Organizational Readiness for Enterprise AI Frameworks

Assessing organizational readiness for AI with an enterprise framework works at 10 to 200 people if you change one thing: where the framework asks whether you have a department, ask instead which person holds that job. Enterprise frameworks assume an AI Center of Excellence, a chief data officer, a governance committee, and an IT team. A 90-person manufacturer usually has none of those, but it still has someone who sets direction, someone who controls spend, someone who decides what data can go where, and someone who fixes things when they break. Readiness at this size means those people are named, and a row with nobody's name on it is the finding.

In September 2026 we read 27 pages ranking for "assessing organizational readiness for enterprise ai framework" and five related searches, and they agreed on a lot: a median of six dimensions, with data, governance, technology, and people each showing up on 21 or more of them. They were thin on who. Only 3 said which role in the company owns which dimension, and only 1 named a company size at all. None mentioned what we think is the likeliest reason a mid-sized firm will care about AI governance this year: a large customer's supplier questionnaire asking about it.

This page covers what enterprise frameworks mean by organizational readiness and how to translate each dimension to a named person at your size. It then cuts the best-known governance framework down to the six parts that matter, and sets out when you really do need the enterprise version. It sits under our guide to AI readiness assessment services, which covers the whole assessment.

Key Takeaways

• Enterprise AI readiness frameworks score strategy, leadership, governance, operating model, skills, and culture as well as data and technology. Most assume departments a 10-to-200-person firm doesn't have.
• Of 27 ranking framework pages we read in September 2026, only 3 mapped roles to dimensions, only 1 named a company size, and none covered customer AI questionnaires.
• In the EU, 14.0% of firms with 10 to 49 employees employed an IT specialist in 2024, against 78.4% of firms with 250 or more. At your size, the functions exist but sit with people, not departments.
• NIST's AI Risk Management Framework has 72 subcategories and says organizations may "select from among" them. Six cover what a mid-sized operation needs.
• Only 38% of organizations had a formal, comprehensive AI policy in ISACA's 2026 poll, and 39% of respondents didn't know whether they had a documented way to shut an AI system down.
• The main reason to adopt enterprise-grade governance is outside pressure: a vendor questionnaire used by thousands of organizations has asked about AI since its 2024 edition.

Table of Contents

• What organizational readiness means in an enterprise framework
• What the framework assumes you have
• Translate each dimension to a named person
• Governance, cut down from NIST's 72 subcategories
• When you need the enterprise version
• How organizations evaluate readiness with a framework
• A worked example: a 150-person food co-packer
• Where Avolis fits
• Frequently Asked Questions
• Continue Learning

What Does Organizational Readiness Mean in an Enterprise AI Framework?

In an enterprise AI framework, organizational readiness is everything scored apart from data and technology. That means a direction for AI, a leader who owns it, rules for using it, a way of delivering it, people with the skills, and a culture that will change how it works. Frameworks label those differently, but the same handful of areas comes up on almost every page.

Two of the 27 pages didn't name dimensions we could count, so the table uses 25. Our methodology page compares the component lists themselves, and the point here is narrower: how many of them are about the organization. The crosswalk further down splits strategy into direction and sponsorship and folds use-case value into strategy, since at this size the same person usually holds both. Six of the eight rows are about the organization rather than its systems, and they're the ones the big consultancies' own research says matter most. When Boston Consulting Group surveyed 1,000 senior executives in 59 countries in 2024, it found the AI leaders "follow the rule of putting 10% of their resources into algorithms, 20% into technology and data, and 70% in people and processes" (BCG, "AI Adoption in 2024: 74% of Companies Struggle to Achieve and Scale Value", October 2024). McKinsey's 2026 survey found the same pattern from a different angle: "Nearly three-quarters of high performers report fundamentally redesigning workflows because of their AI use," while "just one-quarter of other respondents report doing so" (McKinsey, "The state of AI in 2026: On the road to ROI", August 2026).

Both surveys lean heavily toward large companies, so read them as a direction rather than a benchmark for a 60-person contractor. The direction is still useful, because it tells you that the organizational rows of an enterprise framework aren't paperwork attached to the real assessment. They are the part that predicts whether anything gets used.

What Does the Framework Assume You Have?

Most enterprise frameworks assume three things a mid-sized operation doesn't have: specialist staff, standing committees, and a separate team whose job is AI. The assumption is usually built into the questions rather than stated, which is why a capable 120-person business can score badly without being badly run. Maturity models have the same problem, as our AI maturity model page explains.

The pages we read made the assumption visible in different ways. Five named an AI Center of Excellence (a central team that runs AI work for the whole company) or a C-level AI or data role as part of the model. Six named a steering committee or governance board, and six named machine-learning operations platforms, the tooling a data-science team uses to deploy and monitor its own models. A framework that asks "Does your AI Center of Excellence have an approved charter?" has no good answer for a firm that will never need one.

The staffing gap by size is large and well measured. In the EU's 2024 survey of enterprise technology use, 14.0% of firms with 10 to 49 employees employed an IT or communications specialist, against 43.0% of firms with 50 to 249 and 78.4% of firms with 250 or more (Eurostat, Enterprises that employ ICT specialists by size class of enterprise, dataset isoc_ske_itspe, updated June 2026). That's specialists of any kind, not AI specialists, so the AI figure would be lower still.

So who does the work at smaller firms? The UK government's 2026 survey of 2,112 businesses asked who looks after cyber security, which is the closest well-measured stand-in for "who handles technology risk." In small businesses, "very few had someone specifically in an IT-role" doing it, and the most common job was the general office manager, at 26%. Small and medium businesses were also the likeliest to outsource cyber security, at 64% and 70%, against 42% of large ones (Department for Science, Innovation and Technology and Home Office, Cyber Security Breaches Survey 2025/2026, April 2026, sections 3.6 and 3.7). In other words, the function exists, and it sits with the office manager and an outside provider.

The governance profession has noticed. The International Association of Privacy Professionals' 2025 report on AI governance jobs observed that "larger companies can split these tasks into several roles," while "smaller companies will look for AI governance professionals who can cover all these areas." It also noted that asking whether a company has an AI governance committee is a maturity sign for a large firm "but might be irrelevant for a smaller company" (IAPP and Credo AI, AI Governance Profession Report 2025, April 2025). That's the premise of this page, stated by the people who staff enterprise AI governance for a living.

Translate Each Dimension to a Named Person

To assess organizational readiness with an enterprise framework at your size, go through it one dimension at a time. Replace each question about a structure with two questions about a person: who holds this job today, and what would show they're doing it? Keep the dimension, change the unit. A committee becomes a name, and a charter becomes something you can put on the table.

Three things about the table matter more than any single row. First, one person will often hold three or four rows, and that's fine. An owner who sets direction, controls spend, and signs off on risk is doing the job of three enterprise executives, and the framework shouldn't mark that down. Even in large companies the job tends to sit with a few executives. In McKinsey's March 2025 survey, whose sample leans toward big firms, 28% of respondents at organizations using AI said the CEO oversaw AI governance (a smaller share at the largest firms), and respondents reported that "two leaders are in charge" on average (McKinsey, The state of AI: How organizations are rewiring to capture value, March 2025).

Second, a row where nobody's name fits is the most useful thing the exercise produces, because it's where an AI project will stall. The row we'd check first is the operating model, because it fails quietly: a tool gets bought, it works for a month, and when it breaks nobody inside the business owns fixing it or calling the vendor. Third, the evidence column is deliberately concrete. "We have a strong culture" doesn't count, while "the supervisors ran the scheduling-software rollout and the crews used it within three weeks" does. That's the same distinction between what people say and what they can show that our methodology page sets out for the whole assessment.

Why a name beats a score: a 1-to-5 rating on "governance maturity" tells you how someone felt about governance on the day they filled in the form. A name tells you whom to call when an AI tool emails the wrong price to a customer. The first is hard to act on and easy to inflate. The second is either there or it isn't, and if it isn't, you know exactly what to fix.

The culture row gets one line here on purpose, because change readiness has its own method, built around the managers who decide whether a new tool is used. Our page on how consultants assess AI readiness in organizations covers it for firms of 50 to 200 people.

Governance, Cut Down From NIST's 72 Subcategories

The governance row is where enterprise frameworks overshoot furthest, and it's also where a mid-sized firm has the most to gain from borrowing a little of their discipline. The US National Institute of Standards and Technology's AI Risk Management Framework is the best-known public reference, and it's voluntary and free. It has four functions (govern, map, measure, and manage), 19 categories, and 72 subcategories (NIST, Artificial Intelligence Risk Management Framework (AI RMF 1.0), NIST AI 100-1, January 2023).

Seventy-two items would bury a 60-person company, and NIST doesn't ask for that. The framework says it gives "flexibility to organizations of all sizes," acknowledges that "small to medium-sized organizations" may "face different challenges than large organizations," and states that "some organizations may choose to select from among the categories and subcategories" (pp. 2, 9, and 21 as printed). So here are the six subcategories we'd keep, in plain terms.

That's six of 72, and together they fit on two pages: the inventory sheet and the one-page rule for staff. The staff rule itself, meaning what customer and pricing information can go into which tools, is covered under decision 1 on our "should we implement AI?" page, so we won't repeat it here. If you'd rather sort each tool as you list it, our AI readiness assessment for SMBs turns the same inventory into a keep, standardize, or stop decision.

Many organizations haven't done even this much. In ISACA's 2026 poll of more than 3,400 professionals in IT audit, governance, security, and privacy roles, only 38% said their organization had a formal, comprehensive AI policy. Another 30% had a limited one, and 25% had none. And 39% said they didn't know whether their organization had a documented process for shutting down or overriding AI systems if things go wrong, which is MANAGE 2.4 in the table above (ISACA, "AI Use Accelerates While Governance and ROI Lag", May 2026).

The UK survey found a similar gap among ordinary businesses. Of the businesses using or considering AI, 24% had security practices in place to manage the risks of using it, and 38% planned to add them within a year (Cyber Security Breaches Survey 2025/2026, section 3.11). If you set up the six items in the table, you'll have more written down than most respondents in either survey reported.

When Do You Need the Enterprise Version?

You need enterprise-grade AI governance when someone outside the business asks for it, and that's more and more often a customer. A mid-sized manufacturer, contractor, or property manager selling to large companies or to government will start to see AI questions in supplier questionnaires, and those questions are written against enterprise standards.

The questionnaires are already in circulation. The Standardized Information Gathering (SIG) questionnaire is a template large companies send vendors to check their controls, and more than 500 organizations license it. Its 2024 edition lists NIST's AI framework among the standards it maps to (Shared Assessments, "Accepting the SIG", 2023), and that edition added artificial intelligence as one of two new risk domains (Mitratech, "SIG 2024: Key Updates and Considerations", November 2023). The 2026 edition, scheduled for release on September 19, 2025, adds references to ISO/IEC 42001, the AI management standard covered below (Shared Assessments, "2026 SIG Workbook: Key Updates and Enhancements", July 2025). The Cloud Security Alliance's AI Controls Matrix, released in 2025, runs to 243 control objectives across 18 domains and has a separate role for the "AI Customer," the business that uses AI services rather than building them (Cloud Security Alliance, AI Controls Matrix).

Government contracting points the same way. The White House Office of Management and Budget's April 2025 memo on AI purchasing tells federal agencies to consider whether a solicitation (the request for bids) should require "disclosure of AI use as part of any given contract's performance" (OMB Memorandum M-25-22, Driving Efficient Acquisition of Artificial Intelligence in Government, April 2025, section 3h). A contractor that drafts its submittals with AI could be asked to say so.

The pressure comes from the top of the supply chain. In the UK survey, 48% of large businesses reviewed the cyber risks posed by their immediate suppliers, against 22% of small businesses and 12% of micro businesses (Cyber Security Breaches Survey 2025/2026, section 3.10). Those reviews are how questionnaires reach mid-sized firms, and in templates like the SIG, AI is now one of the sections.

ISO/IEC 42001, published in December 2023, is the standard those questionnaires increasingly point to. ISO describes it as "the world's first AI management system standard" and says it's for "organizations of any size" (ISO, ISO/IEC 42001:2023). It's certifiable. One accredited auditor describes the requirement as "38 distinct controls organized into 9 control objectives," meaning 38 specific practices grouped under nine goals, which a certified company has to address or justify leaving out. The certificate is then kept up with yearly check-up audits (Schellman on the Cloud Security Alliance blog, May 2025).

Here's when we'd move past the six-item version:

• A customer's questionnaire asks for it. Answer honestly with what you have first. Consider certification only if a customer makes it a condition of the contract.
• You bid on government work where the solicitation asks you to disclose or govern AI use.
• You handle regulated information (patient records, financial account data, or data on children) and plan to put any of it into an AI tool.
• AI will act for you with customers without a person checking it first, such as quoting, scheduling, or answering questions.
• An owner, lender, or buyer asks. If AI use comes up in due diligence, the other side will usually bring its own checklist.
• You're past 200 people or several sites, and one person can no longer hold a row by knowing everyone.

If none of those apply, the enterprise version costs more than it protects. If one does, the crosswalk table above is still your starting point, because a questionnaire asking "who is accountable for AI risk?" wants a name, and you'll already have it.

How Organizations Evaluate Readiness for AI Adoption With a Framework

An AI adoption readiness assessment built on a framework comes down to five moves: pick one, strip out the rows that assume departments, name a person for each remaining row, collect the evidence, and then test the work itself. The first four take a meeting and a week, and the fifth is where the real assessment starts.

1. Pick one framework and don't blend several. A free one from a vendor or a public body is fine. Our methodology page has seven questions for judging one before you rely on it.
2. Strip out the structures. Cross out every question that asks whether a committee, center, or officer exists, and write next to it the function it stands for.
3. Name a person for each row, in one meeting with the owner and the two or three people who run operations and the office. Expect the same names to repeat.
4. Collect the evidence in the week after, using the last column of the crosswalk table. A row with a name but no evidence is a maybe, and a row with no name is a gap.
5. Test the work. The organizational rows tell you whether you can adopt AI safely. They don't tell you what to build, which depends on how often a workflow runs, how long it takes, and where it stalls. Our methodology page explains why the workflow, not the company is the thing to score.

The output should be short: a filled-in crosswalk with every gap named, the six-item governance sheet, and a list of the workflows you'll look at next. If you've done the first four steps and still have a radar chart instead of a list of names, the framework won the argument and you lost the afternoon.

A Worked Example: A 150-Person Food Co-Packer

This is an illustrative composite, not a client, and its figures are examples rather than benchmarks. Picture a 150-person contract food manufacturer that packs sauces and dressings under grocery-chain and brand labels, with about 110 people on two production shifts, a quality team of six, a four-person customer service desk, and an office of twelve led by a controller. The owner has never thought much about AI governance until the largest customer, a national grocery chain, sends its annual supplier questionnaire with a new section on artificial intelligence.

The owner downloads a free enterprise readiness framework to get ready and scores the company at about 2 out of 5 on nearly every dimension. There's no AI strategy, no chief data officer, no governance committee, and no AI team. The controller points out that the score doesn't help answer a single question on the grocery chain's form, which asks things like "Who is accountable for AI risk?" and "Do you maintain an inventory of AI systems?"

So they redo it as a crosswalk, and most rows fill in within an hour. The owner holds strategy, sponsorship, and risk sign-off. The controller already approves every software purchase, so governance goes to them. The quality manager has run two software rollouts on the floor, both of which stuck, so there's evidence for the culture row. The gaps turn out to be three. Nobody has a list of the AI in use. Customer service has been pasting order details into a personal chat account. And nobody owns the one AI feature that matters most: the production-planning software added an AI demand forecast last year, and the planner uses it, but no one checks it against actual orders.

The questionnaire went back in eight days, and the customer didn't ask for certification. The company didn't need a committee or a center to answer it. It needed four names and two sheets of paper, and the exercise turned up two real risks nobody had been watching: customer order details in a personal account, and a forecast shaping production with nobody checking it.

What the example shows: the enterprise framework's score was accurate and useless at once. The company really didn't have the structures it asked about, but it didn't need them. The crosswalk kept every dimension the framework cared about and changed only the unit, from structures to people, and that was enough to answer a demanding customer honestly.

Where Avolis Fits

The crosswalk and the governance sheet are things you can do yourself in a week, and we'd encourage that before paying anyone. Our two-week diagnostic starts where step 5 above begins, with the work itself. We go through each workflow in scope with the people who run it, measure volumes, time per task, and handoffs, map where the work stalls, and rank what's worth building by impact and effort. Every engagement with us starts with that diagnostic, and the ranked result is yours whatever you decide next.

If you're choosing someone to do that work with you, start with which AI consulting company to choose. If you want to know why organizational gaps like an unowned tool sink so many projects, see why AI projects fail.

Frequently Asked Questions

What is organizational readiness for AI?

Organizational readiness for AI is everything an assessment scores apart from data and technology. That's a direction for AI, a leader who owns it, rules for using it, a way to deliver it, people with the skills, and a culture that adopts change. At 10 to 200 people, each of those jobs needs a named person.

How do organizations evaluate AI adoption readiness with a framework?

Pick one framework, then cross out every question about a committee, center, or officer and write in the function it stands for. Name the person who holds each function, collect evidence that they do it, and treat any row without a name as the gap to fix. Then assess the workflows themselves, which decide what to build.

Does a mid-sized company need an AI Center of Excellence?

A company under 200 people almost never needs one. A Center of Excellence is a central team that runs AI work across a large company. At smaller firms, the same jobs are done by an outside partner plus one internal owner per AI workflow, who knows how it works and who fixes it when it breaks. Name those owners instead.

Does a small business need the NIST AI Risk Management Framework?

It's free, voluntary, and meant for organizations of all sizes, but a small business doesn't need all 72 subcategories. NIST says organizations may select from them. Six cover what matters most at this size: an inventory of AI in use, named owners, leadership sign-off, vendor checks, a way to switch AI off, and an error log.

What should we do when a customer sends an AI questionnaire?

Answer honestly with what you have. Expect questions about who is accountable, whether you keep an inventory of AI tools, how customer data is handled, and whether AI can be overridden. A named owner, a one-page rule, and an inventory sheet answer most questions like these. Pursue ISO/IEC 42001 certification only if the customer requires it.

Continue Learning

An enterprise framework is a useful checklist of what an organization has to be able to do. At 10 to 200 people, read each row as a job to be named, not a department to be built.

Start with the row that has nobody's name on it.

Assessing readiness:

• AI readiness assessment services
• AI readiness assessment methodology
• AI readiness assessment for SMBs
• How consultants assess AI readiness in organizations
• AI maturity model
• Should we implement AI?

Choosing who does it:

• Which AI consulting company should I choose?
• Why do AI projects fail?

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Sources

All sources retrieved 2026-09-25.

• Boston Consulting Group, "AI Adoption in 2024: 74% of Companies Struggle to Achieve and Scale Value," press release on Where's the Value in AI?, October 24, 2024, retrieved 2026-09-25 — https://www.bcg.com/press/24october2024-ai-adoption-in-2024-74-of-companies-struggle-to-achieve-and-scale-value
• McKinsey & Company, "The state of AI in 2026: On the road to ROI," August 25, 2026, retrieved 2026-09-25 — https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
• McKinsey & Company, The state of AI: How organizations are rewiring to capture value, March 2025, retrieved 2026-09-25 — https://www.mckinsey.com/~/media/mckinsey/business%20functions/quantumblack/our%20insights/the%20state%20of%20ai/2025/the-state-of-ai-how-organizations-are-rewiring-to-capture-value_final.pdf
• Eurostat, Enterprises that employ ICT specialists by size class of enterprise, dataset isoc_ske_itspe, EU27, 2024, updated June 15, 2026, retrieved 2026-09-25 — https://ec.europa.eu/eurostat/databrowser/view/isoc_ske_itspe/default/table
• Department for Science, Innovation and Technology and Home Office, Cyber Security Breaches Survey 2025/2026, April 30, 2026, retrieved 2026-09-25 — https://www.gov.uk/government/statistics/cyber-security-breaches-survey-20252026/cyber-security-breaches-survey-20252026
• IAPP and Credo AI, AI Governance Profession Report 2025, April 16, 2025, retrieved 2026-09-25 — https://iapp.org/resources/article/ai-governance-profession-report
• National Institute of Standards and Technology, Artificial Intelligence Risk Management Framework (AI RMF 1.0), NIST AI 100-1, January 2023, retrieved 2026-09-25 — https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
• ISACA, "AI Use Accelerates While Governance and ROI Lag, Says New ISACA Research," May 5, 2026, retrieved 2026-09-25 — https://www.isaca.org/about-us/newsroom/press-releases/2026/ai-use-accelerates-while-governance-and-roi-lag-says-new-isaca-research
• Shared Assessments, "Accepting the SIG" (fact sheet), November 2023, retrieved 2026-09-25 — https://sharedassessments.org/wp-content/uploads/sa-uploads/2024/09/Accepting-SIG-November-2023.pdf
• Mitratech, "SIG 2024: Key Updates and Considerations," November 20, 2023, retrieved 2026-09-25 — https://mitratech.com/resource-hub/blog/sig-2024/
• Shared Assessments, "Coming Soon: 2026 SIG Workbook: Key Updates and Enhancements," July 18, 2025, retrieved 2026-09-25 — https://sharedassessments.org/blog/2026-sig-workbook-updates/
• Cloud Security Alliance, AI Controls Matrix, retrieved 2026-09-25 — https://cloudsecurityalliance.org/artifacts/ai-controls-matrix
• Office of Management and Budget, Memorandum M-25-22, Driving Efficient Acquisition of Artificial Intelligence in Government, April 3, 2025, retrieved 2026-09-25 — https://www.whitehouse.gov/wp-content/uploads/2025/02/M-25-22-Driving-Efficient-Acquisition-of-Artificial-Intelligence-in-Government.pdf
• International Organization for Standardization, ISO/IEC 42001:2023, Information technology: Artificial intelligence: Management system, December 2023, retrieved 2026-09-25 — https://www.iso.org/standard/42001
• Schellman, "ISO 42001: Lessons Learned from Auditing and Implementing the Framework," Cloud Security Alliance blog, May 8, 2025, retrieved 2026-09-25 — https://cloudsecurityalliance.org/blog/2025/05/08/iso-42001-lessons-learned-from-auditing-and-implementing-the-framework

On the page review. "The 27 pages" means the distinct organic results for "assessing organizational readiness for enterprise ai framework," "ai adoption readiness assessment framework for organizations," "how organizations evaluate readiness for ai adoption," "ai adoption readiness assessment," "enterprise ai readiness framework," and "organizational readiness for ai framework," retrieved and read on 2026-09-25. They came from IT-services firms and agencies (11), software vendors (9), change-management consultancies (2), one large accounting and consulting firm, three academic sources, and a nonprofit research body. Two didn't name dimensions we could count, so the dimension table uses 25. Three more (Udemy, Infosys, and a ScienceDirect article) couldn't be retrieved and aren't counted. "Mapped roles to dimensions" means the page said which role answers or owns which dimension, not just who should take part. The one page that named any company size was an academic preprint defining midsize firms as 50 to 250 employees, which is why we say "only 1 named a company size" rather than that none covered firms under 200. It's a snapshot of one day's results, not a market survey.

On the enterprise surveys. BCG and McKinsey survey senior executives, mostly at large companies. In McKinsey's March 2025 survey, 42% of respondents worked at organizations with more than $500 million in annual revenue, and the 28% CEO figure was lower at those larger firms. We use both surveys for direction, not as benchmarks for firms of 10 to 200 people.

On the size data. The Eurostat figure counts firms employing IT or communications specialists of any kind, and the UK figures are about cyber security, the closest well-measured stand-in for who handles technology risk. Neither is a measure of AI staffing. The UK survey's small businesses have 10 to 49 employees and its medium businesses 50 to 249.

On the ISACA poll. Its respondents are IT audit, governance, security, and privacy professionals, not a random sample of businesses, and the release doesn't split results by organization size. The three policy figures add to 93%, and the release doesn't say how the remaining 7% answered.

On the co-packer. It's an illustrative composite, not a client. Its headcount, the questionnaire's wording, and the eight-day turnaround are representative, not measured.

On first-party claims. Descriptions of how the Avolis diagnostic works describe our own method. They are not independent research and are not offered as benchmarks.

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About Avolis Research Group

Avolis Research Group is Avolis's in-house research practice, focused on how operations-heavy small and mid-sized businesses actually adopt AI. It synthesizes primary economic research, government survey data, and results from real implementations into practical, vendor-neutral guidance.

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