Getting Started With AI Service Desk Solutions
Explore some AI service desk solutions.
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Nothing can hold back the dawn. We’re all witnessing the increasing momentum of digital transformation enabling and driving the addition of AI capabilities to organizations’ existing ITSM tools.
AI’s ability to predict and offer solutions is helping users solve problems on their own before requiring the help of a technician is the key to timely service and increased productivity — on both ends.
And by relieving the service desk of low-level issues, freeing technicians to focus on critical tasks, AI reduces maintenance overhead costs and improves existing IT infrastructure. The savings from chatbots alone are expected to reduce business costs by $8 billion by 2022.
Where and how should your organization adopt AI technology? Embracing AI and Machine Learning (ML) today is not merely an option but the essential lifeblood needed to survive and thrive. It’s become a truism, but it’s always worth restating: Today’s customers demand access to effective, informed support at all times, instantly, and on their own terms. AI’s ability to meet this high bar even while cutting costs makes it a CIO’s dream.
Unquestionably, customer service is a critical factor in determining where customers invest their money and loyalty. Fortunately, the technology is available now to help us address these non-negotiable customer demands and resulting challenges.
AI and Machine Learning are a natural fit for customer service requests, with their countless benefits for both customer and company. These include:
- Shortening or even eliminating waiting time
- Creating a reliable, consistent and comfortable service experience
- Speedy resolution of standard tickets
- Supporting human service agents as they take on true complexities
- Structuring of essential information so it’s accessible and available to service agents
- Accurate routing of customer requests within the organization
Getting Started With AI and Machine Learning
IT self-service is nothing new. But these days it’s becoming much more sophisticated, with chatbots and intelligent search recommendations to help guide users to the right solution. In addition to self-service, one of the biggest benefits of AI for the help desk and overall IT support function is that it removes the manual overhead of high-volume, low-value service desk activities. IT automation of repetitive tasks frees people to focus on more fulfilling and higher-value activities. AI is expected to increasingly help IT support teams in other areas too, including predictive analytics for incident management, demand planning, and workflow improvement.
AI Is a Data Story
The amazing computational power of AI begins with and relies on data. It’s data that shapes AI and gives it its power and relevance. So consider data as fuel for your AI engine that drives your applications. AI solutions need current, quality, accurate data based on the GIGO (Garbage In Garbage Out) principle, so begin now to collect and store manufacturing and logistics data that AI solutions can be trained on and learn.
The success of your AI and ML applications depends on the quality of the collected data.
Monica Rogati’s Data Science Hierarchy of Needs is a pyramid illustration of the aspects needed to add intelligence to an organization’s systems. Fundamental to the pyramid is the need at the bottom to gather the right data, in the right formats and systems, and in the right amount.
This means start aggregating documents, information, and knowledge used by humans in manufacturing and logistics teams to do their day-to-day jobs. This data will be processed by AI solutions as part of the training. Don’t worry about centralizing your data gathering; new solutions can process data in place across wide sources.
Your Top Ten(s)
Get basic and identify top 10 repetitive tasks, actions, and workflows that are performed in your manufacturing and logistics organizations. Once you’ve done that, identify the top 10 requests/questions for information and knowledge by internal or external users interacting with apps and systems.
Provide conversational AI-driven access and interface for manufacturing and logistics users; conversational AI is the new “Google search” or next-gen Amazon Alexa that solutions and providers can use to answer users.
Start With Data
Focusing on data initially will help you understand your process from the bottom up and resolve problems more quickly. When you begin your AI journey with a data-first approach, you illuminate the process and help reap quantifiable results in terms of both numbers and product. And don’t forget, the empires of Google, Amazon, and Facebook rest on foundations of data.
Once you gather your data into visualizations and statistical processes, you’re closer to gaining the insights you need; you will find that you have greater control and ability to increase your efficiency and productivity, cut waste, save costs and obtain the industry-specific results you’re seeking.
Avoiding “Dirty Data”
As you embark on your AI adoption journey, you’re likely to discover that your data lives in various formats stored across systems — MES, ERP, SCADA, and so on. You may also find that manual production processes have yielded comparatively little relevant data for analysis and what there is, is inconsistent. So even a data scientist might have a hard time extracting useful meaning from it.
Fortunately, you can convert the data into a common format and system for building models. But even with this scrubbed and spotless data, you face the challenge of data quantity regarding the process you’re trying to improve or the problem you’re dealing with.
So make sure you have enough use cases and that they reflect all of the data variables to explain a failure or other anomaly. Having sufficient data allows you to build models and algorithms to predict failures, for example. Your ability to clean the data will save you having engineers take the time to do that process. This allows those engineers to focus on building models and solving problems rather than laboriously cleaning data.
AI and machine learning (ML) are going to have a huge impact on manufacturing, for example. These technologies offer the computational power needed to solve problems that humans can’t possibly solve. They will ultimately be able to provide prescriptive answers to production issues manufacturers have been asking for centuries. Namely, how to produce your offering as efficiently as possible, with zero waste and the least amount of downtime. So the disruptive new technologies of AI will give CIOs game-changing ability to create compelling experiences and generate substantial operational gains.
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