Virtual assistants, driverless cars, and robots are just some of the devices that are powered by artificial intelligence (AI). The AI technology that makes these devices work can include Voice Assistants, Natural Language Processing (NLP), or Machine Learning (ML). These technologies are no longer a hype but a reality today for many IT organizations. Unfortunately, there is an unfounded opinion on the market that deploying AI for ITSM is a difficult task. However, there is no need for resources or clean data, mainly because of Unsupervised AI.

In reality, IT Service Management (ITSM) has huge potential to benefit from AI as ITSM service desk agents perform a variety of transactional tasks. AI also helps IT meet the growing expectations of users in terms of faster and more efficient service with the latest technology. So, let’s discuss the common misbeliefs and why they are wrong.

1. You need to invest in long ITSM integration efforts and data cleansing cycles before adopting conversational AI. FALSE. Advanced conversational AI solutions tackle both of these challenges by showcasing the benefits of the latest transfer learning AI technologies, which reduce training times from hours to minutes and require very little data from the organization itself.

2. You need large upfront investments in resources and personnel. FALSE. Cloud computing has emerged to tackle this problem and allow a business of any size to be infrastructure-ready and adopt all the latest technologies and solutions. Conversational AI is delivered to companies as SaaS (software as a service). It requires no infrastructure to get up and running and can be quickly implemented and customized for any business. Managing such a solution doesn’t demand a large personnel commitment. Typically, companies need to commit no more than one-half, or one fully dedicated resource.

3. You need to bring data scientists on staff. FALSE. Contrary to the supervised learning AI model, conversational AI gets powered by more complex and advanced unsupervised learning techniques. With unsupervised learning, models automatically learn by themselves and extract relevant information. Closely-looped automation ensures that models get regularly refreshed to incorporate new knowledge, and with time, models grow smarter and operate with fresh/updated information. Therefore, no data scientist is needed.

4. You need to train AI models. FALSE. Today, reinforcement learning techniques in conjunction with unsupervised machine learning techniques are applied. With such a method, an AI agent employs trial and error to find a solution to any given model task. To allow the model to learn what the user would want it to, the AI agent gets rewards or penalties for performing actions. With this approach, the model adapts based on what the user acknowledges as a ‘good action’.

5. Continuous real-time learning is not possible. FALSE. A major weakness of most market-available conversational AI solutions is their inability to learn new knowledge during the conversation. In contrast, intelligent conversational AI solutions are equipped with sophisticated algorithms and job automation, enabling the system to learn and become more knowledgeable interactively. And the process never stops!

Conclusion

We hope we helped clarify the misunderstandings and false beliefs that deploying AI for ITSM takes a tremendous amount of effort, time, and resources.

Our AI-driven, cloud-based AI Service Desk solution will modernize your operations and your service desks, all the while scaling with you through digitally transforming your business to provide autonomous self-service resolutions to digital users across your organization.

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 Kim del Fierro is the VP of Marketing at Aisera. 

If you want to learn more about our AI Service Desk solution, feel free to request a demo.