,245,243,250,246,247,244],"class_list":["post-1119","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized","tag-ai-deployment","tag-ai-implementation","tag-ai-in-business-operations","tag-ai-integration","tag-enterprise-ai","tag-machine-learning","tag-production-ai"],"acf":[],"yoast_head":"\n
Your AI Project Is 80% Done. So Why Is the Last 20% Taking 6 Months? - journai<\/title>\n<meta name=\"description\" content=\"Your AI model works, but production is still months away. Discover why integration, security, data, testing, monitoring, and workflows make the last 20% of AI projects so difficult.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.journai.us\/blog\/ai-project-80-percent-done-last-20-percent\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Your AI Project Is 80% Done. So Why Is the Last 20% Taking 6 Months? - journai\" \/>\n<meta property=\"og:description\" content=\"Your AI model works, but production is still months away. Discover why integration, security, data, testing, monitoring, and workflows make the last 20% of AI projects so difficult.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.journai.us\/blog\/ai-project-80-percent-done-last-20-percent\/\" \/>\n<meta property=\"og:site_name\" content=\"journai\" \/>\n<meta property=\"article:published_time\" content=\"2026-10-19T13:30:09+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.journai.us\/blog\/wp-content\/uploads\/2026\/10\/your-ai-project-is-80-taking-6-months.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1200\" \/>\n\t<meta property=\"og:image:height\" content=\"670\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"admin\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"admin\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"8 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/www.journai.us\\\/blog\\\/ai-project-80-percent-done-last-20-percent\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.journai.us\\\/blog\\\/ai-project-80-percent-done-last-20-percent\\\/\"},\"author\":{\"name\":\"admin\",\"@id\":\"https:\\\/\\\/www.journai.us\\\/blog\\\/#\\\/schema\\\/person\\\/7cc0b6c5446cf0e93a553236a1067cca\"},\"headline\":\"Your AI Project Is 80% Done. So Why Is the Last 20% Taking 6 Months?\",\"datePublished\":\"2026-10-19T13:30:09+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/www.journai.us\\\/blog\\\/ai-project-80-percent-done-last-20-percent\\\/\"},\"wordCount\":1501,\"commentCount\":0,\"image\":{\"@id\":\"https:\\\/\\\/www.journai.us\\\/blog\\\/ai-project-80-percent-done-last-20-percent\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/www.journai.us\\\/blog\\\/wp-content\\\/uploads\\\/2026\\\/10\\\/your-ai-project-is-80-taking-6-months.png\",\"keywords\":[\"Ai Deployment\",\"Ai Implementation\",\"Ai In Business Operations\",\"Ai Integration\",\"Enterprise Ai\",\"Machine Learning\",\"Production Ai\"],\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\\\/\\\/www.journai.us\\\/blog\\\/ai-project-80-percent-done-last-20-percent\\\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/www.journai.us\\\/blog\\\/ai-project-80-percent-done-last-20-percent\\\/\",\"url\":\"https:\\\/\\\/www.journai.us\\\/blog\\\/ai-project-80-percent-done-last-20-percent\\\/\",\"name\":\"Your AI Project Is 80% Done. So Why Is the Last 20% Taking 6 Months? - journai\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.journai.us\\\/blog\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/www.journai.us\\\/blog\\\/ai-project-80-percent-done-last-20-percent\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/www.journai.us\\\/blog\\\/ai-project-80-percent-done-last-20-percent\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/www.journai.us\\\/blog\\\/wp-content\\\/uploads\\\/2026\\\/10\\\/your-ai-project-is-80-taking-6-months.png\",\"datePublished\":\"2026-10-19T13:30:09+00:00\",\"author\":{\"@id\":\"https:\\\/\\\/www.journai.us\\\/blog\\\/#\\\/schema\\\/person\\\/7cc0b6c5446cf0e93a553236a1067cca\"},\"description\":\"Your AI model works, but production is still months away. Discover why integration, security, data, testing, monitoring, and workflows make the last 20% of AI projects so difficult.\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/www.journai.us\\\/blog\\\/ai-project-80-percent-done-last-20-percent\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/www.journai.us\\\/blog\\\/ai-project-80-percent-done-last-20-percent\\\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/www.journai.us\\\/blog\\\/ai-project-80-percent-done-last-20-percent\\\/#primaryimage\",\"url\":\"https:\\\/\\\/www.journai.us\\\/blog\\\/wp-content\\\/uploads\\\/2026\\\/10\\\/your-ai-project-is-80-taking-6-months.png\",\"contentUrl\":\"https:\\\/\\\/www.journai.us\\\/blog\\\/wp-content\\\/uploads\\\/2026\\\/10\\\/your-ai-project-is-80-taking-6-months.png\",\"width\":1200,\"height\":670,\"caption\":\"Your AI Project Is 80 Taking 6 Months\"},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/www.journai.us\\\/blog\\\/ai-project-80-percent-done-last-20-percent\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/www.journai.us\\\/blog\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Your AI Project Is 80% Done. So Why Is the Last 20% Taking 6 Months?\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/www.journai.us\\\/blog\\\/#website\",\"url\":\"https:\\\/\\\/www.journai.us\\\/blog\\\/\",\"name\":\"journai\",\"description\":\"\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/www.journai.us\\\/blog\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Person\",\"@id\":\"https:\\\/\\\/www.journai.us\\\/blog\\\/#\\\/schema\\\/person\\\/7cc0b6c5446cf0e93a553236a1067cca\",\"name\":\"admin\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/02f1622ed995228b18771be3c13cb56d6198f96b08eec483abf8283e00c90a46?s=96&d=mm&r=g\",\"url\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/02f1622ed995228b18771be3c13cb56d6198f96b08eec483abf8283e00c90a46?s=96&d=mm&r=g\",\"contentUrl\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/02f1622ed995228b18771be3c13cb56d6198f96b08eec483abf8283e00c90a46?s=96&d=mm&r=g\",\"caption\":\"admin\"},\"sameAs\":[\"https:\\\/\\\/journai.us\\\/blog\"],\"url\":\"https:\\\/\\\/www.journai.us\\\/blog\\\/author\\\/admin\\\/\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Your AI Project Is 80% Done. So Why Is the Last 20% Taking 6 Months? - journai","description":"Your AI model works, but production is still months away. Discover why integration, security, data, testing, monitoring, and workflows make the last 20% of AI projects so difficult.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.journai.us\/blog\/ai-project-80-percent-done-last-20-percent\/","og_locale":"en_US","og_type":"article","og_title":"Your AI Project Is 80% Done. So Why Is the Last 20% Taking 6 Months? - journai","og_description":"Your AI model works, but production is still months away. Discover why integration, security, data, testing, monitoring, and workflows make the last 20% of AI projects so difficult.","og_url":"https:\/\/www.journai.us\/blog\/ai-project-80-percent-done-last-20-percent\/","og_site_name":"journai","article_published_time":"2026-10-19T13:30:09+00:00","og_image":[{"width":1200,"height":670,"url":"https:\/\/www.journai.us\/blog\/wp-content\/uploads\/2026\/10\/your-ai-project-is-80-taking-6-months.png","type":"image\/png"}],"author":"admin","twitter_card":"summary_large_image","twitter_misc":{"Written by":"admin","Est. reading time":"8 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/www.journai.us\/blog\/ai-project-80-percent-done-last-20-percent\/#article","isPartOf":{"@id":"https:\/\/www.journai.us\/blog\/ai-project-80-percent-done-last-20-percent\/"},"author":{"name":"admin","@id":"https:\/\/www.journai.us\/blog\/#\/schema\/person\/7cc0b6c5446cf0e93a553236a1067cca"},"headline":"Your AI Project Is 80% Done. So Why Is the Last 20% Taking 6 Months?","datePublished":"2026-10-19T13:30:09+00:00","mainEntityOfPage":{"@id":"https:\/\/www.journai.us\/blog\/ai-project-80-percent-done-last-20-percent\/"},"wordCount":1501,"commentCount":0,"image":{"@id":"https:\/\/www.journai.us\/blog\/ai-project-80-percent-done-last-20-percent\/#primaryimage"},"thumbnailUrl":"https:\/\/www.journai.us\/blog\/wp-content\/uploads\/2026\/10\/your-ai-project-is-80-taking-6-months.png","keywords":["Ai Deployment","Ai Implementation","Ai In Business Operations","Ai Integration","Enterprise Ai","Machine Learning","Production Ai"],"inLanguage":"en-US","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/www.journai.us\/blog\/ai-project-80-percent-done-last-20-percent\/#respond"]}]},{"@type":"WebPage","@id":"https:\/\/www.journai.us\/blog\/ai-project-80-percent-done-last-20-percent\/","url":"https:\/\/www.journai.us\/blog\/ai-project-80-percent-done-last-20-percent\/","name":"Your AI Project Is 80% Done. So Why Is the Last 20% Taking 6 Months? - journai","isPartOf":{"@id":"https:\/\/www.journai.us\/blog\/#website"},"primaryImageOfPage":{"@id":"https:\/\/www.journai.us\/blog\/ai-project-80-percent-done-last-20-percent\/#primaryimage"},"image":{"@id":"https:\/\/www.journai.us\/blog\/ai-project-80-percent-done-last-20-percent\/#primaryimage"},"thumbnailUrl":"https:\/\/www.journai.us\/blog\/wp-content\/uploads\/2026\/10\/your-ai-project-is-80-taking-6-months.png","datePublished":"2026-10-19T13:30:09+00:00","author":{"@id":"https:\/\/www.journai.us\/blog\/#\/schema\/person\/7cc0b6c5446cf0e93a553236a1067cca"},"description":"Your AI model works, but production is still months away. Discover why integration, security, data, testing, monitoring, and workflows make the last 20% of AI projects so difficult.","breadcrumb":{"@id":"https:\/\/www.journai.us\/blog\/ai-project-80-percent-done-last-20-percent\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.journai.us\/blog\/ai-project-80-percent-done-last-20-percent\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.journai.us\/blog\/ai-project-80-percent-done-last-20-percent\/#primaryimage","url":"https:\/\/www.journai.us\/blog\/wp-content\/uploads\/2026\/10\/your-ai-project-is-80-taking-6-months.png","contentUrl":"https:\/\/www.journai.us\/blog\/wp-content\/uploads\/2026\/10\/your-ai-project-is-80-taking-6-months.png","width":1200,"height":670,"caption":"Your AI Project Is 80 Taking 6 Months"},{"@type":"BreadcrumbList","@id":"https:\/\/www.journai.us\/blog\/ai-project-80-percent-done-last-20-percent\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.journai.us\/blog\/"},{"@type":"ListItem","position":2,"name":"Your AI Project Is 80% Done. So Why Is the Last 20% Taking 6 Months?"}]},{"@type":"WebSite","@id":"https:\/\/www.journai.us\/blog\/#website","url":"https:\/\/www.journai.us\/blog\/","name":"journai","description":"","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.journai.us\/blog\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Person","@id":"https:\/\/www.journai.us\/blog\/#\/schema\/person\/7cc0b6c5446cf0e93a553236a1067cca","name":"admin","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/secure.gravatar.com\/avatar\/02f1622ed995228b18771be3c13cb56d6198f96b08eec483abf8283e00c90a46?s=96&d=mm&r=g","url":"https:\/\/secure.gravatar.com\/avatar\/02f1622ed995228b18771be3c13cb56d6198f96b08eec483abf8283e00c90a46?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/02f1622ed995228b18771be3c13cb56d6198f96b08eec483abf8283e00c90a46?s=96&d=mm&r=g","caption":"admin"},"sameAs":["https:\/\/journai.us\/blog"],"url":"https:\/\/www.journai.us\/blog\/author\/admin\/"}]}},"_links":{"self":[{"href":"https:\/\/www.journai.us\/blog\/wp-json\/wp\/v2\/posts\/1119","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.journai.us\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.journai.us\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.journai.us\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.journai.us\/blog\/wp-json\/wp\/v2\/comments?post=1119"}],"version-history":[{"count":1,"href":"https:\/\/www.journai.us\/blog\/wp-json\/wp\/v2\/posts\/1119\/revisions"}],"predecessor-version":[{"id":1123,"href":"https:\/\/www.journai.us\/blog\/wp-json\/wp\/v2\/posts\/1119\/revisions\/1123"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.journai.us\/blog\/wp-json\/wp\/v2\/media\/1120"}],"wp:attachment":[{"href":"https:\/\/www.journai.us\/blog\/wp-json\/wp\/v2\/media?parent=1119"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.journai.us\/blog\/wp-json\/wp\/v2\/categories?post=1119"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.journai.us\/blog\/wp-json\/wp\/v2\/tags?post=1119"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}