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When FOX Sports prepared for the expanded FIFA World Cup, the challenge was not simply broadcasting more matches.
The tournament included 48 teams, 104 matches, 16 host cities, and generated more than 11,500 hours of recordings, 23,000 files, and 85,000 subclips. Production teams needed to find specific moments, interviews, and highlights within seconds while delivering content to audiences across multiple platforms in near real time. To support that scale, FOX Sports embedded AI deeply into its production operations, using Gemini-powered search, automated tagging, intelligent content discovery, and agentic workflows that helped teams locate and use content faster than ever before.
The technology challenge was not simply deploying AI. It was making AI useful across a large, distributed workforce.
As FOX Sports explained, the goal was to help more than 1,200 team members quickly find the information they needed while maintaining the reliability expected from one of the world's largest sporting events. Their approach demonstrates something many organizations are now discovering: successful AI adoption requires more than access to powerful models. It requires visibility, governance, and a clear understanding of where AI can create value.
Before organizations deploy Gemini, AI agents, or enterprise automation initiatives, they first need to understand how AI is already being used and which workflows are ready for deeper automation.
Most organizations already have AI inside their environment. Employees use browser-based AI assistants. Teams experiment with AI productivity tools. Developers install AI coding assistants. Departments adopt workflow automation platforms. Business users increasingly rely on AI features embedded inside applications they use every day.
The challenge is that many organizations cannot see the complete picture. AI adoption often happens faster than governance.
Different teams introduce different tools. Some applications are approved. Others are not. Some employees interact directly with AI platforms, while others use AI through existing productivity tools without realizing it. Before organizations can build enterprise-scale AI experiences like those seen at FOX Sports, they need visibility into what is already happening across their environment.
The FOX Sports deployment succeeded because the organization understood how AI would support real business workflows.
The objective was not AI for the sake of AI. The objective was helping teams discover content faster, automate repetitive tasks, and support production decisions in real time. AI became valuable because it was connected to meaningful work.
Organizations pursuing similar outcomes need the same level of understanding. The AI Insights capability within the Google Endpoints Readiness Tool helps organizations gain visibility into AI activity across their environment. It provides insight into installed AI applications, browser-accessed AI tools, local LLMs, workflow automation platforms and browser-integrated AI experiences.
This creates a more complete picture of AI adoption across the enterprise. Instead of relying on assumptions, IT teams can understand which AI technologies are being used, where they are being used and whether they align with organizational policies.
As AI adoption grows, governance becomes increasingly important. Organizations need to understand not only which AI tools exist within the environment but also whether those tools should be approved for business use.
This challenge becomes more significant as employees adopt AI-powered applications independently. Browser extensions, local AI models, AI workflow platforms, and embedded assistants can all introduce governance and security considerations if they operate outside organizational oversight.
AI Insights helps organizations move beyond simple discovery. By identifying sanctioned and unsanctioned AI technologies across devices and browsers, organizations gain a clearer understanding of where governance policies may need to be strengthened. This allows teams to build AI strategies based on visibility rather than assumptions. For many organizations, this becomes an essential foundation before broader AI initiatives begin.
One of the most interesting aspects of the FOX Sports story is that the value did not come from a single AI application. The value came from workflows. AI-powered search helped teams find content faster. Automated tagging accelerated asset management. AI-assisted content discovery reduced manual effort. Different capabilities worked together to support larger business processes.
This reflects a broader shift happening across enterprises. Organizations are moving beyond isolated AI tools and toward agentic workflows that span multiple systems, applications, and business processes. At the same time, Gemini Enterprise is making AI agents more persistent. Long-running agents can maintain context, support multi-step tasks, and participate in workflows that continue across hours or even days.
That creates an important question.
Which workflows are actually ready for that level of automation?
Not every business process should become an AI-driven workflow.
Some processes are highly repetitive and structured. Others depend heavily on human judgment. Some workflows are excellent candidates for automation, while others require more review before AI can be introduced effectively.
This is where Agentic Workflows within the Google Endpoints Readiness Tool provides value. Rather than starting with automation ideas and searching for a use case, organizations can begin by understanding how work currently happens. Teams gain visibility into workflow patterns and can identify processes that may be suitable for future AI-driven automation initiatives.
This creates a more practical path toward agent adoption. Instead of automating based on enthusiasm, organizations can prioritize workflows that already demonstrate strong readiness characteristics.
The FOX Sports story highlights a reality many organizations are now facing. AI is no longer a future initiative. It is already becoming part of how people work. The organizations that succeed will not necessarily be those with the most AI tools. They will be the organizations that understand how AI is being used, where governance is required, and which workflows are genuinely ready for automation.
The Google Endpoints Readiness Tool helps organizations build that understanding.
Through AI Insights, organizations gain visibility into AI adoption across devices, browsers, applications, local models, and workflow platforms. Through Agentic Workflows, they can evaluate workflow readiness before expanding into deeper Gemini-powered automation. Because successful AI adoption does not begin with deploying another AI tool. It begins with understanding the environment where AI will operate. FAQs
FOX Sports shows how AI can support real business workflows through content discovery, automation, and intelligent search at scale.
AI usage visibility helps organizations understand which AI tools, applications, models, and workflow platforms are being used across their environment.
AI Insights identifies AI technologies across devices and browsers and helps distinguish sanctioned and unsanctioned usage for better governance.
Agentic workflows use AI agents to support multi-step tasks and business processes, often across multiple applications or systems.
Assessing workflow readiness helps organizations identify processes that are suitable for AI automation before investing in more complex agentic workflows.
Organizations invest heavily in security technologies, yet many still struggle with a fundamental challenge: turning security strategy into operational reality.
A useful example comes from Roche, one of the world's largest healthcare and pharmaceutical companies. As its workforce increasingly relied on browser-based applications and cloud services, the organization needed deeper visibility into browser activity and stronger controls over how sensitive information moved across the web. Traditional security approaches could protect devices and networks, but they often lacked visibility into what was happening inside the browser itself.
The challenge Roche faced is not unique. Many organizations exploring Chrome Enterprise Premium (CEP) have similar goals. They want better browser security, stronger access controls, richer telemetry, and improved visibility into user activity. However, achieving those outcomes requires more than purchasing licenses. It requires a deployment strategy that moves from readiness assessment to implementation efficiently and at scale.
For Roche, browser security was becoming increasingly important because employees were accessing thousands of web-based applications daily. The security team needed a better understanding of how users interacted with corporate data, what actions were taking place within the browser, and where potential risks existed.
After implementing Chrome Enterprise capabilities, Roche gained centralized visibility into browser activity and security events. According to Google's documented customer story, the organization was able to identify suspicious behavior and respond rapidly to potential threats. In one case, Roche detected and stopped an attempt to exfiltrate a large amount of corporate information within hours of enabling the solution.
That outcome highlights an important reality.
Organizations cannot benefit from browser-native security telemetry if their browsers are not properly enrolled, managed, and configured. Security visibility starts long before the first alert appears in a dashboard.It starts with deployment readiness.
The Hidden Work Behind a CEP Deployment
When organizations decide to adopt Chrome Enterprise Premium, the technical objective often seems straightforward.
Enable browser management.
Enroll devices.
Configure reporting.
Activate security capabilities.
Begin monitoring.
In practice, the process is usually more complex. Administrators must determine which devices require enrollment, identify deployment blockers, generate enrollment configurations, configure deployment settings, publish instructions and verify that enrollment has been completed successfully.
These tasks often span multiple tools and administrative workflows.The result is a gap between knowing what needs to happen and actually executing it. Many organizations can identify readiness issues. Fewer can move from readiness findings to deployment actions quickly and consistently.
Learning From the Roche Journey
If Roche were beginning its Chrome Enterprise Premium journey today, the first requirement would not be threat detection.It would be understanding deployment readiness.Before browser telemetry can reach security teams, organizations need answers to critical questions.
Are browsers enrolled?
Are management policies configured?
Are reporting capabilities enabled?
Are target devices ready for deployment?
Are organizational units correctly structured?
Without clear answers, deployment projects can slow down before security outcomes are ever realized.This is where the Google Endpoints Readiness Tool helps organizations establish a foundation for Chrome Enterprise Premium adoption.
The platform enables IT teams to assess their current environment, identify readiness gaps, and understand what must be addressed before deployment begins. Rather than relying on assumptions, organizations gain data-driven visibility into their readiness posture.That visibility creates a more structured path toward deployment.
From Readiness Findings to Deployment Execution
Historically, readiness assessments and deployment activities have often existed in separate workflows.An assessment identifies what needs attention. Administrators then switch tools to perform the required deployment tasks.The process works, but it creates additional operational overhead.
The latest deployment capabilities within the Google Endpoints Readiness Tool help reduce that gap.
Organizations can move from readiness insights directly into deployment workflows from the same platform. Instead of treating assessment and deployment as disconnected activities, teams can manage both stages within a more unified experience. For organizations pursuing outcomes similar to Roche's, this creates a practical advantage.The faster teams can move from identifying readiness gaps to enrolling and configuring browsers, the sooner they can begin realizing the security benefits that Chrome Enterprise Premium provides.
Simplifying Deployment with AI-Assisted Workflows
Large-scale deployments often involve repetitive administrative tasks.
Enrollment information must be located.
Configuration details must be reviewed.
Deployment settings must be verified.
Instructions must be published.
Progress must be monitored.
The Google Endpoints Readiness Tool introduces AI-assisted workflows designed to simplify these operational activities. Through AI-powered interactions, administrators can retrieve deployment-related information, review readiness insights, and support deployment actions through natural language experience.
The objective is not to remove administrative control.Enterprise deployments require governance, approval processes, and accountability.Instead, AI-assisted workflows help reduce manual effort while keeping administrators in control of deployment decisions.For IT teams managing large environments, this can significantly improve operational efficiency while maintaining deployment oversight.
Why Deployment Speed Matters for Security Outcomes
The Roche story demonstrates the value of browser visibility.
Once browser telemetry became available, security teams gained the insight needed to investigate suspicious activity and respond quickly. The ability to detect and stop a significant data exfiltration attempt illustrates how browser-level visibility can strengthen an organization's overall security posture.
However, that visibility depended on successful deployment.
Browsers needed to be managed.
Reporting needed to be enabled.
Security capabilities needed to be configured.
Only then could telemetry flow into security operations.This is why readiness and deployment are closely connected.Organizations often focus on the destination, whether that is stronger security, better governance, improved access controls, or richer browser telemetry.
But achieving those outcomes requires an efficient path from assessment to implementation.The Google Endpoints Readiness Tool helps create that path.By combining readiness assessment, deployment workflows, deployment visibility, and AI-assisted execution, organizations can reduce the operational friction that often slows Chrome Enterprise Premium initiatives.
Turning Readiness Into Results
The Roche case demonstrates what becomes possible when organizations gain meaningful visibility into browser activity. Better insights lead to faster investigations, stronger governance, and improved security outcomes.
But those benefits do not begin with security alerts.Organizations first need to understand their environment, identify deployment requirements, and successfully enroll and manage their browser fleet.
The Google Endpoints Readiness Tool helps support that journey by connecting readiness insights with deployment execution. From identifying gaps to initiating deployment actions and monitoring progress, the platform provides a more direct route from assessment to implementation.Because for organizations pursuing Chrome Enterprise Premium, readiness is not the final destination.It is the starting point.
Chrome Enterprise Premium is Google's enterprise browser security and management solution, providing capabilities for secure access, browser management, and visibility into browser activity.
Deployment readiness helps organizations identify enrollment, configuration, policy, and device-related gaps before implementing CEP at scale.
It helps IT teams assess readiness gaps and connect those findings with deployment workflows, making it easier to move from assessment to implementation.
AI-assisted workflows can help administrators retrieve deployment information, review readiness findings, and support deployment actions through natural-language interactions.
Successful deployment can provide stronger browser visibility, security controls, reporting, and telemetry that help security teams monitor and respond to potential risks.
A Google Workspace migration is more than moving email, calendars, and files. The bigger challenge is understanding how employees work today and which applications, spreadsheets, browser extensions, and workflows they depend on every day. When those dependencies are discovered late, migration teams may need to change rollout plans, investigate applications, or prepare specific user groups at the last minute. That is why understanding the existing endpoint environment should be part of Workspace migration planning from the beginning.
The Google Endpoints Readiness Tool helps IT teams build that visibility across their environment. By examining application and usage information across users and devices, teams can identify areas that may require additional assessment before migration. This makes it possible to plan around actual usage rather than assuming every employee has the same requirements. For large organizations, that difference can significantly influence how migration waves are structured.
Real migration projects show why application and workflow discovery matters. For example, Veolia Australia and New Zealand assessed applications as part of its Google Workspace transformation, including server-based business applications, applications developed by users, and applications installed on user devices. The user-developed applications included macros embedded in products or databases, and the project involved assessing, redesigning and testing those applications.
This is a practical example of why an application inventory alone is not enough. A department may appear to use a standard spreadsheet or application, while behind it sits an automated workflow that supports reporting, calculations, approvals, or other business processes. If that dependency is not identified before migration, the technical team may only discover its importance when users are preparing to move. Readiness information gives migration teams an opportunity to investigate those dependencies while there is still time to plan.
Spreadsheet macros are a particularly useful example because they can represent business logic hidden inside everyday documents. In one documented Google Workspace migration case, the migration team reviewed internal documents and found that no complex Excel macros were being used, which allowed the organization to move more easily toward Google Sheets. The case also recommended auditing spreadsheets beforehand because organizations with more complex macro dependencies may need a different migration approach.
For an IT team preparing a larger environment, the important question is therefore not simply whether spreadsheets exist. The question is where macro-related dependencies exist and which users or business functions rely on them. Those findings can then lead to a closer review of the affected workflows and help determine whether they can transition directly, require modification, or need additional planning. Identifying the dependency early is valuable because it gives the business more time to decide what should happen next.
Consider a company preparing to migrate 2,000 employees to Google Workspace. Most users may primarily work with email, documents, meetings, and browser-based applications, while a smaller finance group may depend on complex spreadsheet workflows. Another operations team may rely on a specialized desktop application, and a sales team may depend on particular browser extensions.
A single migration plan could easily overlook those differences.
With endpoint and application readiness information, IT teams can investigate:
Application usage across users and devices
Macro-usage presence that may require further review
Browser and extension usage supporting daily workflows
Department-specific applications that may need compatibility assessment
Users and devices associated with particular dependencies
This allows migration teams to organize preparation around real usage patterns. Instead of treating the entire organization as one migration group, teams can identify where additional assessment is needed before each migration phase.
Google Workspace migrations can involve thousands of users, multiple departments, and very different business workflows. Google documents phased migration as a way to manage users and data according to factors such as organization size, complexity, and migration requirements. Real customer examples also demonstrate the scale of these projects, including Turner Industries' migration of 4,800 employees, where comprehensive planning and change management were used to support the transition.
The Google Endpoints Readiness Tool adds another layer of visibility before those migration waves begin. By understanding application usage, macro-related dependencies, browser activity, and user or device context, IT teams can identify where additional preparation may be required. That means migration planning can be based on the environment employees actually use rather than assumptions about how they work.
Before you move users to Google Workspace, understand what they depend on today.
Why should macro usage be checked before a Workspace migration?
Macros can contain business logic that supports important workflows, reporting, and automation. Identifying macro-related dependencies early gives IT teams time to investigate those workflows and determine the appropriate migration approach. Through the assessment, users can see whether macros have been used or not.
Does every user need the same migration plan?
No. Different departments can have very different application and workflow dependencies. Readiness information can help teams identify those differences and use them when organizing migration phases.
What should teams assess besides email and files?
Application usage, macro-related workflows, browser activity, extensions, and department-specific applications can all provide useful context. These areas can reveal dependencies that may not be visible from a basic data migration inventory.
AI adoption is expanding rapidly across organizations, but IT teams may not always have a complete view of where AI is being used. Employees can access public AI tools, browser-based applications, local AI models, and automation platforms outside traditional software procurement processes.
Larridin’s September 2026 analysis highlights this growing visibility challenge, citing Writer’s 2026 enterprise AI adoption research, which found that 35% of employees had entered proprietary information into public AI tools. The same research found that 67% of executives believed their organization had experienced a data leak or security breach because of an employee using an unapproved AI tool.
Traditional software inventories are built around applications that IT teams install, manage or approve. AI usage can be much broader because employees can access tools directly through browsers or adopt new AI services without going through established procurement processes. This makes it difficult to understand which AI applications are being used, how they are being accessed, and whether they are sanctioned by the organization. Larridin also reports that enterprise monitoring can uncover three to five times more AI usage than organizations initially expect.
An accurate inventory is an important starting point for AI governance. Before an organization can review how an AI application is being used, assess its relevance to internal policies, or determine whether additional controls are required, it needs visibility into what is actually present across the environment. This becomes increasingly important as AI usage expands beyond traditional installed applications. A broader view needs to include both managed and browser-accessed AI activity.
AI applications are no longer limited to software installed directly on employee devices. Employees can interact with AI through websites, browser-based applications, automation platforms and locally running language models. These different access methods can create separate areas of AI activity that are difficult to understand through conventional application inventories alone. Identifying these sources provides a more complete view of how AI is being used across an organization.
Google Endpoints Readiness Tool’s AI Insights section provides visibility into AI usage across the organization to support AI governance and decision-making. It brings together information about installed AI applications, web-accessed AI, local AI models, and workflow automation platforms. Organizations can also view direct and indirect AI usage and understand the governance status associated with identified AI applications. This gives IT teams a centralized view of AI activity instead of relying only on known or manually reported applications.
Understanding the overall AI footprint is only the first step. IT teams may also need to determine where specific AI activity is occurring and what applications or platforms are involved on individual devices. Google Endpoints Readiness Tool allows administrators to drill down from organization-level AI insights into device-level details, providing additional context around the AI activity detected on each device. This helps connect broad AI usage patterns with the specific devices and applications behind them.
Device-level insights can include installed AI applications, web-based AI applications, local AI models, and agentic automation platforms. Administrators can also review information such as AI categories, usage types, governance status, and application usage details. This level of visibility can help teams identify where AI is being used and understand the different ways employees interact with AI tools. It also provides a more practical starting point for reviewing individual areas that require attention.
Visibility becomes more useful when organizations can apply their own governance context to the AI activity they discover. Google Endpoints Readiness Tool’s Report Generator includes AI Sanctioning, where administrators can review discovered AI applications, workflow platforms, local LLMs, and browser-integrated AI. These items can be classified as Sanctioned or Unsanctioned, helping organizations distinguish tools that have been approved from those that may require further review. Administrators can also configure whether identified AI activity is considered direct or indirect where applicable.
This classification does not determine whether an AI application should ultimately be allowed or restricted. Instead, it gives organizations a structured way to understand their AI footprint and identify applications that may need additional assessment. Combined with device-level visibility, sanctioning helps connect AI discovery with the organization’s own governance approach. The result is a more contextual view of AI activity across the environment.
AI adoption will continue to introduce new applications, workflows, and ways of working. As this happens, relying only on manually maintained software inventories can make it harder to understand the actual AI landscape within an organization. Continuous visibility can help IT teams identify AI activity across different access methods and understand where additional governance review may be needed.
AI Insights in Google Endpoints Readiness Tool brings this visibility into a broader readiness assessment, helping organizations understand AI activity across their environment. From installed applications and browser-accessed AI to local models, workflow platforms, and governance classifications, Google Endpoints Readiness Tool provides the insights needed to build a clearer picture of AI usage. Explore AI Usage Insights with Google Endpoints Readiness Tool. Visit Google Endpoints Readiness Tool
Shadow AI refers to AI tools or services used within an organization without going through established IT approval or governance processes.
Visibility helps organizations understand which AI tools are being used, how they are accessed, and where additional governance or review may be required.
Google Endpoints Readiness Tool AI Insights provides visibility into installed AI applications, web-accessed AI, local AI models, workflow platforms, and AI usage and governance information.
Yes. Google Endpoints Readiness Tool allows administrators to drill down into individual devices to review detected AI applications, web-based AI, local models, and automation platforms.
Yes. Google Endpoints Readiness Tool’s Report Generator provides AI Sanctioning options that allow administrators to classify identified AI applications and related AI technologies as Sanctioned or Unsanctioned.
Moving from traditional Windows environments to Chromebooks can help organizations simplify how employees work, reduce software dependency, and support a more cloud-first workplace.
A real example is MedXM, a U.S. healthcare organization that previously relied on Windows machines, Microsoft Office, and VPN access for employees. MedXM moved toward Chrome Enterprise technologies including Chromebooks, Chrome Browser, and Google Works pace to create a more streamlined working environment.
The results show what a successful endpoint modernization strategy can look like. But for another organization considering the same direction, the first question should be:
Is our current environment actually ready to move from Windows to Chromebooks?
That readiness question is exactly where Chrome Readiness Assessment (CRA) fits. CRA is an enterprise readiness assessment platform that helps IT and security teams understand whether their current environment is ready for Chrome Enterprise Premium, ChromeOS, and secure enterprise browsing. For organizations considering a Windows-to-Chromebook transition, CRA helps turn that broader readiness question into practical visibility around application and device readiness, showing what may be ready to move, what needs verification, and where potential blockers require attention before rollout.
MedXM previously operated with Windows machines equipped with Microsoft Office, while healthcare workers and remote employees also relied on VPN access to reach the company network. The additional steps made everyday work slower and more complex.
MedXM introduced Chromebooks, Chrome Browser, and Google Workspace as part of its move toward a more cloud-based environment.
According to the customer story, MedXM:
Reduced software-related expenses by 40%
Made routine employee tasks faster through cloud technology
Improved collaboration through Google Workspace
Planned to replace additional outreach-center and clinician computers with Chromebooks
Explore the MedXM Chrome Enterprise customer story
The story demonstrates the potential business value of moving away from a heavily desktop-dependent environment.
But reaching that point requires more than selecting new devices.
Every Windows environment is different.
Some employees may already spend most of their day in browser-based applications and cloud services. Their transition to Chromebooks may be relatively straightforward.
Others may rely on Windows applications, specialized software, peripherals, or workflows that require additional review.
That means an organization cannot assume every user or device should migrate at the same time.
Before investing in a wider Chromebook rollout, IT teams need to understand:
Which applications are already suitable for ChromeOS
Which applications need further verification
Which applications may create migration blockers
Which devices and user groups may be stronger candidates for an earlier move
Where additional planning is needed before migration
This is where readiness data becomes important.
Chrome Readiness Assessment helps organizations understand their existing environment before moving users from Windows to ChromeOS.
CRA evaluates application readiness using clear categories:
Chrome Ready : applications with strong ChromeOS readiness
Possibly Ready: applications that require additional review or verification
Blocker: applications that may prevent or complicate migration
Unknown: applications where readiness information still needs to be reviewed
These categories help IT teams identify where migration may be straightforward and where further investigation is needed before rollout.
CRA also helps teams review device readiness so the migration does not have to be treated as one organization-wide switch.
Instead, organizations can begin building migration waves around actual readiness.
See how CRA supports ChromeOS readiness planning
MedXM shows the outcome organizations may be working toward: a more cloud-first environment built around Chromebooks, Chrome Browser, and modern productivity tools.
CRA focuses on the stage before that transformation.
For an organization currently operating a large Windows fleet, CRA helps answer practical questions such as:
“Which users could move first? Which applications still depend on Windows? Where are the issues?”
Instead of beginning migration with assumptions, IT teams can use readiness insights to decide where the transition can start and where more preparation is required.
A Windows-to-Chromebook migration affects more than hardware.
Applications, user workflows, access requirements, peripherals, and existing dependencies all influence whether the change works smoothly for employees.
If those dependencies are discovered only after Chromebooks have already been deployed, IT teams may face avoidable disruption and support pressure.
CRA helps surface those issues earlier.
The goal is not simply to determine whether ChromeOS is suitable for the organization. It is to understand which parts of the organization are ready for ChromeOS today and which parts need more preparation.
MedXM’s experience shows how moving toward Chromebooks and cloud-first tools can support a simpler way of working. The organization reported lower software-related expenses, faster routine tasks, and plans to expand Chromebook adoption further.
For organizations considering a similar transition, readiness should come first.
Chrome Readiness Assessment helps teams evaluate their current Windows environment, identify application and device readiness, surface potential blockers, and plan a more practical path toward Chromebook adoption.
The goal is not to move every device at once. It is to understand where the organization is ready to move first.
Did MedXM use Chrome Readiness Assessment?
The MedXM customer story does not state that MedXM used CRA. The case study is used as a real-world example of an organization moving from a Windows-based environment toward Chromebooks and Chrome Enterprise technologies.
How does CRA connect to the MedXM use case?
CRA supports organizations considering a similar Windows-to-ChromeOS transition by helping them evaluate application and device readiness before rollout.
Can CRA identify applications that may affect ChromeOS migration?
Yes. CRA categorizes applications as Chrome Ready, Possibly Ready, Blocker, or Unknown so IT teams can identify where additional review may be needed.
Does every Windows device need to move to a Chromebook at the same time?
No. Readiness visibility can help organizations plan migration in stages, moving stronger candidates first while reviewing applications, devices, or workflows that still require attention.
Artificial intelligence is becoming a regular part of how employees work. From browser-based AI assistants and installed AI applications to local LLMs and workflow automation platforms, AI tools are appearing across enterprise environments faster than many organizations can track.
The challenge is not simply knowing that AI is being used. It is understanding what AI tools are being used, how they are being accessed, whether they are approved, and where governance gaps may exist.
To help organizations address this challenge, Chrome Readiness Assessment (CRA) is introducing AI Insights, a new capability designed to provide visibility into AI usage across devices, browsers, and applications while supporting governance through AI sanctioning controls.
AI tools can enter the workplace through multiple channels.
Some are installed directly on employee devices. Others are accessed through browsers. Employees may also use local AI models or connect AI-powered workflow automation platforms to business processes.
AI Insights brings these different sources together into a single view, helping organizations understand:
Installed AI applications across devices
Web-accessed AI applications used through browsers
Local AI models (LLMs) running on endpoints
AI workflow and automation platforms
Direct and indirect AI usage patterns
Approved (sanctioned) and unapproved (unsanctioned) AI tools
This provides a clearer picture of how AI is being adopted throughout the organization.
The AI Insights dashboard provides a consolidated overview of AI usage across the environment.
Organizations can quickly view:
Total installed AI applications detected
Browser-accessed AI applications
Local AI models discovered on devices
Workflow automation platforms in use
Direct and indirect AI application counts
Sanctioned and unsanctioned AI usage statistics
Rather than manually gathering information from multiple sources, teams can review AI activity from a centralized dashboard and identify areas that may require further investigation or governance.
Visibility becomes more valuable when organizations can understand AI usage at the device level.
AI Insights provides detailed device-level views that allow teams to review:
Installed AI applications on individual devices
Browser-accessed AI tools used by employees
Local AI models detected on endpoints
Workflow platforms associated with each device
Direct and indirect AI usage classifications
Governance status, including sanctioned and unsanctioned items
This helps security, IT, and governance teams understand where AI is being used and which devices may require additional review.
Not all AI interactions occur in the same way.
AI Insights distinguishes between:
Direct AI usage refers to applications specifically designed as AI tools or AI-powered solutions.
Examples may include:
ChatGPT
Gemini
Perplexity
AI coding assistants
Dedicated AI productivity applications
Indirect AI usage refers to traditional applications or services that incorporate AI functionality as part of a broader product experience.
Examples may include:
Productivity platforms with embedded AI features
Browsers with built-in AI assistants
Business applications enhanced by AI capabilities
This distinction helps organizations better understand how AI enters everyday workflows and where governance policies may need to be applied.
Visibility alone is not enough. Organizations also need a way to define which AI tools are approved for use.
AI Insights introduces an AI Sanctioning workflow within CRA's Report Generator, allowing organizations to review discovered AI technologies and determine whether they should be sanctioned or unsanctioned.
Items can be categorized and governed across multiple areas, including:
Installed AI applications discovered across devices can be reviewed and marked as sanctioned or unsanctioned according to organizational policy.
AI-enabled workflow and automation platforms can be evaluated to determine whether their usage aligns with governance requirements.
Locally installed AI models and LLM frameworks can be identified and reviewed to ensure compliance with security and governance standards.
Modern browsers increasingly include built-in AI assistants and copilots.
AI Insights identifies browser-integrated AI capabilities such as:
Gemini in Chrome
Copilot in Microsoft Edge
Leo in Brave
Firefox AI assistant capabilities
Aria in Opera
Organizations can review these browser-integrated AI features and determine whether they should be sanctioned for use within the environment.
Once governance decisions are defined, AI Insights reflects those decisions throughout reporting and device-level views.
Organizations can quickly identify:
Sanctioned AI tools approved for business use
Unsanctioned AI tools that may require review
Devices using unapproved AI technologies
Governance trends across departments and user groups
This enables teams to move from simple discovery toward active AI governance.
AI adoption is accelerating across every industry. The organizations that succeed will be those that can balance innovation with visibility, governance, and control.
With AI Insights, Chrome Readiness Assessment helps organizations understand AI activity across their environment, distinguish between different types of AI usage, and establish governance through sanctioning workflows and reporting.
By providing visibility into installed AI applications, browser-accessed AI tools, local LLMs, workflow platforms, browser-integrated AI capabilities, and governance status, AI Insights helps organizations make more informed decisions about AI adoption at scale.
For IT professionals, readiness reports provide valuable insights, but finding the right information can take time.
Chrome Readiness Assessment gives organizations a detailed view of their environment and helps teams understand their overall readiness. But when a user needs one specific answer, they may still need to move through multiple report sections before finding it.
CRA MCP introduces a new way to work with that readiness data. It brings Model Context Protocol support into the CRA Report Generator, allowing tested MCP-compatible clients to connect to the current generated CRA report through a controlled MCP Server.
The report-querying and data retrieval flow has currently been tested with CommandLyne and the Claude Desktop app. With this connection, users can ask natural-language questions, explore follow-ups, and receive focused answers based on CRA readiness data. Instead of manually searching through the report, teams can ask the report what they need to know.
CRA MCP also brings readiness intelligence closer to action. Where approved, CEP enrollment token deployment can be supported through a separate Device Action Tools section. This deployment capability is currently in beta and is supported through CommandLyne only.
CRA MCP is not just about making reports easier to read. It helps teams move from readiness data to controlled next steps while keeping setup approval, MCP access control, tool-level confirmation, and deployment visibility inside CRA.
CRA MCP is a new integration feature that allows supported AI agents to interact with CRA readiness data through an MCP Server inside the Report Generator.
Instead of manually searching through different report sections, users can connect a tested MCP client and ask questions about the current generated report. At this stage, the report-querying experience has been tested with CommandLyne and the Claude Desktop app.
For example, a user may want to know which devices are blocked, which applications need review, what browser risks were found, which workflows show automation activity, or what is affecting CEP readiness. CRA MCP allows the connected client to request the right information from the report and return a focused answer.
The Report Generator remains the source of truth. The MCP Server controls access. The connected MCP client provides the conversational experience.
Traditional reports are useful, but they depend on the user knowing where to look.
CRA MCP makes the report more interactive. Instead of opening multiple pages, filtering tables, and reading through different sections, users can ask direct questions and receive data-backed responses.
This helps different teams use the same report in different ways.
An IT administrator can ask for technical blockers. A manager can ask for a short readiness summary. A security team can ask about browser and extension exposure. A partner can ask for the main customer risks and the next areas to review.
The same readiness report becomes easier to explore because users can ask for the exact view they need.
Before device actions can be used, CRA requires approval during the setup flow.
In the installation or wizard process, the user must allow Remote Device Actions. This approval authorizes the Report Generator on that machine to send signed configuration actions to devices in the current installation round.
This is an important control point.
Remote Device Actions do not mean CRA can send any command to a device. The flow is limited to supported built-in actions. Instructions are signed, devices pick them up during their check-in cycle, and outcomes can be reviewed later.
Once this setup approval is completed, the MCP Server and Actions capabilities become available in the Report Generator.
The MCP Server sits inside the CRA Report Generator as a controlled connection layer.
When the MCP Server is enabled, users can create access tokens for tested MCP clients. These tokens allow approved clients, such as CommandLyne or the Claude Desktop app, to connect to CRA and request information from the current generated report.
The connected client does not directly own the CRA data. It sends a request through the MCP Server. The MCP Server validates access, checks whether the requested tool is available, retrieves the supported information from the current report, and returns only the relevant data.
The flow is simple:
User asks a question → AI agent sends the request → MCP Server validates access → CRA Report Generator provides the report data → AI agent returns the answer
This gives users a natural-language experience while keeping CRA as the trusted source of readiness information.
CRA MCP does not open report access automatically.
Users must enable the MCP Server and create named access tokens for the MCP clients they want to connect. The report-querying and data retrieval flow has currently been tested with CommandLyne and the Claude Desktop app.
This gives organizations control over which tested clients can connect to CRA.
Access can also be removed when needed. If a token is revoked, the connected client can no longer access CRA readiness data through that token.
This matters because CRA reports may include sensitive environment information, such as device status, application usage, browser risks, migration blockers, workflow activity, and CEP readiness. Token-based access keeps the AI connection useful without making it uncontrolled.
CRA MCP also includes a separate section called Device Action Tools.
This is different from the read-only MCP tools.
Read-only tools help users retrieve and understand report data. Device Action Tools can support a specific approved action when the organization enables them.
These tools are not available by default. The deploy_chrome_enrollment_token tool is available under the Device Action Tools section within the MCP Server page. This CEP enrollment token deployment capability is currently in beta and is supported through CommandLyne only.
To enable the CEP enrollment token action, the user must select the action tool under Device Action Tools. When the user selects the tool, CRA shows a warning modal asking whether AI clients should be allowed to use that action.
This creates another approval layer.
If the user confirms, the selected CEP enrollment token action tool becomes available through the approved CommandLyne MCP flow. If the user does not confirm, the tool remains disabled.
This means the AI agent is not given general device control. It can only call the specific CEP enrollment token action tool that has been approved.
Actions triggered through MCP do not disappear into the background.
After the approved CommandLyne agent deploys the CEP token, the user can go to the Actions page and open the Deployments tab. There, the deployment card appears for the enrollment token action.
The card shows the deployment summary, including how many instructions were published and how many are still pending. Users can then view the devices connected to that deployment.
Inside the device list, users can see which devices are pending, which devices have applied the action, and which devices are still waiting for check-in. Device-level details also help teams review information such as the device name, group, action type, published time, expiry, last upload time, reported outcome, agent version, and action history.
This keeps the process transparent.
Even when the action is triggered through CommandLyne, the Report Generator still gives users a place to verify progress and confirm the outcome.
The business value of CRA MCP is that it connects readiness insight with controlled execution.
A readiness report can show what needs attention. An AI agent can help explain the finding. A controlled Device Action Tool can then support a specific approved next step.
This reduces the gap between analysis and action.
For IT teams, it means less time moving between report review and operational follow-up. For security and management teams, it means AI-supported workflows can still stay inside approved boundaries. For partners, it creates a stronger customer experience because the discussion can move from “what did the report find?” to “what controlled next step can we take?”
Because the CEP token deployment flow is currently in beta, it also creates a foundation for future expansion. More supported actions can be introduced later while keeping the same control model: setup approval, MCP access control, tool-level confirmation, and deployment tracking.
CRA MCP brings readiness intelligence closer to action while keeping approval, access control, and deployment visibility inside the Report Generator.
CRA MCP should not be understood as open-ended AI control over devices.
The AI agent cannot freely change CRA data, run arbitrary commands, or perform unrestricted device operations.
Read-only MCP tools are used to query and summarize report data. Device Action Tools are separate, disabled by default, and only available after explicit approval. The current CEP token deployment capability is beta and limited to the approved CommandLyne flow.
This separation makes the feature practical for enterprise environments. Teams can benefit from AI-assisted readiness exploration while keeping action boundaries clear.
CRA MCP supports AI-connected workflows, but users do not have to use an AI agent for every action.
Users who prefer a manual flow can go to the Actions area in the Report Generator, choose the supported action, configure the required details, and deploy it directly from the interface.
This manual path is useful when teams want to handle the action themselves while still using the Report Generator to track deployment status.
CRA MCP turns CRA reports into a more interactive layer of readiness intelligence.
The Report Generator continues to hold the trusted data. The MCP Server controls which tested clients can connect. Access tokens protect the connection. Read-only tools provide focused answers. Device Action Tools support approved next steps. The Actions page keeps deployment tracking visible.
Together, these capabilities make CRA MCP more than a report-querying feature.
It helps organizations ask better questions, understand readiness faster, and move toward controlled action with confidence. With the CEP token deployment capability currently in beta through CommandLyne, CRA also sets the foundation for more controlled action workflows in future releases.
Which MCP clients are currently tested with CRA MCP?
The report-querying and data retrieval flow has currently been tested with CommandLyne and the Claude Desktop app.
What happens when the CEP enrollment token Device Action Tool is selected?
CRA displays a warning modal asking whether AI clients should be allowed to use that action. If the user confirms, the action becomes available through the approved CommandLyne MCP flow. If the user does not confirm, the tool stays disabled.
Can an AI agent deploy a CEP token?
Yes, where approved, but this capability is currently in beta and supported through CommandLyne only. The CEP enrollment token Device Action Tool must be enabled, confirmed, and accessed through a valid MCP token before CommandLyne can support the deployment action.
Will more Device Action Tools be added later?
Yes. The CEP token deployment flow is the current beta action, and future releases can expand the Device Action Tools area with more supported actions.
Deploying Chrome Enterprise Premium licenses across a large enterprise can be a challenging task. When hundreds or thousands of devices are involved, even a simple deployment step can require significant time and coordination. IT teams need a way to move from being ready for Chrome Enterprise Premium to actually putting it into use across their environment without adding unnecessary complexity.
That is where an upcoming capability in Chrome Readiness Assessment (CRA) comes in. CRA is evolving to help organizations take the next step after understanding their environment and readiness. The goal is to make Chrome Enterprise Premium deployment more streamlined and easier to manage across an enterprise.
Before rolling out Chrome Enterprise Premium, organizations need to understand whether their devices and environment are ready. CRA helps provide that visibility, giving IT teams a clearer picture of their current environment and the areas that may need attention before deployment.
But knowing that your environment is ready is only part of the journey. The next step is putting Chrome Enterprise Premium into use across the organization's browser environment. With the upcoming CRA MCP capability, organizations will be able to bring readiness information and CEP deployment closer together, helping teams move forward with greater efficiency.
Imagine an organization preparing to introduce Chrome Enterprise Premium across a large number of devices. Managing that process device by device can quickly become difficult, especially when IT teams are already handling multiple deployment and security priorities. A more connected approach can help reduce the effort involved and make it easier to move through the deployment process.
The upcoming capability will connect CommandLyne, CRA, and the MCP experience to help organizations work toward deploying Chrome Enterprise Premium licenses across their browser environment. Rather than treating deployment as a completely separate step after readiness assessment, the experience is designed to help organizations move more naturally from understanding their environment to taking action..
CRA has traditionally helped organizations understand their readiness for Chrome Enterprise Premium deployment by providing visibility into their environment and identifying factors that may impact a successful rollout. These insights have helped IT teams answer an important question: “Are we ready?” With its upcoming MCP capabilities, CRA is evolving beyond readiness assessment to help organizations act on those insights as well. Rather than stopping at identifying readiness, teams will be able to move closer to deploying Chrome Enterprise Premium licenses across their browser environment through a more connected and streamlined experience.
With this upcoming CRA MCP capability, organizations will have a new way to streamline their journey toward Chrome Enterprise Premium deployment at scale. It is another step toward making CRA more than a readiness tool, it is becoming a more connected part of the journey from knowing where you stand to moving forward.
A simpler path from readiness to Chrome Enterprise Premium deployment is coming soon.
Stay tuned.
It is an upcoming capability that will help connect CRA readiness information with the process of deploying Chrome Enterprise Premium across an organization's browser environment. The goal is to make the journey from readiness to deployment more streamlined.
It is primarily intended for IT teams and organizations managing Chrome Enterprise Premium deployments across large browser environments.
CRA MCP provides a way to connect CRA with supported AI-assisted experiences such as CommandLyne. For this upcoming capability, that connected experience will help bring CRA readiness information closer to the next step of deploying Chrome Enterprise Premium.
This capability is currently coming soon. More details about the deployment experience will be shared as it progresses.
AI is becoming part of everyday work. Employees may use AI applications installed on their devices, access AI tools through their browsers, experiment with local AI models, or use workflow automation platforms to bring AI into their daily processes. For IT teams, understanding this growing AI activity is becoming just as important as knowing which applications are installed across the organization.
Chrome Readiness Assessment is expanding its AI visibility to help organizations understand more of this activity in one place. With the upcoming AI Usage Insights experience, CRA will bring together a broader view of AI activity across devices and browsers, helping teams see how AI is being accessed and used across their environment.
CRA already helps organizations identify AI applications installed on devices. This gives IT teams an initial view of the AI tools present across their environment, but installed applications are only one part of the picture. Employees can also access AI services through their browsers, use workflow automation platforms, or work with AI models directly on their devices.
The upcoming AI Usage Insights section is designed to bring these different forms of AI activity into a more complete view. Instead of looking only at what is installed, organizations will be able to get a better understanding of how AI is being accessed and where it is appearing across their environment.
AI usage can look very different from one device to another. One employee may have AI applications installed on their device, while another may access several AI websites through their browser. Others may be using platforms such as n8n or Google Workspace Studio to build and automate workflows, creating another layer of AI activity for IT teams to understand.
AI activity does not always happen through a familiar application or website. Some users may run or work with local large language models directly on their devices, creating AI activity that can be difficult to identify through traditional application or browser visibility alone.
More visibility also brings an important question for IT teams: Which AI tools are approved for use?
AI Usage Insights will help organizations look at AI applications through a governance lens, allowing administrators to distinguish between sanctioned and unsanctioned AI activity. Organizations will also have the flexibility to customize the domains included in their assessment. One Place to Understand Your AI Environment
As AI adoption grows, having scattered information about AI activity can make it harder for IT teams to understand what is really happening across their environment. AI Usage Insights brings these different signals together within a dedicated experience in CRA, making AI activity easier to explore and understand.
From installed AI applications and browser-accessed tools to workflow automation platforms and local AI models, organizations will have a broader view of their AI environment.
AI adoption is moving quickly, and knowing which applications are installed is no longer the whole story. Organizations need to understand where AI is being accessed, how it is being used, and which tools are part of their working environment.
With the upcoming AI Usage Insights experience, CRA is taking a broader approach to AI visibility. It is designed to help organizations move from simply knowing “What AI applications are installed?” to understanding “How is AI being used across our environment?”
A clearer view of your organization's AI usage is coming soon. Stay tuned.
AI Usage Insights is an upcoming CRA experience designed to provide a broader view of AI activity across an organization's environment. It will bring together information about installed AI applications, browser-accessed AI, workflow automation platforms, local AI models, and AI governance.
The existing view focuses on AI applications installed on devices. AI Usage Insights expands this visibility by also showing AI accessed through browsers and other forms of AI activity across the environment.
The upcoming experience will provide visibility into installed AI applications, browser-accessed AI applications and websites, workflow automation platforms, and locally detected AI models.
Yes. The upcoming experience will allow administrators to categorize AI activity as sanctioned or unsanctioned, helping organizations distinguish approved AI tools from those that may require review.
AI Usage Insights is currently coming soon. More details about the feature and its capabilities will be shared as it becomes available.