How to Predict and Remediate IT Incidents Before They Affect Business Outcomes [Webinar Recap]

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Author: Guy Nadivi

The ability to proactively predict  and remediate IT incidents BEFORE they occur, rather than react to them after they’ve already happened, is one of the key value propositions of a new IT operations category called AIOps, which stands for Artificial Intelligence for IT Operations.

Leveraging the AI part of AIOps to mitigate problems before they become problems is a game changer for IT. So we’ve partnered with Loom Systems, who like ourselves are a Gartner Cool Vendor in their category, to demonstrate how two best-of-breed providers can integrate their respective platforms to create an enterprise-grade AIOps solution. In doing so, we believe the result is an early glimpse at the self-healing data center of tomorrow, and we think you’ll be intrigued to experience how you can peek over the horizon to see  and automatically remediate incidents before they impact end-users.

Let’s start with the obvious question many of you might have on your mind – what is AIOps? It is after all, a term that kind of snuck up on all of us.

The term AIOps, like a lot of buzzwords in our industry, was originated by Gartner. In this case, a Sr. Director Analyst named Colin Fletcher coined it in 2016, and its earliest published appearance (as best I can tell) was in early 2017.

Interestingly though, Colin told me he originally meant the term to refer to Algorithmic IT Operations.

Since then it’s evolved to refer to Artificial Intelligence for IT Operations.

Now we all know how it is in IT marketing. New buzzwords are used to refresh a category and create excitement. So is AIOps basically just a recycling of the term “IT monitoring”? Are IT monitoring and AIOps basically the same? Twins, so to speak, but with different names?

Here’s the definition for IT Monitoring, courtesy of an internet publication many of you are probably aware of called TechTarget:

  “IT monitoring is the process to gather metrics about the operations of an IT environment’s hardware and software to ensure everything functions as expected to support applications and services.   Basic monitoring is performed through device operation checks, while more advanced monitoring gives granular views on operational statuses, including average response times, number of application instances, error and request rates, CPU usage and application availability.”    

The operative words there are “gather metrics” – “through device operation checks”.

This reflects one of the primary characteristics of IT Monitoring – namely that it’s passive in nature.

And here’s Colin Fletcher’s original definition for AIOps:

“AIOps platforms utilize big data, modern machine learning and other advanced analytics technologies to directly and indirectly enhance IT operations (monitoring, automation and service desk) functions with proactive, personal and dynamic insight. AIOps platforms enable the concurrent use of multiple data sources, data collection methods, analytical (real-time and deep) technologies, and presentation technologies.”

Unlike IT Monitoring, AIOps is proactive and far more sophisticated. So AIOps is a LOT MORE than just IT Monitoring.

At this point you may be asking yourself, “OK, but how can this benefit me?”

As we all know, in today’s Digital Era, most businesses are digital or undergoing a digital transformation, which means that IT systems are replacing many traditional physical business processes, and that in turn means more work for IT Operations.

In fact, IT Operations engineers have become responsible for the customers’ digital experience. When your organization’s systems are misbehaving, underperforming, or worse not working at all, your customers’ satisfaction is affected, which often leads to customer churn.

It’s that simple.

End users often use applications or websites and love how simple and intuitive they can be. In IT though, we all know that building something to look nice and simple, can actually be quite difficult. That’s because there are usually many technologies under the hood that need to work together seamlessly in order for these digital experiences to run smoothly.

As if that wasn’t enough, let’s add some more complexity:

With Cloud Computing on the one hand, and Microservices architectures on the other, things become even more complex, for the following reasons:

  1. Cloud computing means abstraction – that can lead to struggles understanding what the impact of a performance issue on a host will do to other components of your applications.
  2. These environments change dynamically, making it harder to stay on top of everything.
  3. Microservices often require disparate data sources, each generating its own logs and metrics, making tracing and correlation an inherent part of root cause analysis (RCA).

So, the increased complexity of digital businesses architectures, coupled with the explosion of different data types, and the elevated expectations consumers have these days for seamless end user experiences, makes the life of IT Operations teams quite challenging.

Enter AIOps.

AIOps is a set of tools that enable achievement of optimum availability and performance by leveraging machine learning technologies against massive data stores with wide variance. The big idea here is to use machines to deal with machines.

Here are some examples of the challenges customers often look to address by implementing AIOps:

  • Outage prevention – organizations in the process of cloud migration or architecture change, often look for modern technologies like AIOps to help them prevent outages before the business is affected. This is a marked difference from 2 years ago when the market was just focused on noise reduction. Artificial intelligence and machine learning have raised expectations of how much more is possible.
  • Capturing different data feeds – this means it’s not just about alerts anymore. There’s a huge need to consolidate logs, metrics, and events together, and to make sense out of them as a whole.
  • Consolidation of tools – this one is mainly about the workflow of the users. They’d like AIOps to make their daily lives easier and consolidate everything into one system.

A monitoring architecture for modern enterprises that can do all of the above would be a real-life example of a self-healing architecture.

Everything starts with observability. Many enterprises use one or more infrastructure monitoring tools. Application Performance Management (APM) monitors do a great job in monitoring performance, but are very limited for the application stack and log management, rendering them a bit unhelpful for triage and forensic investigations.

These monitoring tools are usually focused on specific data feeds or IT layers, and they emit alerts when things go wrong. However, these can lead to confusing alert storms.

This is another reason why organizations are beginning to leverage AIOps to work for them and make sense out of it all. Think of AIOps as a robot that turns monotonous data into information you cannot ignore. In our case, turning logs into predictions or early stage detection of an outage.

Now that you know something is about to break, can you prevent it from happening? That’s exactly the idea of self-healing. When working with an intelligent automation platform like Ayehu, you can build simple (or complex) remediation workflows, that can take the alert from Loom Systems and automatically remediate the incident BEFORE it becomes something more calamitous.

In your monitoring architecture, you want the Automation tool to seamlessly interact with both the AIOps solution and your ITSM platform, to open a ticket and update it as you’re taking remedial action.

When configured properly, this architecture can resolve issues before they affect the business, while also documenting what happened for future reference.

Gartner concurs with this approach.

In a paper published earlier this year (ID G00384249 – April 24, 2019), they wrote that:

  “AI technologies play an important role in I andO, providing benefits such as reduced mean time to response (MTTR), faster root cause analysis (RCA) and increased I andO productivity. AI technologies enable I andO teams to minimize low-value repetitive tasks and engage in higher-productivity/value-oriented actions.”    

No ambiguity there.

A little further down in the same paper, Gartner gave the following recommended actions, representing their most current advice to infrastructure and operations leaders regarding AIOps and automation:

  “Embark on a journey toward driving intelligent automation. This involves managing and driving AI capabilities that are embedded by infrastructure vendors, in addition to reusing artificial intelligence for operations (AIOps) capabilities to drive end-to-end (from digital product to infrastructure) automation.”    

With AIOps + Automation, it’s possible to predict and prevent network outages or other major disruptions by proactively detecting the conditions leading up to them and automatically remediating them BEFORE disaster strikes. Given how costly a service interruption can be to an enterprise, avoiding issues before they happen will be a critical function in the self-healing data center of tomorrow.

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Your Top Artificial Intelligence Adoption Questions, Answered

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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.

5 Things to Avoid for a Successful Intelligent Automation Rollout

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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.

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ITOps: Best practices to improve performance and service quality

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ITOps best practicesThere’s no doubt about it. Intelligent automation is the biggest driver for increasing the overall performance of ITOps and service quality for businesses today. It allows IT management and personnel to streamline their workflows by automating the time consuming day to day tasks that bog them down, allowing technology to do the heavy lifting so they can focus on more important business-critical issues.

Intelligent automation can be applied to almost any pain point your organization may face, from frequent password resets to service restarts to disk space cleanups and much, much more. The key is to begin with a few small things so that the value can be easily identified and then work up to include more complex projects and workflows to utilize automation to its fullest potential.

Best Practices for Systems and IT Operations Managers:

As with anything else in business, there are certain “best practices” that have been established and should be implemented to achieve optimum results with automation. Here is a brief list of guidelines for system and ITOps managers to follow:

  • Pick one or two pain points with value. What simple processes or small tasks are important to your organization but are bogging your team down? Pick points that you can quickly and easily measure the value of once you’re up and running.
  • Once you’ve got your list of pain points, it’s time to sell the value of your automation project to the key decision makers within the organization. Go over the benefits in detail and be prepared to counter any objections and show evidence of projected ROI (try our free ROI calculator). The more prepared you are ahead of time, the better your chances of winning over the “powers that be”.
  • Carefully evaluate available intelligent automation tools to help you choose the right product and then learn as much as you can about the one you choose so that you can truly convey the benefits that it will have for your business operations.
  • Foster IT automation skills within your team. Make it clear to IT personnel that automation isn’t something to fear. That it’s not there to eliminate their jobs, but rather to make them more efficient and productive, and to provide the opportunity to enhance their skills, become more marketable and achieve more growth in their careers.
  • Encourage communication between IT teams and business people. Devops and automation go hand in hand, with the shared goal of bridging the gap between IT personnel and those on the operational end of the technology. For optimum results, a solid relationship built on trust and open communication should be developed and fostered.
  • Develop key performance indicators and measure results. Once you’re up and running with automation, it’s critical that progress is continuously monitored, measured, analyzed and modified accordingly. Develop a list of which performance indicators are most important to your organization and then measure regularly to ensure optimum results.

In summary, organizations that follow these practices will not only increase agility and reliability, but they will also have a more productive, happier staff. ITOps teams that know how to utilize these tools will have more opportunities for growth, both within the workplace and beyond, as demand for these skills continues to grow.

In the end, it’s a triple win: employees, your business and your customers all benefit in multiple ways through automation. So, the question then becomes not “should you automate”, but rather, “why haven’t you started yet?”

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The 7 Secrets of Effective Digital Transformation

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If you’ve ever read the book The 7 Habits of Highly Effective People by Stephen Covey, you’re familiar with the concept of “beginning with the end in mind.” Putting that into context in terms of digital transformation means organizations must determine what their goals are before they begin adopting a ton of shiny new technologies. Unfortunately, many otherwise intelligent business leaders make the mistake of focusing so much on technological innovation that they miss the mark altogether.

This is not to say that technology isn’t a key driver of digital transformation. The problem often lies in a misunderstanding of what digital transformation actually is. According to a recent report by Altimeter, despite the fact that a growing number of enterprises are investing in innovative technologies, the majority of them are still lacking in terms of meeting customer expectations due in large part to a lack of digital literacy. The report also concludes that the main obstacles to achieving the solidarity and collaboration necessary for true, effective and lasting digital change are ego, politics and fear.

When an organization begins with a tech-first approach, it risks missing the point about what digital transformation is truly all about. In many instances, company leaders – CIOs in particular – fall into the trap of attempting to build new technology atop an old and crumbling legacy foundation. There’s an erroneous belief that all it takes to keep up with disruption is continuously adopting the latest and greatest apps and programs. New tech is great, but it must be adopted as a component of the digital transformation process, rather than its fundamental basis.

To demystify the whole digital transformation concept and improve your chances of success, here are a few expert tips to keep in mind.

The human element should be front and center.

Yes, the term is “digital” transformation, but in reality, it’s more about human transformation than anything else. That’s really what’s at the heart of any successful change. Technology is essential, yes, but it’s equally, if not more important that your people are all on the same page and moving together at the right speed. One of the biggest challenges to transforming a business is bringing its workforce up to speed, in particular, getting them current with the skills needed to facilitate change.

Experts unilaterally agree that the key to achieving true digital transformation is having a team of individuals who are curious, motivated by and passionate about the mission. Only then can you successfully usher in the innovative technologies you need to move forward.

A great example of this is Pitney Bowes. Several years ago, the company began initiating a shift to align itself with the changing world of tech. Specifically, they focused on evolving in 10 key areas such as machine learning, analytics, mobile, SaaS and APIs. But while leadership recognized the critical need for a strong technical strategy, they also prioritized the development and implementation of a solid people strategy as well.

The company organized curriculum for each of the 10 key areas of disruption and every one of the 1,200 employees was tasked with immersing themselves in one of those 10 topic areas for a period of one full year. The results have been beneficial to both sides – the company, by enhancing its workforce, and the employees, who have enriched their skills and improved their personal value proposition. Additionally, with staffers becoming subject matter experts in their chosen topics and subsequently collaborating together, many new and valuable relationships have been forged. This is advantageous to everyone involved.

Take the time to really understand your customers.

Ask any business leader what they believe the biggest driver of digital transformation is and they’ll probably cite the evolving behaviors and preferences of their clientele. Yet, according to the Altimeter report, a remarkable few (less than half) actually bother to truly understand their digital customers.

The few that are actually getting it right have done so by taking an outside-in approach. In other words, they take the time to determine what’s missing or broken that can solve a need and then focus their efforts on doing just that, tying in key performance indicators (KPIs) and ROI to demonstrate success. Rather than looking at internal processes, these innovators examine the customer experience first to identify opportunities to add value.

The key takeaway? If you aren’t meeting what your customers want or need, your efforts to achieve digital transformation will inevitably fall short and you will risk being left behind. The best technology, the best policies and procedures, the best laid plans – none of that will matter if the end result doesn’t make the lives of your clientele easier. That’s the end result that should be your focus from day one.

Establish new teams.

Spearheading digital transformation shouldn’t be a side project. If you want it done right, you need to have a team of individuals who are 100% dedicated to the cause. Teams should be made up of various people with different strengths and diverse backgrounds. For instance, you might have a project manager, a lead developer and someone who is focused on the customer experience. You could then supplement this with members from other roles, such as QA, development, ops and finance.

When an idea for a new initiative arises, the team’s job should be bringing it to fruition – at least to some degree – as quickly as possible. It’s not about achieving perfection right away. Digital transformation involves evolution, which means your team should be ready to go through a cycle of development – try things out, assess how they work and then adapt and improve accordingly. This agile methodology may require a paradigm shift, which is why it’s so important to have a dedicated team.

Cultivate collaboration as you deploy technology.

As mentioned, digital transformation isn’t entirely about technology. Yes, technology is a critical component, but it takes people to really achieve successful change, and that requires ongoing collaboration. Trailblazing ideas, sharing best practices, building a community – these things drive innovation and continuous improvement.

Use Pitney Bowes as an example once again. While they were designing their curricula around their 10 targeted technology areas, leadership also hosted global innovation roundtables to enhance collaboration efforts. As a result, they were able to identify cases in which there were common problems with their integration, delivery and operational practices. This enabled a fast and effective resolution across the board. Furthermore, because of the improved collaboration, workers acknowledged feeling much more engaged, as opposed to being just another “cog in the wheel.”

Don’t give in to the resistance.

It’s human nature to fear change, and that fear often manifests itself as resistance amongst workers. Logically speaking, the larger the enterprise, the greater the push back is likely to be. If you want to successfully shift to a digital ecosystem, you simply cannot let the naysayers get you down.

That’s not to say you should steamroll over them and ignore their concern. It’s more about your approach. Over communication and clear articulation, not just about what is happening, but how and most importantly, why, is key. It’s also important to develop a group of early adopters and innovators – those who embrace the proposed changes, as they can become your champions.

At the end of the day, digital transformation is really about people transformation.

Think like a startup.

As organizations become larger, greater divides between various groups and departments begin to occur. This results in silos of information, which can hinder communication and the ability to collaborate effectively.

To avoid this, try to adopt more of a startup mentality – one that focuses on operating nimbly and making sure that projects are being carried out in the correct way. Be cognizant of any walls and barriers that exist and focus on eliminating those and encouraging unilateral communication across the board. Encourage teams, departments and divisions to work closely together with a goal of making strategic decisions more quickly and rolling out smaller changes faster.

Take a bottoms up approach.

According to the aforementioned Altimeter survey, only 40% of the companies polled say their digital transformation initiative is overseen by an executive-mandated steering committee. Getting buy-in from the C-suite is certainly important, but how you go about doing so can make all the difference in the world.

Many organizations have had tremendous success by flipping the typical top-down narrative to more of a bottoms up approach. In other words, they focus on obtaining buy-in from all levels of hierarchy within, bringing together a diverse group of workers to collaborate together to create a digital transformation strategy.

This provides the opportunity to go through checks and balances to determine what makes the most sense and is directionally appropriate. Only when every ‘I’ is dotted and every ‘T’ crossed is the strategy presented to the C-suite for approval.

Conclusion

Is technology an important part of digital transformation? Of course. But if that’s all you’re focused on, you will inevitably come up short. Instead, focus on the people and policies that matter most, get all your ducks in a row and start with the end in mind. Do so and your organization can be counted among the success stories.

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5 Tips for Managing the Changes of Digital Transformation

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Today’s business leaders are focused on digital transformation. What they often fail to consider is how much transformations like these alter the very essence of their organization. In some instances, a company might modify its entire business offering after going through a successful transformation. This may require a complete realignment of how you approach the market, how you use technology, how you engage your customers and how your employees see their roles as well as the business as a whole.

At the end of the day, change is about speed. It’s about competitiveness. It’s about innovation. To be successful in today’s digital environment, organizations must be agile and ever-evolving. The problem is, change isn’t always easy – especially when it comes to people. Getting your employees on board requires strong, deliberate leadership. This is where change management comes into play. To follow are five truths that change leaders must embrace in order to be successful.

Start with a vision.

You cannot drive change unless and until you have a clear and accurate picture of what you’re trying to achieve. When you develop a vision of the end-state, it becomes easier to understand the ‘why’ of what you’re doing, and when you can get others to appreciate this ‘why,’ you’ll get buy-in. Just be careful not to be too rigid with your vision. Make sure you leave room for adjustments along the way. 

Involve the stakeholders.

Remember, change management is really about people, and these people will either resist or embrace the proposed changes. To mitigate detractors and maximize drivers, identify who will be most affected by the changes you are proposing and then get them involved in the process as early as possible. If you can make them feel a sense of control over what’s happening, they’re more likely to become advocates for your cause.

Listen.

The nature of digital transformation is that it is fluid. You will inevitably reach points at which you must pivot in order to progress. There may also be a number of tradeoffs or roadblocks you haven’t yet considered. Listening to those most closely affected can provide insight as to what courses may need to be corrected. Additionally, giving people a voice can help get them on board. Invite people to share their questions, concerns and feedback.

Communicate, communicate, communicate.

Having a clear vision of your digital transformation won’t do much good unless you share that vision with everyone else. Being honest, forthcoming and transparent right from the start can do wonders for overcoming employees’ fear of change. Use as many tools as are available to you, from email and newsletters to intranet sites, videoconferencing, town halls and more. Do everything you can to instill that vision in your employees.

Learn as you go.

The fifth rule for change leaders is to recognize that as you push forward toward your goal, new and unexpected challenges can and will arise. Your success in achieving digital transformation will depend largely on your ability to adjust to those challenges. Be prepared to regularly reevaluate to make sure you’re still on the right track and course-correct as needed. Being agile is what will ultimately get you to your end game.

Of course, having the right tools in your corner can also help make managing change easier. Ayehu supports digital transformation through seamless integrations, rapid adoption and even faster time-to-value. Click here to take Ayehu for a test drive for 30 days.  

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4 steps to minimize MTTR

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Any seasoned IT professional will tell you that one of the biggest challenges they face in their day to day job is reducing mean time to resolution (MTTR), or the amount of time it takes to get key systems back up and running after an incident. Down time in any industry can have a significant impact on both internal operations and external service levels. And the longer it takes to get things resolved, the worse the problems can become. Intelligent automation can make minimizing MTTR even easier and more effective.

Managing mean time to resolution involves 4 main steps:

  • Identifying the problem
  • Uncovering the root cause of the problem
  • Correcting the problem
  • Testing to verify that the problem as successfully been resolved

How quickly you can achieve the first step will ultimately depend on the quality of the monitoring system you have in place. Having a basic system can only get you so far and leaves a lot of room for costly error. Depending on how many incoming alerts your organization fields, staying on top of them can be too much for a small IT department. That means serious issues could slip through the cracks and cause major problems down the road. Enhancing your system with intelligent automation can create a highly effective, closed-loop solution, ensuring that all critical incidents requiring attention are prioritized and addressed accordingly.

Once an incident is identified, the next step is determining its root cause. This is the costliest part of the MTTR equation because it takes time, resources and manpower. Obviously, the more serious the issue, the more quickly it needs to be addressed. It may require “all hands on deck” to help uncover the cause so it can be corrected. It’s also important to maintain visibility and accountability at all times throughout the process. Who is handling the problem? What steps have been taken so far to get to the bottom of it? Has anything been missed? Again, automation can address this by providing real-time status of incidents, ownership, severity and priority in one central dashboard.

As soon as the problem has been properly diagnosed, the third step is taking the necessary actions to resolve it as quickly and effectively as possible. With most incidents, time is of the essence, so developing a solution is critical. One of the biggest benefits of integrating intelligent automation into your incident management process is that it can actually predict MTTR based on historic events. This can provide a guideline for the resolution process and alleviate some of the stress that naturally arises during a downtime. The IT team will be able to work quickly and efficiently to implement a solution that will get systems back up and running fast, limiting the negative effects on the company.

The final step in the MTTR process is testing to ensure that the problem is, indeed, resolved. It’s also important to assess each process to identify areas that can be improved. Being proactive and leveraging artificial intelligence can help to determine the best way to deal with similar incidents and can even help to avoid them completely.

In conclusion, managing the mean time to resolution process involves careful monitoring and the right tools, specifically intelligent automation. This can provide the most timely and effective response and a faster overall turnaround, thereby reducing or even eliminating impact on the business.

If your current incident response strategy isn’t producing these results or you’d like to learn more about how IA can dramatically reduce your MTTR, take Ayehu for a test drive or download a free 30 day trial.

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Want Big Improvements in ITSM? Start Small.

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Have you ever faced a challenge so big that you didn’t know where to begin, so you simply did nothing? It’s happened to the best of us, and it happens far too often in the world of ITSM. In many cases, IT decision makers know improvements must be made, but the looming costs and risks have them circling their wagons, afraid to take that leap and get started. If you are in this situation currently, the solution is simple: get back to basics. Rather than taking on huge, risky and expensive ITSM projects, start where you are and take it one step at a time. By starting small, you’ll eventually achieve the big change you seek.

As an example, let’s take a larger ITSM organization that provides support service to external clients across the globe. An operation like this has a ton of service desks, some of which support multiple customers, others are dedicated to individual customers and still others are designated for internal users. Some service desks provide only logging and dispatch while others also offer level 1 support. Across all of these service desks, there are a wide variety of tools and technologies being employed.

In this scenario, the vast majority of communication with customers, for all the service desks, takes place via telephone. Decision-makers within the organization recognize the need for more efficient communication channels (i.e. self-service portals and chatbots). The problem is, in a company that large, the costs of implementation would be tremendous. IT leaders have actually already identified a tool capable of supporting such a complex environment, but because they lack confidence and adequate justification that the project would run smoothly and produce ROI, they can’t nail down funding.

Believe it or not, the solution to this monster of a dilemma is actually quite obvious. Rather than attempting to roll out chat for their entire user base – a significant and costly undertaking – all they really need to do is begin where they are and implement in several smaller steps.

For instance, IT could introduce an easy-to-use, out-of-the-box chatbot platform and designate a small group of users to begin using this tool when they need IT support. IT would then closely monitor the process to identify any issues – either on the service desk or end user side – and work to resolve those issues before scaling up. Over time, the IT team will have:

  • Pinpointed and addressed many issues as they relate to chat-based interactions with the service desk
  • Identified which types of requests are best suited for chat and which are better handled via other channels
  • Figured out how to best encourage users to transition from telephone support to chat
  • Gathered and analyzed data regarding the volume of requests each agent can manage using chat
  • Determined exactly how much time and money could be saved if scaled to the entire organization

Rolling out chatbot support this way is far less risky, and by starting with a tool that is easy to use and doesn’t require coding and/or a ton of training, the overall cost of implementation also remains low. As the project is slowly scaled up, the IT team will have all the information they need to determine whether full implementation would be a cost-effective investment with quantifiable proof to back them up. They’ll also already know what needs to be done in order to ensure success as the project expands.

The good news is, starting small is easy when you have the right tool in your corner. Ayehu is specifically designed to provide rapid time-to-adoption and enhanced simplicity to even the most complex ITSM projects. Don’t believe us? Try it free for 30 days and experience it for yourself. You have nothing to lose!

5 Ways to level up your service desk using it process automation

Intelligent Automation Solutions – 7 Questions to Ask

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When it comes to IT operations, there’s no question that it is crucial for organizations to increase their efficiency. In fact, with the ever-growing complexity, service levels and performance, greater operational efficiency becomes not just an option, but a competitive necessity. Intelligent automation solutions can help your IT operations become more efficient, thereby improving your organization’s bottom line.

That being said, not every automation tool is the same. And with so many to choose from, the selection process can be confusing and downright overwhelming. Here are seven important questions that should help you make a more informed decision.

1. What are the integration points? Before choosing your intelligent automation solution, you should first verify that it will have touch points and triggers that integrate with your data center systems, including different OS, legacy systems, integration with help desk such as ServiceNow, monitoring and management systems, etc. The more integrated the better.

2. What should be expected in terms of deployment effort? Evaluate how much time and effort will be required for deployment (setup, configuration, implementation, etc.). You want to make sure that the time-frame proposed works with your organizational needs. Also, set expectations so that everyone is on the same page throughout the process and there are no unexpected surprises.

3. What is the required skill set for this particular platform? Part of the implementation process involves training and adapting to the new system. Before moving forward, you should understand what the estimated learning curve will be for generating automated workflows independently. You’ll also want to determine ahead of time if any scripting will be required. (Tip: Look for a solution that does not require coding or programming required. This enables rapid adoption and time-to-value.)

4. Does this product feature out-of-the-box functionality? Some intelligent automation solutions provide ‘pre-canned’ templates for various commonly performed tasks. If the platform you are considering offers this, the next step is to determine whether these generic templates can easily be tailored to fit your unique business environment and processes. Templates are a great place to start, but the more customizable, the better.

5. What level of human intervention is available? Even the simplest automated processes will require some type of human decision at some point during the process. The question to ask is whether you will be able to embed decision-making logic into workflows for remote decisions on process execution. This allows management to maintain control over the process from start to finish.

6. What kind of scheduling functionality does it offer? While some automated processes will be triggered by system events, others, such as repetitive tasks, will need to be scheduled. You’ll want to make sure the automation platform you select provides for full scheduling capability.

7. Does it take into account regulatory compliance? Meeting regulatory compliance requirements is a critical part of every organization’s success. When evaluating an intelligent automation solution, be sure to find out whether it can help you with these initiatives. For instance, does the tool provide tracking of events, reports and knowledge management that will help the organization comply with regulations?

These are the seven most important things to consider when evaluating automation for your organization. By asking these key questions and understanding what to look for, you will have a much better chance of selecting an intelligent automation solution that will be a perfect fit for your business and ensure project success.

eBook: 10 time consuming tasks you should automate

How to Build an AI Team

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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.

Ready to get started with AI but not sure how? For a limited time, we’re offering a free trial of Ayehu’s Next Generation Intelligent Automation platform. Use the full product for 30 days and don’t pay a penny. Get yours before it’s too late!

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