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AI / Operations

The Benefits and Limitations of AI for Service Optimization

When you venture beyond the realm of the purely technical, AI ceases to be a source of useful insights, and you'll need humans to make the decisions that AI can't.
May 23rd, 2023 10:00am by
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At first glance, artificial intelligence might seem like the perfect solution for IT service optimization — and it is, in some cases. By quickly parsing complex sets of data and generating insights based on them, AI can help organizations to identify and act on opportunities for streamlining their IT services.

But that doesn’t mean AI can optimize every IT service at every business. There are important limitations on the extent to which IT teams can apply AI to their service optimization strategy.

Keep reading for a breakdown of what AI technology — including but not limited to the generative AI tools that have been the source of much hype in recent months — can realistically achieve in the context of service optimization, and for a look at which types of processes will always require a human touch.

The Benefits of AI for Service Optimization

Before looking at examples of what AI can and can’t do in the realm of service optimization, let’s discuss why you’d want to use AI to optimize your services in the first place.

The main reason is that service optimization often requires analyzing large amounts of data and AI can perform that work much faster and more efficiently than humans.

To put this in context, imagine that you want to streamline IT processes within your organization. You could do that by having your IT team manually look over data sources like help-desk tickets, determine which types of requests take the longest to fulfill, and then produce recommendations on how to make those processes faster. That’s doable, but it would take a long time, and it would constitute a major distraction for your IT team.

Alternatively, you could deploy an AI tool that automatically analyzes all of your tickets — alongside, perhaps, other data sources that provide insight into why certain requests take a long time to fulfill — then generates recommendations on where and how to optimize services. This approach will yield results in a fraction of the time that it would take to glean the same insights manually.

IT Services That AI Can Help to Optimize

You can leverage an AI-based service optimization approach for any type of IT process that meets the following criteria:

  • You have a substantial set of data that an AI tool can analyze to understand how the process works and identify opportunities to make it better.
  • The process does not involve complex human interactions that require emotional intelligence to understand fully.

Plenty of core IT services fit both of these bills. Apart from using AI to analyze help-desk tickets in order to improve IT services aimed at end users, other services that are good candidates for AI-powered optimization include:

  • Infrastructure management: AI can analyze logs, metrics and other infrastructure data to understand what your organization’s infrastructure requirements are and provide guidance on optimizing infrastructure management. In turn, it could help you reduce spending on unnecessary infrastructure, plan hardware refresh processes and so on.
  • Network management: AI can analyze network traffic patterns to help you identify bottlenecks or predict outages, leading to better network performance for your organization.
  • Software development: Businesses that build software can take advantage of AI to optimize their software delivery processes by, for example, predicting how long a sprint should last or how many changes they can reasonably implement with each release cycle. AI tools could do this by analyzing logs from CI/CD tools, along with data like application deployment speed and frequency.

The list could go on, but the point is straightforward enough: Almost any IT service that generates systematic data, and that involves technical resources or processes, can probably be improved with the help of AI-based insights.

When Not to Use AI for Service Optimization

On the other hand, services are typically not good candidates for AI-assisted optimization when they have one or more of the following characteristics:

  • They are not associated with a data source and therefore can’t be optimized by AI tools that analyze data.
  • They require ethical decision-making, which AI is typically not equipped to handle.
  • They involve creative decision-making or ideation, something that AI can’t do well because it’s incapable of generating total novel concepts.
  • They necessitate emotional intelligence or the building of trust — tasks that AI can’t perform well.
  • They involve adapting to unstructured or unpredictable environments, such as servers that have suffered a never-before-seen type of cyberattack. In this context, AI is of little use because it can’t reliably anticipate the conditions it needs to work with.

Project management is an example of a real-world situation where AI-based service optimization is unlikely to yield value. You can certainly automate some aspects of project management, and you can record some data about project operations through tools like Jira. But that data represents only part of what goes into an effective project. Every project has unique requirements, making it hard to optimize upcoming projects based on data you collect about past projects.

Plus, most projects involve extensive interactions between humans. They also require trust and accountability among stakeholders. Those are factors that AI tools are not adept at assessing or optimizing.

This means that optimizing project management processes requires more than deploying an AI tool and seeing what it recommends. You need a nuanced understanding of each project’s requirements, as well as knowledge of how to build trust and manage human relationships.

Negotiating with vendors is another example of a common process that is very difficult to streamline using AI. As with project management, there are complex human components at play with negotiation. Although AI tools might be able to help with some aspects of negotiation, such as helping you understand how vendor pricing trends have varied over time, they can’t tell you exactly how to interact with a vendor or exactly which pricing terms to ask for. Nor can they build the trust relationships that are necessary for instilling confidence that a vendor will deliver on promises.

Conclusion

AI offers enormous potential to make a variety of common IT and business processes faster, more efficient, more scalable and less costly. But it’s important to understand the limitations of AI as a service optimization solution. When you venture beyond the realm of the purely technical, AI ceases to be a source of useful insights, and you’ll need humans to make the decisions that AI can’t.

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