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Preparing for the Future of Work: Transitioning to Intelligent Automation

These days, a growing number of organizations are making the shift toward integrating intelligent automation as a critical component of their business model. But it should never be about automating just for the sake of automating. That’s not going to help you win the AI race. The future of work will involve more of a hybrid approach that balances artificial with human intelligence for a “best of both worlds” kind of environment.

From the top down, it’s important to identify and address the challenges companies will face when implementing initiatives around critical areas, such as analytics, intelligent process automation, digital and AI. There is no shortage of lessons to be learned and common themes from which to gain knowledge. For those charged with preparing their organizations for what the future of work will inevitably become, here are five key insights to keep in mind.

Expect the unexpected.

While it’s true that business leaders can learn from others who have gone before, the fact is, every hybrid AI initiative is unique. As such, it’s important to design fluid systems that are capable of accommodating requirements, expectations and business challenges that are often unexpected. To some degree, the future of work is a moving target. Systems that can adapt and evolve will be the ultimate key to success.

Mistakes will happen.

It’s been said that failure is the key to success. This is an important mantra to keep top of mind when preparing for the future of work. Remember that when it comes to any type of change process, mistakes are inevitable. When and if you do fail, the key is to fail fast and bounce back by reflecting, regrouping and iterating your subsequent attempts with a greater understanding of what you’ll need to do in order to succeed. Decision-makers must also recognize that change is a necessary investment that requires the right communication and resources at the right time.

Start with small, measurable wins first.

Automating at scale isn’t something that takes place overnight – at least not if it’s done correctly. As you move toward the future of work and strive for digital transformation, it’s wise to start with smaller wins that can quickly generate ROI. To begin, focus on the tasks already being performed by the organization that are menial, repetitive and mature. Capturing those quick wins early will gain you buy-in and provide a solid foundation upon which to build out an organization-wide automation strategy.

Identify and communicate the ‘hows’ and ‘whys’ across the enterprise.

When it comes to good governance, it’s critical that executives carefully develop their strategic plans around intelligent automation. More importantly, they must openly and consistently communicate the hows and whys behind their decisions to everyone across the organization. As mentioned, incorporating the valuable skills of humans in with the benefits of automation and AI is the ideal scenario. As such, proactively reskilling, retraining and reorganizing employees will become essential over the coming months and years.

Automate accordingly to address business problems.

Some organizations find it necessary to enlist the help of consultants or outsourced companies to help them identify the best processes to begin the automation journey. Don’t be afraid to admit if this assistance is something that could benefit your organization. Either way, the goal is to gain a deeper understanding of business priorities so you can identify quick successes. Ideally, the automation strategy should be one that is a joint initiative between the IT department and the rest of the business.

Ultimately, what these five insights have in common is that they require executive buy-in, AI investment that is strategic and a shift toward a business model that involves a convergence of innovative technologies. Master these five steps and your organization will be much better positioned to be successful in the future of work.

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4 Tips for Successful Adoption of AI at Scale

Utilizing artificial intelligence on a smaller scale is relatively simple in nature. At the enterprise level, however, it isn’t always so straightforward. This may be a contributing factor to the recent survey results from Gartner indicating that while 46% of CIOs have plans for implementing AI, only a mere 4% have actually done so. Those early adopters no doubt have faced and overcome many challenges along the way. Here are four lessons that you can learn from that will make adopting AI at scale easier.

Start small.

Desmond Tutu once said that the best way to eat an elephant is “one a bite at a time.” Just because your end goal is to have enterprise-wide adoption of AI doesn’t mean you have to aim for that large of an outcome right off the bat.

Most often, the best way to initiate a larger AI project is to start with a smaller scope and aim for “soft” rather than “hard” outcomes. In other words, rather than primarily seeking direct financial gains, focus instead on things like process improvement and customer satisfaction. Over time, the benefits gained by achieving these smaller “soft” goals will lead you to your larger objectives anyway.

If decision-makers in your organization require a financial target in order to start an AI initiative, Gartner VP and distinguished analyst Whit Andrews recommends setting the target as low as possible. He suggests the following: “Think of targets in the thousands or tens of thousands of dollars, understand what you’re trying to accomplish on a small scale, and only then pursue more-dramatic benefits.

Focus on augmentation vs. replacement.

Historically, significant tech advances have often been associated with a reduction in staff. While cutting labor costs may be an attractive benefit for executives, it’s likely to generate resistance amongst staffers who view AI as a threat to their livelihood. A lack of buy-in from front line employees may hinder progress and result in a less favorable outcome.

To avoid this, shift your approach to one that focuses on augmenting human workers as opposed to replacing them. Ultimately, communicating that the most transformational benefits of AI lie in the technology’s ability to enable employees to pursue higher value and more meaningful work. For instance, Gartner predicts that by 2020, 20% of organizations will have workers dedicated to overseeing neural networks.

Make an effort to engage employees and get them excited about the fact that an AI-powered environment will enhance and elevate the work they do.

Prepare for knowledge transfer.

The majority of organizations are not adequately prepared for AI implementation. In particular, most lack the appropriate internal skills in data science and, as a result, plan on relying heavily on external service providers to help bridge the gap. Furthermore, Gartner predicts that 85% of AI projects initiated between now and 2022 will deliver erroneous outcomes due to inaccurate or insufficient data and/or lack of team knowledge/ability.

In order for an AI project to work at scale, there must be a robust knowledge-base fueled by accurate information and there must be adequately trained staff to manage it. Simply put, relying on external suppliers for these things isn’t a feasible long-term solution. Instead, IT leaders should prepare in advance by gathering, storing and managing data now and investing in the reskilling of existing personnel. Building up your in-house capabilities is essential before taking on large-scale AI projects.

Seek transparent solutions.

Most AI projects will inevitably involve some type of software, system, application or platform from an external service provider. When evaluating these providers, it’s important that decision-makers take into account not only whether the solution will produce the appropriate results, but also why and how it will be most effective.

While explaining the in-depth details of something as complex as a deep neural network may not always be possible, it’s imperative that the service provider be able to, at the very least, provide some type of visualization as to the various choices available. At the end of the day, the more transparency that is present, the better – especially when it comes to long-term projects.

For more information on how to incorporate artificial intelligence into your strategic planning for digital transformation, check out this resource from Gartner. And when you’re ready to move forward with your AI initiative, give Ayehu NG a try free for 30 days. Click here to start your complementary trial.

Your Top Artificial Intelligence Adoption Questions, Answered

According to Gartner, the number of organizations implementing some type of artificial intelligence (i.e. machine learning, deep learning and automation) has grown by 270% over the past four years. One big reason for this boost is the fact that executives and decision makers are beginning to recognize the value that these innovative technologies present.

That’s not to say they’re all on board. Are CEOs getting savvier about AI? Yes. Do they still have questions? Also yes – particularly as it relates to the adoption/deployment process. Let’s take a look at a few of the top questions and answers surrounding the topic of artificial intelligence below, along with some practical advice for getting started.

Is a business case necessary for AI?

Most AI projects are viewed as a success when they further an overarching, predefined goal, when they support the existing culture, when they produce something that the competition hasn’t and when they are rolled out in increments. At the end of the day, it’s really all about perspective. For some, AI is viewed as disruptive and innovative. For others, it might represent the culmination of previous efforts that have laid a foundation.

To answer this question, examine other strategic projects within the company. Did they require business cases? If so, determine whether your AI initiative should follow suit or whether it should be standalone. Likewise, if business cases are typically necessary in order to justify capital expenditure, one may be necessary for AI. Ultimately, you should determine exactly what will happen in the absence of a business case. Will there be a delay in funding? Will there be certain sacrifices?

Should we adopt an external solution or should we code from scratch?

For some companies, artificial intelligence adoption came at the hands of dedicated developers and engineers tirelessly writing custom code. These days, such an effort isn’t really necessary. The problem is, many executives romanticize the process, conveniently forgetting that working from scratch also involves other time-intensive activities, like market research, development planning, data knowledge and training (just to name a few). All of these things can actually delay AI delivery.

Utilizing a pre-packaged solution, on the other hand, can shave weeks or even months off the development timeline, accelerating productivity and boosting time-to-value. To determine which option is right for your organization, start by defining budget and success metrics. You should also carefully assess the current skill level of your IT staff. If human resources are scarce or if time is of the essence, opting for a ready-made solution probably makes the most sense (as it does in most cases).

What kind of reporting structure are we looking at for the AI team?

Another thing that’s always top-of-mind with executives is organizational issues, specifically as they relate to driving growth and maximizing efficiencies. But while this question may not be new, the answer just might be. Some managers may advocate for a formal data science team while others may expect AI to fall under the umbrella of the existing data center-of-excellence (COE).

The truth is, the positioning of AI will ultimately depend on current practices as well as overarching needs and goals. For example, one company might designate a small group of customer service agents to spearhead a chatbot project while another organization might consider AI more of an enterprise service and, as such, designate machine learning developers and statisticians into a separate team that reports directly to the CIO. It all comes down to what works for your business.

To determine the answer to this question, first figure out how competitively differentiating the expected outcome should be. In other words, if the AI effort is viewed as strategic, it might make sense to form a team of developers and subject matter experts with its own headcount and budget. On a lesser scale, siphoning resources from existing teams and projects might suffice. You should also ask what internal skills are currently available and whether it would be wiser to hire externally.

Practical advice for organizations just getting started with AI:

Being successful with AI requires a bit of a balancing act. On one hand, if you are new to artificial intelligence, you want to be cautious about deviating from the status quo. On the other hand, positioning the technology as evolutionary and disruptive (which it certainly is) can be a true game-changer.

In either case, the most critical measures for AI success include setting appropriate and accurate expectations, communicating them continuously and addressing questions and concerns with swiftness and transparency.

A few more considerations:

  • Develop a high-level delivery schedule and do your best to adhere to it.
  • Execution matters, so be sure you’re actually building something and be clear about your plan of delivery.
  • Help others envision the benefits. Does AI promise significant cost reductions? Competitive advantage? Greater brand awareness? Figure out those hot buttons and press them. Often.
  • Explain enough to illustrate in the goal. Avoid vagueness and ambiguity.

Today’s organizations are getting serious about AI in a way we’ve never seen before. The better your team of decision makers understands about how and why it will be rolled out and leveraged, the better your chances of successfully delivering on that value, both now and in the future.

Need Scalability? Intelligent Automation is the Answer

Imagine how much your company could accomplish if you had a veritable army of employees at your disposal. More importantly, what if these employees were perfectly happy waiting in the background for the next time you needed them? Believe it or not, that’s precisely what you’ll get with intelligent automation. Let’s take a look at the surprising way artificial intelligence can provide your business with the scalability you need to stay on top of your game.

Instant Access

With human workers, it’s not feasible to dramatically increase your workforce whenever the need arises, nor is it easy to decrease your numbers when things get slow. There’s a complicated process behind all of this and time is not on your side. With intelligent automation, however, you have a team of robots who are ready, willing and able to get the job done at any given moment.

Consistency

Bringing different employees up to speed via on-boarding and training can be challenging and time-consuming, especially in today’s fast-paced, digital age. Not to mention the fact that you have to initiate the entire process over again every time someone new joins the team. Robots, on the other hand, can be “trained” in groups of any size with the outcome being routine and perfect consistency across the board.

Cost Savings

Recruiting, hiring, training and retaining talented employees costs money. In addition to intelligent automation providing the ability to scale up or scale down instantly as well as train and deploy thousands of bots while maintaining complete consistency, all of this can be done at a reduced cost to the business.

Now, let’s take a look at a few real-world applications of these benefits.

Scenario 1

Your business is launching a new product and, as a result, will incur a substantial increase in transactions. Your current workforce is already maxed out and you don’t have the time or the ability to hire any additional employees. Intelligent automation can step in and bridge the gap, handling the influx of work at any capacity necessary without the major hassle and expense of staffing. Then, once things settle down, you can scale back down to normal as needed.

Scenario 2

Business has been particularly lucrative as of late and you’ve had to increase output significantly to meet the increased demands of your customers. Suddenly, the market takes a turn for the worse and your numbers start to rapidly decline. With intelligent automation in place, you won’t have to face the possibility of laying employees off. Rather, you could just scale back the number of robots.

Scenario 3

One of your biggest competitors has launched a new product or service and you’re scrambling to develop and implement something similar. Chances are you can’t afford to hire a slew of new employees to help bring your comparable product or service to market and doing so would take too long. Conversely, putting too much pressure on existing team members could result in costly mistakes and QA issues. AI, on the other hand, is available at the ready to take on whatever is necessary for you to remain competitive.

Without question, intelligent automation has the potential to bring your business to the next level. Are you ready? Give us a call today or download your free 30 trial now to get started.

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5 Things to Avoid for a Successful Intelligent Automation Rollout

For all the talk we do here at Ayehu about how to make intelligent automation work for your organization, one area we don’t usually cover is how and why these types of projects often fail. Sometimes even though the reason for adopting automation is on target, the outcome isn’t quite what one had hoped for. This can lead to costly double-work and the frustration of having to start over again. To improve the chances of your automation project going off without a hitch, here are the 5 most common mistakes so you’ll know exactly what to avoid.

Focusing on tools and tasks instead of people.

It may seem ironic, particularly given the widespread opinion that artificial intelligence is somehow out to replace humans, but one of the biggest reasons an automation project fails is because it was designed around a task or tool instead of the people who it was ultimately designed to help. The fact is, intelligent automation is meant to streamline operations and make the lives of your IT team better, not worse. Focus on how the project will benefit your human workers and the results will be much greater.

Failing to adequately calculate and communicate ROI.

For an automation project to be carried out successfully, the projected benefits and long-term gains must be determined and demonstrated upfront. This includes taking into account the early costs associated with adopting a platform and helping decision makers understand the time-frame for seeing positive returns. Without this, you risk upper management pulling the plug too early due to lack of results. (If you’re not sure how to calculate ROI on an IT automation project, here’s a helpful guideline.)

Not setting appropriate expectations.

Sometimes an intelligent automation project is deemed a “failure” simply because it did not meet the (often unrealistic) expectations of certain stakeholders. That’s why it’s so important that those in charge of planning, testing and implementing any AI project include communication of the expected time-frame as well as the potential for issues and delays that may inevitably arise. When “the powers-that-be” know what to expect ahead of time, there are no surprises to have to deal with during the process.

Automating broken processes.

Another common cause of an automation project failure occurs when those in charge attempt to automate a process that isn’t working properly in the first place. Not only is this a huge waste of time and resources, but it simply won’t work, which means backtracking, adjustments and a whole host of other delays will ultimately occur. Before starting any automation project, be certain everything you’re planning to automate is relevant and ready.

Not using the right platform.

Just like most things in IT, not every automation platform is created equal. Some organizations fall into the trap of purchasing the cheapest tool they can find only to learn that, as usual, you get what you pay for. Others make the mistake of investing in a product that they think is top-of-the-line, only to discover that it has way more features than they really need, making it a complex waste of money. The key to successfully carrying out an intelligent automation project is to do your research and select a platform that is robust but easy to use and scalable to fit your specific needs.

Thinking about trying automation but not sure where to begin?

Check out these common tasks and processes that can and should be automated and then download your free 30 day trial of Ayehu NG to experience it for yourself.

eBook: 10 time consuming tasks you should automate

And the Employee of the Year Award goes to: A robot?

Imagine: you’re in the annual company meeting and the CEO announces who has been the busiest, most productive employee over the past 12 months. Now, imagine if he completes that statement not with the name of you or any of your co-workers, but instead points to your intelligent automation platform. In other words, a robot.

Can a robot really take the place as employee of the year? The answer is an emphatic yes. Here’s why.

It’s no secret that the IT department is continually being asked to do more with less. In other words, be as productive and efficient as possible while simultaneously limiting the amount of resources being used to do so. As a result, IT leaders have to find a way to maximize output while also minimizing the time and costs associated with producing that output. With humans, this simply isn’t possible without either extending the hours worked or hiring additional staff, neither of which will achieve the goal of keeping expenditures down.

Another area of pressure IT leaders experience is that of user satisfaction. Internal service levels and external customer expectations are increasing at a rapid rate, and if you can’t meet those demands effectively, you will lose your competitive advantage. In other words, your bottom line will suffer. Yet again, in order for human agents to achieve these goals is to either work more or hire additional team members.

Enter intelligent automation, or a team of artificially intelligent robots who can basically take on all the time consuming day to day tasks that the help desk currently handles. Whether it’s password resets, system monitoring, incident management or some other complex workflow, intelligent automation can be leveraged for it all. As a result, the work will be completed faster, with fewer mistakes, driving productivity and efficiency up while also bringing costs down.

Many IT professionals erroneously view these robots as a threat to their very livelihood. After all, if a machine can be used to do all these tasks, what’s the point of keeping humans on staff? Why not automate the entire operation? Well, for starters, many automated processes still require some type of human input.

Secondly, while intelligent automation may take some of the work away from humans, it will inevitably free those humans up to apply their skills to more complex and important business matters. So, it’s not a replacement, but rather a shift in responsibility. Furthermore, experts predict that AI will actually create 58 million new jobs by 2020. As such, it should be embraced rather than feared.

So, when the big boss stands up and hails automation as the company’s busiest employee, that doesn’t necessarily mean bad news for the people who work there. As long as the technology is leveraged properly and viewed as the powerful and innovative tool it truly is, its role as employee of the year is something that will drive the ongoing success of the organization and make the lives of human workers easier and/or more meaningful.

Give the employee of the year a test drive in your own company and experience the power and impact of intelligent automation for yourself by starting your free 30-day trial of Ayehu.

The Rise of Artificially Intelligent Service Management (AISM)

It’s been said that the best way to serve customers is to anticipate their needs, whether it’s a restaurant concierge offering to walk patrons to their vehicles with an umbrella overhead on rainy evenings or rolling out an update on a software product. The same concept can be applied in the IT realm, specifically in IT service management (ITSM).

The fact is, with today’s technology, it’s entirely possible to predict that certain situations will occur, from simple password reset requests to servers crashing. It’s not really a matter of if these things will happen, but rather when. And if you know what’s coming, you can be prepared to respond and, in many cases, even head problems off at the pass.

That’s where artificial intelligence comes into play. Thanks to AI and machine learning technologies, ITSM professionals can now predict potential problems faster and with a much higher degree of accuracy. As a result, the end user (or “customer”) enjoys a much more positive experience. In other words, everybody wins.

What is Artificially Intelligent Service Management?

The core principles of ITSM remain sound. The introduction of AI into the mix doesn’t change this. Instead, it enhances it. AISM simply takes the fundamental concepts and processes of ITSM – incident response, service request management, etc. – and leverages newer and better technologies to make them even more effective. In the context of IT service management, AI can be applied to improve, simulate and/or replace the work of a human agent.

You may be asking yourself, “Isn’t this really just automation?” The answer isn’t necessarily cut and dry. The truth is, we’ve been automating processes and workflows for decades, and ITSM is no stranger to this technology. The difference is that with AI, these processes and workflows become more intelligent and independent. Rather than just carrying out predefined or scripted instructions, AI is capable of identifying and carrying out required actions all on its own.

How does AISM work?

Now, let’s take a look at how AI can enhance the execution of ITSM activities.

Support Request Management

The basics of ITSM: an end user needs assistance. They either pick up the phone to call the help desk, send an email request, submit a support ticket or browse the self-service options (if available). The steps necessary to fulfill that incoming request are then followed and the user receives his or her desired outcome. The problem is, that outcome could potentially take hours, days, weeks or even longer.

Now, let’s look at that scenario with AISM at the helm. The end user initiates contact and immediately receives two-way support from an intelligent bot. They request what they need and the bot – relying on underlying technologies of machine learning, deep learning, neural networks and natural language processing – understands the request and responds accordingly. Rather than waiting for a human to take action, AISM can produce results for the end user within seconds.

Incident Management

The ability to react, respond to and correct an incident is one of the most basic components of ITSM. Traditionally, a form would be filled out. Perhaps the analyst might do a little research. Ultimately, the task is assigned to a team. There it might sit untouched for a while before it is either rejected, resolved or possibly even assigned to another team altogether. In the end, the incident is resolved, but after much back and forth and passing of the torch.

Enter AISM. The end user reports a problem via his or her self-service portal and an incident is immediately created. Thanks to artificial intelligence, however, that same end user may instantly be prompted with various suggestions that are pulled from the underlying knowledge base. This may result in resolution right away.

If not, it is turned over to a support analyst who is automatically provided with suggested resolution methods. The AI can even advise who the incident should be assigned to, what relevant implications may exist, the scope of the situation and more.

Problem Management

In a traditional ITSM setting, problem management would often involve a person taking the time to review prior incident patterns and trends and develop possible resolutions. Along the way, however, many twists, turns, delays and bottlenecks exist. For instance, let’s say a support agent grows weary of addressing the same incidents over and over. The problem may be investigated further. Perhaps some knowledge may be created and a change is even identified. But, given the chaotic nature of the ITSM environment, time passes and nothing really gets done.

Now, take that same scenario in the context of AISM. Instead of a frustrated human agent taking the initiative to identify and resolve problems, machine learning technology continuously scans patterns of data to pinpoint and present potential issues that should be investigated. What’s more, thanks to data processing and learning across multiple patterns of work, AI is even capable of proposing a solution, backed by data-driven risk and impact analyses. In other words, it takes the guess-work out of decisions.

AISM – From Reactive to Proactive and Beyond

Getting back to our original point – that the best customer experiences are anticipatory in nature – AISM enhances service management by facilitating the shift from reactive (meeting needs when they occur) to proactive (predicting and preventing issues from happening in the first place). There are three key ways AISM can do this:

  • Guidance – The end user has a need and AISM uses a connection with endpoint tools to identify and make suggestions based on that need.
  • Learning – Building a knowledge base used to be a hassle. Not with AISM. Thanks to machine learning and AI tracking systems, the knowledge base can naturally grow based on issues encountered over time.
  • Strategy – AISM is capable of identifying and recommending both changes to existing core services as well as new innovations to improve for the future.

Conclusion

As you can see, AISM follows many of the same principles, processes and best practices of ITSM. It’s just faster and more accurate. And with AI being leveraged to intelligently automate complex tasks at just about every operations level, IT professionals will be freed up to spend more time innovating and evolving to help achieve business goals.

Buckle up folks, because AISM is poised to be a true game-changer.

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7 Steps for Getting Started with AIOps

Today’s IT teams are dealing with a growing mountain of data. What’s more, they’re finding themselves having to use a multitude of tools in order to monitor and manage that data. In situations of technical outages, this can make it incredibly difficult and time-intensive to identify and resolve underlying issues. Anyone in business knows that even just a tiny amount of down-time can have a serious and costly impact on the bottom line. And it’s the IT team that bears the brunt of the burden.

As Padraig Byrne, Senior Director Analyst at Gartner put it:

“IT operations is challenged by the rapid growth in data volumes generated by IT infrastructure and applications that must be captured, analyzed and acted on. Coupled with the reality that IT operations teams often work in disconnected silos, this makes it challenging to ensure that the most urgent incident at any given time is being addressed.”

Take, for example, the two largest supermarket chains in Australia. Last year, both experienced severe technical issues which forced them to shut down several stores while they worked on fixing the problem. Not only did those companies lose revenue during the shutdown, but they also suffered a serious blow to their reputation. In other words, customers were not happy.

To better and more quickly identify, resolve and prevent outages and other problems, organizations are turning to artificial intelligence for IT operations (AIOps) – the long-term impact of which Gartner’s Byrne predicts will be nothing short of “transformative.”

What is AIOps

In simplest of terms, AIOps combines data science and machine learning functionality to enhance and/or replace the majority of IT operations functions. This includes performance and availability monitoring, event analysis and correlation, ITSM and automation. To put it even more simply, AIOps platforms gather and analyze all of the data produced by IT to extract what’s of value and present meaningful insights.

And AIOps is quickly gaining ground. Gartner predicts that by 2023, the exclusive use of AIOps to monitor infrastructure and applications will reach 30% – up from just 5% in 2018.

“IT leaders are enthusiastic about the promise of applying AI to IT operations, but as with moving a large object, it will be necessary to overcome inertia to build velocity,” comments Byrne. “The good news is that AI capabilities are advancing, and more real solutions are becoming available every day.”

How to Get Started with AIOps

Step 1: Don’t put it on the back burner.

If you really want to reap the benefits of AI for your IT operations, the time to jump on the AIOps bandwagon is now. Don’t make this an afterthought or push it out as some far-off future initiative. Even if the actual deployment isn’t imminent, start preparing yourself and others within your organization by becoming familiar with artificial intelligence and machine learning capabilities today. This way, in the event that priorities shift and you need to implement sooner, you’ll already be a few steps ahead of the game.

Step 2: Be careful when choosing your initial test case.

The concept of AIOps at scale may seem overwhelming, but keep in mind that truly transformative initiatives almost always start small. Focus first on capturing knowledge, testing frequently and iterating as needed. You don’t need to be an expert right out of the gate, and not every project you spearhead will be a resounding success. Just be mindful of what you’re starting with and work your way up from there.

Step 3: Work on developing and demonstrating your proficiency.

If you are leading the AIOps charge in your organization, you’ll inevitably be the go-to subject matter expert, at least initially. It will be up to you to communicate and convey the value of the technology to your colleagues and others in leadership. Wear your role with pride and start assembling a team of others who can champion the cause alongside you. Start by identifying gaps that exist in skills and experience, and then create a plan to address those gaps together.

Step 4: Don’t be afraid to experiment.

There are already many AIOps platforms on the market that are incredibly complex and subsequently cost-prohibitive. As with any tech product or solution, it’s wise do experiment and test the waters. Keep in mind that more features doesn’t necessarily equate to a better product. Your organization may not need all those bells and whistles. If possible, take advantage of product demos and free trials. This will enable you to evaluate AIOps uses and applications specific to your business needs without having to invest too heavily or commit to one particular solution.  

Step 5: Expand your vision beyond the IT department.

Data management is a massive component of AIOps. Take a step back and examine your organization. Chances are very high that your existing teams are already skilled in this area and that there are data and analytics tools already present within your organization. Resist the urge to reinvent the wheel and be willing to expand your vision to look beyond the IT department. It could save you tremendous time, effort and money.

Step 6: Standardize whenever possible and modernize wherever it makes sense.

You can prepare your existing infrastructure so that it is capable of supporting an AIOps implementation in the future by developing a consistent automation architecture, immutable infrastructure patterns and infrastructure as code (IaC).

Step 7: Consider build-vs-buy.

Understand that there are a number of variables involved in making a shift to AIOps. Likewise, the platforms available on the market today will continue to evolve, as will the infrastructure and applications for which you are responsible currently. Be mindful of this as you weigh whether to purchase a solution or build one of your own. Ideally, the best answer will likely be a combination of the two, so be prepared to figure out which approach best applies where and by how much.

Over the past few years, AIOps has developed from an emerging category to an IT necessity. Successful companies are beginning to leverage AIOps to automate and improve IT operations by applying machine learning to their data. Furthermore, forward-thinking organizations will use AIOps to draw valuable insights from their IT data that will help drive strategic business decisions.

If AIOps is on your to-do list (and it certainly should be), the steps outlined above should help you to, at the very least, lay the groundwork so that when the time comes to implement, the process will go faster and much more smoothly.

Why wait? Experience the next generation of IT automation, powered by machine learning and artificial intelligence and get started on the fast track to successful AIOps deployment. Start your free 30 day trial of Ayehu today!

Making the Case for Artificial Intelligence in Your Organization

Recent statistics published in Forbes revealed that while 82% of IT and business decision makers agree that company-wide strategies to invest in AI-driven technologies would offer significant competitive advantages, only 29% said their companies have those strategies in place.

Why such a big divide? In many situations, it’s a simple lack of buy-in. In fact, Forbes Insights research also revealed that while 45% of IT stakeholders express “extreme urgency” regarding the application of AI within their organizations, only 29% see that same sense of urgency among their C-suite. Among the board of directors, that percentage drops down to just 10%.

Leaders who want to reap these benefits and advance AI within their organizations must overcome these odds by making a strong, solid business case around how artificial intelligence will deliver in terms of business benefits, such as operational efficiency, competitive advantage and revenue growth. Here are a few recommendations on how to accomplish this goal.

Illustrate success through real-life case studies.

There’s nothing more powerfully persuasive than a real-life story. C-suite executives and board members don’t want to hear about hypotheticals. They want to see numbers – quantifiable proof of ROI – before they’ll be willing to sign on the dotted line and invest in AI. After all, it’s pretty hard to argue against benefits like lowered costs, improved service levels and other key business advantages.

Demonstrate AI’s decision-making support.

One of the hardest parts of an executive’s job is making critical business decisions. If you can show them how artificial intelligence can address and resolve this major pain point, you’ll make believers out of even the biggest skeptics. Simply put, AI provides the ability to digest, process and analyze data to unlock invaluable insight and boosting confidence through data-driven decision support.

Position AI as the cornerstone to successful digital transformation.

These days, everybody’s talking about digital transformation. In fact, it’s widely believed that moving to digital operations and offering digital services will be absolutely essential in order to remain competitive in the modern economy. If you can position AI as the catalyst for making this happen, you’ll get emphatic yesses across the board. And since analytics is the core to what drives digital experiences, the connection to AI shouldn’t be too difficult.

Link AI with the power to innovate.

40% of IT leaders list driving innovation and implementing new tech as one of their top concerns. In today’s rapidly changing landscape, staying in-step is no longer enough. To remain competitive and achieve sustainable success, organizations must find a way to stay a few steps ahead. Easier said than done? Not when you have artificial intelligence in your corner. AI offers business leaders the opportunity to garner engagement from all levels of the organization, creating a truly collaborative environment where ideation and innovation thrive.

Reinforce the power of AI for optimizing client experience.

In business, you’re only successful if your customers are happy. Leveraging machine learning and artificial intelligence can help businesses to become far more responsive to their clients, ultimately delivering a better experience overall. And it’s a win-win, because not only do customers receive a higher level of service, but because AI frees up employees to focus more on high-value initiatives, the organization benefits from greater productivity. Happier clients + more efficiency = a better bottom line.  

It’s important to point out that AI, just as with any technology, shouldn’t just be implemented for the sake of it. It should be leveraged because it’s the best and most effective solution to a specific business problem or opportunity. When presenting your case, be sure to tie the technology and its capabilities directly to these problems and/or opportunities, and demonstrate exactly who will benefit and how. This will make your case far more compelling and improve your chances of success.

Want to really wow those key decision-makers? Download your free trial of Ayehu, and you’ll have a full 30 days to create a use case of your own that will demonstrate quantifiable ROI within your own organization. Click here to get started!

EBOOK: HOW TO MEASURE IT PROCESS AUTOMATION RETURN ON INVESTMENT (ROI)

3 Ways Virtual Assistants are Transforming the Service Desk

A few years ago, the chatbot phenomenon swept the consumer world. Today, people are becoming more and more at ease using conversational AI and virtual assistants to do everything from set their doctor appointments to planning travel. Yet, despite this consumer-driven craze, one area that seems to have been left largely in the dark is the IT help desk. Surprisingly (and frustratingly) enough, for many organizations, even something as basic as requesting more storage and resetting your password still requires opening and waiting for a ticket to be serviced.

The truth is, what once began as an innovative service to help employees has somehow evolved into more of a costly distraction. Budget-conscious executives have come to view the IT service desk, not as a core component of the business, but as an expensive necessity. As such, the help desk has long been the target of cost-cutting reductions. Yet, despite these efforts, one recent report indicates that the expenses surrounding service desks are actually on the rise. Today, a typical help desk is massively overloaded and majorly underfunded.

Enter the virtual assistant. Unlike the many other “solutions” CIOs tried in the past, chatbot technology has the potential to dramatically disrupt and ultimately transform the modern service desk in a way that is both positive and sustainable. This will happen in three distinct ways, as follows.

Automating the humdrum.

According to Gartner, password resets account for 40% of all service desk requests. In this way, help desk support agents can feel like mere robots, repeatedly responding to the same requests over and over (and over) again. Why not transition these mundane, repetitive tasks to actual robots? AI-driven virtual assistants can handle everything from simple tasks to complex workflows. This frees up human agents to focus on higher-level initiatives.

The best part? Chatbots are available 24 hours a day, 7 days a week, 365 days a year. They work weekends and holidays and they don’t require overtime. This means not only can you offer round-the-clock support, but scaling to higher volumes will not require an increase in headcount. The tremendous value this promises has led many large, global enterprises to begin deploying virtual assistants.

Removing the human from intuitive tasks.

Under normal circumstances, a typical service order can take more than a full business day to resolve. This process generally includes several interactions between support analysts and often requires escalation to subject matter experts. Next generation chatbot technology is now capable of using historical interactions – such as voice transcripts, prior transactions and other preexisting data – to learn, engage, suggest and recommend resolutions. Even complex troubleshooting can be handled almost, if not entirely by virtual assistants.

Revamping the user experience.

The IT industry has spent a fortune in an attempt to improve employee self-service. Yesterday’s setup was centered on the creation and maintenance of an institutional knowledge base where users could log in and search for answers to their questions in lieu of opening a help desk ticket. The results of these queries were often mixed. Today, thanks to advances in artificial intelligence technology, a user can type, text or even speak their question and a virtual assistant can engage in a meaningful exchange to resolve the issue.

Despite getting off to a markedly slow start, large enterprises around the globe are beginning to recognize the value that conversational AI brings to the table. As such, we are seeing a rapidly growing number of organizations “hiring” virtual assistants to help transform their service desks into the highly effective, cost-efficient and innovative business benefits they’ve always dreamed of being.

Get started with virtual assistant technology and see how it can revolutionize your help desk by downloading your free 30 day trial of Ayehu today.