{"id":6485,"date":"2026-04-02T12:40:13","date_gmt":"2026-04-02T12:40:13","guid":{"rendered":"https:\/\/guenix.digital\/?page_id=6485"},"modified":"2026-04-02T14:16:55","modified_gmt":"2026-04-02T14:16:55","slug":"common-failures-in-ai-adoption","status":"publish","type":"page","link":"https:\/\/guenix.digital\/en\/common-failures-in-ai-adoption\/","title":{"rendered":"Common Failures in AI Adoption"},"content":{"rendered":"\t\t
\n\t\t\t\t
\n\t\t\t\t\t
\n\t\t
\n\t\t\t\t
\n\t\t\t\t\t

Common Failures in AI Adoption<\/h1>\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t\t

Most institutions know AI adoption is complex.\nFew understand exactly where and why it breaks down.<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
\n\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

This page maps the two categories of failure that consistently derail institutional AI initiatives. Structural failures in governance and data, and human resistance signals that operate below the surface of apparent compliance.<\/em><\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

\n\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t

When an AI initiative fails, the instinct is to look at the technology.<\/h3>\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
\n\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

Was the algorithm accurate enough? <\/strong><\/p>

Was the data clean enough? <\/strong><\/p>

Was the platform the right choice?<\/strong><\/p>

These are the wrong questions. The technology rarely fails. The institution fails to govern it, own it, and sustain it.<\/p>

Failures in institutional AI adoption fall into two distinct categories. The first is structural, failures in governance, data, and programme design. The second is human, resistance signals that operate beneath the surface of apparent compliance.<\/p>

Both categories are predictable. Both are preventable. Neither requires better technology.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t\t\t\t
<\/div>\n\t\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t

Part 1. Structural failures<\/h3>\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

These failures happen before a single person resists or a single tool is deployed. They are embedded in how the institution approached the initiative from the start.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t\t\t\t\t\t
1. Governance absent at the start<\/h5>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

Decisions about AI adoption are made without clear authority, defined responsibilities, or explicit limits. When something goes wrong and something always does, no one knows who is accountable. The initiative continues without anyone formally owning it.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t\t\t\t\t\t
2. Tools selected before readiness is assessed<\/h5>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

Vendor pressure, peer influence, or leadership enthusiasm leads to tool selection before the institution understands its governance gaps, risk exposure, or capacity constraints. The tool arrives before the institution is ready to absorb it.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t\t\t\t\t\t
3. Pilots succeed but never scale<\/h5>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

A controlled pilot produces results. But because it was not designed as a programme, with governance, documented processes, and a continuity plan, it never becomes institutional practice. The pilot team disperses. The learning disappears.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t\t\t\t\t\t
4. Training creates skills, not capacity<\/h5>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

Teams are trained on AI tools. When a key person leaves, the knowledge leaves with them. Skills without systems, ownership, and documentation are not institutional capacity. They are individual competence on a temporary contract.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t\t\t\t\t\t
5. Speed prioritised over sustainability<\/h5>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

Pressure to demonstrate results leads to rushed adoption. Short-term metrics improve. Long-term fragility is built in. The initiative collapses when the pressure eases, the project funding ends, or the champion moves on.<\/p>

For a detailed framework on addressing structural failures, see Key Topic 01 \u2014 AI Governance Framework for Institutions.<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"Why\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
\n\t\t\t\t\t\t\t
<\/div>\n\t\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t

Part 2. The seven resistance signals<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

Human resistance to AI adoption is rarely vocal. It does not announce itself in opposition meetings or formal objections.<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t\t\t\t\t\t

It operates beneath the surface, through polite compliance, strategic delays, and invisible workarounds that slowly hollow out the initiative from within.<\/p>

Resistance is rarely irrational. In high-accountability institutional environments, it is almost always a rational response to unclear responsibilities, unrealistic expectations, or genuine governance concerns.<\/p>

Understanding resistance as a governance signal rather than a change management problem changes how you respond to it.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"Governance,\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
\n\t\t\t\t\t\t\t
<\/div>\n\t\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t

Part 2. The seven resistance signals<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

Human resistance to AI adoption is rarely vocal. It does not announce itself in opposition meetings or formal objections.<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t\t\t\t\t\t

It operates beneath the surface, through polite compliance, strategic delays, and invisible workarounds that slowly hollow out the initiative from within.<\/p>

Resistance is rarely irrational. In high-accountability institutional environments, it is almost always a rational response to unclear responsibilities, unrealistic expectations, or genuine governance concerns.<\/p>

Understanding resistance as a governance signal rather than a change management problem changes how you respond to it.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"Governance,\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t
\n\t\t\t\t\t
\n\t\t
\n\t\t\t\t
\n\t\t\t\t\t






01<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
\n\t\t\t\t
\n\t\t\t\t\t

Signal 01. The Corporate Fad Dismissal<\/h3>\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

What it looks like: <\/strong><\/p>

Staff mentally file the AI initiative under another initiative that will pass. They attend sessions, nod at appropriate moments, and continue exactly as before. The dismissal is rarely spoken. It is communicated through minimal engagement and references to previous abandoned initiatives.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t\t\t\t\t\t

Red flag: Minimal note-taking or follow-up questions during sessions. Conversations immediately shift to other topics after meetings. References to previous initiatives that were quietly abandoned. Surface compliance with no genuine change in practice.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t\t\t\t\t\t

Governance response:<\/p>

Address the history directly.<\/strong><\/p>

Acknowledge past initiatives that failed and explain specifically what is structurally different this time.<\/p>

Create visible early results that make dismissal harder to sustain.<\/p>

Connect the initiative explicitly to institutional accountability requirements, not just efficiency gains.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

\n\t\t\t\t\t
\n\t\t
\n\t\t\t\t
\n\t\t\t\t\t






02<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
\n\t\t\t\t
\n\t\t\t\t\t

Signal 02. The Perpetually Rescheduled Meeting<\/h3>\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

What it looks like: <\/strong><\/p>

Implementation meetings are consistently postponed, delegated to junior staff, or reduced in frequency. Each rescheduling sends an unspoken message: this initiative is not a genuine priority. Over time, even committed champions begin to lose confidence.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t\t\t\t\t\t

Red flag: Key decision-makers consistently absent or replaced by substitutes. Meetings rescheduled multiple times before occurring. Gradual reduction in meeting frequency without explanation. Decisions perpetually deferred due to absent parties.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t\t\t\t\t\t

Governance response:<\/strong><\/p>

Make attendance and engagement visible through transparent tracking. Require executive sponsors to explicitly designate implementation meetings as non-negotiable. Identify which meetings require named decision-makers to attend personally and enforce it. Passive non-attendance is a governance failure, not a scheduling problem.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

\n\t\t\t\t\t
\n\t\t
\n\t\t\t\t
\n\t\t\t\t\t






03<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
\n\t\t\t\t
\n\t\t\t\t\t

Signal 03. Endless Documentation Demands<\/h3>\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

What it looks like: <\/strong><\/p>

What it looks like: Every answer generates new questions. Every decision requires additional specifications. Teams request levels of technical detail far beyond what is needed for business decisions. The documentation demands never converge. They expand. This is bureaucratic delay masquerading as diligence.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t\t\t\t\t\t

Red flag: Every answer generates three new questions on the same topic. Technical specifications requested far beyond business decision needs. The same information requested repeatedly in different formats. Documentation of edge cases becomes a prerequisite for any progress.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t\t\t\t\t\t

Governance response:<\/strong><\/p>

Establish clear documentation standards upfront and get stakeholder agreement before proceeding. Implement strict time-boxing for documentation phases. Calculate and communicate the organisational cost of delay. Documentation should enable action, not replace it.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

\n\t\t\t\t\t
\n\t\t
\n\t\t\t\t
\n\t\t\t\t\t






04<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
\n\t\t\t\t
\n\t\t\t\t\t

Signal 04. Knowledge Hoarding<\/h3>\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

What it looks like: <\/strong><\/p>

What it looks like: Subject matter experts attend all the right meetings and express support. But they provide only surface-level information, systematically withholding the deep operational knowledge that would make the AI system genuinely useful. This resistance stems from a rational fear of professional obsolescence.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t\t\t\t\t\t

Red flag: Vague responses to specific knowledge extraction questions. Overemphasis on exceptions and edge cases to demonstrate irreplaceable complexity. Describing routine processes as highly nuanced or judgment-based. Reluctance to document tacit knowledge or decision-making frameworks.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t\t\t\t\t\t

Governance response:<\/strong><\/p>

Address the underlying fear directly. Help experts understand that teaching AI systems to handle routine tasks demonstrates higher-level capability. It does not replace their expertise. Create new roles that give experts status through knowledge architecture rather than knowledge hoarding. Use multiple extraction methods including observation, scenario workshops, and cross-validation across experts.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

\n\t\t\t\t\t
\n\t\t
\n\t\t\t\t
\n\t\t\t\t\t






05<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
\n\t\t\t\t
\n\t\t\t\t\t

Signal 05. Enthusiastic Non-Delivery<\/h3>\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

What it looks like: <\/strong><\/p>

Team members express strong commitment in meetings, volunteer for tasks, and agree to deadlines. Then nothing is delivered. Progress reports sound substantive but contain few concrete results. This pattern is particularly damaging because these individuals receive recognition for apparent support while quietly preventing progress.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t\t\t\t\t\t

Red flag: Consistent gap between expressed enthusiasm and actual output. Deliverables repeatedly delayed for seemingly legitimate reasons. Progress updates that sound impressive but contain no concrete results. Departments expressing commitment to AI while making no operational adjustments.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t\t\t\t\t\t

Governance response:<\/strong><\/p>

Create visibility around progress through specific milestone tracking rather than narrative updates. Replace large distant deliverables with small frequent ones that make inaction apparent quickly. Establish clear accountability structures with named ownership. When patterns of commitment without follow-through persist, address them directly and specifically.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

\n\t\t\t\t\t
\n\t\t
\n\t\t\t\t
\n\t\t\t\t\t






06<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
\n\t\t\t\t
\n\t\t\t\t\t

Signal 06. The Sudden Data Quality Crisis<\/h3>\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

What it looks like: <\/strong><\/p>

Data that has been sufficient for daily operational decisions is abruptly declared inadequate just as AI implementation begins. The proposed remediation timeline is open-ended. The quality standards applied to AI data are significantly higher than those applied to current operations. The timing is the signal.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t\t\t\t\t\t

Red flag: Data concerns emerge late in the process after initial approvals. Remediation timelines are excessive or deliberately vague. Quality standards applied to AI data far exceed those used for current decisions. Reluctance to proceed even in areas with acknowledged data quality.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t\t\t\t\t\t

Governance response:<\/strong><\/p>

Separate legitimate data concerns from delay tactics. Acknowledge real issues while pushing back on perfectionism. Remind teams that current decision-making already accommodates data limitations. Implement a minimum viable data approach. Identify the smallest dataset needed for meaningful initial implementation, then improve through use rather than before use.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

\n\t\t\t\t\t
\n\t\t
\n\t\t\t\t
\n\t\t\t\t\t






07<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
\n\t\t\t\t
\n\t\t\t\t\t

Signal 07. The Uniqueness Exception<\/h3>\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

What it looks like: <\/strong><\/p>

Departments consistently insist their operations are too specific for standard approaches. Every proposed solution meets explanations of why it cannot apply here. Requests for custom development accumulate. Parallel legacy systems are maintained indefinitely. The underlying message: the normal rules do not apply to us.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t\t\t\t\t\t

Red flag: Excessive emphasis on minor differences that do not prevent standardisation. Rejection of solutions working successfully in comparable departments. Requests for custom development that would create unsustainable complexity. Insistence on maintaining legacy systems in parallel with no defined end date.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t\t\t\t\t\t

Governance response:<\/strong><\/p>

Acknowledge that some contextual adaptation is appropriate while establishing that complete exemption is not an option. Create a structured exception request process that requires departments to document specific operational impacts rather than general uniqueness claims. Use a prove-it approach: establish small pilots to test whether claimed barriers actually materialise in practice.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

\n\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

Resistance signals and structural failures are not obstacles to governance. <\/strong><\/p>

They are the reasons governance must come first.<\/strong><\/p>

The organisations that succeed in AI adoption are not those with the most advanced technology. They are those that governed the human and structural dimensions of adoption with the same rigour they applied to the technical ones.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

\n\t\t\t\t\t
\n\t\t
\n\t\t\t\t
\n\t\t\t\t\t

How Guenix helps<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

Guenix works with institutions to identify structural and human failure risks before they become costly.<\/p>

The AI Governance & Data Readiness Diagnostic assesses your institution’s readiness across governance, data, and trust architecture. The result identifies your most critical failure risks before any tool is deployed.<\/p>

For a complete overview, read our guide on Responsible AI & Digital Adoption.\u00a0<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t

\n\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tRequest a diagnostic conversation<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tExplore first \u2192 Take the free diagnostic<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

Common Failures in AI Adoption Most institutions know AI adoption is complex. Few understand exactly where and why it breaks down. This page maps the two categories of failure that consistently derail institutional AI initiatives. Structural failures in governance and data, and human resistance signals that operate below the surface of apparent compliance. When an<\/p>","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"om_disable_all_campaigns":false,"_surecart_dashboard_logo_width":"180px","_surecart_dashboard_show_logo":true,"_surecart_dashboard_navigation_orders":true,"_surecart_dashboard_navigation_invoices":true,"_surecart_dashboard_navigation_subscriptions":true,"_surecart_dashboard_navigation_downloads":true,"_surecart_dashboard_navigation_billing":true,"_surecart_dashboard_navigation_account":true,"_uag_custom_page_level_css":"","_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"site-sidebar-layout":"no-sidebar","site-content-layout":"page-builder","ast-site-content-layout":"full-width-container","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"disabled","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"class_list":["post-6485","page","type-page","status-publish","hentry"],"acf":[],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t