Tag Archives: automation

The New Role of the Service Desk Agent

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What is the future service desk agent? Instead of fearing the future, it’s time to redefine the role.

AI is disrupting almost every part of IT and the service desk is no exception. In fact, service desk agents may be more impacted by AI than any other part of IT.

This has some service desk agents worried about losing their job to a bot. Some of them may even be resistant to incorporating AI into their organization because of this fear.

IT leaders who want to embrace AI must work with their service desk agents to identify opportunities for AI success. Bots are already here and service desk agents should embrace that because bots are ushering in a renaissance for the service desk. The service desk agent role isn’t being outsourced or replaced because of AI. The service desk agent role is being redefined – and this new role is a reason for excitement.

But, before we talk about this new role, let’s first address a common question.

What is the role of the service desk agent?

The service desk agent is typically the first point of contact for IT consumers who need help. Their role generally involves troubleshooting IT consumer issues and providing basic support while escalating complex or more advanced problems to others within IT. Their role involves executing the processes in place to escalate those problems and managing IT consumer expectations and needs. Providing excellent customer service is a critical part of this role.

That description looks good on paper, but what does a service agent actually do?

Historically, service desk agents are performing menial and tedious tasks (like resetting passwords), answering and routing calls and contacts, and strictly following predefined scripts.

But now, bots using AI and machine learning can do those menial tasks service desk agents have historically done – but they can do it faster. Unlike humans, bots are available 24/7, so they’ll never miss a call. Bots will follow those predefined procedures and can perform tedious tasks, like resetting passwords, faster than a human.

So, of course, some service desk agents are looking at this new technology and thinking they can’t compete, or that they are being replaced. But this is where the new opportunity begins.
Finally, with the help of AI, service desk agents can get out from underneath those time-consuming, yet easy-to-solve issues that dominate their days. They are freed from the monotonous tasks that take up their time but don’t utilize their unique skill sets. AI is not going to replace roles. It’s just replacing how low-level tasks are performed.

So what will the service desk agent do when bots and other AI-related technologies are performing those low-level tasks? Here are three opportunities for the future service desk agent.

AI and Automation Experts

You can’t just plug in an automation tool or “turn on AI” and expect it to work perfectly. AI technologies and automation tools only work if they have the proper setup and are managed correctly.
Service desk agents can become AI and automation experts by configuring and managing those technologies. They can be the architects of knowledge bases and automation procedures. Service desk agents can become the go-to experts in helping the organization identify automation opportunities, as well as what needs to be done to implement that automation. We’re just beginning to see how impactful AI and automation can be for organizations and someone will need to continue to lead the organization into the automation age as more technologies are introduced. The service desk agent is perfect for that role.

Problem Solvers

Not all user issues or requests can be addressed by automation or by a bot. There will always be bigger and more complex issues that need to be addressed. With service desk agents no longer bogged down performing menial tasks, they can tackle those bigger user issues that exist within the business.

IT often becomes so busy with small technical requests that they end up applying too many fixes that are only short-term solutions. With bots and AI-enabled technology dealing with those small requests, service desk agents can use their time to create those long-term solutions. They’ll have the bandwidth to innovate and think creatively to identify their solutions. As an added bonus, this work will contribute to the business in larger and more valuable ways and service desk agents will feel more rewarded and appreciated for their work.

IT Ambassadors

Finally, service desk agents will have more opportunities to collaborate with key users. Service desk agents will be able to invest the needed time to understand the business impact of incidents, educate users regarding technology, and identify ways the IT consumer and IT can work together to create a better overall experience.

Good service desk agents will leverage those outstanding soft skills to communicate with empathy and operate from a place of patience. They become ambassadors for the IT organization. If more IT consumers feel seen, heard, and understood by service desk agents, then users will start to see service desk agents as partners, instead of order-takers. This opens the door for IT to be included in bigger conversations around business objects, goals and strategies.

What Should Service Desk Agents Do Now?

The service desk renaissance is here! IT leaders and service desk agents can help usher it in within the organization by championing AI. Service desk agents should aim to become the experts in this new technology, educating themselves on what is available, and then identifying opportunities for using automation and AI technologies within the organization. Upon becoming knowledgeable about AI and how it can support the business, service desk agents should build the business case for implementing AI into the service desk (just be sure you’re avoiding these 5 signs before you start to do so!).

Disruption due to technology is a good thing. It has been happening since the dawn of time and the best way to protect yourself and your team is to embrace it and learn how to work with it. The sooner that service desk agents and IT teams are able to see that AI use will be an asset and not a threat, the sooner your renaissance will begin.

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Doug Tedder is a panelist on BrightTalk’s “ITSM in 2020: Experts’ Predictions” webinar

December 3, 2019:  Doug Tedder, principal consultant of Tedder Consulting LLC, will appear as a panelist on the December 12 BrightTalk webinar “ITSM in 2020:  Experts’ Predictions”.

Doug joins Claire Agutter,  Director of Scopism and ITSM Zone, and Roy Atkinson,  Senior Writer/Analyst for ICMI and HDI of InformaTech on the panel to discuss what 2020 will mean to ITSM.

The webcast airs at 11:00am ET on December 12. 2019.  To register for the webcast, visit https://tinyurl.com/v64ahf3 .

How AI Will Change DevOps

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Intelligent machines aren’t coming, they’re already here. The question is: how will they change things? We’ve already discussed the future of AI and ITSM,  but today I want to take a deeper look at how AI will impact DevOps.

Much of DevOps is about the automation of tasks. It focuses on automating and monitoring every step of the software delivery process. DevOps encourages enterprises to set up repeatable processes that promote efficiency and reduce variability. Artificial Intelligence (AI) and Machine Learning (ML) can help improve those efficiencies and automate even more of the process so DevOps practitioners can focus on bigger, more complex initiatives.

DevOps experts have a lot to gain by adopting AI and ML. According to ServiceNow’s report “The Global Point of View”,  85% of C-level executives believe AI can offer value in terms of accuracy and rapidity of decision making. 60% of C-level executives surveyed said decision automation can contribute to their organization’s top-line growth. But according to that same report, only 27% of have hired team members with skills in machine learning.

For current DevOps practitioners and IT leaders, it will pay off to start understanding how AI will change DevOps and how you can exploit these newer technologies

Data Accessibility

Perhaps the biggest impact AI and ML will have on DevOps is the capability to access and correlate data from disparate sources. DevOps activities generate large amounts of data. That data can contribute to many aspects of IT: streamlining workflows, monitoring systems, and diagnosing issues. However, the quantity of data can often become overwhelming for teams. So rather than looking directly at the data developers define tolerance thresholds and use only breaches of those thresholds as conditions for action. But by doing this, they are identifying only outliers and ignoring the majority. That can create larger problems because IT organizations are unable to see an informed, deep view with only outlier data.

AI can be used to collect data from multiple sources and prepare that data for evaluation. ML can then be used to identify and predict any alarming patterns and create recommendations based on those patterns. This helps to keep DevOps in a “predictive state” as opposed to a reactive one and provides continuous feedback throughout the process.

And even when there are alerts that may cause DevOps teams to be “reactive,” AI can help with that as well. Many DevOps teams may be accustomed to receiving a high number of alerts, but ML can help manage those alerts and prioritize them based on factors such as past behavior or the magnitude of the current alert. This way DevOps teams can continue to move quickly and efficiently and “fail fast,” as they are often encouraged to do.

Mask Operational Complexity

AIOps is an emerging application of AI and ML that helps DevOps teams have a consolidated and unified view of all components of the toolchain (and more). Using AIOps, an engineer can view all the alerts and relevant data produced by the tools in a single place and the team will have a holistic view of an application’s health. In many cases, the AIOps tool can take automated actions in response to data patterns and conditions, using ML and associated algorithms.

Improved Testing

While integration testing is done as part of trunk code updates, AI and ML can be used to perform deep integration and regression testing to identify potential poor development practices.

Even more automation

DevOps wants to automate as much as possible, but for many organizations, automation is focused on code deployment. Through the use of AI and ML, infrastructure configurations or builds can be automated, tasks within processes automated, processes orchestrated, as well as remediation responses to alerts.

Where to Start Adopting AI with DevOps

Before you rush out and invest in an AI or ML tool, you need to establish your DevOps foundation so that you’re ready and capable of handling the changes AI can bring to your organization.

Remember, AI can only do what it has been designed to do. Just as with ITSM, good AI will not fix poor DevOps practices. You can start preparing for adopting AI into your DevOps environment by reviewing three key factors first.

  • Processes – If your processes are not documented, clear or follow then AI can’t automate them! Start with a clear, documented process and then AI can step in to help it operate more efficiently.
  • Standardization – The more standardized the environment, the easier it will be to introduce AI / ML into the environment. Standardization reduces variably and integration challenges. Standardize not only on the infrastructure, but also standardize tools and APIs as well.
  • Map the complete value stream – Some DevOps teams only look at the IT value stream as including only software development and deployment, which are important, but not inclusive of everything that is done to deliver value to a business. The complete IT value stream must include not only operations and support, but QA, security, portfolio management, and the consumer.

The future of DevOps is bright. AI and ML can revolutionize how DevOps operates but your team needs to be primed and prepared to handle these changes prior to purchasing an AI tool. Additionally, working with your team to embrace AI will contribute to their career advancements as AI and ML continue to play larger roles in DevOps and IT as a whole.

Interested in learning about AI? Contact Tedder Consulting to learn about AI workshops and consulting.

Looking for DevOps training? Learn about our DevOps training here.

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What is DevOps?

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“What is DevOps?”

Taken at face value, the question may seem a bit rhetorical, but allow me to explain.

There is no single definition of “DevOps”. If you ask five people “What is DevOps?”, you’ll likely get five different answers.

Need some examples?

Here’s how Gartner has defined DevOps[1]: “A change in IT culture, focusing on rapid IT service delivery through the adoption of agile, lean practices in the context of a system-oriented approach.  DevOps emphasizes people (and culture), and seeks to improve collaboration between operations and development teams.  DevOps implementations utilize technology – especially automation tools that can leverage an increasingly programmable and dynamic infrastructure from a life cycle perspective.”

 Take a look at “The DevOps Handbook[2] by Gene Kim and others.  It states that DevOps is “…the outcome of applying the most trusted principles from the domain of physical manufacturing and leadership to the IT value stream.  DevOps relies on bodies of knowledge from Lean, Theory of Constraints, the Toyota Production System, resilience engineering, learning organizations, safety culture, human factors, and many others…the result is world-class quality, reliability, stability, and security at ever lower cost and effort; and accelerated flow and reliability throughout the technology value stream, including Product Management, Development, QA, IT Operations, and InfoSec.”

 One of my favorite definitions is from Rob England (aka “The IT Skeptic”). Rob describes DevOps as follows[3] : “Agile is the approach of working with complex systems anywhere. Lean is the approach of optimizing the flow of work anywhere. DevOps is the application of Agile and Lean to the acceleration of value work through IT.”

DevOps Defined?

Perhaps the best definition of DevOps is credited to Jez Humble, one of the co-authors of “The DevOps Handbook” who coined the term “CALMS”.[4] CALMS is a conceptual framework for integrating development and operational (DevOps) teams within an organization.  CALMS is an acronym for:

  • Culture – A culture of shared responsibility
  • Automation – Automate as many tasks as possible
  • Lean – Visualization of work-in-progress; limit batch sizes; eliminate waste; continuous improvement, focus on customer value
  • Measurement – Data is collected on everything, with mechanisms for providing visibility into all systems
  • Sharing – There are user-friendly channels for facilitating on-going communications

In most DevOps thinking and reading, CALMS seems to be a common theme.  But I also encounter a lot of DevOps “anti-thinking” as well.

What DevOps is not

Here are a few examples of DevOps “anti-thinking” that I’ve encountered.  DevOps is not:

  • A standard – There is no documented “DevOps” standard.
  • A tool – There are literally hundreds of tools (I’m not sure if this is a good or bad thing) that claim to be “DevOps” tools. The point is buying a tool does not make your organization a “DevOps” organization.
  • A best practice – There is no defined “body of knowledge” or designated reference for DevOps. In fact, there are literally hundreds of books about DevOps, and some (highly visible) examples of organizations that have successfully adopted DevOps thinking. But there is no designated DevOps “best practice”.
  • Just automation – While automation is perhaps one of the most visible aspects of DevOps, automation alone is not “DevOps”.
  • A silver bullet for IT issues – Just saying that you’re “doing” DevOps is not the same as addressing communication issues, collaboration challenges, wasteful processes, poor measurement practices, eliminating technical debt, and other organizational problems.

What is DevOps?

So, what is DevOps?  At its core, DevOps is a mindset; a way of working together. DevOps is:

  • About building trust and teamwork, sharing knowledge, taking accountability – Teams build trust when members can rely upon one another. Trust is built when team members openly and willingly share their knowledge and contribute to the success of the team.  When mistakes happen, the member making the mistake acknowledges the error; but more than that, there is no blame; the team works together to resolve the error.
  • About tearing down the walls that exist between working groups within IT – DevOps can only be successful when all parts of the IT team are part of the success.
  • About creating a culture of experimentation and learning – Too many organizations work in fear of failure. Much about DevOps is about empowering and learning.
  • About improving the productivity of the overall organization – Sometimes that means searching out and eliminating waste or streamlining the process. If it’s taking too long to get code to trunk and then tested, what can be done to improve that?  If standing up a new development environment requires manual intervention, what could be done to standardize that work so that manual intervention is no longer required?

Avoiding DevOps “Ditches”

Knowing what DevOps is and is not will help you get on the road to success. Here are five ditches to avoid to achieve success with DevOps.

  • Purchasing and implementing tools before anything else – DevOps adoptions taking this approach usually fall into the old “hammer and nail” syndrome (“when all you have is a hammer, every problem looks like a nail”). Well, just because you own a hammer and can swing it doesn’t make you a carpenter.  One of the most common mistakes with DevOps adoption is too much focus on tool implementation and automation of (often poorly or even undefined) processes. When all you have done is implement a tool without designing processes or developing teams, you’ve not “DevOps”- you’re just using a tool – and likely not getting the full benefit of that tool.
  • Getting hung up on semantics – Developers and operations both have developed their own specific languages for what each group does – and this often causes confusion. Take the time to define and agree on a shared terminology.
  • Developers running over everyone else in the organization under the flag of “we’re doing DevOps” – Once, while at a client site, I heard a “DevOps engineer” (seriously) tell a sysadmin that “I’m about to automate you out of a job.” Not a very collaborative, teamwork attitude, huh?  DevOps is about building teams whose members trust and rely on each other- not trying to dominate or control.
  • Arbitrarily throwing out existing process and procedures – Although a process may not be performing as effectively and efficiently as desired, don’t overlook the fact that there was a good reason why that process was defined and implemented in the first place. In my experience, process design and implementations have been treated as “one time” activities and rarely revisited to identify improvements or needed changes.  Before getting rid of an existing process, first understand the reason for the process, and if that reason still exists today.  Then look for ways to improve that existing process, rather than just throwing it away.
  • Ignoring the need for organizational change – DevOps adoption represents a significant change in the way IT does its work. Without good communication, appropriate training, and a shift to a “thinking / experimentation / learning” mindset, DevOps adoption will fail.

Keep CALMS and DevOps on

Knowing what DevOps is and is not is key for success in adopting DevOps.  Following the CALMS model ensures that you’re addressing the complete extent of DevOps adoption, and not just being fixated on a single aspect.

Need help answering the question “What is DevOps?”  Want to build a fundamental understanding of DevOps?  Tedder Consulting offers the DASA-accredited DevOps Fundamentals class.  Visit  our training page to register for an upcoming class! 

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Picture Credit: Pixabay

[1] https://www.gartner.com/it-glossary/devops , retrieved 8/11/2018.

[2] Kim, Gene, et al. “The DevOps Handbook”. IT Revolution Press, LLC. 2016. Portland, OR

[3] http://www.itskeptic.org/content/laymans-definition-devops, retrieved 8/11/2018.

[4] https://whatis.techtarget.com/definition/CALMS , retrieved 8/11/2018.

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