Tag Archives: Artificial Intelligence

Alexa Is NOT the Service Management Star You’ve Been Searching For

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Have you been hearing the news?

There’s a brand new rising star in the Service Management world.

She’s tech-savvy, has fantastic people skills and is extraordinarily productive.

Her name is Alexa. But I hate to break it to you- I don’t think she’s going to be as revolutionary as everyone says.

If you couldn’t tell by now, I’m not talking about a “real” person. I’m referring to Alexa, Amazon’s much-loved voice assistant. While Alexa has been in the consumer market for years, she’s now making the move into service management. There have been many signs that Alexa is about to become the hot new tool in service management.

  • Amazon has already outlined Alexa for enterprise and business solutions
  • ServiceNow is showing partners how to build and integrate Alexa Skills with the ServiceNow platform
  • FreshService is already outlining ways Alexa can assist ITSM

There’s no question that AI, machine learning and digital assistants, including Alexa, will play a role in the future of service management. I’m not here to argue that. However, I will argue that we shouldn’t be focusing on the technology but the environment where the technology will play a role. If you put Alexa in the right environment, she can thrive (and so can your organization) but if you implement Alexa with the hope that she’ll make the environment a better one, then you’re going to have useless technology on your hands and you’ll still have a long line of tickets, frustrated users and stressed out service desk technicians.

So let’s discuss how you can put Alexa (or any voice assistant) in the right environment.

What Role Will Alexa Play?

Let me start by saying that the idea of AI in ITSM is a fantastic concept. Natural language processing (NLP) can make it easier for users to find the content they need to fix their problems. Machine learning looks at data, identifies patterns or conditions, and develops new actions in response. Virtual assistants combine the two to automate tasks for technicians, providing faster solutions for end users. This allows service desk technicians to have more time and energy to focus on bigger, more complex issues.

It’s exciting to think we can live in a world that could nearly eliminate the need for manual opening, closing, and management of support tickets. It’s thrilling to someday see a sales rep saying “Alexa, open a support ticket for a broken printer,” and then Alexa quickly assigns the ticket in the correct way to the correct person. And in the not far off future, Alexa may be able to provide context for possible solutions for more complex problems using past cases, making it even easier for technicians to troubleshoot. Just imagine how remarkable that would be!

And while all of this is exciting, there’s something to remember: Alexa doesn’t come “out of the box” with this capability. She’ll never replace the humans who currently work on the service desk because she can’t gain any knowledge or accomplish any process without guidance from them.

Who is The Future Star of SM?

Like any new service desk technician, Alexa won’t be ready or able to do any of those things without the proper training, frameworks and an accurate and relevant knowledge base. She’s not the rising star of Service Management. In fact, the star of Service Management is something you already have: the foundations provided by your service management implementation.

I know what you’re thinking. Knowledge management, frameworks, and communication aren’t as exciting as AI. Who wants to pay attention to that when you can say “Alexa, tell me how many tickets are open”?

But, Alexa won’t know how many tickets are open unless she can access that information. She can’t access that data if it is not set up for her. Simply put, without the foundations of Service Management. AI won’t work in your organization. You must have proper frameworks, the right data, and inter-department communication in order to enable Alexa (or any voice assistant) to work properly.

If you’re not sure if your foundations can be put to the AI test, check on these three things.

1. Knowledge Management
AI can’t work well without good data. You need to document, gather, record and store all your knowledge into an easy-to-read knowledge base. According to Gartner, “Through 2020, 99% of AI initiatives will fail due to a lack of established knowledge management foundation.”

It takes time to optimize a knowledge base system that is all-encompassing and easy-to-access. You already have a great knowledge base: it’s your team. Use this as an opportunity to engage your team and begin preparing them for AI. No one understands what needs to be in a knowledge base quite like the people who field tickets and solve issues every day.

2. Create flexible frameworks
There’s no space for rigid approaches to the use of frameworks. Flexibility is key to success with AI. Are your frameworks and methodologies capable of adjusting to keep up with evolving projects and services? Luckily, in recent years there have been updates to traditional ITSM frameworks, such as ITIL® that allow for such flexibility. There have also been new approaches introduced, such as VeriSM™, which allows for flexibility in delivering service management. If you haven’t updated your approach to using frameworks or offered your team the opportunity to achieve new certifications in these frameworks, now is the time to do so!

3. Extend Service Management outside of IT
The success of Alexa and other voice assistants doesn’t just depend on IT. It depends on an organization of self-service, shared processes and communication. Alexa won’t have the capability to change her process depending on the context who is requesting support – unless the entire enterprise works together to manage data, share information and create effective processes that work for everyone.

Enterprise Service Management is now gaining steam. As more of these technologies are introduced, I predict ESM will become more and more commonplace. Innovative leaders are jumping on the bandwagon now and you should too.

I am just as excited about the possibilities that Alexa and other digital assistants can bring to service management as everyone else. I share these thoughts because I want a world where AI plays a major role in delivering good service management. That’s why I want every IT leader to know and master these foundational pieces for AI enablement. Because they will pave the way for massive success with Alexa or any other voice assistant or AI technology that comes your way.

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The AI Playbook – 3 Key ITSM Plays to Make When Implementing AI

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AI is one of the fastest growing tech trends across all industries. 20% percent of business executives said their companies plan to implement AI across their enterprise in 2019, according to research from PricewaterhouseCoopers.

AI is the approach of using technologies like machine learning or bots to automate simple and repetitive tasks. The power of AI is clear. It allows for services to be delivered faster to the end user. It eases the burden of resource-strapped teams by automating simple tasks, allowing those teams to focus on larger or more strategic initiatives. It also keeps organizations competitive as new technology has created new consumer expectations that demand speed and agility.

While AI is making a splash for good reason, it is not a sole solution. Investing in AI won’t fix every issue in an organization. In fact, if implemented in the wrong environment, AI can slow down an organization and cause even more problems.

Before you jump and invest a chunk of your budget into an AI tool, you need to first review your ITSM environment. If you want to win at AI implementation, you need these plays in your playbook.

1. Clean up or create your processes

It’s simple: automation only works if you have a process to automate. If there’s no process, your AI tool has nothing to automate. AI will only master what it’s fed. You need to evaluate your current processes and workflows. Look for gaps where the process is slow due to human intervention, bandwidth issues or approval processes. Identify what is too convoluted, unclear or undocumented, too fluid or constantly shifting. This exercise will give you a clear view of what’s needed in your process and what is prime for automation.

When cleaning up your processes, you’ll want to get your entire team involved. You want buy-in from every member and you need to see the big picture of how each member contributes to a process. Meet with your team to map out your processes. Work with them to understand what each step requires and where automation can play a role.

2. Enable cross-department collaboration

AI will not work well in a siloed organization. Many AI tools facilitate integration with multiple backend systems and work across departments to deliver solutions. If your marketing team has a completely different tool, process and system than the sales team and those two departments are unable to come together to create shared processes and systems that deliver an end result, then AI won’t be able to make it better.

Every department must work together to effectively implement AI. They have to create shared processes, enable communication and clearly understand what is needed from each department to deliver a service, product, or result. Handoffs have to be smooth for automation to be able to step in and handle it.

Where in your organization is there confusion over how departments interact with one another? Are there communication issues that need to be addressed? What are the expectations and outputs of each department? It’s absolutely required that every team be on the same page when it comes to processes, approvals, goals, communications, and expectations.

IT leaders should find buy-in from other leaders to help teams integrate successfully. The goal for every leader should be a successful AI implementation that actually speeds up results. When each leader understands that this is only successful with inter-department collaboration, they will be more willing to encourage their teams to work with IT.

3. Identify and map value streams

Mapping value streams evaluates the tools, people and processes in the lifecycle of a service. Mapping value streams gives you two important things: visualization and metrics. Value stream mapping helps organizations visualize of how value and information flow through an organization. By doing so, organizations can see if any steps can be eliminated, refined, consolidated or most, importantly — automated. These metrics and data will help you be able to pinpoint exactly where AI can work, how it should work and what metrics you should use to measure it.

Mapping value streams will make it clear how AI could drive business value. This makes it easier to prioritize future implementations and integrate more AI solutions within your organization.

There’s one last important note for every IT leader to address.

It’s the elephant in the room, so to speak. Staff often feels threatened by AI so every IT leader must be able to express to their teams how AI can fuel their success. There should be no worry that staff will automate their way out of a job.

Instead, focus on the opportunities this can create. What projects are you unable to accomplish because your team is stuck doing manual, tedious, and mundane tasks? What successes are you held back from due to the limitations of manual work? Successful use of automation does require a shift in organizational culture. To create an atmosphere of acceptance, you need to focus on the potential for new projects, more exciting initiatives and a larger role in contributing to business goals.

Lastly, recognize that AI implementation is not one big project. Start small automating something of use and value. Pay attention to your metrics and adjust as the organization needs. Keeping an open mind and flexible approach to these implementations will be key to keeping them successful.

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