---
title: "How to Evaluate an AI Readiness Platform in 2026"
url: "https://atlan.com/know/ai-agent/ai-readiness-platform-evaluation/"
description: "Evaluate an AI readiness platform by testing context, certification, runtime delivery, traceability, and portability, not a one-time readiness score."
author: "Karthik Pasupathy"
author_role: "Contributing Writer — AI Context & Agents"
published: "2026-09-28T00:00:00.000Z"
updated: "2026-09-28T00:00:00.000Z"
---

---

An AI readiness platform is software that keeps running, continuously supplying AI systems with the data, business context, controls, and evidence a given use case needs to produce reliable answers. A one-time readiness assessment only measures a moment; a platform is what keeps those conditions current as data, policies, and models change under it. Atlan operates this way as the Context Layer for AI, connecting, certifying, and delivering business context to agents at runtime, then keeping a record of what fed every answer.

### Run an AI Readiness Data Audit

Runs a twelve-check audit of whether your data is ready for AI agents, grouped into meaning, trust, boundaries, and reproducibility, and returns a specific fetch list for every unknown. [Read the skill](/skills/ai-ready-data-audit.md).

*Paste into a new chat*

```
Use the skill at https://atlan.com/skills/ai-ready-data-audit.md to audit whether your data is actually ready before you evaluate a platform. Ask me for whatever it needs.
```

*Run once in a terminal*

```
curl -fsSL --create-dirs \
  -o ~/.agents/skills/ai-ready-data-audit/SKILL.md \
  https://atlan.com/skills/ai-ready-data-audit.md
```

*For an agent*

```
curl -fsSL https://atlan.com/skills/ai-ready-data-audit.md
```


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A qualifying solution should prove five things:

* **Unified business context:** Brings together data, definitions, lineage, ownership, quality signals, and usage history from across the business.
* **Use-case readiness:** Verifies that an agent can access the data, definitions, policies, and operational context required to complete a specific task reliably.
* **Governed approval:** Gives domain owners a review path to certify context and apply the appropriate permissions, policies, and approval rules before agents use it.
* **Runtime delivery:** Supplies approved context to AI agents through MCP, APIs, and other supported interfaces while preserving permissions and policy controls.
* **Reproducible outcomes:** Records the sources, rules, policy decisions, and versions used so teams can explain or recreate an agent's output later.

Jump to: [Why generic AI falls short](#why-does-a-generic-ai-model-give-an-average-answer-about-your-business) | [Why readiness scores fail](#why-does-ai-readiness-as-a-score-keep-failing) | [What a platform must do](#what-must-an-ai-readiness-platform-do) | [How to evaluate](#how-should-you-evaluate-an-ai-readiness-platform) | [Build vs. buy](#should-you-build-buy-or-use-the-capabilities-bundled-with-your-existing-platform) | [How Atlan fits](#how-does-atlan-support-continuous-ai-readiness) | [FAQs](#faqs-about-evaluating-an-ai-readiness-platform)

---

## Why does a generic AI model give an average answer about your business?

A generic AI model relies on patterns learned across many organizations because it cannot see how your company defines its customers, metrics, priorities, and exceptions. Ask the model which customers matter most, and it may rank them by revenue even when your business prioritizes metrics like renewal risk, margin, and product adoption.

The answer can sound credible because its reasoning reflects patterns abstracted from the model's training data. That reasoning is still partial: it leaves out the company-specific definitions, policies, priorities, and exceptions the agent needs to complete the task appropriately.

Atlan addresses that gap as the Context Layer for AI, a shared infrastructure for company-specific meaning. See [what an AI context platform actually is](https://atlan.com/know/ai-agent/context-layer/ai-context-platform/), [what an enterprise context layer is](https://atlan.com/know/what-is-the-enterprise-context-layer/), and [why agents need one](https://atlan.com/know/why-ai-agents-need-an-enterprise-context-layer/).

Schema access tells an agent which tables and columns exist. It doesn't reveal which customer definition is approved, which source takes priority when records conflict, whether the data is current enough, or which exclusions and policies apply to the task. That's the gap a [context layer for AI agents](https://atlan.com/know/context-layer-for-ai-agents/) is built to close.

In tests covering 12,751 text-to-SQL pairs across 95 databases, the BIRD researchers found that realistic questions require value comprehension and external knowledge, not semantic parsing alone, according to [Li et al. (2023)](https://arxiv.org/abs/2305.03111). The [context layer](https://atlan.com/know/ai-readiness/context-layer-101/) explains how to supply that meaning, while [text-to-SQL in the enterprise](https://atlan.com/know/ai-agent/data-for-ai/text-to-sql-for-enterprise/) shows why table access alone isn't enough.

According to the [Stanford HAI AI Index 2025](https://hai.stanford.edu/ai-index/2025-ai-index-report), 78% of surveyed organizations reported using AI in 2024, up from 55% in 2023. Most reported gains stayed modest, so adoption is outpacing readiness.



---

## Why does AI readiness as a score keep failing?

AI readiness depends on whether a model has the right context for a specific task. A model may summarize support tickets accurately, yet fail to determine whether a customer qualifies for a refund, because that call requires current policies, contract terms, customer tiers, exceptions, and approval rules. As those inputs change, a once-high score goes stale unless the model gets the updated context.

In a 2024 survey of 1,203 data management leaders, Gartner found that 63% said their organizations lacked the right data management practices for AI, or weren't sure they did. According to [Gartner's 2025 forecast](https://www.gartner.com/en/newsroom/press-releases/2025-02-26-lack-of-ai-ready-data-puts-ai-projects-at-risk), 60% of AI projects unsupported by AI-ready data will be abandoned through 2026.

Measure AI readiness first at the use-case level. An enterprise roll-up should reflect business importance and risk while preserving the status of each use case, rather than hiding gaps in a simple average.

Tie each score to dated evidence: the data, definitions, policies, permissions, procedures, and evaluation results used to calculate it. Recalculate when those inputs, the model, or the task changes. An [AI readiness assessment](https://atlan.com/know/ai-readiness/ai-ready-data/) establishes the baseline, a [checklist of what makes data AI-ready](https://atlan.com/know/ai-agent/data-for-ai/what-makes-data-ai-ready/) turns that baseline into concrete fixes, and [preparing enterprise data for agents](https://atlan.com/know/ai-agent/data-for-ai/how-to-prepare-enterprise-data-for-ai-agents/) keeps it relevant as the underlying [AI data architecture evolves](https://atlan.com/know/ai-readiness/data-architecture-ai/). A dedicated [AI-ready data checklist](https://atlan.com/know/ai-agent/data-for-ai/ai-ready-data-checklist/) is worth running alongside that baseline before you talk to any vendor.

---

## What must an AI readiness platform do?

If readiness is specific to a use case and changes as its context changes, the platform has to do more than assess gaps. It must keep the data, definitions, policies, procedures, and evidence for each use case connected, approved, up to date, and available to agents at runtime.

A credible AI readiness platform should support eight connected capabilities:

| Capability | What it means |
| :---- | :---- |
| Connect | Link data, definitions, lineage, owners, policies, and usage into a shared context map. |
| Enrich | Combine technical metadata with business definitions, relationships, operating procedures, and domain-expert input. |
| Certify | Let domain experts test, edit, approve, version, and retire context for a specific use case. |
| Keep current | Detect changes to sources, schemas, definitions, quality, or policies, and identify the affected use cases. |
| Attach policy | Associate classifications, permitted uses, permissions, and approval rules to the context an agent receives. |
| Trace | Record the source data, transformations, context version, permissions, policy decisions, and procedures behind an agent output or action. |
| Serve to agents | Deliver approved context through MCP, APIs, and other runtime interfaces without bypassing access controls. |
| Preserve deterministic logic | Store and version approved metrics, queries, notebooks, rules, checks, retries, and procedures separately from generated responses. |

Together, these capabilities form an operating loop. Context gets enriched and certified, kept current, and delivered at runtime, turning a [context layer for AI agents](https://atlan.com/know/context-layer-for-ai-agents/) from a diagram into something agents can actually call. Skip any one step and it reverts to incomplete or stale documentation.

The loop begins with connected evidence. [Data contracts for AI](https://atlan.com/know/ai-agent/data-for-ai/data-contracts-for-ai/) state what a source should provide, [lineage built for AI](https://atlan.com/know/ai-agent/data-for-ai/data-lineage-for-ai/) shows its origin and changes, and [quality signals agents can read](https://atlan.com/know/data-for-ai/data-quality-for-ai-agent/) indicate fitness for use. Business definitions and ownership make those signals reviewable by domain experts.

Policy has to stay attached once context enters an agent workflow. The [NIST AI Risk Management Framework](https://www.nist.gov/itl/ai-risk-management-framework) organizes AI risk work around four areas: Govern, Map, Measure, and Manage. A platform can support that lifecycle through an [AI governance framework](https://atlan.com/know/ai-readiness/ai-governance-framework/), controls for [managing AI risk](https://atlan.com/know/ai-readiness/ai-risk-management/), context for [governing models](https://atlan.com/know/ai-readiness/ai-model-governance/), and rules for [handling PII in AI pipelines](https://atlan.com/know/ai-agent/data-for-ai/how-to-handle-pii-in-ai-pipelines/).

For high-risk systems, the [European Commission's AI Act overview](https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai) identifies obligations related to data quality, logging, documentation, human oversight, accuracy, and transparency. A readiness platform should preserve evidence of compliance with [applicable EU AI Act requirements](https://atlan.com/know/ai-readiness/the-eu-ai-act-summary/); it doesn't determine compliance on its own.

Runtime delivery completes the loop. The [Model Context Protocol](https://modelcontextprotocol.io/docs/2026-07-28/getting-started/intro) connects AI applications to external data and tools, but a readiness platform still has to determine which context is approved, what an agent may access, and which policies apply. Only then can it serve that context through MCP or APIs, or make it queryable through SQL. This is [why MCP matters for agents](https://atlan.com/know/mcp/why-mcp-matters-for-ai-agents/), but on its own, MCP can't establish readiness.

---

## How should you evaluate an AI readiness platform?

Evaluate with your own data, questions, permissions, and failure cases. A feature tour can't prove that meaning survives across your stack.

Ask each vendor to demonstrate the following:

1. **How does the platform assemble the context required for the task?** Start with one use case and see whether it brings together the relevant data, definitions, lineage, ownership, and operational context across systems.

2. **Who can improve and certify that context?** Have a domain expert add business meaning, resolve a conflicting definition, approve the change, and retire the outdated version.

3. **What happens when the context changes?** Change a source, definition, or policy during the demonstration. The platform should identify the affected use cases and flag the context for revalidation.

4. **How are permissions and policies enforced?** Run the same task as two different users. Check what each agent can retrieve, what it must withhold, and where approval is required.

5. **Can you see the logic behind the output or action?** Pick one result and trace the source data, transformations, business definitions, deterministic procedures, policy decisions, and context version used.

6. **Can you reproduce an earlier result?** Select a previous result and reconstruct what the agent knew, which rules applied, and what it was permitted to access at that time.

7. **How does the agent receive context at runtime?** Run an actual agent workflow and retrieve approved context through MCP, APIs, SQL, or another supported interface.

8. **What remains portable when the technology stack changes?** Switch a supported model, agent framework, cloud, or data tool and check whether the certified context, policies, and deterministic assets remain usable.

For a formal scorecard, use [evaluation criteria for the context layer](https://atlan.com/know/ai-agent/context-layer/context-layer-evaluation-criteria/) and compare vendors against [a reference architecture](https://atlan.com/know/ai-agent/context-layer/context-layer-reference-architecture/). Score [model independence](https://atlan.com/know/ai-agent/context-layer/model-agnostic-context-layer/) explicitly, since that's where lock-in hides.

None of these eight questions matter as much as the last step: trace one output or action to its underlying logic. Stopping at a document, prompt, or generated explanation doesn't prove the context was current, approved, and reproducible.

---

## Where do adjacent categories stop?

Most buyers already have data quality tools, data catalogs, semantic layers, and compliance workflows. These remain useful, but they may not keep all the context for a specific task connected, approved, up to date, and available at runtime.

Understanding these boundaries helps you see what your existing stack already covers, and what's still missing:

| Category | What it contributes | Where it usually stops |
| :---- | :---- | :---- |
| Data quality tool | Tests data for completeness, validity, freshness, consistency, and anomalies | Doesn't establish whether an agent used the approved definition, policy, or procedure for the task |
| Compliance workflow | Records risks, controls, approvals, review decisions, and supporting evidence | May approve an AI use case without connecting those controls to the exact approvals, controls, and exceptions an agent uses at runtime |
| Data catalog | Makes data assets, ownership, glossary terms, and lineage discoverable | Human-readable documentation doesn't automatically become certified, callable context tied to an agent's output or action |
| Semantic layer | Translates technical schemas into shared business terms and centrally defines measures, dimensions, entities, relationships, and hierarchies for consistent use across tools | Primarily standardizes analytical meaning rather than the broader lineage, ownership, operating procedures, decision history, and use-case evidence required for an agent task |

A [data catalog built for agents](https://atlan.com/know/data-catalog-for-ai/) can supply discovery, ownership, definitions, and lineage, while a [semantic layer](https://atlan.com/know/semantic-layer/) preserves consistent meaning between entities. Data quality and compliance systems add signals and controls on top.

The buying question is whether your stack unifies these inputs into a readiness layer that detects changes, routes context for certification, applies permissions during the task, and preserves evidence. Separate quality results, catalog entries, metric definitions, and approval records don't add up to usable context for an agent.

---

## Should you build, buy, or use the capabilities bundled with your existing platform?

Choose based on operating capacity, the systems and agents involved, and the time available to reach production. Each option assigns integration and maintenance work differently.

Compare the three paths using these questions:

| Option | Choose it when | Main trade-off | Question to answer |
| :---- | :---- | :---- | :---- |
| Build | Your context and procedures are strategic, and your team can maintain the infrastructure | Greater control requires dedicated engineering and long-term ownership | Can the team support it after the first use case launches? |
| Buy | Multiple agents need shared context across tools, clouds, and models | Faster deployment depends on product fit and organizational adoption | Can the vendor prove portability and one complete task path? |
| Use capabilities bundled with your existing platform | Your cloud, data, analytics, or AI platform already includes the context, but you need semantic, policy, or agent capabilities to bridge the gap | A faster start can create dependence on that platform's data, models, and runtime | Will the context remain usable if you change a model, cloud, or agent framework? |

Compare the [total cost of building, buying, or bundling](https://atlan.com/know/ai-agent/context-layer/context-layer-tco-build-vs-buy-vs-bundle/) and [time to value](https://atlan.com/know/ai-agent/context-layer/ai-context-platform-time-to-value/). Include ongoing updates, evaluations, policy changes, and new use cases in that math, not just the initial cost.

---

## How do you know your organization's AI readiness is improving?

Measure improvement at the use-case level, not as one company-wide number. For each agent task, establish a baseline for context completeness and task reliability, then track it over time.

Track these five measures together:

* **Context coverage:** Percentage of required data, definitions, policies, procedures, and owners mapped for the use case.
* **Certification rate:** Percentage of required context approved by the appropriate domain expert and still within its review period.
* **Task success rate:** Percentage of tasks completed correctly against a stable evaluation set.
* **Time to production:** Time from use-case approval to deploying the corresponding agent workflow with certified context, permissions, and monitoring.
* **Reproducibility:** Percentage of outputs or actions that teams can reconstruct using the original sources, logic, policies, and context version.

No single measure proves readiness on its own. High task success can hide broad permissions or unreproducible logic, and strong context coverage doesn't prove correct use.

Review each use case before rolling up to an organization-wide view weighted by business importance and risk. Connect improvement to fewer manual investigations, less rework, shorter deployments, and faster issue resolution using this framework for [measuring context-layer returns](https://atlan.com/know/context-layer-roi/).

---

## Who should own the AI readiness platform?

One executive should be accountable for AI outcomes, whether that's the Chief AI Officer (CAIO), the head of AI, the Chief Data Officer (CDO), the Chief Technology Officer (CTO), or another leader with an AI mandate. Day-to-day ownership stays distributed underneath that person.

Assign four clear areas of responsibility:

| Responsibility | Typical owner |
| :---- | :---- |
| Use-case priorities and outcomes | CAIO, head of AI, or executive sponsor |
| Shared context infrastructure | CDO, data platform leader, or AI platform leader |
| Definitions, policies, and approvals | Domain, security, legal, and risk leaders |
| Agent integration and monitoring | Product and engineering teams |

Document who approves each use case, operates the platform, certifies its context, and accepts its risk before production. See [who owns AI governance internally](https://atlan.com/know/ai-agent/who-owns-ai-governance-caio-vs-cdo-vs-cto/) for a closer division of responsibilities.

---

## How does Atlan support continuous AI readiness?

Atlan supports continuous AI readiness as the Context Layer for AI. Instead of producing a one-time score, it connects the context behind each use case, helps teams enrich and certify it, keeps that context current, and delivers it to agents under the appropriate permissions.

The readiness requirements described above map to Atlan's capabilities as follows:

| Readiness requirement | Atlan capability | How it helps |
| :---- | :---- | :---- |
| Connect business context | [Enterprise Data Graph](https://docs.atlan.com/agents/how-tos/build-an-enterprise-context-layer) | Connects metadata, lineage, business definitions, ownership, usage, quality, and policy across the data estate |
| Enrich context at scale | [Context Agents](https://atlan.com/context-agents/) | Uses table structure, query history, lineage, and glossary terms to generate descriptions, READMEs, and SQL intelligence for review |
| Test and certify by use case | [Context Engineering Studio](https://atlan.com/context-engineering-studio/) | Assembles a context repository for one use case, tests it against expected questions, and gives domain experts a review step before deployment |
| Keep context open and versioned | [Context Lakehouse](https://atlan.com/context-lakehouse/) | Stores context in an Iceberg-native architecture with historical state, SQL access, and open interfaces |
| Trace provenance and quality | [Data lineage](https://atlan.com/data-lineage/) and [Data Quality Studio](https://docs.atlan.com/product/capabilities/governance/data-quality) | Shows where data came from, how it changed, and whether it meets the quality checks required for the task |
| Attach ownership and policy | Policy context and access controls | Registers AI models and applications, tracks versions and owners, and keeps classifications and policy context connected to them |
| Serve context at runtime | [Atlan MCP](https://atlan.com/mcp/) | Lets agents retrieve definitions, lineage, quality, and other approved context through MCP while respecting permissions already defined in Atlan |
| Preserve reusable logic | Context repositories | Keeps metrics, dimensions, relationships, rules, and linked source assets inspectable and reusable across supported tools and agent workflows |

Together, these capabilities create a continuous loop. The Enterprise Data Graph connects existing context. **Context Agents** help fill gaps. Domain experts test and certify the result in **Context Engineering Studio**. The **Context Lakehouse** preserves it. **Atlan MCP** makes it available when an agent performs a task.

Two customers describe this shift in their own words. Workday's Joe DosSantos, VP of Enterprise Data and Analytics, put it plainly: "Our beautiful governed data, while great for humans, isn't particularly digestible for an AI." Mastercard's Chief Data Officer, Andrew Reiskind, frames the same shift from the other side of the table: "We've moved from privacy by design to data by design to now context by design."

---

## Real stories from real customers: readiness that held up under agent workflows



      "Atlan captures Workday's shared language to be leveraged by AI via its MCP server. As part of Atlan's AI labs, we're co-building the semantic layer that AI needs."


      Joe DosSantos, VP Enterprise Data & Analytics, Workday




    Watch Now




      "AI initiatives require more context than ever. Atlan's metadata lakehouse is configurable, intuitive, and able to scale to hundreds of millions of assets. As we're doing this, we're making life easier for data scientists and speeding up innovation."


      Andrew Reiskind, Chief Data Officer, Mastercard




    Watch Now


---

## What should you do before choosing an AI readiness platform?

Start with one important agent task that produces slow, disputed, or generic results. Define its data, business rules, policies, owner, and acceptable error rate before you talk to a single vendor.

Use this short sequence to compare your options:

1. **Choose the use case:** Pick a task with measurable value and known context gaps.

2. **Define the proof standard:** Require certified context, permission-aware retrieval, traceability, and portability.

3. **Run the same test:** Compare build, buy, and bundled options using that task and your data. Record the ongoing work each option leaves behind.

Evidence from a real task beats a readiness percentage every time. You can [check agent readiness](https://tools.atlan.com/tools/ai-agent-context-readiness/) with Atlan's checklist, or go straight to a conversation.

  Book a Demo

---

## FAQs about evaluating an AI readiness platform

### 1. What is an AI readiness platform, and how is it different from a data catalog or governance tool?

An AI readiness platform keeps the context and controls for specific AI tasks up to date, approved, and available at runtime. A data catalog organizes metadata for discovery and understanding, while an AI governance tool supports risk, policy, and accountability. A readiness platform connects those functions to use-case testing, runtime delivery, and traceability. Because products can span categories, evaluate capabilities rather than labels.

### 2. Do we need an AI readiness platform, or can our warehouse vendor cover it?

A warehouse vendor's bundled capabilities may suffice when the relevant data, semantics, policies, and agents all stay inside that environment. Coverage gets less certain when context spans operational systems, clouds, BI tools, or agent runtimes. Test the same use case across those boundaries and check whether its definitions, permissions, and traceability remain intact. Choose based on that result, not the feature list.

### 3. What should be on an AI readiness platform evaluation checklist?

Check whether the platform can connect, enrich, certify, update, govern, trace, and deliver context while preserving versioned logic and procedures. Test role-aware access, historical reconstruction, ownership, and portability across models and tools. Require each vendor to demonstrate these capabilities with one of your tasks, your data, and your permission sets.

### 4. How do we measure whether we are getting more AI-ready over time?

Measure readiness by use case, not by a single organization-wide percentage. Track context coverage, certification rate, task success against a stable evaluation set, time to production, and reproducibility. Record the context version, owner, review date, and business outcome. Aggregate only after reviewing individual use cases, weighted by importance and risk.

### 5. Who owns the platform internally: the CDO, CAIO, or head of AI?

The Chief AI Officer, head of AI, or another executive with an AI mandate should own use-case outcomes. The Chief Data Officer or data platform leader typically owns shared context infrastructure, while security, legal, and risk leaders define controls. Domain experts certify definitions and procedures, and product and engineering teams operate the agent workflow. Titles vary by company, but decision rights still need to be documented.

### 6. How does an AI readiness platform handle obligations such as the EU AI Act?

An AI readiness platform supports compliance work but doesn't establish compliance on its own. Under the EU AI Act, obligations depend on intended purpose, risk classification, and whether the organization is a provider or deployer. High-risk systems can require risk management, data quality documentation, traceability, logging, human oversight, and monitoring. The platform connects evidence and approvals, while legal and risk teams determine applicability.

### 7. How long before an AI readiness platform makes a difference?

There's no universal timeline. Time to value depends on the first use case, access to sources, existing definitions, and domain review capacity. Measure from use-case approval to the first certified context package and production task, then expand after that task clears its success and error thresholds.

### 8. What happens when agents run across more than one cloud or model vendor?

Agents should access approved context independently of any model, cloud, or agent framework. APIs and MCP can deliver the same versioned definitions, policies, procedures, and evidence across runtimes. Teams should still test each model and runtime, because their behavior and tool support can differ.

### 9. Why does the same question give a different answer each time, and how do we audit an old answer?

Answers vary because generation is probabilistic, and because models, data, context, permissions, or procedures change underneath the question. Version the metrics, queries, checks, policies, and procedures that need to stay stable. To audit an answer six months later, retain its sources, context version, instructions, permissions, policy decisions, model and runtime version, tool calls, and outputs. Without records captured at the time of the response, full reconstruction may be impossible.

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## Sources

1. AI Index Report 2025, Stanford Institute for Human-Centered Artificial Intelligence, 2025. https://hai.stanford.edu/ai-index/2025-ai-index-report
2. Lack of AI-Ready Data Puts AI Projects at Risk, Gartner, 2025. https://www.gartner.com/en/newsroom/press-releases/2025-02-26-lack-of-ai-ready-data-puts-ai-projects-at-risk
3. AI Risk Management Framework, National Institute of Standards and Technology. https://www.nist.gov/itl/ai-risk-management-framework
4. AI Act regulatory framework, European Commission. https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai
5. Can LLM Already Serve as a Database Interface? Li et al., 2023. https://arxiv.org/abs/2305.03111
6. What is the Model Context Protocol? Model Context Protocol documentation. https://modelcontextprotocol.io/docs/2026-07-28/getting-started/intro
7. Re:Govern 2025: The Data & AI Context Summit Recap, Atlan, 2025. https://atlan.com/regovern-2025-recap/