{"id":8190,"date":"2026-06-02T05:35:36","date_gmt":"2026-06-02T05:35:36","guid":{"rendered":"https:\/\/nextagile.ai\/blogs\/?p=8190"},"modified":"2026-06-04T07:10:48","modified_gmt":"2026-06-04T07:10:48","slug":"ai-readiness-assessment","status":"publish","type":"post","link":"https:\/\/nextagile.ai\/blogs\/gen-ai\/ai-readiness-assessment\/","title":{"rendered":"AI Readiness Assessment: The Enterprise Checklist Most Companies Skip Until a Pilot Fails"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">Most enterprises do not start their AI journey with a readiness assessment.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">They start with a demo.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Someone in leadership sees a chatbot summarize reports in 12 seconds. A team experiments with a public LLM. A vendor promises \u201cAI-powered transformation\u201d in six weeks. Budget gets approved. A pilot begins.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Then things slow down.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The model cannot access clean internal knowledge. Legal gets nervous about data exposure. Teams disagree on ownership. Nobody knows which workflows should actually be automated. The pilot works in controlled demos but breaks under real operational conditions.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">At that point, companies usually assume they picked the wrong model.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In practice, the model is rarely the main problem.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The real issue is that the organization was never operationally ready for AI deployment in the first place.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">That is what an AI readiness assessment is supposed to uncover before money gets burned on tooling, licenses, or consulting-led experimentation.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">At NextAgile, we use something called the AARI framework; AI Adoption Readiness Index &#8211; to evaluate whether an enterprise is realistically prepared for production AI systems, not just isolated experiments.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The framework looks at eight areas that repeatedly determine whether AI initiatives move beyond the pilot phase:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">leadership alignment<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">data quality<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">infrastructure maturity<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">governance<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">operational processes<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">AI capability<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">internal adoption culture<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">financial preparedness<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Some organizations score high technically but fail culturally. Others have executive enthusiasm but fragmented systems and unusable data. Occasionally, we see companies with strong engineering teams but no governance model at all.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Those projects usually stall later, not earlier.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The point of an AI readiness assessment is not to prove that your company is \u201cinnovative.\u201d It is to identify where implementation friction is likely to appear before deployment pressure increases.<\/span><\/p>\n<h2><b>What an AI Readiness Assessment Actually Measures<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">A proper AI readiness assessment is less about AI itself and more about organizational conditions around AI.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">That distinction matters.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Many enterprises assume readiness means:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">buying enterprise LLM access<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">experimenting with copilots<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">hiring a few AI engineers<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">setting up a vector database<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">launching a proof of concept<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Those are implementation activities. They are not readiness indicators.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">We have seen organizations with expensive AI tooling still struggle to answer very basic operational questions:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Who owns model outputs?<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Which data sources are reliable?<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">What happens when an answer is wrong?<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Can internal systems expose APIs cleanly?<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Who signs off on automation risk?<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Which workflows should never be autonomous?<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Those gaps become visible only after deployment pressure starts building.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">An assessment helps surface those issues early.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The AARI framework evaluates eight weighted dimensions because not all readiness factors carry equal operational impact.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For example, weak branding does not kill AI initiatives.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Weak data foundations usually do.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">That is why Data Foundations carries the highest weight in the model.<\/span><\/p>\n<h2><b>The AARI Framework: 8 Areas That Decide Whether AI Survives Beyond Pilots<\/b><\/h2>\n<p>&nbsp;<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-8220 size-full\" src=\"https:\/\/nextagile.ai\/blogs\/wp-content\/uploads\/2026\/06\/The-AARI-Framework-8-Areas-That-Decide-Whether-AI-Survives-Beyond-Pilots-1.png\" alt=\"AARI Framework 8 Areas\" width=\"1200\" height=\"800\" title=\"\" srcset=\"https:\/\/nextagile.ai\/blogs\/wp-content\/uploads\/2026\/06\/The-AARI-Framework-8-Areas-That-Decide-Whether-AI-Survives-Beyond-Pilots-1.png 1200w, https:\/\/nextagile.ai\/blogs\/wp-content\/uploads\/2026\/06\/The-AARI-Framework-8-Areas-That-Decide-Whether-AI-Survives-Beyond-Pilots-1-300x200.png 300w, https:\/\/nextagile.ai\/blogs\/wp-content\/uploads\/2026\/06\/The-AARI-Framework-8-Areas-That-Decide-Whether-AI-Survives-Beyond-Pilots-1-1024x683.png 1024w, https:\/\/nextagile.ai\/blogs\/wp-content\/uploads\/2026\/06\/The-AARI-Framework-8-Areas-That-Decide-Whether-AI-Survives-Beyond-Pilots-1-768x512.png 768w, https:\/\/nextagile.ai\/blogs\/wp-content\/uploads\/2026\/06\/The-AARI-Framework-8-Areas-That-Decide-Whether-AI-Survives-Beyond-Pilots-1-600x400.png 600w, https:\/\/nextagile.ai\/blogs\/wp-content\/uploads\/2026\/06\/The-AARI-Framework-8-Areas-That-Decide-Whether-AI-Survives-Beyond-Pilots-1-150x100.png 150w\" sizes=\"auto, (max-width: 1200px) 100vw, 1200px\" \/><\/p>\n<h3><b>D1. Strategic Vision and Leadership Alignment (15%)<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">A surprising number of AI initiatives still operate without executive ownership.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The technical teams move ahead, but leadership has not agreed on:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">why AI is being adopted<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">which business outcomes matter<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">what level of risk is acceptable<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">whether the organization wants augmentation or automation<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Without alignment at that level, AI remains experimental for much longer than expected.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">One common pattern:<\/span><span style=\"font-weight: 400;\"><br \/>\n<\/span><span style=\"font-weight: 400;\">leadership wants productivity gains while operational teams fear process disruption. Nobody resolves the contradiction early, so adoption slows quietly over time.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This dimension evaluates:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">executive sponsorship<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">AI investment clarity<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">organizational direction<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">accountability structure<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">long-term ownership<\/span><\/li>\n<\/ul>\n<h3><b>D2. Data Foundations and Information Architecture (20%)<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">This is usually where projects fail first.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Not because companies lack data.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Most enterprises have too much of it.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The problem is:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">duplicated documentation<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">outdated knowledge repositories<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">inconsistent permissions<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">fragmented ownership<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">poor metadata<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">inaccessible internal systems<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">A retrieval system is only as reliable as the information ecosystem behind it.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">We have seen organizations attempt enterprise search deployments while internal documentation still lived across:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">email threads<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">PDFs<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">SharePoint folders<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Slack exports<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">local spreadsheets<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">undocumented tribal knowledge<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">No model fixes that.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This dimension evaluates:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">data accessibility<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">governance standards<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">freshness controls<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">knowledge organization<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">pipeline maturity<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">retrieval readiness<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">In practice, this area predicts implementation success more consistently than model selection.<\/span><\/p>\n<h3><b>D3. Infrastructure and Operational Readiness (15%)<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">A pilot environment is not the same thing as production infrastructure.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Many companies can get an LLM running internally. Far fewer can operate it reliably at scale.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The problems usually appear later:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">latency spikes<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">API dependency issues<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">rising inference costs<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">monitoring gaps<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">deployment bottlenecks<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">inconsistent environments<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">vendor lock-in<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Operational AI requires:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">monitoring<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">deployment pipelines<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">model version control<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">observability<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">rollback processes<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">cost visibility<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Without those systems, even successful pilots become difficult to maintain.<\/span><\/p>\n<h3><b>D4. LLM and Agentic AI Readiness (15%)<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">This dimension looks at whether the organization actually understands modern AI architectures beyond surface-level terminology.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Right now, many enterprises use words like:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">agents<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">autonomous workflows<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">copilots<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">reasoning systems<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">without operational clarity behind them.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">A lot of \u201cagentic AI\u201d discussions are still presentation-layer optimism.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Before enterprises attempt orchestration or autonomous execution, they usually need to answer simpler questions first:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Can retrieval quality be trusted?<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Are prompts governed?<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Are outputs reviewed?<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Can workflows be audited?<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Do systems expose stable actions through APIs?<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Organizations that skip these fundamentals often jump prematurely into complex multi-agent experiments that create more operational noise than value.<\/span><\/p>\n<h3><b>D5. Governance and Responsible AI (10%)<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Governance usually enters the conversation later than it should.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Initially, teams focus on capability:<\/span><span style=\"font-weight: 400;\"><br \/>\n<\/span><span style=\"font-weight: 400;\">\u201cWhat can the model do?\u201d<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Eventually the discussion changes:<\/span><span style=\"font-weight: 400;\"><br \/>\n<\/span><span style=\"font-weight: 400;\">\u201cWhat happens if this goes wrong?\u201d<\/span><\/p>\n<p><span style=\"font-weight: 400;\">That shift tends to happen after exposure risk becomes visible.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In regulated industries especially, governance cannot remain theoretical.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Enterprises need practical answers around:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">human review<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">audit trails<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">explainability<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">access controls<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">data residency<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">compliance obligations<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">output accountability<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">A common mistake is assuming governance slows innovation.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In reality, unclear governance slows deployment far more aggressively because nobody is comfortable approving production rollout.<\/span><\/p>\n<h3><b>D6. Culture, Talent, and Internal Adoption (10%)<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">This dimension is consistently underestimated.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Technical teams often assume employees will naturally adopt AI if the tools are useful enough.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">That is not how organizational behavior works.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In many companies, resistance is not explicit. It shows up quietly:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">low usage<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">process avoidance<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">shadow workflows<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">distrust in outputs<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">lack of experimentation<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">passive non-adoption<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Teams with strong AI literacy usually adapt faster even when tooling is imperfect.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Teams with weak adoption culture struggle even with good systems.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The organizations that scale AI most effectively tend to normalize experimentation early. Leadership uses the tools openly. Employees are allowed to test workflows without fear of looking replaceable.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">That cultural signal matters more than most executives realize.<\/span><\/p>\n<h3><b>D7. Process Maturity and Workflow Structure (10%)<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">AI systems perform poorly inside chaotic operational environments.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Before automation works well, workflows need:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">clear decision paths<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">documented exceptions<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">system interoperability<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">ownership boundaries<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">measurable outcomes<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">A surprising number of enterprises still rely heavily on undocumented manual work.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">People know how processes operate, but the organization itself does not.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">That creates major problems for AI deployment because agents require structured environments to operate reliably.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">If workflows are inconsistent between teams, the AI layer inherits that inconsistency immediately.<\/span><\/p>\n<h3><b>D8. Financial Readiness and ROI Discipline (5%)<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">This dimension carries the lowest weight, but it still matters.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Not because AI budgets are rare.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Because many organizations still cannot evaluate AI investment realistically.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Early-stage projections often assume:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">immediate productivity gains<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">fast automation<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">rapid adoption<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">lower operating costs<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Actual deployment timelines are usually slower.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Enterprises that succeed with AI tend to approach ROI incrementally:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">narrow use cases first<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">measurable operational improvements<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">controlled scaling<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">phased rollout economics<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Organizations chasing transformational outcomes too early often overbuild before proving operational value.<\/span><\/p>\n<h2><b>The Five AARI Maturity Levels<\/b><\/h2>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-8196 size-full\" src=\"https:\/\/nextagile.ai\/blogs\/wp-content\/uploads\/2026\/06\/The-Five-AARI-Maturity-Levels.png\" alt=\"The Five AARI Maturity Levels\" width=\"1200\" height=\"800\" data-sitemapexclude=\"true\" title=\"\" srcset=\"https:\/\/nextagile.ai\/blogs\/wp-content\/uploads\/2026\/06\/The-Five-AARI-Maturity-Levels.png 1200w, https:\/\/nextagile.ai\/blogs\/wp-content\/uploads\/2026\/06\/The-Five-AARI-Maturity-Levels-300x200.png 300w, https:\/\/nextagile.ai\/blogs\/wp-content\/uploads\/2026\/06\/The-Five-AARI-Maturity-Levels-1024x683.png 1024w, https:\/\/nextagile.ai\/blogs\/wp-content\/uploads\/2026\/06\/The-Five-AARI-Maturity-Levels-768x512.png 768w, https:\/\/nextagile.ai\/blogs\/wp-content\/uploads\/2026\/06\/The-Five-AARI-Maturity-Levels-600x400.png 600w, https:\/\/nextagile.ai\/blogs\/wp-content\/uploads\/2026\/06\/The-Five-AARI-Maturity-Levels-150x100.png 150w\" sizes=\"auto, (max-width: 1200px) 100vw, 1200px\" \/><\/p>\n<h3><b>Level 1:\u00a0 Initial (AARI 1.0 to 1.9)<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">At this stage, most AI activity is exploratory.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Teams experiment independently. Leadership discussions are still conceptual. Data environments are fragmented. Governance is either absent or informal.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This is not the stage for production deployment.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Companies here usually need to stabilize:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">data ownership<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">internal documentation<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">leadership alignment<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">operational priorities<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">before scaling anything AI-related.<\/span><\/p>\n<h3><b>Level 2: Developing (AARI 2.0 to 2.9)<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Organizations at this stage have started building pilots.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">There may be:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">early RAG experiments<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">API integrations<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">isolated automation projects<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">small internal AI teams<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">The risk here is scaling too quickly before governance and operational structure mature.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">A successful prototype can create false confidence.<\/span><\/p>\n<h3><b>Level 3: Defined (AARI 3.0 to 3.4)<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">This is where organizations begin operating AI systematically instead of experimentally.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Typical characteristics include:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">centralized data environments<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">operational deployment pipelines<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">defined governance controls<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">monitored workflows<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">structured evaluation systems<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">At this stage, companies can usually support production AI use cases with controlled oversight.<\/span><\/p>\n<h3><b>Level 4: Strategic (AARI 3.5 to 4.4)<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">AI becomes embedded into operational decision-making.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Organizations here often:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">orchestrate multiple systems<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">automate cross-functional workflows<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">measure AI impact consistently<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">integrate governance directly into delivery pipelines<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">This is also the stage where organizational complexity increases significantly.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Scaling AI operationally is much harder than launching it.<\/span><\/p>\n<h3><b>Level 5: AI-Native (AARI 4.5 to 5.0)<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Very few enterprises are genuinely operating at this level today despite the branding language used in the market.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">AI-native organizations redesign operations around AI capability instead of layering AI onto legacy structures.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">That requires:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">mature governance<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">strong internal adoption<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">highly interoperable systems<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">disciplined operational management<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">continuous evaluation processes<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Most enterprises are not there yet.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">And forcing \u201cAI-native\u201d positioning prematurely usually creates more confusion internally than progress.<\/span><\/p>\n<h2><b>The Most Common AI Readiness Mistakes We Keep Seeing<\/b><\/h2>\n<h3><b>Mistake 1: Buying AI Infrastructure Before Fixing Information Quality<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">This happens constantly.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">An organization invests in:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">enterprise AI licenses<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">vector databases<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">orchestration frameworks<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">copilots<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">external consulting<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">while internal documentation remains disorganized and unreliable.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The deployment technically works.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The outputs do not.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Teams then blame the model when the underlying retrieval layer was never trustworthy to begin with.<\/span><\/p>\n<h3><b>Mistake 2: Treating AI Readiness as a One-Time Workshop<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Readiness changes continuously.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Teams evolve. Governance evolves. Regulations evolve. Systems evolve.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">A company that was \u201cready\u201d twelve months ago may no longer be ready after scaling complexity increases.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The organizations that adapt best usually revisit readiness quarterly, especially during active deployment phases.<\/span><\/p>\n<h3><b>Mistake 3: Underestimating Organizational Resistance<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Most enterprises assume resistance will look dramatic.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Usually it looks administrative.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">People stop using the system quietly.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Managers bypass workflows.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Teams revert to manual processes because trust never formed properly.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">AI adoption problems are often operational psychology problems before they become technical problems.<\/span><\/p>\n<h2><b>What Happens After the Assessment<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">A readiness assessment should not end with a maturity score.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">That is another common enterprise mistake:<\/span><span style=\"font-weight: 400;\"><br \/>\n<\/span><span style=\"font-weight: 400;\">turning the assessment into a presentation artifact.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The useful output is the operational gap analysis.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">After scoring, the next step is usually prioritization:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">which risks need immediate attention<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">which workflows are realistic candidates<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">which systems need cleanup<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">where governance is weakest<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">where adoption friction is likely to emerge<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">In most organizations, the first 90 days matter far more than the long-term AI vision deck.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Early execution quality tends to shape internal trust for the next phase of adoption.<\/span><\/p>\n<h2><b>Conclusion<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">Most failed AI initiatives do not fail because the underlying model was weak.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">They fail because the organization underestimated the operational changes required to support AI responsibly at scale.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The companies getting long-term value from AI today are usually not the ones chasing the loudest AI narrative.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">They are the ones doing slower foundational work:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">cleaning internal knowledge systems<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">clarifying governance<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">restructuring workflows<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">improving adoption culture<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">defining ownership properly<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">introducing AI incrementally instead of theatrically<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">That work is less marketable than \u201cAI transformation.\u201d<\/span><\/p>\n<p><span style=\"font-weight: 400;\">It is also the work that determines whether deployment survives beyond the pilot phase.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">If your organization is exploring AI seriously, the readiness work cannot stay theoretical for long. At some point, leadership needs a clear view of where the operational gaps actually are; whether that is data quality, governance, workflow maturity, infrastructure, or internal adoption. NextAgile works with enterprises to assess these gaps pragmatically and build an execution roadmap that fits the organization\u2019s current maturity instead of forcing AI transformation prematurely. If you want to evaluate where your enterprise stands today, reach out to <\/span><a href=\"mailto:consult@nextagile.ai\"><span style=\"font-weight: 400;\">consult@nextagile.ai<\/span><\/a><span style=\"font-weight: 400;\"> and the team can help you work through the assessment and next steps.<\/span><\/p>\n<h2><b>Frequently Asked Questions<\/b><\/h2>\n<h3><b>1.How long does an AI readiness assessment usually take?<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">It depends less on company size and more on how fragmented the organization already is.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In some enterprises, the assessment moves quickly because the leadership team, data owners, and engineering teams already operate with reasonable alignment. In others, even identifying who owns critical systems takes time.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For most mid-sized and large organizations, the process usually stretches across a few weeks. The actual scoring is not the slow part. The discussions around data quality, governance gaps, workflow ownership, and deployment expectations usually take longer than expected.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">If the scope is narrower, for example, evaluating only AI infrastructure readiness or governance maturity, the assessment can move faster.<\/span><\/p>\n<h3><b>2.What is considered a \u201cgood\u201d AI readiness score?<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">A high score matters less than an honest one.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Some organizations try to position themselves as AI-mature because leadership pressure already exists around transformation initiatives. That usually creates problems later when operational gaps begin surfacing during deployment.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In practical terms, organizations crossing the mid-level range are generally in a position to run structured production pilots with tighter controls and realistic expectations.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Lower scores are not necessarily bad news. In many cases, they prevent companies from scaling AI prematurely before the foundations are stable enough to support it.<\/span><\/p>\n<h3><b>3.Which area causes the most AI deployment failures?<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Data problems still cause more deployment failures than model problems.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Not because companies lack information, but because enterprise knowledge is often inconsistent, duplicated, outdated, or difficult to retrieve cleanly.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">A surprisingly common scenario is this:<\/span><span style=\"font-weight: 400;\"><br \/>\n<\/span><span style=\"font-weight: 400;\">the AI demo works well in testing, but once real internal documents enter the system, output quality becomes unreliable very quickly.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Culture-related issues are close behind. Teams that do not trust the workflows, leadership, or governance model usually slow adoption quietly, even when the technical implementation is solid.<\/span><\/p>\n<h3><b>4.How is the AARI framework different from standard AI readiness tools?<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">A lot of readiness tools available today are designed to support a specific ecosystem or platform strategy.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The AARI framework was built more from implementation experience than from software positioning.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The focus is less on \u201chow advanced your AI ambitions sound\u201d and more on identifying where operational friction is likely to appear once deployment moves beyond experimentation.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The weighting model also matters. Not every dimension creates the same level of implementation risk. Weak governance and weak data foundations tend to create much larger downstream problems than organizations initially expect.<\/span><\/p>\n<h3><b>5.Does this framework work for GCCs and India-based enterprises?<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Yes, although the operational realities are often different from US or Europe-centric AI rollout models.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Many GCCs and Indian enterprises operate inside more layered approval structures, tighter compliance expectations, hybrid legacy environments, and stricter data movement considerations.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">That changes how readiness should be evaluated.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In practice, governance, infrastructure dependencies, vendor constraints, and data residency requirements tend to play a much larger role in India-based enterprise AI programs than many global frameworks assume initially.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Most enterprises do not start their AI journey with a readiness assessment. They start with a demo. Someone in leadership sees a chatbot summarize reports in 12 seconds. A team experiments with a public LLM. A vendor promises \u201cAI-powered transformation\u201d in six weeks. Budget gets approved. A pilot begins. Then things slow down. The model&#8230;<\/p>\n","protected":false},"author":19,"featured_media":8217,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[145],"tags":[],"class_list":["post-8190","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-gen-ai"],"_links":{"self":[{"href":"https:\/\/nextagile.ai\/blogs\/wp-json\/wp\/v2\/posts\/8190","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/nextagile.ai\/blogs\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/nextagile.ai\/blogs\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/nextagile.ai\/blogs\/wp-json\/wp\/v2\/users\/19"}],"replies":[{"embeddable":true,"href":"https:\/\/nextagile.ai\/blogs\/wp-json\/wp\/v2\/comments?post=8190"}],"version-history":[{"count":6,"href":"https:\/\/nextagile.ai\/blogs\/wp-json\/wp\/v2\/posts\/8190\/revisions"}],"predecessor-version":[{"id":8250,"href":"https:\/\/nextagile.ai\/blogs\/wp-json\/wp\/v2\/posts\/8190\/revisions\/8250"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/nextagile.ai\/blogs\/wp-json\/wp\/v2\/media\/8217"}],"wp:attachment":[{"href":"https:\/\/nextagile.ai\/blogs\/wp-json\/wp\/v2\/media?parent=8190"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/nextagile.ai\/blogs\/wp-json\/wp\/v2\/categories?post=8190"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/nextagile.ai\/blogs\/wp-json\/wp\/v2\/tags?post=8190"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}