Ivanti Blog: Posts by

The Rise of Experience-Level Agreements (XLAs) in Practice: A Deep Dive into ITSM Transformation

Fri, 21 Nov 2025 15:17:59 Z

For decades, the backbone of IT Service Management (ITSM) has been the Service-Level Agreement (SLA). While effective for tracking the nuts and bolts of IT delivery, SLAs have one critical blind spot: they say little about how users actually feel about their IT experiences.

This is where Experience-Level Agreements (XLAs) and Digital Employee Experience (DEX) fill in the rest of the picture. They provide a new paradigm that's rapidly gaining traction, promising to transform ITSM by focusing on the human experience behind the numbers.

But what exactly are XLAs and how do they differ from traditional SLAs? Moreover, how does Digital Employee Experience factor into successful XLAs?

What are XLAs?

Experience-Level Agreements are a set of metrics and commitments designed to ensure that the end-user experience is consistently positive and meets (or exceeds) expectations.

While you should use XLAs and traditional SLAs in tandem, the latter focuses on technical performance and uptime. XLAs prioritise the user's overall experience, including factors like ease of use, responsiveness and emotional satisfaction.

By focusing on the user's perspective, XLAs help organisations align their services with user needs, driving satisfaction and loyalty.

Understanding how SLA, XLA and DEX work together

XLAs, SLAs and DEX work in tandem to provide you with a 360-degree view of your technical and user experience metrics.

	Description	Example metrics
Service-Level Agreements (SLAs)	A contractual agreement between service providers and customers that define the expected service quality, availability and responsiveness. SLAs focus on technical metrics.	Uptimes. Response times. Resolution rates. Etc.
Experience-Level Agreements (XLAs)	A contractual agreement between providers and customers that focuses on end-user experience, measuring the quality of service from the user's perspective. XLAs include not just technical performance, but also factors like usability, accessibility and overall satisfaction.	End user satisfaction score. Net promotor score (NPS). Digital experience score (DEX).

Unlike SLAs and XLAs, Digital Employee Experience (DEX) is not another type of agreement. DEX refers to the quality of digital experiences employees have while performing their job functions. It encompasses the technology, tools and digital environments they interact with daily.

Based on their experiences across these tools and environments, a DEX score is created (which is measured as part of the XLA). A positive DEX is crucial for employee productivity, engagement and overall job satisfaction.

How DEX enables successful XLAs

Digital experience analytics are an important metric in the success of XLA's. By ensuring that employees have a positive digital experience, organisations:

Improve productivity — When employees can work efficiently and effectively, they're more likely to deliver excellent service to customers.
Enhance customer experience — A positive DEX often translates to a better customer experience, as employees are better equipped to meet customer needs.
Drive business outcomes — By focusing on DEX, organisations can drive business outcomes, such as increased revenue, reduced costs and improved competitiveness.

Best practices for implementing XLAs

Implementing XLAs requires a careful, strategic approach to ensure that you not only measure user satisfaction effectively, but also drive meaningful improvements in service delivery.

By following the best practices below, organisations can successfully integrate XLAs into their operations and foster a culture centred on positive user experiences.

Define clear user experience metrics: Identify the key metrics that you will use to measure user experience. These should be specific, measurable, and aligned with business goals.
Leverage technology: Use advanced analytics, AI and machine learning to gather and analyse user data. These tools can help identify trends and areas for improvement. DEX tools enable you to gather and measure your IT experience across devices, users and the organisation.
Engage users: Regularly collect feedback from users through surveys, feedback forms and testing. This provides valuable insights into their needs and preferences. Ensure that surveys utilise AI to identify and highlight sentiment.
Train and empower IT teams: Train your IT teams in user experience principles and give them the autonomy to make decisions that improve the user experience.
Continuous improvement: Treat XLAs as a living document. Review it regularly and update it based on user feedback and changing business needs.

Challenges and lessons learned

Implementing XLAs isn’t just about new tools — it's a mindset/cultural change. Common hurdles include:

Defining experience — What counts as a “good” experience? It can be subjective and varies across roles.
Cultural buy-in — XLAs need sponsorship from leadership and buy-in from front-line IT teams.
Continuous improvement — XLAs aren’t static. They require regular review and recalibration as user needs evolve.
More than surveys — Surveys are just one method of measurement. Ensure that DEX tools are enabled to capture real technology metrics.

Organisations leading the XLA charge invest in regular feedback loops, co-design goals with business units and link experience outcomes directly to ITSM performance reviews.

The future: Why XLAs matter

In practice, XLAs and DEX are more than buzzwords — they’re the next evolution of service management. As technology becomes ever more entwined with business success, experience is the product. IT teams empowered by XLAs move beyond “keeping the lights on" to driving employee engagement, customer loyalty and strategic differentiation.

For any organisation focused on ITSM transformation, embracing XLAs means listening harder, acting faster and thinking bigger about the real purpose of technology: enabling people to do their best work.

Read the Digital Experience Report to understand how DEX impacts productivity, satisfaction and retention.

Is Your ITSM Environment Ready for Agentic AI? Probably Not (Yet)

Wed, 16 Jul 2025 16:01:36 Z

Agentic AI is making waves across various industries, and its potential to transform ITSM is undeniable. With all the talk about agentic AI capabilities, you are probably daydreaming about streamlining ITSM processes through automated incident management, automated change risk analysis, improved efficiency through service desk automation, or enhanced service quality with predictive maintenance. But you also may start to wonder: How real is all of this? Is my organisation truly ready for agentic AI?

I would suggest that for most organisations, the answer is no!

Don't get me wrong; organisations are listening to the conversation and are keen on the potential long-term benefits. And some may indeed be ready for to implement agentic AI properly.

Ambitious, but underprepared: why most organisations aren’t ready for agentic AI

The reason I doubt that organisations are adequately prepared is that for agentic AI to function optimally, you require good clean data, seamlessly automated workflows that are well defined, and comprehensive flexible integration across all systems intended for agentic AI operations.

We see the importance of clean, integrated data in the acquisition of data management companies by other tech vendors, notably Salesforce and Informatica. Reconciled, normalised data gives AI agents the best possible intelligence to work with so they can provide the best outcomes and most accurate information.

You should always consider: How will AI agents benefit you and your organisation? Can your journey to autonomous work be achieved through workflows? Even Anthropic, a leader in the AI space talks about using the simplest solution possible and only implementing AI agents for complex requirements. In some cases, agents are not only more costly but also less efficient than pre-built workflows.

Preparation today, agentic AI tomorrow

As you consider pursuing agentic AI, there are several factors to examine. With planning and preparation, you can be successful, and this checklist will help guide you on your journey.

Planning

When you start your journey to agentic AI, clearly outline your goals for introducing agentic AI into your service management environment while ensuring that they align with your overall organisation's goals. Some common objectives are:

Reduce the workload on IT staff by automating repetitive and time-consuming tasks.
Improve the speed and accuracy of incident resolution.
Enhance the end-user experience with seamless automated request fulfilment.

With your goals defined, what does success look like, and how will you measure it? These will vary based on your expectations and defined goals, but some common metrics could be:

% of tasks successfully automated by AI.
Decrease in errors within tasks handled by AI compared to manual tasks.
User satisfaction score (CSAT).
% of users opting for AI-driven self-service solutions.
Reduction in the average cost of resolving a ticket with AI.
% of incidents escalated to human agents after AI intervention.

Related content: AITSM: How AI Is Redefining IT Service Desk Automation, part of Ivanti’s Digital Employee Experience Research Report Series

Data preparation

Within the context of service management, there are many sources of data that may be required, including sources that are purely ITSM-related, such as a CMDB or asset repository. As you move towards the wider enterprise, you may also need data from CRM, HR, facilities, finance or other departments.

Regardless of where you are getting the data from, you need to consider the following:

Is your data quality reliable? Is the data consistent? How accurate is the information? Is there a more trusted source (or master) that should be considered as the default? Often data is inconsistent across multiple systems, making it harder for an AI agent to make well-informed decisions.
How accessible is your data? How many different data sources are there, and will integration via APIs be required? Are the data sources up to date?
Is your CMDB ready to be used by agentic AI? Do you have critical CIs in your CMDB, and more importantly, how up to date are the relationships and the information?
Are you required to adhere to data privacy compliance? Do you need to adhere to data protection regulations such as GDPR, CCPA or others? It is possible that regulations may demand that you anonymize sensitive data and keep it within country.

Defined workflows and automation

Not only is having high-quality data vital, but having good, functioning workflows to drive tasks is a critical component of successful agentic AI. In some cases, your existing workflows can be repurposed for use within an agentic AI process; however, some workflows will need to be modified to meet the request/response requirements of an AI agent.

Some things to consider for workflows:

Are your workflows able to be used by AI agents? Your existing workflows may not be structured in the most effective way for AI agents to utilise the entire workflow to deliver accurate results.
Will the workflows only be working with data within your organisations, or will it need to access external data sources? When accessing external data sources, what security measures are in place to ensure the security and accuracy of the data within the responses?
Ensure all agent AI and related workflows have audit trails enabled, so you can log and monitor AI actions and decisions. This ensures the correctness of the AI and can be used in the event of compliance or security issues.

Governance and compliance

Earlier we discussed compliance requirements for data and the need to meet regulatory or statutory requirements. Governance and compliance must also apply to the entire AI ecosystem within your organisation to deliver responsible, secure and trusted responses and actions.

Here are a few things to consider:

Define policies for how AI is to be used within your organisation and assign people or teams who are accountable for protecting the data and evaluating ethical considerations.
Ensure that regulatory or statutory compliance standards are understood across the entire AI workflow and met. For example, if an AI agent requires access to health-related data and data collected from European users, then you would need to consider both HIPAA and GDPR.
Identify potential risks throughout your environment and meticulously mitigate those linked with the AI deployment.

Testing and validation

Start your journey to agentic AI in a controlled environment. Set up a pilot programme to test the functionality, ensuring you measure against your goals and gather feedback from users. It is important to have a continuous improvement programme in place for adjustments and enhancements. Be patient, as good results may take time as the AI learns and adjusts. Always monitor and adjust as required and define a process for handling issues or failures.

Agentic AI is an evolution, not a revolution

Discussion around AI is everywhere, and while there is a lot of interest from organisations, many are still rightly cautious about how to proceed and what is best for their organisation. As we have seen over the last few years, AI has progressed very quickly from generative to agentic. Keeping up with emerging technologies and planning for future enhancements is vital.

Ivanti is well positioned with AI capabilities that can enable you to grow. Find out more about what Ivanti can offer for your AI journey.

How to Use Generative AI for Knowledge Management

Tue, 27 Jun 2023 14:30:00 Z

In the blog “How Generative AI Can Benefit Knowledge Management”, we looked at the benefits of AI to knowledge management to enhance the quality, automating the creation of content and enabling more engaging content. In enabling generative AI to become part of the knowledge management framework introduces concerns about accuracy, data bias, privacy and security.

Now, it’s time to look at how we can make it work well together...

How to use generative AI with knowledge management

Despite concerns of using generative AI in daily operations, this technology has the potential to be a powerful tool to optimise knowledge management. By carefully considering the potential drawbacks and taking steps to mitigate them, organisations can use generative AI to improve their knowledge management practices.

Here are the five things to consider when using generative AI for knowledge management:

Making sure to identify the type of data that will be used to train the generative AI model. Identification of the data type will help to ensure that the data used is accurate and reliable. Are you going to be using existing knowledge articles, incident data, problem data or combinations of all?
Having identified the data type, generative AI is only as good as the data it's trained on. The old saying ‘garbage in, garbage out’ still applies. Ensure that the data you've identified above is accurate, complete and up-to-date.
Monitoring the output of the generative AI model for signs of bias, misinformation, completeness and accuracy. This can help to ensure that the information generated by the model is reliable.
Developing policies and procedures to manage the risks associated with using generative AI for knowledge management. This is an important step in ensuring the success of your project. These policies and procedures should address issues such as data security, privacy and ethical considerations. They should be designed to ensure that using generative AI for knowledge management is conducted in a responsible and ethical way.
Putting an approval process in place before any knowledge information is shared publicly to ensure that the generated outputs are reviewed and authorised.

By taking these steps, organisations can use generative AI to improve their knowledge management practices while minimising the risks.

Combine generative AI and knowledge management with caution

The effectiveness and impact of generative AI on knowledge management will depend on how it's used and implemented. It's important to carefully evaluate the benefits and risks before deciding whether to incorporate it.

Here are some potential pros and cons:

Pros

1. Automatic generation of relevant content

Generative AI can be used to automatically create knowledge articles from existing data sources, such as product documentation, customer support tickets and employee training materials.

With 32% of IT professionals reporting an increase in helpdesk tickets since the move to remote working, there’s a significant opportunity for enhancement of the knowledge base that can enable quicker and more effective issue resolution, freeing up IT professionals to focus on more strategic tasks, such as developing new knowledge management initiatives and improving the quality of existing knowledge articles.

2. Improved search accuracy

Generative AI can help improve search accuracy by personalising the delivery of knowledge to employees, based on their individual needs and preferences. With an average employee spending 3.6 hours a day searching for information, any time savings in the way knowledge is delivered to them is a win.

Enabling easier and quicker access to information will ultimately enhance your employees’ digital experience.

3. Enhanced automation

Generative AI can assist in automating routine task – even if it's not directly related to the creation of knowledge management articles.

With 85% of IT professionals rating automation and AI investments as profitable ventures, identifying new ways of streamlining their processes can free up time for IT professionals to focus on more complex issues.

Cons

1. Risk of misinformation

Generative AI can potentially produce incorrect or misleading information, which can lead to serious consequences in the IT field. For example, the introduction of malware, or the incorrectly recommending turning off functionality which is used to secure the IT environment from malicious actors.

2. Dependence on AI-generated content

If companies become too reliant on AI-generated content, they may not prioritise the human-generated one or critical thinking skills, leading to a potential loss of expertise. Despite all the discussion around generative AI, human oversight is still required to validate accuracy and approve the generated information.

3. Ethical concerns

There are ethical concerns surrounding the use of generative AI, like potential bias in the data used to train the model, which can perpetuate existing inequalities.

There’s no doubt that generative AI can be a valuable tool for IT knowledge management and while a new exciting technology, there's still much to be learned about the benefits and pitfalls that it may bring.

Each organisation needs to review the potential impact individually and choose an appropriate AI solution that meets their own need for privacy, accuracy and security.

Tips for implementing generative AI for knowledge management

Start small and scale up

It's better to start with a small pilot project and then scale up using generative AI as you gain experience.

Get buy-in from stakeholders

It's important to get buy-in from stakeholders before deploying generative AI in production. This will help ensure that the model is used effectively and that its outputs are trusted.

Monitor the model's performance

It's important to monitor the model's performance after it's been deployed in production. This will help identify any potential problems with the model and improve the model's accuracy.

Continuously improve the model

Generative AI models are constantly being improved. It's important to continuously enhance the model by retraining it on new data and addressing any potential problems that may occur.

Learn more about this topic – watch our webinar on Generative AI for InfoSec & Hackers: What Security Teams Need to Know.

How Generative AI Can Benefit Your Knowledge Management

Tue, 20 Jun 2023 15:24:02 Z

There has been growing interest in the capabilities of generative AI since the release of tools like ChatGPT, Google Bard, Amazon Large Language Models and Microsoft Bing. With the hype comes concerns about privacy, PII, security and, even more importantly, accuracy. And rightly so.

Organizations are treading cautiously with their acceptance of generative AI tools, despite seeing them as a game changer. Many businesses are trying to find the sweet spot that enables them to capitalize on the benefits now, while identifying more strategic uses for generative AI for the future; all without compromising on security.

One area where immediate gains can be obtained within an organization is their knowledge management. This initiative has been challenging for many organizations, yet it’s one that can drive employee productivity and deliver significant benefits to support teams, who'd typically maintain knowledge manually.

How generative AI and knowledge management intersect

Generative AI refers to a type of artificial intelligence that can create new content, such as images, text or even music, based on existing data. It uses machine learning algorithms to analyze and learn from large datasets. From there, it generates new content based on that analysis.

Knowledge management, on the other hand, is the process of capturing, organizing and sharing knowledge within an organization. It involves collecting information from various sources, storing it in a centralized database and making it easily accessible to employees when they need it.

Many organizations manually maintain their knowledge management, opening the door for out of date or poorly written content. By automating many of the tasks involved in knowledge management, generative AI can help improve the efficiency and effectiveness of your knowledge management processes.

Some of the specific ways in which generative AI can optimize knowledge management include:

1. Automating the creation of knowledge articles

Generative AI can automatically create knowledge articles from existing data sources, such as product documentation, customer support tickets and employee training materials. This automation can free up IT professionals to focus on more strategic tasks, such as developing new knowledge management initiatives and improving the quality of existing knowledge articles.

2. Improving the quality of knowledge

Generative AI can improve the quality of knowledge by identifying and correcting errors, archiving old information, as well as by adding context and additional information to knowledge articles. This can help ensure that employees have access to accurate and up-to-date information.

3. Generate new ideas and insights

Generative AI can generate new ideas and insights by combining existing knowledge in new ways. For example, HR, facilities and IT all have articles that talk about onboarding and offboarding employees with an organization.

Generative AI may look at these and produce a merged knowledge article that discusses the end-to-end process of onboarding and offboarding across all three areas. This can save an employee from having to search across three different areas.

4. Solve problems more quickly

Generative AI can quickly solve problems by identifying patterns and trends in data. This can help organizations make better decisions and improve their overall performance.

For example, generative AI may look across IT incidents over a defined period of time and identify a common method of resolution for a group of common issues. Based on its findings, it can generate a knowledge article for service desk agents to resolve issues quicker, and for employees to resolve them themselves via self-service.

5. Create more engaging content

Generative AI can create more engaging content by personalizing it for each user, helping organizations improve their customer experience. Knowledge articles, particularly within HR knowledge, are personalized based on region or language. Being able to generate content unique to their persona will greatly enhance the use and experience to the employee.

What are the drawbacks to generative AI?

Generative AI solutions paired with knowledge management have the potential to revolutionize many industries and fields. However, it isn't without its drawbacks, including:

1. Security and privacy

Generative AI systems used for knowledge management may contain sensitive or confidential information. So, it's crucial to ensure that they are secure and protected against cyberthreats. Additionally, there may be concerns around privacy, particularly if the AI is generating content that includes personal or identifying information.

For example, generative AI can create realistic-looking malware and phishing attacks. These attacks can be used to steal personal information, financial data or other sensitive information.

2. Quality and accuracy

While generative AI models can produce impressive outputs, their quality and accuracy can vary widely depending on the input data and the complexity of the task. The old saying of "garbage in, garbage out" still applies. It can also be difficult to ensure that the AI has access to accurate and up-to-date information, which can affect the quality of what it generates.
For example, training data for ChatGPT is collected from the internet and updated regularly. However, the current version of ChatGPT is trained on data that was collected up to September 2021. This means that ChatGPT may not be able to answer questions about current events or topics that have been in the news since September 2021.

3. Data bias

Generative AI models can inadvertently reflect the biases and prejudices present in the data they're trained on, leading to biased or inaccurate results. This data bias is especially concerning in knowledge management applications, where accuracy is critical.
For example, if a model is trained on a dataset of text that's predominantly from the United States, the model may be less likely to generate text relevant to people from other countries.

Generative AI provides an organization with opportunities to enhance knowledge management through improved quality, engaging content and automation. But there are cautions along the way. Learn what you need to have in place to successfully use generative AI with knowledge management in your organisation.

The Need for Enterprise Service Management

Wed, 15 Mar 2023 14:42:49 Z

Key takeaways

ESM is the practice of managing technology services to ensure that they meet the needs of the organization and its employees.
It ensures services are delivered consistently, efficiently and cost-effectively – while meeting the needs of the business and customers.
The benefits of Enterprise Service Management (ESM) include increased employee satisfaction, boosted employee engagement, reduced costs, increased efficiency and improved compliance.
Comprehensive processes and automation help ensure your organization provides the highest quality of service to customers while optimizing available resources.
There are four key components of an effective ESM strategy: clear goals and objectives, comprehensive processes, automation and monitoring and feedback.

Organizations today are becoming more reliant on technology to provide quality services to their employees. As a result, there is an increased need for Enterprise Service Management (ESM).

What is Enterprise Service Management?

ESM is a set of processes and practices used for managing the delivery of an organization’s technology services. ESM ensures services are delivered consistently, efficiently and cost-effectively – while meeting the needs of the organization and customers.

Employees expect a similar experience in their day-to-day business life, whether they engage with IT or any of the many other service providers in a company. This includes human resources (HR), Legal, Facilities, project management, security, sales, marketing, R&D and finance departments.

Benefits of Enterprise Service Management

The benefits of Enterprise Service Management are numerous. Some of the most important include:

1. Improved employee satisfaction

ESM looks at how technology helps or hinders employees when doing the work they need to do - when they need to do it. ESM ensures services are delivered consistently and efficiently, leading to improved employee productivity and satisfaction. By providing clear expectations, enhancing communication and creating a sense of ownership, ESM enables employees to feel more engaged with the teams who are delivering the services they have requested.

For example, if an employee can see the status of a request, they know what stage it is in and when it can be expected to be finished This allows employees and business departments to focus more on providing business value and ensuring that IT boosts productivity versus hindering employee efficiency.

2. Boosted employee engagement

Delivering an omnichannel experience across a self-service portal, Microsoft Teams, virtual agent, and email allows employees to choose the way they work that best fits their needs and preferences, increasing their engagement by providing an easy way to find and request information and services across a variety of modes.

3. Reduced costs

ESM can help reduce costs significantly by streamlining processes and eliminating unnecessary complexity. Automation is key here; ticket classification, assignment and flexible workflows can be automated, ensuring the right team is handling the ticket from the beginning. This not only reduces costs but leads to improved efficiency and customer satisfaction.

4. Increased efficiency

ESM helps increase efficiency and reduce errors by ensuring services are delivered on time and meet the organization's priorities. This can be achieved through standardized workflows, documented processes, automated assignments and interconnected systems, resulting in higher quality services across the organization.

5. Improved compliance

ESM enables organizations to meet industry regulations and standards. Through detailed auditing and reporting, organizations can ensure their compliance with service levels, security protocols and other regulations. This helps organizations protect their data and assets and gain

a competitive advantage in the market by ensuring compliance with relevant regulations.

Features of an effective Enterprise Service Management strategy

Employees expect easy and instant access to IT and non-IT services and automatic fulfilling of their requests.

While the IT service desk or help desk are top of mind when discussing service management, this is only one element for an ESM strategy. Some other key capabilities, which are an opportunity for introducing Enterprise Service Management to non-IT services, include:

Self-service portal.
Easy knowledge access.
Ticket automation.
Case management.

An effective ESM strategy must have four key components:

1. Clear goals and objectives

It is essential to have a comprehensive understanding of an organization's goals and objectives when it comes to ESM. This could begin with Human Resources (HR) or Facilities having an explicit idea of advantages.

To start small, if email is the normal form of communication, it would make sense to incorporate automated email creation into the new ESM platform and implement the self-service portal or virtual agent as a second step.

However, it is even more important to enhance the backend processes by utilizing automation and workflow technology. This will allow for streamlined operations and more efficient processes, resulting in greater productivity.

Additionally, having a well-defined ESM strategy helps ensure the organization keeps up with the ever-changing needs of the customer base and offers high-quality service all the time.

2. Comprehensive processes

From the initial design to the final delivery, ESM processes should be comprehensive and detailed, encompassing a wide range of service delivery stages. It is important to document and outline the services and processes that your organization plans to offer. These services and processes may be limited at first, but they will inevitably expand as your organization matures.

Additionally, it is essential to identify the most significant processes that will give the best employee satisfaction and the highest efficiencies. Having comprehensive processes in place helps ensure your organization can provide the highest quality of service to the customers while optimizing the resources available.

3. Automation

Enabling automation in your organization will streamline processes and reduce errors. Automatically assigning work to the right team will significantly reduce response and resolution time and improve turnaround time for customer requests. Workflows that automatically manage approvals, tasks and notifications help maintain high levels of accuracy and quality.

Combining automation with existing manual steps will significantly improve the speed, accuracy and quality of organizational operations and improve the employee experience.

4. Monitoring and feedback

It is important to monitor the performance of ESM processes and provide feedback to ensure that they are meeting the organization's goals and objectives. To do this effectively, it is necessary to put in place dashboards, analytics and other reporting tools that will provide visibility into the team's workload and identify any imbalances or inconsistencies.

Monitoring will help you observe any trends or changes in workload, and measure how well service level targets are being met. This helps ensure that all organizational goals and objectives are achieved. Additionally, it gives managers the ability to make better decisions on how to adjust and prioritize resources accordingly.

The need for Enterprise Service Management (ESM) is becoming increasingly important as organizations become more reliant on technology to provide quality services to their employees.

ESM can help to reduce costs, improve employee satisfaction, and increase efficiency, while also ensuring that an organization is compliant with industry regulations and standards. An effective ESM strategy must include clear goals and objectives, comprehensive processes, automation, monitoring and feedback.

From improving employee satisfaction to significantly reducing cost, implementing an effective ESM strategy helps your organization stay productive and efficient in this new era of Everywhere Work. As IT’s responsibility for managing non-IT assets grows, ESM will help IT teams broaden their influence and deliver business value to the organization. Learn more about how Ivanti can help with your Enterprise Service Management strategy.