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We are customers of ServiceNow IT Operations Management, using it for the ticketing systems and user management and change management. Change management and user management are the major use cases for us with ServiceNow IT Operations Management.

We quite rarely use the configuration management part of ServiceNow IT Operations Management, but we sometimes have to. User management and change management are continuously required, and we have to generate tasks for those, but the configuration management part is used quite rarely.

The main and best thing about ServiceNow IT Operations Management is that it helps to organize user management tickets in an organized form. You don't have to go after the work, and you can always see the process where you are and the tasks you have to do on a special day. It's an organized way of managing your own work, especially when it comes to changes. I used Service Manager at my previous employer, but ServiceNow IT Operations Management is much better than that.

The predictive AIOps capability helps to maintain service availability for us with ServiceNow IT Operations Management.

It's a kind of organized way that gives us a schedule to organize our work with ServiceNow IT Operations Management. I can't really tell you an example since I'm so used to it that I can't imagine how we would organize our work in another way, and I appreciate it very much.

We use the centralized dashboard in ServiceNow IT Operations Management.

When it comes to the dashboard of ServiceNow IT Operations Management, I appreciate that I can see everything on the same page. I can organize the most important things, and in my case, change management and user management are the most important. On the dashboard, I can keep these two at the top of the screen and always see new changes or tickets that indicate my intervention, and that's what I really appreciate about the dashboard. We don't have too many incidents in our system, so I focus mainly on new tickets that require my attention, and the dashboard helps me see what to do.

We don't use the orchestration part of ServiceNow IT Operations Management here.

The CMDB part of ServiceNow IT Operations Management is a little bit too complicated, and it is a bit slow at our company. Sometimes, especially when it comes to user management and audits, we need detailed lists about changes and user management from ServiceNow IT Operations Management, and that can take a lot of time. That is the only issue I can list about ServiceNow IT Operations Management.

I have been working here for five years, and this company has used ServiceNow IT Operations Management for quite a long time, more than 10 years.

The technical support for ServiceNow IT Operations Management is good at our company.

Negative

I used Service Manager at my previous employer, but ServiceNow IT Operations Management is much better than that.

I work with ServiceNow IT Operations Management.

I don't really use the real-time analytics aspects of ServiceNow IT Operations Management, but my boss utilizes that part for lists and analytics, especially regarding work and ticket numbers. It is useful mainly for our management.

I am a SWIFT administrator in the company.

I would rate ServiceNow IT Operations Management an eight.

I am not looking for a monitoring solution, but I am a product manager comparing some products based on research I conducted on the PeerSpot website regarding monitoring products such as Splunk, AppDynamics, and LogicMonitor.

I search as a researcher and product manager comparing platforms regarding monitoring products that I researched on your website to understand which platform is good and which platform has the pros and cons. We are using monitoring platforms such as OpenText, ManageEngine, and we also have some ITSM product.

OpsBridge in OpenText is what we use, and in ManageEngine, I use Network Operation Manager and Service Desk. I have been working with the OpenText product for almost two years.

We have a platform where we are collecting metrics, logs, and traces for OpenText AI Operations Management, and if there is an anomaly, we directly open a ticket in our ITSM system.

We are managing multiple infrastructure types including networks, virtual machines, applications, and cloud applications. We collect metrics and receive alarms, and we open automation ticket systems.

OpenText AI Operations Management has an agent system where you can directly add plugins and collect metrics and logs. It is ready to use. It is really difficult to collect these kinds of things such as metric data with other systems, but it is easier with this platform.

We are planning to use the anomaly detection feature in OpenText AI Operations Management, but we have not yet implemented it. We are familiar with anomaly detection, but anomaly detection is a different feature than what we currently use. We are collecting data and setting some thresholds, which are manual thresholds. Anomaly detection is different and not automatic.

The dashboards in OpenText AI Operations Management are helpful. The user experience is somewhat outdated, but it is pretty good. It would be beneficial to have a more modern UI in OpenText AI Operations Management. For example, the ability to filter the dashboards would be helpful. A modern UI library would be beneficial.

OpenText AI Operations Management could benefit from a fully integrated AI system, which we call AIOps. A fully integrated AI operations system would be valuable in the future. Additionally, the UI experience could be more comfortable.

We are not using predictive features in OpenText AI Operations Management such as anomaly detection. In the future, we may be able to operate with our customer, but we need to decide this collectively. You need to see the big picture and understand what the customer's pain points are to find the right tuning.

Normally, predictive features can be more useful, but this is an end-to-end solution that needs to be customized. You need to understand the exact customer needs. The predictive analytics feature is very close to being integrated, but it is not fully integrated at this time.

Stability and scalability with OpenText AI Operations Management depend on the metrics, how many agents you are working with, and how large the infrastructure is. We are following approximately 10,000 metrics and logs, and the platform performs pretty well.

With other large systems, there might be issues. However, there are no issues for us at this time.

Scalability with OpenText AI Operations Management depends on the metrics, how many agents you are working with, and how large the infrastructure is. We are following approximately 10,000 metrics and logs, and the platform performs pretty well.

With other large systems, there might be issues. The stability and scalability depend on architectural considerations and the company's specific situation. There are no issues for us.

My team works with the customer success team for technical support and customer service for OpenText AI Operations Management. They are analyzing and installing things. They have some questions, but I am not directly involved in those interactions.

We tested Zabbix, Grafana, and InfluxDB for collecting monitoring data. However, you need to operate these solutions by writing the agent or agentless configurations, which is more difficult. These solutions are not packaged software. You can collect data and create API calls and SNMP traps, but these kinds of things are difficult to implement.

These kinds of configurations need to be packaged. If you go to your customer, you need time to operate and integrate these systems with other solutions. These platforms are open to development, which is more helpful if you need specific things that you can control. You need to write the new package to achieve this.

OpenText AI Operations Management and other companies have a guarantee for their packages, licenses, and everything else.

The initial setup is not just about OpenText AI Operations Management. You need to collect data, you need a CMDB and other things. You need to analyze the customer first. Then you need to understand the customer and their pain points. After that, it is easy to control which metrics, logs, and automation you want to use.

There is nothing difficult in this product. However, the important thing is that you really need to analyze the customer needs first. After that, you can easily take everything under control.

If the company is experienced, implementation can be straightforward. From my company's perspective, we know AIOps and IT Operations Management. We understand all the perspectives of this.

Once we understand the customer needs, we can directly integrate the solution through the customer success team or their professional team. It can be easy to do this through their marketplace. However, from another company's perspective, they might need some training. They might need to go through channels for implementation. It depends on the company. I prefer to download directly from their marketplace and proceed from there.

We tested Zabbix, Grafana, and InfluxDB for collecting monitoring data. However, you need to operate these solutions by writing the agent or agentless configurations, which is more difficult. These solutions are not packaged software.

You can collect data and create API calls and SNMP traps, but these kinds of things are difficult to implement. These kinds of configurations need to be packaged. If you go to your customer, you need time to operate and integrate these systems with other solutions.

These platforms are open to development, which is more helpful if you need specific things that you can control. You need to write the new package to achieve this. OpenText AI Operations Management and other companies have a guarantee for their packages, licenses, and everything else.

I have not worked with the pattern recognition feature in OpenText AI Operations Management.

OpenText AI Operations Management has an agent system where you can directly add plugins and collect metrics and logs. It is ready to use. It is really difficult to collect these kinds of things such as metric data with other systems, but it is easier with this platform.

We are not using predictive features in OpenText AI Operations Management such as anomaly detection. In the future, we may be able to operate with our customer, but we need to decide this collectively. You need to see the big picture and understand what the customer's pain points are to find the right tuning.

Predictive features can be more useful, but this is an end-to-end solution that needs to be customized. You need to understand the exact customer needs. The predictive analytics feature is very close to being integrated, but it is not fully integrated at this time.

I do not have a partnership with OpenText AI Operations Management.

I am not certain about whether I am working with the cloud version of OpenText AI Operations Management or if it is deployed on-premises or in a hybrid cloud model. However, I know that OpenText AI Operations Management is easily connected to AWS and Azure because they have ready plugins.

I am not familiar with the licensing and pricing of OpenText AI Operations Management. I know they collect metrics count, users, and use subscription models. I am a technical person, not in sales.

Please do not include my name and my company name when you publish the feedback on your website. I do not need my name and my company's information to be revealed. My overall rating for OpenText AI Operations Management is seven out of ten.