8 min read

Freshservice vs Other ITSM Solutions:
_{The Ultimate Comparison Guide for 2025}

Written by

Srihari Jagannathan

Published on

17 January 2022

Introduction

Mi tincidunt elit, id quisque ligula ac diam, amet. Vel etiam suspendisse morbi eleifend faucibus eget vestibulum felis. Dictum quis montes, sit sit. Tellus aliquam enim urna, etiam. Mauris posuere vulputate arcu amet, vitae nisi, tellus tincidunt. At feugiat sapien varius id.

Eget quis mi enim, leo lacinia pharetra, semper. Eget in volutpat mollis at volutpat lectus velit, sed auctor. Porttitor fames arcu quis fusce augue enim. Quis at habitant diam at. Suscipit tristique risus, at donec. In turpis vel et quam imperdiet. Ipsum molestie aliquet sodales id est ac volutpat.

Office setting — Image caption goes here

Dolor enim eu tortor urna sed duis nulla. Aliquam vestibulum, nulla odio nisl vitae. In aliquet pellentesque aenean hac vestibulum turpis mi bibendum diam. Tempor integer aliquam in vitae malesuada fringilla.

Elit nisi in eleifend sed nisi. Pulvinar at orci, proin imperdiet commodo consectetur convallis risus. Sed condimentum enim dignissim adipiscing faucibus consequat, urna. Viverra purus et erat auctor aliquam. Risus, volutpat vulputate posuere purus sit congue convallis aliquet. Arcu id augue ut feugiat donec porttitor neque. Mauris, neque ultricies eu vestibulum, bibendum quam lorem id. Dolor lacus, eget nunc lectus in tellus, pharetra, porttitor.

"Ipsum sit mattis nulla quam nulla. Gravida id gravida ac enim mauris id. Non pellentesque congue eget consectetur turpis. Sapien, dictum molestie sem tempor. Diam elit, orci, tincidunt aenean tempus."

Tristique odio senectus nam posuere ornare leo metus, ultricies. Blandit duis ultricies vulputate morbi feugiat cras placerat elit. Aliquam tellus lorem sed ac. Montes, sed mattis pellentesque suscipit accumsan. Cursus viverra aenean magna risus elementum faucibus molestie pellentesque. Arcu ultricies sed mauris vestibulum.

Conclusion

Morbi sed imperdiet in ipsum, adipiscing elit dui lectus. Tellus id scelerisque est ultricies ultricies. Duis est sit sed leo nisl, blandit elit sagittis. Quisque tristique consequat quam sed. Nisl at scelerisque amet nulla purus habitasse.

Nunc sed faucibus bibendum feugiat sed interdum. Ipsum egestas condimentum mi massa. In tincidunt pharetra consectetur sed duis facilisis metus. Etiam egestas in nec sed et. Quis lobortis at sit dictum eget nibh tortor commodo cursus.

Odio felis sagittis, morbi feugiat tortor vitae feugiat fusce aliquet. Nam elementum urna nisi aliquet erat dolor enim. Ornare id morbi eget ipsum. Aliquam senectus neque ut id eget consectetur dictum. Donec posuere pharetra odio consequat scelerisque et, nunc tortor. Nulla adipiscing erat a erat. Condimentum lorem posuere gravida enim posuere cursus diam.

Introduction

Dolor enim eu tortor urna sed duis nulla. Aliquam vestibulum, nulla odio nisl vitae. In aliquet pellentesque aenean hac vestibulum turpis mi bibendum diam. Tempor integer aliquam in vitae malesuada fringilla.

"Ipsum sit mattis nulla quam nulla. Gravida id gravida ac enim mauris id. Non pellentesque congue eget consectetur turpis. Sapien, dictum molestie sem tempor. Diam elit, orci, tincidunt aenean tempus."

Conclusion

AI ITSM Starter Checklist for CIOs

Introduction: Modernizing ITSM with AI

CIOs are under increasing pressure to reduce IT service costs, improve operational efficiency, and enhance user experience. Agentic AI-powered ITSM solutions can:

● Automate up to 60% of IT tickets.

● Reduce costs by 30-50%.

● Improve ticket resolution times by up to 90%.

Before you implement ITSM on your system, it’s important to understand how it actually works. Here’s an ITSM workflow right from raising a ticket to resolution of it. AI-Powered ITSM Workflow: From Ticket to Resolution

1. INPUTS

Sources of service requests and issues

· Self-Service Portal

· Email & Chat

· Phone & Voicemail

· Monitoring Tools (AIOps, Alerts)

· Internal Systems (HR, Finance, DevOps)

2. AI LAYER

Core of automation & intelligence

· AI Ticket Classification & Routing (e.g., NLP + intent detection routes ticket to correct queue.)

· Automated Responses & Resolutions (e.g., LLMs, RPA for password resets or software installs.)

· AI Knowledge Suggestions (Context-aware KB surfacing for users and agents.)

· Predictive Analytics (Identify patterns for proactive issue prevention.)

· Virtual Agents/Chatbots (Resolve Level 1 issues instantly.)

3. OUTPUTS

What the system delivers after AI processing

· Resolved Automatically

· Routed to Human Agent

· Escalated Based on Priority

· Tracked for Metrics:

o Resolution Time

o CSAT

o Deflection Rate

o SLA Adherence

4. Continuous Feedback Loop

Real-time data trains and improves AI models

· User feedback on resolution accuracy

· Agent corrections fed back to AI

· Knowledge base updates

· AI performance metrics (success rate, false positives)

Use this checklist to ensure a strategic, step-by-step AI implementation in your ITSM workflows.

✅ Step 1: Identify ITSM Processes for AI Automation

● Audit current ITSM workflows and identify bottlenecks (e.g., ticket resolution time, backlog issues).

● Identify high-volume, repetitive tasks suitable for automation (e.g., password resets, software requests, ticket routing).

● Assess existing AI-readiness (cloud vs. legacy ITSM tools, integration capabilities).

● Define key success metrics (ticket deflection rate, cost savings, CSAT improvements).

✅ Step 2: Select the Right AI-Powered ITSM Platform

● Evaluate AI-driven ITSM tools like Freshservice, Jira Service Management, and Atomic Work for automation capabilities.

● Ensure the platform supports AI-driven ticket routing, predictive analytics, and chat-based self-service.

● Assess integration capabilities with existing IT infrastructure (e.g., ITOM, ITAM, AIOps).

● Review AI-powered knowledge management & automated resolutions for efficiency.

● Compare cost, scalability, and licensing models to maximize ROI.

✅ Step 3: Implement AI in Phases (Pilot → Scale)

● Launch a pilot AI implementation (e.g., automate one workflow, such as AI-powered ticket triage).

● Monitor performance metrics (resolution time, automation success rate, agent workload reduction).

● Expand AI-driven automation across multiple ITSM functions (incident response, service requests, self-healing IT, change management).

● Optimize AI based on user feedback & real-time performance tracking.

✅ Step 4: Measure ROI & Optimize for Scale

● Track AI-driven performance against baseline metrics (CSAT, resolution time, ticket deflection rate).

● Analyze cost savings from reduced manual workload & faster ticket resolutions.

● Continuously train AI models on real-time ITSM data for improved accuracy.

● Expand AI adoption to broader Enterprise Service Management (ESM) use cases (HR, finance, facilities, etc.)

Avoid These Common Pitfalls in AI-Powered ITSM Implementations

Even the best AI initiatives can fall short if certain traps aren’t avoided. Here are the top mistakes CIOs make—and how to stay clear of them:

❌ Over-Automating Without Quality Training Data

AI models need context. Automating complex tasks without feeding the AI historical ticket data, intents, or edge cases can result in poor performance and frustrated users.

✅ Solution: Start with high-volume, low-complexity use cases (like password resets). Train AI using clean, labeled ticket history, and build from there.

❌ Ignoring Change Management for IT Teams

Resistance from IT agents and support staff can derail even the smartest AI rollout.

✅ Solution: Involve agents early, show how AI reduces repetitive workload, and provide training to work alongside AI tools (not against them).

❌ Selecting Tools with Limited Integration Capabilities

AI-powered platforms that don’t talk to your CMDB, monitoring tools, or HR systems create data silos and missed insights.

✅ Solution: Choose platforms with robust integration options (APIs, webhooks) and compatibility with your existing ITOM/ITAM/AIOps stack.

❌ Lack of Clear Success Metrics

Without tracking the right KPIs, you won’t know if your AI implementation is succeeding.

✅ Solution: Establish baseline metrics (e.g., average resolution time, deflection rate, CSAT) and measure improvement over time.

❌ Trying to Scale AI Too Fast

Rolling out AI across all ITSM processes at once leads to chaos and poor adoption.

✅ Solution: Pilot one workflow, learn from it, i

‍

Introduction

Dolor enim eu tortor urna sed duis nulla. Aliquam vestibulum, nulla odio nisl vitae. In aliquet pellentesque aenean hac vestibulum turpis mi bibendum diam. Tempor integer aliquam in vitae malesuada fringilla.

"Ipsum sit mattis nulla quam nulla. Gravida id gravida ac enim mauris id. Non pellentesque congue eget consectetur turpis. Sapien, dictum molestie sem tempor. Diam elit, orci, tincidunt aenean tempus."

Conclusion

AI ITSM Starter Checklist for CIOs

Introduction: Modernizing ITSM with AI

CIOs are under increasing pressure to reduce IT service costs, improve operational efficiency, and enhance user experience. Agentic AI-powered ITSM solutions can:

● Automate up to 60% of IT tickets.

● Reduce costs by 30-50%.

● Improve ticket resolution times by up to 90%.

1. INPUTS

Sources of service requests and issues

· Self-Service Portal

· Email & Chat

· Phone & Voicemail

· Monitoring Tools (AIOps, Alerts)

· Internal Systems (HR, Finance, DevOps)

2. AI LAYER

Core of automation & intelligence

· AI Ticket Classification & Routing (e.g., NLP + intent detection routes ticket to correct queue.)

· Automated Responses & Resolutions (e.g., LLMs, RPA for password resets or software installs.)

· AI Knowledge Suggestions (Context-aware KB surfacing for users and agents.)

· Predictive Analytics (Identify patterns for proactive issue prevention.)

· Virtual Agents/Chatbots (Resolve Level 1 issues instantly.)

3. OUTPUTS

What the system delivers after AI processing

· Resolved Automatically

· Routed to Human Agent

· Escalated Based on Priority

· Tracked for Metrics:

o Resolution Time

o CSAT

o Deflection Rate

o SLA Adherence

4. Continuous Feedback Loop

Real-time data trains and improves AI models

· User feedback on resolution accuracy

· Agent corrections fed back to AI

· Knowledge base updates

· AI performance metrics (success rate, false positives)

Use this checklist to ensure a strategic, step-by-step AI implementation in your ITSM workflows.

✅ Step 1: Identify ITSM Processes for AI Automation

● Audit current ITSM workflows and identify bottlenecks (e.g., ticket resolution time, backlog issues).

● Identify high-volume, repetitive tasks suitable for automation (e.g., password resets, software requests, ticket routing).

● Assess existing AI-readiness (cloud vs. legacy ITSM tools, integration capabilities).

● Define key success metrics (ticket deflection rate, cost savings, CSAT improvements).

✅ Step 2: Select the Right AI-Powered ITSM Platform

● Evaluate AI-driven ITSM tools like Freshservice, Jira Service Management, and Atomic Work for automation capabilities.

● Ensure the platform supports AI-driven ticket routing, predictive analytics, and chat-based self-service.

● Assess integration capabilities with existing IT infrastructure (e.g., ITOM, ITAM, AIOps).

● Review AI-powered knowledge management & automated resolutions for efficiency.

● Compare cost, scalability, and licensing models to maximize ROI.

✅ Step 3: Implement AI in Phases (Pilot → Scale)

● Launch a pilot AI implementation (e.g., automate one workflow, such as AI-powered ticket triage).

● Monitor performance metrics (resolution time, automation success rate, agent workload reduction).

● Expand AI-driven automation across multiple ITSM functions (incident response, service requests, self-healing IT, change management).

● Optimize AI based on user feedback & real-time performance tracking.

✅ Step 4: Measure ROI & Optimize for Scale

● Track AI-driven performance against baseline metrics (CSAT, resolution time, ticket deflection rate).

● Analyze cost savings from reduced manual workload & faster ticket resolutions.

● Continuously train AI models on real-time ITSM data for improved accuracy.

● Expand AI adoption to broader Enterprise Service Management (ESM) use cases (HR, finance, facilities, etc.)

Avoid These Common Pitfalls in AI-Powered ITSM Implementations

Even the best AI initiatives can fall short if certain traps aren’t avoided. Here are the top mistakes CIOs make—and how to stay clear of them:

❌ Over-Automating Without Quality Training Data

AI models need context. Automating complex tasks without feeding the AI historical ticket data, intents, or edge cases can result in poor performance and frustrated users.

✅ Solution: Start with high-volume, low-complexity use cases (like password resets). Train AI using clean, labeled ticket history, and build from there.

❌ Ignoring Change Management for IT Teams

Resistance from IT agents and support staff can derail even the smartest AI rollout.

✅ Solution: Involve agents early, show how AI reduces repetitive workload, and provide training to work alongside AI tools (not against them).

❌ Selecting Tools with Limited Integration Capabilities

AI-powered platforms that don’t talk to your CMDB, monitoring tools, or HR systems create data silos and missed insights.

✅ Solution: Choose platforms with robust integration options (APIs, webhooks) and compatibility with your existing ITOM/ITAM/AIOps stack.

❌ Lack of Clear Success Metrics

Without tracking the right KPIs, you won’t know if your AI implementation is succeeding.

✅ Solution: Establish baseline metrics (e.g., average resolution time, deflection rate, CSAT) and measure improvement over time.

❌ Trying to Scale AI Too Fast

Rolling out AI across all ITSM processes at once leads to chaos and poor adoption.

✅ Solution: Pilot one workflow, learn from it, i

‍

Introduction

Dolor enim eu tortor urna sed duis nulla. Aliquam vestibulum, nulla odio nisl vitae. In aliquet pellentesque aenean hac vestibulum turpis mi bibendum diam. Tempor integer aliquam in vitae malesuada fringilla.

"Ipsum sit mattis nulla quam nulla. Gravida id gravida ac enim mauris id. Non pellentesque congue eget consectetur turpis. Sapien, dictum molestie sem tempor. Diam elit, orci, tincidunt aenean tempus."

Conclusion

AI ITSM Starter Checklist for CIOs

Introduction: Modernizing ITSM with AI

CIOs are under increasing pressure to reduce IT service costs, improve operational efficiency, and enhance user experience. Agentic AI-powered ITSM solutions can:

● Automate up to 60% of IT tickets.

● Reduce costs by 30-50%.

● Improve ticket resolution times by up to 90%.

1. INPUTS

Sources of service requests and issues

· Self-Service Portal

· Email & Chat

· Phone & Voicemail

· Monitoring Tools (AIOps, Alerts)

· Internal Systems (HR, Finance, DevOps)

2. AI LAYER

Core of automation & intelligence

· AI Ticket Classification & Routing (e.g., NLP + intent detection routes ticket to correct queue.)

· Automated Responses & Resolutions (e.g., LLMs, RPA for password resets or software installs.)

· AI Knowledge Suggestions (Context-aware KB surfacing for users and agents.)

· Predictive Analytics (Identify patterns for proactive issue prevention.)

· Virtual Agents/Chatbots (Resolve Level 1 issues instantly.)

3. OUTPUTS

What the system delivers after AI processing

· Resolved Automatically

· Routed to Human Agent

· Escalated Based on Priority

· Tracked for Metrics:

o Resolution Time

o CSAT

o Deflection Rate

o SLA Adherence

4. Continuous Feedback Loop

Real-time data trains and improves AI models

· User feedback on resolution accuracy

· Agent corrections fed back to AI

· Knowledge base updates

· AI performance metrics (success rate, false positives)

Use this checklist to ensure a strategic, step-by-step AI implementation in your ITSM workflows.

✅ Step 1: Identify ITSM Processes for AI Automation

● Audit current ITSM workflows and identify bottlenecks (e.g., ticket resolution time, backlog issues).

● Identify high-volume, repetitive tasks suitable for automation (e.g., password resets, software requests, ticket routing).

● Assess existing AI-readiness (cloud vs. legacy ITSM tools, integration capabilities).

● Define key success metrics (ticket deflection rate, cost savings, CSAT improvements).

✅ Step 2: Select the Right AI-Powered ITSM Platform

● Evaluate AI-driven ITSM tools like Freshservice, Jira Service Management, and Atomic Work for automation capabilities.

● Ensure the platform supports AI-driven ticket routing, predictive analytics, and chat-based self-service.

● Assess integration capabilities with existing IT infrastructure (e.g., ITOM, ITAM, AIOps).

● Review AI-powered knowledge management & automated resolutions for efficiency.

● Compare cost, scalability, and licensing models to maximize ROI.

✅ Step 3: Implement AI in Phases (Pilot → Scale)

● Launch a pilot AI implementation (e.g., automate one workflow, such as AI-powered ticket triage).

● Monitor performance metrics (resolution time, automation success rate, agent workload reduction).

● Expand AI-driven automation across multiple ITSM functions (incident response, service requests, self-healing IT, change management).

● Optimize AI based on user feedback & real-time performance tracking.

✅ Step 4: Measure ROI & Optimize for Scale

● Track AI-driven performance against baseline metrics (CSAT, resolution time, ticket deflection rate).

● Analyze cost savings from reduced manual workload & faster ticket resolutions.

● Continuously train AI models on real-time ITSM data for improved accuracy.

● Expand AI adoption to broader Enterprise Service Management (ESM) use cases (HR, finance, facilities, etc.)

Avoid These Common Pitfalls in AI-Powered ITSM Implementations

Even the best AI initiatives can fall short if certain traps aren’t avoided. Here are the top mistakes CIOs make—and how to stay clear of them:

❌ Over-Automating Without Quality Training Data

AI models need context. Automating complex tasks without feeding the AI historical ticket data, intents, or edge cases can result in poor performance and frustrated users.

✅ Solution: Start with high-volume, low-complexity use cases (like password resets). Train AI using clean, labeled ticket history, and build from there.

❌ Ignoring Change Management for IT Teams

Resistance from IT agents and support staff can derail even the smartest AI rollout.

✅ Solution: Involve agents early, show how AI reduces repetitive workload, and provide training to work alongside AI tools (not against them).

❌ Selecting Tools with Limited Integration Capabilities

AI-powered platforms that don’t talk to your CMDB, monitoring tools, or HR systems create data silos and missed insights.

✅ Solution: Choose platforms with robust integration options (APIs, webhooks) and compatibility with your existing ITOM/ITAM/AIOps stack.

❌ Lack of Clear Success Metrics

Without tracking the right KPIs, you won’t know if your AI implementation is succeeding.

✅ Solution: Establish baseline metrics (e.g., average resolution time, deflection rate, CSAT) and measure improvement over time.

❌ Trying to Scale AI Too Fast

Rolling out AI across all ITSM processes at once leads to chaos and poor adoption.

✅ Solution: Pilot one workflow, learn from it, i

‍

Introduction

Dolor enim eu tortor urna sed duis nulla. Aliquam vestibulum, nulla odio nisl vitae. In aliquet pellentesque aenean hac vestibulum turpis mi bibendum diam. Tempor integer aliquam in vitae malesuada fringilla.

"Ipsum sit mattis nulla quam nulla. Gravida id gravida ac enim mauris id. Non pellentesque congue eget consectetur turpis. Sapien, dictum molestie sem tempor. Diam elit, orci, tincidunt aenean tempus."

Conclusion

AI ITSM Starter Checklist for CIOs

Introduction: Modernizing ITSM with AI

CIOs are under increasing pressure to reduce IT service costs, improve operational efficiency, and enhance user experience. Agentic AI-powered ITSM solutions can:

● Automate up to 60% of IT tickets.

● Reduce costs by 30-50%.

● Improve ticket resolution times by up to 90%.

1. INPUTS

Sources of service requests and issues

· Self-Service Portal

· Email & Chat

· Phone & Voicemail

· Monitoring Tools (AIOps, Alerts)

· Internal Systems (HR, Finance, DevOps)

2. AI LAYER

Core of automation & intelligence

· AI Ticket Classification & Routing (e.g., NLP + intent detection routes ticket to correct queue.)

· Automated Responses & Resolutions (e.g., LLMs, RPA for password resets or software installs.)

· AI Knowledge Suggestions (Context-aware KB surfacing for users and agents.)

· Predictive Analytics (Identify patterns for proactive issue prevention.)

· Virtual Agents/Chatbots (Resolve Level 1 issues instantly.)

3. OUTPUTS

What the system delivers after AI processing

· Resolved Automatically

· Routed to Human Agent

· Escalated Based on Priority

· Tracked for Metrics:

o Resolution Time

o CSAT

o Deflection Rate

o SLA Adherence

4. Continuous Feedback Loop

Real-time data trains and improves AI models

· User feedback on resolution accuracy

· Agent corrections fed back to AI

· Knowledge base updates

· AI performance metrics (success rate, false positives)

Use this checklist to ensure a strategic, step-by-step AI implementation in your ITSM workflows.

✅ Step 1: Identify ITSM Processes for AI Automation

● Audit current ITSM workflows and identify bottlenecks (e.g., ticket resolution time, backlog issues).

● Identify high-volume, repetitive tasks suitable for automation (e.g., password resets, software requests, ticket routing).

● Assess existing AI-readiness (cloud vs. legacy ITSM tools, integration capabilities).

● Define key success metrics (ticket deflection rate, cost savings, CSAT improvements).

✅ Step 2: Select the Right AI-Powered ITSM Platform

● Evaluate AI-driven ITSM tools like Freshservice, Jira Service Management, and Atomic Work for automation capabilities.

● Ensure the platform supports AI-driven ticket routing, predictive analytics, and chat-based self-service.

● Assess integration capabilities with existing IT infrastructure (e.g., ITOM, ITAM, AIOps).

● Review AI-powered knowledge management & automated resolutions for efficiency.

● Compare cost, scalability, and licensing models to maximize ROI.

✅ Step 3: Implement AI in Phases (Pilot → Scale)

● Launch a pilot AI implementation (e.g., automate one workflow, such as AI-powered ticket triage).

● Monitor performance metrics (resolution time, automation success rate, agent workload reduction).

● Expand AI-driven automation across multiple ITSM functions (incident response, service requests, self-healing IT, change management).

● Optimize AI based on user feedback & real-time performance tracking.

✅ Step 4: Measure ROI & Optimize for Scale

● Track AI-driven performance against baseline metrics (CSAT, resolution time, ticket deflection rate).

● Analyze cost savings from reduced manual workload & faster ticket resolutions.

● Continuously train AI models on real-time ITSM data for improved accuracy.

● Expand AI adoption to broader Enterprise Service Management (ESM) use cases (HR, finance, facilities, etc.)

Avoid These Common Pitfalls in AI-Powered ITSM Implementations

Even the best AI initiatives can fall short if certain traps aren’t avoided. Here are the top mistakes CIOs make—and how to stay clear of them:

❌ Over-Automating Without Quality Training Data

AI models need context. Automating complex tasks without feeding the AI historical ticket data, intents, or edge cases can result in poor performance and frustrated users.

✅ Solution: Start with high-volume, low-complexity use cases (like password resets). Train AI using clean, labeled ticket history, and build from there.

❌ Ignoring Change Management for IT Teams

Resistance from IT agents and support staff can derail even the smartest AI rollout.

✅ Solution: Involve agents early, show how AI reduces repetitive workload, and provide training to work alongside AI tools (not against them).

❌ Selecting Tools with Limited Integration Capabilities

AI-powered platforms that don’t talk to your CMDB, monitoring tools, or HR systems create data silos and missed insights.

✅ Solution: Choose platforms with robust integration options (APIs, webhooks) and compatibility with your existing ITOM/ITAM/AIOps stack.

❌ Lack of Clear Success Metrics

Without tracking the right KPIs, you won’t know if your AI implementation is succeeding.

✅ Solution: Establish baseline metrics (e.g., average resolution time, deflection rate, CSAT) and measure improvement over time.

❌ Trying to Scale AI Too Fast

Rolling out AI across all ITSM processes at once leads to chaos and poor adoption.

✅ Solution: Pilot one workflow, learn from it, i
1. Day 1 – Initial Outreach - The Value Hook

Subject: Quick check-in on [Tool Name]?

Body: Hey [First Name] —

Hope all’s running smoothly since we wrapped your [Tool Name] implementation.

We usually recommend a short sync around this point to:

● Spot underused features or missed wins

● Share benchmarks from teams doing similar work with [Tool Name]

● Flag optimization areas before they slow things down

Even light adjustments post-launch can unlock 2–3x more ROI. Would [insert date/time] work for a quick check-in?

Best, [Your Name]

2. Day 7 – The “Client Trends” Follow-Up

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