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Intelligent ITSM Automation – Your Secret Formula for Success

One of the more surprising trends in recent history has been the implementation of IT Service Management (ITSM) in areas that are outside of the IT realm, such as facilities management and human resources. Similar to IT, these functions can derive significant business value from standardizing, automating and streamlining workflows and processes. Furthermore, by cutting costs and skyrocketing efficiency, intelligent ITSM automation can help all lines of business roll out newer and better capabilities for the benefit of the entire organization.

Widespread Benefits of Intelligent ITSM Automation

According to a recent survey by PMG, nearly three quarters of the 300 respondents listed self-service automation as beneficial to the entire organization. 68 percent agreed that automation can help lower the costs of IT operations. 82 percent acknowledged that automation has fundamentally changed the way cloud and virtual environments are managed while 65 percent credit automated technology as instrumental in integrating and managing Big Data.

Nearly all survey respondents, however, (98 percent) agreed that automation already provides clear and measurable business benefits, including:

  • Enhanced customer satisfaction
  • Increased productivity and subsequent gains
  • Better knowledge sharing
  • New product delivery
  • Data-driven decision-making

It’s no surprise, then, that intelligent ITSM automation is now being leveraged to streamline manual processes across entire organizations, including IT help desks, HR departments, customer contact centers and more. Extending automation outside IT departments into other business units within the company is becoming much more commonplace.

Aligning Intelligent ITSM Automation with Business Goals

Of course, in order for intelligent ITSM automation to truly generate measurable benefits across the enterprise, it must be aligned as closely as possible with broader organizational goals. This isn’t a significant challenge, however, thanks to ITSM’s ability to facilitate better communication throughout the company. By eliminating miscommunication, businesses achieve greater efficiencies. When IT becomes less of an island and more a part of overall business operations, everyone benefits because they’re all on the same page.

Obstacles to Intelligent ITSM Automation

While the majority of business leaders agree on the many benefits intelligent ITSM automation has to offer, there are still certain key challenges that exist and must be overcome. One of the biggest obstacles is the lack of a holistic approach to automation, which results in silos that are not integrated and therefore are not being leveraged to their fullest potential. In some instances, separate automated processes actually work against rather than with one another, slowing down progress and creating, rather than eliminating inefficiency.

One of the contributors to these silos of automation is different departments that deploy automation individually, without the IT team’s knowledge and assistance. Other respondents to the survey cited business leaders who create their own automated solutions using incorrect tools or non-standard processes. Clearly these issues must be addressed and overcome if intelligent ITSM automation is to become truly beneficial. Ideally, the IT department should take the lead on developing and implementing an interdepartmental, enterprise-wide strategy for automation.

The first step? Choosing the right platform. See AI-powered, intelligent ITSM automation in action today by requesting a product demo. Or experience it for yourself with a free 30-day trial.

IT Process Automation Survival Guide

How to Build an AI Team

Once viewed as a technology of the distant future, AI is quickly becoming an integral component of many an IT/business strategy. The rapid advancement of data science and machine learning technology, combined with the accessibility and affordability of artificial intelligence platforms in the cloud, are enabling companies in every industry to uncover new ways to extract business value from data. But in order to fully capitalize on AI, an organization must first assemble a strong team. Let’s take a look at three steps for creating such a team in your business.

Learn what successful AI looks like.

When establishing a department dedicated to AI, it’s important to recognize that successful artificial intelligence initiatives require a variety of different roles and skillsets. If you are focused solely on one role – data scientist for example – you will almost assuredly come up short. Instead, take a more well-rounded approach paying particular attention to three distinct areas: a person (or people) who can generate data, a person (or people) who can interpret that data, and a person (or people) who can make judgments about that data.

Recruit/train (and retain) top talent.

It’s no secret that skilled AI professionals are in high demand. In order to develop a good AI team, recruitment and retention are key. The good news is, you don’t necessarily have to look outside of your company to do so. In fact, developing AI talent from internal staff can be just as, if not more effective – particularly given the talent shortage. Investing in training and upskilling can produce a higher return on your investment than external recruiting.

And remember, it’s not just about assembling a team. You also need to focus on keeping turnover at bay. Offering things like professional development and autonomy can make long-term employment with you more attractive.

Tap freelancers.

What if your company simply isn’t prepared or doesn’t have the budget to hire a Ph.D. in computer science? What if your existing staff is too small, doesn’t have the potential or lacks the bandwidth to recruit internally? There are still other ways to get started with AI. Some organizations have had tremendous success hiring artificial intelligence specialists via online talent marketplaces, like Upwork. By eliminating the need to hire in-house, and all the ancillary expenses that come with such an arrangement, you can tap into global AI talent at an affordable price.

With Gartner forecasting that 85% of CIOs will be piloting AI projects by the year 2020, it’s abundantly clear that artificial intelligence is the way of the future. Having a team of skilled individuals dedicated to your AI initiatives can help you maximize the long-term benefits and give your organization the competitive advantage it needs to thrive in the digital era.

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Overcoming the AI Talent Shortage

Artificial intelligence has become a huge contributor in the battle for digital transformation. In fact, according to a recent PwC report, global GDP could reach up to $15.7 trillion as a result of AI. But no organization can fully realize the value of this technology without adequate talent at the helm.

The PwC report goes on to point out: “If your business is operating in one of the sectors or economies that is gearing up for fast adoption of AI, you’ll have to move quickly if you want to capitalize on the openings, and ensure your business doesn’t lose out to faster-moving and more cost-efficient competitors.’’

Thanks to this rapid development and adoption, however, many companies are now dealing with an AI talent shortage. This problem exists even in sectors where adoption is slower or the potential for disruption is lower. In fact, staffing skills are the number one challenge for the majority of CIOs looking to adopt AI. The firm also predicts that by the year 2020, 85% of CIOs will be piloting AI projects. In order for that to happen, something has to give.

Roles will evolve, but the need for people will remain constant.

There has long been whisperings that artificial intelligence will eliminate jobs and replace human workers. But while some tasks will certainly be shifted to machine, the need for humans will still exist. They will simply need to master new skills that will enable them to work alongside AI. Those skills, which cannot be replicated by machines, include creativity, communication, leadership and emotional intelligence.

Meanwhile, the demand for data scientists, AI and robotics engineers and other experienced tech specialists continues to grow, and at a fast pace. Unfortunately, given the rapid rate of change and low barrier to entry, these talented individuals are becoming harder and harder to come by. As a result, forward-thinking organizations are focusing their efforts to helping existing employees develop the skills they need to navigate the changing workplace landscape.

Building a Pipeline

One way that organizations are addressing the shortage of AI talent is to establish relationships with resources such as universities and trade schools. This enables them to engage in learning projects, become involved through speaking and mentorship and – most importantly – tap into emerging talent at an early stage, before students enter the workforce.

Internships provide valuable real-world experience and the opportunity to hone their skills and network with other like-minded professionals through meaningful, hands-on projects. The company benefits through the development and ongoing growth of a talent pool from which to draw. It’s a win-win.

Development from Within

The beauty of AI is that it’s a technology that can draw interest from individuals with many different backgrounds. For instance, people with strengths in math, statistics and engineering make excellent candidates for working with machine learning. As such, many companies are discovering that they are already sitting on a gold mine in terms of sourcing talent for their AI initiatives. Internal training and development can be an incredibly effective alternative to external staffing efforts.

For those organizations that lack existing talent or simply don’t have the capacity to transition current employees into new AI-related roles, there is also the option to hire for soft skills and train for the rest. For instance, many futuristic leaders are seeking out candidates that are highly collaborative, possess aptitude and are open to learning new things. Once hired, they can then work on growing AI experts from within.

Closing Thoughts

Whichever way you look at it, the AI talent shortage is very real and it’s not something that can easily be solved, at least not for the foreseeable future. Organizations looking to adopt AI and work toward digital transformation must begin thinking outside the box to solve their staffing needs. In many cases, that means making connections, nurturing relationships and building talent in-house.

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Is Intelligent Automation Really Replacing Human Workers?

When intelligent automation first hit the market, some thought it was too far-fetched to ever become a reality. But as more and more organizations began recognizing the many benefits – from increased productivity and efficiency to lower costs and fewer errors – people started worrying, wondering whether this technology would spell the end of the human workforce as we knew it. Would artificial intelligence really start taking over jobs? To answer that question, those asking it must look inward.

In reality, the impact automation has on the workforce will depend largely on how humans themselves respond. When faced with the rising adoption of AI, workers will likely take one of two paths. The first group will continue to focus on the type of work they’ve always done, but do so more efficiently thanks to the assistance of machine learning. The second will take this as a golden opportunity to pursue their ambitions, further their education to broaden their skill sets, put their creativity and innovation to work and move on to more value-added, meaningful work. In either case, the organization will benefit, as will most of the employees.

In particular, roles that have a primary focus on people, such as customer support and HR, have the potential to benefit greatly from intelligent automation. Instead of being bogged down by repetitive, menial tasks that can easily (and more quickly) be handled by software, agents will be freed up to tackle more complex issues requiring a human touch. Furthermore, the improved allocation of resources afforded by AI will enable agents to prevent issues from occurring in the first place. This can dramatically improve both customer and employee satisfaction rating.

This concept can also be applied to the IT help desk. Rather than waiting until system problems arise and scrambling to fix them in a timely and effective manner, help desk agents can use the extra time automation provides them with to monitor and proactively address technical issues before they occur. Imagine how impressed the CEO will be when he gets a call from IT letting him know his hard drive was about to fail, but it’s been taken care of.

In both of these scenarios, the human worker is enhancing their interactions with their colleagues and/or customers. And since intelligent automation is there to take on the routine, manual tasks, the human agents themselves are also able to improve.

The reality is, very few organizations are focusing on using AI to eliminate jobs. Instead, they are focused on automating tasks, which in turn will improve productivity, streamline how work is completed, eliminate errors and cut costs. In other words, companies implementing automation are not doing so to replace human workers, but rather to augment and make their lives easier. As a result, everyone benefits – from employees and management to clientele and ultimately the organization’s bottom line.

Still not completely sold on the idea of intelligent automation and the value this technology can bring to your business? Don’t take our word for it. Try it for yourself. Click here to download a free 30 day trial of Ayehu. You have nothing to lose!

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3 Business Areas that are Ideal for Machine Learning

At the current rate, AI systems worldwide are on pace to hit nearly $50 billion in revenues by the year 2020. The proof is in the pudding. And if you’re not yet leveraging the power of machine learning, you can bet your competitors are. The good news is, you don’t need a massive budget or a team of experienced data scientists to start putting machine learning to use in your business. In fact, to follow are three practical areas where almost any organization can get started with ML technologies.

Internal/External Support

If you have an IT help desk for employees or a support team dedicated to customer inquiries, you have a great opportunity to leverage machine learning technology. Chatbots can be trained to handle everything from the most basic FAQs to complex issues, working in tandem with human agents.

Not only will a chatbot strategy free up your support staff to focus on more important business initiatives, but it’ll also improve service levels, so it’s a win-win. (Not sure where to start? Here’s a step-by-step guide to implementing bots along with some tips for what not to do.)

Cybersecurity

According to research by Ponemon, the average cost of a single ransomware attack is $5 million. And that’s just one strategy hackers use. If you think cybersecurity is not a big deal, think again. The problem is, cyber criminals are becoming savvier and using more sophisticated methods by the day. Staffing enough people to handle the onslaught isn’t just challenging. It’s next to impossible.

The good news is, machine learning can be used to augment your IT security team, providing an added layer of protection against potential breaches. Intelligent automation can work around the clock, constantly monitoring and analyzing mountains of data and identifying/addressing anomalies before they have a chance to wreak havoc.

Human Resources

While there are certainly areas of the human resources function for which a human touch is still needed, such as discussing sensitive matters with employees, the reality is, the vast majority of today’s HR processes and workflows can easily be automated.

For instance, machine learning algorithms can be used to weed through job applicants, saving recruiters time and aggravation, while intelligent automation can handle new employee onboarding far faster and more efficiently than a human agent could. To get you started, check out these 5 tips for optimizing HR with automation.

Of course, none of these things will be possible without the right technology. Thankfully, you don’t have to be an AI guru to leverage machine learning, nor do you have to hire a team of experts. In fact, you don’t even have to know how to code. Experience the power of plug-and-play intelligent automation by requesting an interactive demo of Ayehu or jump right in with your free 30-day trial today!

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5 Surefire Ways to Derail Your Machine Learning Project

The process of implementing something new typically involves making mistakes, heading in the wrong direction and then figuring out a way to right those wrongs and avoid those risks in the future. Adopting machine learning is no exception. If you aren’t careful, the mistakes you make can become encoded, at least for the time being, into your business processes. As a result, these errors will occur at scale and will be difficult to control.

On the other hand, when you proactively detect errors and take the steps to address and correct them right away, you’ll have much more success with the technology. To follow are five common pitfalls that can wreak havoc on your machine learning project so you’ll know what to watch for.

Lack of clear understanding.

Simply put, you cannot adequately solve a problem that you don’t fully understand. The same can be said for machine learning initiatives. If you don’t completely understand what problem you are actually trying to solve, the risk of errors goes up exponentially.

To avoid this, begin with a hypothesis statement. Ask what the problem is that you are trying to resolve and which models you plan on using to address that issue. This is key, because if it’s not done correctly from the start, things can go wrong very quickly.

Poor data quality.

The old adage, “garbage in, garbage out” can easily be applied to machine learning projects. If the quality of the data you are supplying isn’t up to par, the outcome will inevitably suffer. In fact, poor data quality is one of the top concerns of data managers, as it can impact analytics and ultimately influence business decisions in the wrong direction.

The result of these poor decisions can negative affect performance and make it difficult to garner support for future initiatives. Exploratory data analysis (EDA) can help you proactively identify data quality issues so you can prevent problems before they occur.

No specific purpose.

Another common contributor to machine learning failure is implementation without a clear purpose. In order for machine learning to produce ROI, it must be applied properly – not simply because it’s the cool thing to do. In fact, using machine learning when it’s not the best solution to a problem and/or not completely understanding the use case can ultimately cause more harm than good.

In addition to addressing the wrong problem, doing so can involve wasted time and resources, both of which come at a cost. To avoid this, identify the precise problem and desired outcome to determine whether machine learning is the appropriate solution. 

Insufficient resources.

It’s easy to underestimate the amount of resources required to do machine learning right, in particular as it relates to infrastructure. Without adequate processing power, successfully implementing machine learning solutions in a timely manner can be a difficult, if not impossible feat. And without the resources in place to allow for its deployment and use, what’s the point?

To address the expense and complexity of deploying a scalable infrastructure, leveraging a cloud service that can be provisioned on-demand may be the better option. Those wishing to keep things in-house should look for a lightweight, plug-and-play solution that doesn’t require coding and can be deployed across on-premises and private cloud platforms.

Poor planning and lack of governance.

It’s not unusual for a machine learning project to start off with tremendous enthusiasm only to lose momentum and ultimately end up grinding to a halt. When this happens, poor planning and lack of governance is most often to blame. For those projects that don’t cease, a lack of guidelines and limits can result in an exorbitant expenditure of resources without the beneficial end results. 

To keep things moving in the right direction, machine learning initiatives must be continuously monitored. In the event that progress begins to wane, it can be wise to take a break and reevaluate the effort. Keeping people engaged in the process is the key.

Machine learning can be a tremendous asset to an organization, but only if it’s planned, implemented and managed properly. By avoiding the five common pitfalls listed above, you can place your company in a much better position and improve your chances of long-term, sustainable success.

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What will 2019 have in store for AI and machine learning?

There’s been plenty of hype about machine learning and artificial intelligence and that buzz isn’t expected to slow down anytime soon.As we prepare for another new year, it’s always a good idea to consider what’s in store for technology and all indications point to 2019 being a major year for AI and ML.

What might we expect to unfold over the coming months? Well,for starters, next year is poised to be one in which those who have been teetering on the fence about adopting machine learning are likely to finally take the plunge. Let’s take a closer look at a few other trends to watch for in 2019.

Cross-Industry Infiltration of Machine Learning

To put it plainly, there simply isn’t a single industry that would not benefit in some way from machine learning technology. As more decision-makers begin to recognize this, more widespread adoption will occur alongside the ideation of newer and more innovative ways to use ML.

A great example of this is the U.S. Army. Over the next year, they will be rolling out the use of machine learning sensors to predict when combat vehicles are in need of repair. The health care industry is another field that is finding new uses for AI. For instance, algorithms now exist that can predict – with 95% accuracy – the probability of a patient’s death. Physicians can use this data to literally save lives.

It’s safe to say that as we ramp up adoption of AI and ML,forward-thinking companies will continue to discover new ways to leverage these technologies to read, interpret and apply data for greater success.

Increasing Use of Chatbots

Most of us utilize AI assistants on a daily basis, whether it’s asking Alexa to play our favorite song list or checking with Siri to see how traffic will be for the commute home. These basic interactions are really just the tip of the iceberg.

In 2019, development of chatbots will snowball, making AI assistants an even bigger part of our everyday lives. Not only will they be in our pockets and in our homes, but chatbot technology will continue to make its way into the business world.

For instance, in the IT service management realm, chatbots will be used increasingly to enable end-users to self-remediate while simultaneously freeing up human talent to be focused on more complex projects and business initiatives.

Deepening Interactions between Humans and Machines

The concept of AI being a robot merely capable of performing repetitive, mundane tasks has become antiquated. To the contrary, more and more organizations are recognizing artificial intelligence as an integral part of their workforce, working alongside their human employees and playing a pivotal role in their success. This relationship will only continue to evolve as we push onward into2019 and beyond.

As AI technology advances further, we can expect features and functionality that mimics human behavior in much greater detail. Imagine a chatbot that not only recognizes what a human is saying, but the tone and nuances behind those words. The possibilities are virtually limitless.

And as AI continues to become ingratiated into the fiber of how organizations operate, the fear and uncertainty that clouded human workers in the past will begin to dissipate. In its place will be a newfound respect and an optimism for the new opportunities these innovative technologies will create.

Without question, 2019 will be a critical year for both machine learning as well as AI. The three predictions above may very well just be scraping the surface of what’s truly in store. One thing’s for certain:these technologies are here to stay and they’re changing our world in ways beyond what we ever thought possible.

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How to Successfully Implement a Chatbot Strategy in 5 Steps

Chatbot technology is disrupting almost every industry, with everyone from Verizon and Capital One to NASA jumping onboard. But while artificial intelligent is certainly not a new concept, developing and implementing chatbots in a practical and profitable way is still in its relative infancy. Unlike other, more established technologies, there aren’t necessarily any real standards for using bots. Thankfully, there are things we can learn from those already paving the way. Here are five real-world tips to help your company bring a chatbot strategy to fruition.

Identify audience and need.

For bots to produce ROI, they must solve a specific problem (or set of problems) and/or deliver real, measurable improvement (such as with staff efficiency or productivity). As such, the initial phase of your chatbot strategy should involve identifying who you are trying to help and exactly why. The narrower you can get with this step, the better the outcome. Keep in mind you may have multiple iterations of the same engine, based on the user you are targeting.

Select a platform.

Once you have a clearer picture of your target user and target problem, the next step should involve choosing a platform through which the bots will be built and managed. This is the phase of the project that can overwhelm some decision makers. The good news is, there are platforms (like Ayehu) that are so easy to use and quick to implement that you can be up and running in mere minutes – no coding or scripting required. Even if you have a highly talented IT team, this would be the best case scenario.

Define your measure(s) of success.

One of the biggest challenges of chatbots (and artificial intelligence in general) is proving financial value. The easiest and most straightforward way to approach this is to determine as early as possible which metrics matter the most. What type of ROI do those in the C-suite and/or other stakeholders expect out of this initiative? Bear in mind, also, that some measures of success aren’t as easy to quantify, but are just as – if not more – important, such as end-user engagement levels.

Start fast – don’t wait for perfection.

Many people make the mistake of trying to make things perfect before rolling out their project. Instead, the focus should be on building fast and executing fast, even if that involves some degree of failure in the process. Take, for instance, NASA, which approaches each chatbot initiative as a small startup with the goal of launching as quickly as possible. If you cannot iterate that fast, optimize the process as much as possible. For example, while Verizon was developing their Mix and Match bot, the consumer plan was being developed simultaneously. This made the actual rollout more seamless and successful.

Adjust and learn continuously.

A chatbot strategy isn’t something you set and forget. There is also the need for continuous adaptations and ongoing training to consider. Artificial intelligence is a fluid technology, which means your bots should continue to learn and improve over time. There will almost always be something to add, whether it’s a new term or a tweak in “personality” to better serve end-users. The main thing to remember is that chatbot development is an ongoing process and must be treated as such if it is to be successful.

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The Human Element of AI Implementation

The benefits of artificial intelligence for business are widely acknowledged and understood. So, why are so many organizations still holding back when it comes to adoption? While it may seem logical to blame technical issues for the holdup, in reality what’s standing in the way is far more likely to be human in nature, whether it’s uncooperative employees, confusion over what AI means, unrealistic expectations of executives or something else. The key to navigating these roadblocks is addressing this human element.

Coming Down to Earth

Surprisingly, one of the biggest misconceptions people have about artificial intelligence is that it can solve any problem the business is experiencing. Have an issue? Just throw some intelligent automation at it. But the truth is, as amazing as the technology is, AI simply cannot fix everything. It’s essential that this message is clearly communicated and sufficiently understood. To set more realistic expectations, begin with a specific business objective and then determine holistically how you’re going to align everything to accomplish that goal.

Network to Gain Buy-In

If the human issue you’re struggling to overcome is resistance, networking can do wonders (and no, we’re not talking about computer networking). Once you’ve identified a good problem to solve, work on developing a few proof of concepts and then begin socializing with those throughout the organization. This can raise the level of awareness and help people gain a clearer understanding of what AI is, how it works and what it can help them accomplish. Identifying early adopters and internal champions is also important. These individuals can help explain artificial intelligence in a language that others understand.

Combat Fear of Change

Innovative tech solutions – especially as they relate to automation – are typically marketed on the premise that they’ll streamline efficiency and cut costs. But from an end-user’s perspective, AI can feel like a looming threat ready to inch them out of a job. Will intelligent automation eliminate some jobs? Of course. But that doesn’t mean those human resources can’t still be retained through more strategic allocation. If fear of change is hindering adoption of AI, repurpose your message to present it as a solution that will make their lives better, whether that be reducing their day-to-day drudgery or giving them an opportunity to focus their talents on more meaningful work.

Ultimately, the best strategy for successful AI implementation is to align your systems, process and people. Without the latter part of the equation, you simply will not achieve complete success. By recognizing and addressing the human element, adoption should be smooth sailing.

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How IT Service Management Can Be Transformed With Intelligent Chatbots

In today’s digital landscape, organizations are facing increasing demands to do more with less, keeping expenditure at a minimum and efficient output at a maximum. In response, more and more enterprises are turning to artificial intelligence to bridge the gap. In fact, a recent report by Oracle revealed that 80% of brands either already use or plan to implement AI — specifically chatbot technology — to better serve customers by the year 2020.

But what about internal customers? Couldn’t they, too, benefit from chatbots? The fact is that the IT help desk has become an indispensable component of business success. With increasing pressures to cut costs and a growing demand to drive efficiency, however, IT technicians and administrators often find themselves struggling to keep their heads above water. As a result, delays and bottlenecks impact end-user productivity, and IT talent is wasted.

I believe that intelligent chatbots have the potential to revolutionize the way the service desk is run, transforming inefficient, manual-laden workflows into a streamlined, self-driving operation.

What Are Chatbots?

Chatbots are essentially computer programs that are powered by artificial intelligence and machine learning technology to facilitate automated, digital conversations with people. If you’ve ever used the online chat feature of a website, it’s highly likely that you were interacting with a bot – and chances are, your issue was resolved entirely without the need for any human intervention.

Intelligent chatbots are capable of understanding language, both written as well as spoken, and contextually interpreting that information to a significant degree in order to produce an appropriate response. In addition to pre-programmed data, intelligent bots also have the ability to extract data from various sources, such as wikis, best practices and user guides to help end users resolve issues quickly without having to open help desk tickets.

The Role Of Chatbots In IT Service Management (ITSM)

Some experts estimate that anywhere from 30% to 50% of all Level 1 help desk support functions are repetitive in nature (password resets, anyone?). Not only are these tasks time consuming and monotonous, but they are also quite costly from a human resource perspective.

Paying skilled IT personnel to perform laborious elemental work day in and day out isn’t just a waste of money. It’s a waste of talent. And when the work isn’t meaningful, the risk of employee turnover also goes up.

Meanwhile, from an end-user perspective, sending help desk tickets and waiting for responses impedes productivity. So, not only are IT agents bogged down by tedious requests, but the entire workforce can potentially be impacted.

A Better End-User Experience

Introducing chatbots into the IT service management process enables organizations to shift the regular and repetitive tasks and workflows away from human agents and toward AI-powered software. Intelligent bots are capable of answering simple user inquiries, troubleshooting issues and providing self-service remediation options. When an end user has a problem that they need IT’s assistance to solve, they can get their answer or resolution via a quick chat using a conversational electronic interface — just as customers do when using a live chat.

As a result, end users no longer have to wait for resolution. In fact, in many cases, employees can be empowered to use self-service options to resolve issues entirely on their own.

Cutting Costs

Simply requests like password resets are time-consuming and costly. Consider the time it takes for the end user to get locked out, open a ticket to the help desk and wait, as well as the time it takes the IT agent to manually process the request. Surely there are better ways for talented IT professionals to spend their time and energy.

Shifting simple but essential tasks like this from human to chatbot can save tremendously, both in time and in end-user productivity levels. And this is just one example. Take into account the aforementioned 30% to 50% of other repetitive Level 1 help desk functions, and you’ve got something you can really take to the bank.

Finally, though equally as important, introducing intelligent chatbots into the service desk system can take much of the pressure off of IT personnel. Enter artificial intelligence and machine learning, which, according to Gartner, Inc., will free up to 30% of support capacity for IT service desksby the year 2019. Rather than wasting time and energy on mundane, tiresome tasks, IT workers can use their creativity and cognitive abilities to perform work that interests and challenges them.

Getting Started With AI And Chatbots

If your organization decides to invest in chatbots, maximize your investment by looking for quick wins that solve specific ITSM issues, or tasks that can be automatically performed by a bot. These are typically relatively easy to automate but will produce a fast and measurable return on investment.

A good place to start is with a simple IT service desk chatbot that can create and assign tickets, escalate tickets to real agents, assist end users with questions and provide important updates on critical incident IT and security.

Intelligent bots can take that a step further. In my experience, here are a few good places to start:

• Ticket handling: Categorization, prioritization and assignment of tickets.

• Level 0 support: Leveraging artificial intelligence to provide 24/7, self-service support.

• AIOps: Use of advanced analytics technologies to proactively detect, diagnose and address problems.

• Decision support: Utilization of the predictive capabilities of machine learning algorithms to make better, more data-driven decisions.

Simply put, intelligent bots have the potential to supercharge the IT help desk, skyrocketing the productivity of both the support agents and the end users. This ultimately results in greater efficiency, lower operational costs, improved retention and the opportunity to innovate at a much faster rate. And in today’s digital age, this is what will separate the success stories from the failures.

This article was originally published in Forbes Technology Council. To see the original publication, click here.

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