What are the advantages and disadvantages of artificial intelligence in service optimization?

Artificial intelligence can quickly parse complex data sets and generate insights, helping enterprises identify and take action to streamline their IT services.

But this does not mean that artificial intelligence can optimize every IT service in every enterprise. There are important limits to the extent to which IT teams can apply artificial intelligence to their service optimization strategies.

Benefits of Artificial Intelligence for Service Optimization

Before delving into what artificial intelligence can and cannot do in the field of service optimization, let us first discuss why artificial intelligence should be used to optimize services .

The main reason is that service optimization often requires analyzing large amounts of data, and artificial intelligence can allow humans to perform this job faster, more efficiently, and more scalably.

With this in mind, let’s say you want to streamline IT processes within your enterprise. This can be done by having IT teams manually look at data sources, determine which types of requests take the longest to complete, and then make recommendations on how to speed up those processes. This is doable, but takes a long time and creates a significant distraction for the IT team.

Alternatively, an AI tool can be deployed that automatically analyzes all data sources, can provide insights into why certain requests are taking a long time to complete, and then generates recommendations on where and how to optimize the service. This approach will produce results in a fraction of the time it would take to gather the same insights manually.

AI can help optimize IT services

We can apply AI-based service optimization methods to almost any type of IT process that meets the following conditions:

• With large amounts of data, AI tools can analyze it to understand how processes work and find opportunities to improve them.
• The process does not involve complex human interactions that require emotional intelligence to fully understand.

Many core IT services meet both requirements. In addition to the example of using AI to analyze data to improve end-user IT services, other services are also suitable for leveraging AI for optimization

Infrastructure Management:AI can analyze logs, metrics and other Infrastructure data to understand an enterprise's infrastructure needs and provide guidance for optimal infrastructure management. In turn, it can help reduce unnecessary infrastructure spending, plan hardware refresh processes, and more.

Network Management: Artificial intelligence can analyze network traffic patterns and help identify bottlenecks or predict outages, resulting in better network performance for enterprises.

Software Development: Businesses building software can leverage artificial intelligence to optimize their software delivery processes, for example, predicting how long sprints should last or what can be reasonably implemented in each release cycle How many changes. AI tools can do this by analyzing logs from CI/CD tools, as well as data such as the speed and frequency of application deployments.

The examples are endless, but the point is simple, almost all IT services that generate system data, as well as those that involve technical resources or processes, may be helped by AI-based insights Improve.

When Not to Use Artificial Intelligence for Service Optimization

When services have one or more of the following characteristics, they are generally not suitable for AI-assisted optimization:

• Optimization by AI tools that analyze data cannot be achieved when the data source is irrelevant. When ethical decisions are required, AI is often unable to handle this. Decisions or ideas involving creativity cannot generate completely new ones. Concept
• When emotional intelligence or building trust are required, AI cannot perform these tasks well
• Involves adapting to unstructured or unpredictable environments, such as those exposed to a never-before-seen cyber attack server. Artificial intelligence is less useful in this case because it cannot reliably predict the conditions that need to be addressed.
• As an example of a real-world situation where AI-based service optimization is unlikely to generate value, consider project management, where certain aspects of project management can be automated and some relevant aspects of the project can be documented through tools Operational data. But these figures represent only a portion of the active projects. Since each project has unique requirements, it can be difficult to leverage data from past projects to optimize upcoming projects.
• In addition, most projects involve extensive interactions between people. They also require trust and accountability among stakeholders. These are factors that AI tools are not good at assessing or optimizing for.

To optimize project management processes, not only do AI tools need to be deployed, but their recommendations also need to be reviewed. You'll need a detailed understanding of each project's requirements and the skills to build trust and manage relationships.

Negotiating with suppliers is another example of a common process that is difficult to streamline using artificial intelligence. Like project management, negotiation involves complex human elements. While AI tools may be able to help with some aspects of negotiations, such as helping to understand how supplier pricing trends change over time, they cannot say exactly how to interact with suppliers, or exactly what pricing terms to ask for. Building a trusting relationship is essential for suppliers to have confidence that they will deliver on their promises, and they do not have the ability to deliver on this.

Conclusion

Artificial intelligence has huge potential to improve the speed, efficiency, scalability, and reduce costs of a variety of common IT and business processes. But it’s important to understand the limitations of AI as a service optimization solution. Once the venture involves going beyond the realm of pure technology, AI will cease to be a provider of valuable insights and will require humans to make decisions that are beyond the capabilities of AI.

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