AI in Service Management: Comparing Technologies
AI is Changing Service Management
Artificial Intelligence (AI) is everywhere. Virtually every company today uses some form of AI for developing, analyzing, and even evaluating products, services, or customer interactions. But this requires the right technology or implementation to achieve the desired effect, all with their own advantages and disadvantages.
So which AI technology is best for your service management needs?
This whitepaper will go through five of the most common AI technologies in service management and present their common advantages and disadvantages.
Here is an overview of the AI technologies covered!
Virtual agents or bots are nothing new, with rule-based bots using if/then actions and decision trees being widely used. With the addition of AI, companies implementing bots will see many of the same benefits, such as 24/7 support. But bots have their faults, including customer acceptance and difficulty of implementation compared with the benefits versus alternatives such as FAQs and knowledge base articles.
Agent Assistants are similar to virtual agents, except they interact with your agents, not your customers. Agent assistants can help your support personnel find solutions quicker, reduce training times, and improve efficiencies by helping your team find and analyze information quicker. While agent assistants can help improve many areas, the primary disadvantage compared to other technologies is their reliance on assistance instead of replacing or eliminating.
Prescriptive maintenance (RxM) uses AI to help evaluate and make decisions relating to usage and maintenance. Prescriptive maintenance follows predictive maintenance by using existing knowledge relating to component lifespan and usage patterns to evaluate their maintenance practices. The primary issues of prescriptive maintenance revolve around the complexity needed in most systems and the dependence on accurate analysis and algorithms.
Standard Usage Profiling
AI is dramatically changing the areas of security and identity protection. One potential usage for AI in this space is through standard usage profiling. Standard usage profiling is learning a particular user's habits and behaviors and alerting when something out of the ordinary happens. This is often done through cookies and caching, but AI will allow for greater accuracy and a larger number of data points to be analyzed. While profiling is a sensitive subject, through the use of anonymizing data, we will likely see wider use in the near future.
Business process automation through workflows has dramatically improved operations. As workflow usage has advanced and matured, their complexity has also grown. The addition of AI to workflows will help organizations reduce their reliance on complex if/then combinations. AI workflows can add AI to a wide range of business processes overnight. But they will take more advanced systems and training to analyze the large amounts of data required to reach this level of use and sophistication.
To find out more about these different AI technologies, please download our guide!