How to Future-Proof Your Business with Intelligent Automation

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How to Future-Proof Your Business with Intelligent AutomationAutomation has become a staple in IT operations. Yet, despite its prevalence, many of the automated processes currently being used are antiquated. The fact is, IT infrastructures have evolved substantially over the past decade or so, and continue to do so at a rapid pace. In order to maintain a high degree of integrity, automated processes must also adapt. Adopting more intelligent automation will not only dramatically improve internal operations, but it will position your organization leaps and bounds ahead of your competitors.

What is “intelligent automation?”

Unlike conventional automation tools of the past which were capable only of executing simple, manual and easily defined processes, intelligent automation and orchestration platforms are able to undertake tasks and workflows that are far more complex. Not only is intelligent automation capable of making decisions without the need for human input, but it can also evolve and improve itself over time.

Why is intelligent automation beneficial to IT operations?

IT ops has long been tasked with the overwhelming duty of doing more with less. Today’s IT teams, however, are also being depended on to drive innovation, and at a much more rapid pace than ever before. Intelligent automation facilitates both of these things by streamlining operations and freeing up skilled staff to apply their expertise to more innovative business initiatives. Furthermore, automation is dramatically improving the delivery and integrity of information, which paints IT ops in a positive light.

What are some of the biggest challenges IT ops face today?

Among the many challenges IT operations face in the digital age is the need to increase agility while maintaining as little disruption to existing processes as possible. IT teams must continue to meet (and in many instances exceed) business demand while also looking toward the future and finding newer, better opportunities to grab onto. It’s a delicate balance between meeting the needs of today and anticipating what the needs of tomorrow will be.

Why aren’t more organizations adopting intelligent automation?

Automation in its traditional form has worked well for IT ops, mainly because it can be leveraged on an ad hoc basis, such as with custom scripts and job scheduling. These days, however, things are becoming far more complex, with many different layers of virtualization and applications stacks of varying ages, along with combinations of public and private cloud solutions, all of which must be aggregated to deliver a single, streamlined IT service. In this environment, the concept of integrating intelligent automation can seem overwhelming. The good news is, it’s not nearly as complicated as it may seem.

What’s a good way to get started with intelligent automation?

A great way to introduce intelligent automation into the mix is to start small. Identify the big wins – or those routine, manual and time consuming tasks and workflows that when automated will produce the greatest return on investment quickly. Figure out what your IT operations team is wasting precious time and resources on, and then start deploying robots to do the dirty work. Keep in mind, however, that in order for intelligent automation to truly be beneficial, it must eventually become an integral part of the entire infrastructure. It’s ok to smart small, but make sure you’re working toward the big picture.

Without question, adding intelligence to automation will facilitate far greater productivity and innovation, while simultaneously setting the bar higher in terms of speed and agility for IT operations. Organizations that successfully deploy intelligent automation will easily surpass their competitors who do not, ultimately positioning themselves at the head of the pack.

Where do you want your company to be? Get a jump on the competition and future-proof your business with intelligent automation. Experience it for yourself by taking Ayehu for a test drive today.

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4 Steps to Intelligent Process Automation Breakthrough

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Intelligent process automation is rapidly transforming the global economy, delivering momentous gains to enterprises that adopt it at scale. One recent article by McKinsey revealed that some organizations have been able to automate 50 to 70 percent of their workflows, generating ROI that reaches into the triple-digits. In addition to cutting costs, intelligent process automation can also deliver precision, speed and enhanced customer experience.

In order for organizations to enjoy the full value of intelligent process automation, IT leaders must be willing to take a guiding role. Unfortunately, many IT executives find this challenging, whether due to the increased complexity of IT processes, lack of understanding and/or clarity, inconsistent or fragmented tools that hinder scaling, or the misconception that intelligent process automation cannot be adopted without major re-engineering of existing processes.

How can these challenges be overcome? And how can IT leaders succeed in their automation initiatives? The answer to these questions lies in the following four key steps along the intelligent automation journey.

Step 1: Evaluate the high-level potential value

The first step in becoming an intelligent process automation leader starts with the development of a clear business case. This involves assessing the potential high-level value of the company’s main IT activities. Some examples of what these areas of value might look like include:

Incident Response – A significant number of IT incidents are initiated through support desk requests. These typically result in tickets being created and assigned to Level 1 support agents. While these are the obvious candidates for automation, the portion of tickets that are escalated to specialized L2 and L3 agents are also ripe for the picking, thanks to the advanced technology behind intelligent process automation. And since these activities are generally well-documented, categorizing and prioritizing them by automation potential should be relatively straightforward.

Planned Activities – In addition to the one-offs and unexpected support tickets that crop up, IT is also responsible for performing a number of planned activities on a regular basis. These activities typically include things like backups, upgrades and patching. They may also involve more complex security audits. The amount of time and resources required to perform these duties can collectively add up to around 20 percent of the IT budget. Calculating this figure can help determine the potential savings intelligent process automation can deliver.

Introducing New Applications – From a business perspective, this activity is often viewed as the one that produces the most significant value. It can also account for an additional 20 to 40 percent of the time and resources put forth by IT. These activities are not exclusive of application development, either. They also include such tasks as testing and hosting. This places increasing demand on both the application team as well as the infrastructure group.

Step 2: Dig deeper to identify which specific use cases are best suited for intelligent automation.

Determining how to effectively implement intelligent process automation requires a deep dive to uncover the root causes of issues. It may also involve the untangling of complex systems and the development of an accurate picture of how to leverage automation to extract the greatest value. In other words, the process is a complicated one and requires a certain degree of commitment. Let’s take the three potential use cases above as an example.

Incident Response

Automating IR begins with identifying which incidents are the best candidates, which can be challenging. The goal should always be digging deep enough to uncover the “why” of documented incidents. Without this information, efforts are futile. Text-mining can help by automatically reading ticket descriptions and extracting the necessary insights to sort them into three categories:

  • Automatable
  • Requires machine learning
  • Highly cognitive/manual

This analysis should leave you with a prioritized list of incidents to automate and the type of automation best suited for the job.

Planned Activities

Most enterprise-grade IT departments rely on industry-standard tools to manage their infrastructures. Unfortunately, due to factors such as advanced customization, adjustments due to mergers and specific user requirements, managing these systems often requires a significant amount of manual effort, diminishing the overall value.

For instance, despite the widespread adoption of infrastructure and application monitoring tools, support teams are often unable to respond effectively to the logs being generated, either because there are too many of them or because of the many reasons why they are being generated in the first place. As a result, IT leaders are often unclear on how to approach intelligent automation implementation.

In situations such as this, machine learning technology can be “trained’ to identify the reasons behind alerts and then either recommend or autonomously make better decisions on which action to take. This eliminates much of the complexity for the IT team.

Introducing New Applications

Many IT executives fall into the trap of focusing solely on the reduction of manual labor. As a result, they fail to see and achieve the full value potential of intelligent process automation. Faster and more accurate delivery of applications requires the development and design of a new operating model, with an emphasis on DevOps and agile.

Reviewing this entire process to gain an understanding of how to make the most use of this new operating model can result in entirely new approaches to work. Intelligent process automation can facilitate some of these new ways of working. For instance, automating the testing process will enable applications teams to iterate more quickly. Likewise, developing a self-service model for things like automated server provisioning allows the operations team to become more responsive. The list goes on.

Step 3: Execute your proof of concept

In order to demonstrate the true value and validate your case for intelligent process automation, the next critical step is executing a proof of concept. A great place to start with this is incident management. Organizations that have successfully deployed intelligent process automation for incident management have been able to achieve substantial cost savings in a relatively short period of time.

Thankfully, there are many different incidents that can quickly and easily be automated to support your proof of concept, including such tasks as password resets and employee onboarding. In its most basic form, a proof of concept requires the following:

  • Collaboration with subject matter experts to identify where automation can best be applied and understand all the steps and systems involved in a particular process or workflow.
  • Careful selection of an intelligent process automation platform. Look specifically for products that can be integrated with existing systems and applications and offers pre-packaged, no-code options. (This will enable rapid adoption and time-to-value.)
  • Obtaining necessary IT and overall business approvals with regard to regulatory constraints, security guidelines and access limitations.
  • Ongoing testing and monitoring to capture results and document value

This phase is also an ideal time to consider building stronger internal intelligent automation capabilities; for example, developing a team to spearhead a future automation center of excellence (CoE). This team will ultimately become the foundation and engine that drives all IPA initiatives.

Step 4: Build intelligent automation capabilities to scale

Achieving the full benefits of intelligent process automation requires the development and nurturing of certain skills and capabilities, in addition to rolling out an entirely new company-wide culture. This is essential as successful adoption of IPA requires that automation become embedded into the very heart of the organization itself. There are plenty of ways to accomplish this, but generally speaking, companies that have been successful have done the following three things:

Build on success to expand into new areas of IT (and beyond).

Once the basic tasks and workflows have been automated, it’s time to move on to more advanced level-2 and level-3 activities. The IT team should be expanding beyond incidents to begin leveraging the AI and machine learning technologies to assist with things like analytics and decision support. The goal is to eventually roll out intelligent process automation to as many routine and complex processes as possible.

Spread the word.

With a strong foundation of capabilities and experience, IT leaders can begin to position themselves as subject matter experts for the rest of the organization. This process involves continued outreach, such as connecting with other leaders across the enterprise to advise them of the specific benefits IPA can have for them. This outreach also provides the opportunity to identify additional areas where automation might be beneficial.

Explore the advanced elements of intelligent process automation.

While the majority of organizations have thus far only focused primarily on simple process automation, the future belongs to those with an eye toward artificial intelligence and cognitive learning. These solutions are already making an impact on companies with forward-thinking leaders. The best way to break into this arena is to start working on small AI initiatives. From there, just like basic automation, you can continue to build, expand and grow.

Intelligent process automation is maturing rapidly and quickly becoming a core component of the IT landscape. IT professionals who recognize the importance and understand how to develop their automation capabilities have the potential to become respected leaders in the process – a title that will serve them well throughout their careers.

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Three Steps To Prepare The Enterprise For The Digital Workforce In 2020

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Article originally published in Forbes Technology Council.

There’s no longer any uncertainty or ambiguity. Automation absolutely, positively will impact the way every one of us works. The degree to which that impact occurs will vary, but make no mistake: Humans in every industry and position, from warehouse workers to C-suite executives, will someday soon be working alongside digital workers (a.k.a. virtual agents).

Just what will this future digital enterprise look like? The answer to that lies in how organizations implement artificial intelligence.

The ‘How’ Vs. The ‘What’

For many workers, the way that automation and artificial intelligence technologies are adopted will be more effective than the technology itself. The same goes for organizations as a whole. To succeed in the digital age, business leaders must begin to shift their viewpoint from opportunistic to a more systematic approach. In years past, automating on an ad hoc basis was sufficient. Over time, however, that strategy led to silos that were not adequately governed, nor were they scalable.

The future of automation in tomorrow’s workplace must be rigorous and robust, policy- and data-driven, and, above all, enterprise-centric. In other words, it’s not so much about the “what” as it is about the “how.” This will be the main differentiator between organizations that succeed in achieving digital transformation and those that fall irreparably behind.

Three Steps To Success With Intelligent Automation

1. Build. New technologies like artificial intelligence and machine learning will inevitably affect some workers in adverse ways. This has always been the case, as people continue to be displaced from one economic sector to another. In fact, according to one estimation by McKinsey, up to 30% of the global workforce (and between 400 million and 800 million workers) could be displaced by automation by the year 2030.

But while some jobs will ultimately be eliminated, the current and ongoing technological innovation we are experiencing will simultaneously create new opportunities.

Perhaps it is more than fitting that a fictional Borg from the futuristic Star Trek series uttered the infamous words, “Resistance is futile.” Like it or not, AI and automation technologies are already having an impact on the workplace, and they’re not going away any time soon.

The future of work will ultimately belong to those individuals who are willing to embrace and leverage artificial intelligence to their advantage. This may come in the form of self-automation — that is, the foresight and desire to automate portions of one’s own job in the interest of productivity and efficiency. Organizational leaders can and should meet them in the middle by seeking out key employees who show promise, optimism and a willingness to adapt and reskill, if necessary.

Investing in human capital with the ultimate goal of developing an automation center of excellence will create a compromise between top-down mandated automation and bottom-up, enthusiastic support and participation. This is the ideal scenario and one that will drive ongoing innovation and success. Some key roles to focus on for the future include:

  • Automation architects.
  • Automation engineers.
  • Site reliability and DevOps engineers (SRE).
  • API product managers.
  • Data scientists.

2. Standardize. With the right people and teams in place, the next step toward leveraging intelligent automation for digital transformation should involve the standardization of processes and the creation of best practices.

To start, the focus should be on delivering continuous value rather than aiming for one major change. This is achieved via strategic increments.

Centralized governance will then help to ensure ongoing compliance and support future growth and expansion.

3. Invest. Many will find it surprising that technology is actually the final piece in the automation puzzle. This is due in large part to the old-school, opportunistic way of thinking. The new recommended approach is one that involves a strategic cultural change and focuses on people and processes first, and then tools and technology.

Once these first two factors have been determined, the search for the right automation platform can begin. Ideally, the criteria should include no-code or low-code solutions that are both robust and agile. This will enable the eventual proliferation of automation across the entire enterprise while also supporting the future growth and changing needs of the business and/or industry.

Closing Thoughts

What will the workplace of tomorrow look like? For human workers, it will be markedly different and require new skills and greater adaptability. For the enterprise, it will be a composite of real and artificial intelligence — humans and machines — working together toward a common goal of innovation and success.

Dare to take risks despite your fear. Organizations and their employees who approach these challenges with eagerness and optimism, a willingness to adapt and evolve, and the ability to strike the ideal balance between humans and machines will ultimately be the ones who rise to the top.

Is AI Killing Jobs….or Creating Them?

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Ask a roomful of people how they feel about artificial intelligence in the context of jobs and you’ll undoubtedly received a mixed bag of responses. Some will undoubtedly express their concerns that AI is poised to destroy employment as we know it today. Others will take a more optimistic approach, viewing AI as a tool to help us work more efficiency and accomplish things we couldn’t do with human workers alone. Even the various analysts and economists range widely in their predictions of what role AI will ultimately have in the future of work.

The truth, as is quite often the case, lies somewhere in the middle. There’s no question that intelligent automation will eliminate some jobs, impacting just about every industry and sector across the board. At the same time, however, AI will create new jobs, both in categories we’re familiar with as well as many more that have yet to be developed.

AI = Job Killer?

Automation is nothing new. It’s a technology that’s been embraced and lauded by organizations for decades upon decades. The key differentiator here is the introduction of the word “intelligent.” It’s the cognitive abilities afforded by AI and machine learning that will ultimately enable businesses to optimize their use of human labor. And that’s where the shakeup will inevitably occur.

In fact, a shift has already begun – particularly in areas where the work is highly repetitive, regulatory intensive and prone to error. Why pay humans to perform work that could easily be carried out by an intelligent bot – especially when that bot is capable of understanding the meaning and context of the information at hand?

So, does this mean people are being eliminated from the workplace entirely? Not so fast. In fact, there are plenty of categories of employment that will remain relatively untouched by AI. For instance, many professional services categories will remain intact as they still require a level of responsiveness that cannot yet be replicated by artificial intelligence.

AI = Job Creator.

Studies indicate a surprisingly positive view of AI’s role and ultimate impact on tomorrow’s workplace. In fact, a recent survey revealed that 75% of U.S. workers do not view their jobs as being at risk of elimination – at least not within the next decade.

Additionally, the vast majority (87%) of workers say they wish their employers would automate more tasks and processes. Why? Because they recognize the promise that AI brings in terms of improved efficiency and increased productivity. In other words, most people understand that AI will make their work lives better, not worse.

But, what about those repetitive, error-prone tasks that are already being shifted from human to machine? Won’t those workers end up in the unemployment line? Not if they are willing to change gears. In fact, the rapid adoption of AI and intelligent automation is already creating exciting new roles and opportunities.

Part of the challenge here is that it’s difficult to fathom jobs and employment categories that may not yet exist. Thinking back 20 years, the concept of a role such as social media manager was completely foreign, yet today it’s something most organizations have. Likewise, looking forward 20 years into the future, there will undoubtedly be whole new sectors of the economy that do not exist today. And along with those emerging opportunities, human workers will also need to adapt and evolve.

Without question, the world is experiencing a revolutionary shift, thanks in large part to artificial intelligence technology. Thankfully, the magnitude of the impact that shift will have on the future of work is still largely within our control. Those who are at the greatest risk of redundancy can provide themselves with a safety net by proactively reskilling and reinventing themselves.

Not sure where to begin? Our Automation Academy is a great place to start. Enroll today!

4 Steps for Selling Intelligent Process Automation to the Masses

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Seasoned leaders know that, when beginning any significant project, there are two different paths that can be taken. Path number one offers the shortest route from point A to point B. This is to simply force-feed the project to everyone, essential saying, “We’re doing this and that’s that.” The second path, on the other, may be a little less direct and take a lot longer. On this journey, time is taken to explain the strategy and the reasoning behind it. In other words, saying, “Here’s what we’re doing and why.”

Which of these paths do you think will yield better results? If you chose the second path, chances are you’ve experienced both options before and know firsthand that getting people onboard with change is almost always the wiser choice. Few industries are as familiar with the correlation between major change and feelings of fear, skepticism, resistance and other challenges as leaders in the IT realm. Implementing intelligent process automation is no different.

What’s the best way to overcome the many uncertainties and misconceptions that could delay or derail your automation project? Let’s take a look.

Appeal to their self-interest.

Most people won’t get fully behind a project unless and until they know how it will directly impact their lives – essentially, they want to know what’s in it for them. Self-preservation is human nature. Appealing to this natural instinct can help make your argument much more persuasive. Instead of simply announcing that you will be introducing intelligent process automation into the mix, show them how that change will benefit them.

Will automating a particular workflow finally put an end to those middle-of-the-night phone calls? Will it allow some employees to eliminate low-skill, manual tasks from their workload, freeing them up to focus on more strategic and meaningful work? Will learning how to work alongside artificial intelligence enable ambitious team members to develop new marketable skills that they can use to further their career?

Figure out what’s in it for each of the individuals or teams you are presenting to, as well as how intelligent automation will ultimate benefit customers and the company as a whole, and then communicate that to the masses. Paint a picture of what’s currently causing issues and then demonstrate how automation can help. By the way – the same concept applies when making the case outside of the IT department, to non-technical stakeholders, for example. Just go easy on the jargon.

Connect your proposal to specific business goals.

A big part of making a strong and support-worthy case for anything, really, involves getting people to understand that you’re not simply chasing trends. In other words, you’re not just automating for the sake of automating. If people sense that’s the case, they’re going to lose confidence and likely provide even greater resistance – especially those who are directly impacted, such as the IT team.

The case for intelligent process automation must be driven by a specific business demand, whether it’s reducing expenses, improving service levels, gaining competitive advantage, etc. Unless it’s a core competency of the organization, no automation endeavor should be a means unto itself.

If you want people to back your plan, you need to align it with specific business goals and then clearly and accurately convey those connections. Lay out these goals and explain, step-by-step, how automation will help the company achieve those goals.

Break down your plan into manageable milestones.

One major reason why many automation projects fail is because they are simply overwhelming undertakings. Even if your goal is to automate everything (or close enough), attempting to do so in one fell swoop is simply not realistic nor is it a sustainable strategy.

You’ll make a much stronger, longer-lasting argument when you develop a plan that breaks down your project into smaller, more manageable increments. This also allows for more flexibility to be able to adapt and iterate as needed along the way. At Ayehu, we almost always recommend starting with tasks and workflows that offer the quickest and most measurable wins. This will enable you to continuously prove value and gain ongoing support as you begin to proliferate automation further throughout the organization.

Identify smaller areas where automation will have the biggest immediate effect and then work your way outward from there. Remember, as they say, the proof is ultimately in the pudding. Once you’ve got those smaller wins under your belt, you’ll be in a much better position to sell the big-picture benefits as well.

Sing your own praises.

Well, not necessarily your praises, but those of your automation project. If you’ve followed the steps above, you should begin to generate ROI relatively quickly. It’s in your best interest to promote those positive results early and often. There is no case more convincing than one that features real-world, definitive and measurable results.

This step is especially important for instances where skepticism still abounds. People can resist change and choose to doubt anticipated benefits of intelligence process automation all they want, but this becomes markedly more difficult when they can see and experience those benefits firsthand.

Not only will continuously promoting positive results quiet the critics, but it will also lay the groundwork for automating even more tasks and workflows in the future, which will ultimately lead to becoming a self-driving organization.

Get started on your journey to successful adoption of intelligent process automation today by downloading your free 30-day trial of Ayehu NG.

The Secret to Surviving the Tech-Led Revolution

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The Secret to Surviving the Tech-Led Revolution

Automation has been at the forefront of the digital revolution for decades, primarily because it maximizes efficiency, reduces costs and accelerates service levels. But the cloud, mobile and other innovative technologies – coupled with an ever-growing volume of raw data – have led to dramatically more complex IT environments.

According to ESG’s IT Spending Intentions Survey from 2018, 68% of those surveyed said their IT infrastructures are significantly more complex than they were just two years ago. Furthermore, 39% of respondents listed automated IT operations as a critical component of survival in today’s digital age.

In response to this increasing complexity, organizations are beginning to make the shift toward the next generation of automation – from basic to intelligent. This new level of automation involves technologies like machine learning and artificial intelligence to orchestrate workflows across a multitude of tools, systems and processes.

In fact, with the right platform, it is now possible to fully automate L2 and L3 tasks – functions which have traditionally required the use of human judgment. Now, those insights lie within the data itself and can be extracted, interpreted and leveraged autonomously by AI.

Embracing intelligent process automation is also enabling enterprises to lay the foundation for AIOps, a focus area that experts predict will boom over the next five years or so.

AI and ML: Augmenting IT Operations

AIOps is helping IT teams manage the increasing challenges created by data and digital disruption, leveraging intelligent process automation and orchestration to gain competitive advantage. Thanks to the powerful processing capabilities of artificial intelligence, IT can sort through mind-boggling amounts of data points to find the proverbial needle in a haystack.

The role of humans in this increasingly tech-driven environment is still present, though it too is evolving. Rather than relying on error-prone employees to handle the bulk of the processing work, human cognition and advanced skillsets are being used to define that proverbial needle.

In response to this, more organizations are focusing their efforts on reskilling and upskilling their existing staff to bring them up to speed on ML and AI technologies.

Making the Switch to Autonomous Operations

Autonomous operations (AO) utilizes advanced AI to deliver unassisted responses to IT incidents across the entire infrastructure. Thanks to the self-learning capabilities of ML algorithms, AO is able to continuously improve its ability to identify patterns and carry out the appropriate actions.

Again, human workers are still needed in an AO-driven environment, but in the role of supervisor as opposed to operator. Yet as the software continues to evolve and improve, and as errors consistently decrease over time, full autonomy and a zero-touch IT operations environment will one day become a very real possibility.

The Role of Data

The key to success with intelligent automation is accurate data, as this enables users to write more impactful rules. There is little to no value in static data. These days, it’s all about dynamic information which comes from things like descriptive metadata as well as relational and behavioral data.

In order to harness this dynamic data and gain adequate insights from it, organizations need to develop software-defined IT environments. Intelligent process automation is about the ability to not only proactively identify anomalies, but to also remediate those issues automatically without causing any business disruption.

The Right Way to Automate Intelligently

In today’s competitive landscape, automation is no longer an option but a necessity. That said, there’s a right way and a wrong way to leverage this game-changing technology. Start by weighing the time, effort, complexity and frequency of a given task and then benchmarking these factors against the cost of transitioning that task to intelligent process automation. From there, create a prioritized list. This will help you maximize ROI and harness the full potential of intelligent IT operations.

Not sure where to start? Why not give intelligent process automation a test drive free for 30 full days? Click here to launch your Ayehu trial today.

Transform Your Organization with AI in 5 Steps

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According to IDG’s 2018 State of the CIO report, 73% of IT executives struggle with striking a balance between the need to innovate and the demand to achieve operational excellence. One of the main reasons for this is the fact that IT frequently gets bogged down with a growing list of tools and competing priorities, all of which chip away at precious time and available resources. As a result, more organizations are turning to artificial intelligence as a way to bring technology, data and people together to drive digital transformation. Here’s how you can use AI to do the same in five easy steps.

Step 1: Understand what you can and cannot solve.

While AI has the potential to transform an entire organization, machine learning technology is not yet capable of fully replacing the experience of skilled professionals. Instead, IT teams can leverage automation powered by artificial intelligence to free up skilled workers to do what they do best: apply their expertise to develop solutions for highly prioritized issues.

Machine learning algorithms can sift through mountains of data to spot trends, deliver insights and identify potential solutions. Automation can assist in resolving certain issues. But it’s up to the IT department to apply the deep analysis necessary to achieve business goals.

Step 2: Identify and prioritize problems to address.

Artificial intelligence can help address the two biggest IT challenges: maximizing operational efficiency and improving the customer experience. The role of CIO has taken on much greater importance, with 80% of businesses viewing IT managers as strategic advisors for the business. As such, these individuals, along with others in IT, are responsible for defining key areas of focus for new technology, such as AI solutions. In order to achieve buy-in, new solutions should be presented in a way that closely aligns with broader organization-wide goals.

Step 3: Pinpoint gaps in technology and skills.

The IT skills gap is an ever-present problem, and it doesn’t appear to be going away any time in the near future. In addition to the talent shortage, IT budgets are stagnating. AI solutions can help to mitigate both of these issues by empowering IT teams to do more with less, and at a much faster rate than they could on their own.

Keep in mind, of course, that key skills are still necessary in order to drive these solutions. To address this, many organizations are looking to reskill existing staff. Thankfully, today’s automation tools do not require a PhD to operate them. Regardless, decision-makers should look for a data-based platform that features AI-powered technology.

Step 4: Develop your strategy.

Once you’ve identified which problems AI is capable of solving for your organization, defined the specific challenges you’d like to overcome, achieved buy-in for adoption and assessed what resources you have to work with, the four step is to develop your strategy for deployment. This strategy should include the following main segments:

  • Roadmap – from proof of concept to continuous process improvement
  • Testing Plan – defining what you want to accomplish and what metrics will indicate progress
  • Team – investing in and arranging training for IT staff

Step 5: Prepare for scale.

Any broader AI strategy should involve mapping out data across all systems, services, apps and infrastructure. This includes both structured and unstructured data as well as data in a variety of different formats. It’s essential to select a solution that is capable of ingesting, normalizing and formatting all data sources for analysis.

Further, it’s critical to choose a platform that offers room to mature and scale. And keep in mind, also, that while the “land and expand” concept may work for some companies, others – particularly those with a higher risk tolerance – may be better off to push transformation across the entire organization at once. Generally speaking, however, stable and sustainable change begins by starting small and building on early successes. The key is leaving enough room to grow.

Want to experience some of those early successes now? Launch your free 30-day trial of Ayehu NG and put the power of AI and intelligent automation to work for your organization today!

How to Leverage Intelligent Automation to Better Manage Alert Storms [Webinar Recap]

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Author: Guy Nadivi

As most of you already know, there’s a digital transformation underway at many enterprise organizations, and it’s revolutionizing how they do business. That transformation though is also leading to increasingly more complex and sophisticated infrastructure environments. The more complicated these environments get, the more frequently performance monitoring alerts get generated. Sometimes these alerts can come in so fast and furious, and in such high volume, that they can lead to alert storms, which overwhelm staff and lead to unnecessary downtime.

Since the environments these alerts are being generated from can be so intricate, this presents a multi-dimensional problem that requires more than just a single-point solution. Ayehu has partnered with LogicMonitor to demonstrate how end-to-end intelligent automation can help organizations better manage alert storms from incident all the way to remediation.

The need for that sort of best-of-breed solution is being driven by some consistent trends across IT reflecting a shift in how IT teams are running their environments, and how costly it becomes when there is an outage. Gartner estimates that:

Further exacerbating the situation is the complexity of multi-vendor point solutions, distributed workloads across on-premise data centers, off-premise facilities, and the public cloud, and relentless end-user demands for high availability, secure, “always-on” services.

From a monitoring standpoint, enterprise organizations need a solution that can monitor any infrastructure that uses any vendor on any cloud with any method required, e.g. SNMP, WMI, JDBC, JMX, SD-WAN, etc. In short, if there’s a metric behind an IP address, IT needs to keep an eye on it, and if IT wants to set a threshold for that metric, then alerts need to be enabled for it.

The monitoring solution must also provide an intuitive analytical view of the metrics generated from these alerts to anyone needing visibility into infrastructure performance. This is critical for proactive IT management in order to prevent “degraded states” where services go beyond the point of outage prevention.

This is where automating remediation of the underlying incident that generated the alert becomes vital.

The average MTTR (Mean Time To Resolution) for remediating incidents is 8.40 business hours, according to MetricNet, a provider of benchmarks, performance metrics, scorecards and business data to Information Technology and Call Center Professionals.

When dealing with mission critical applications that are relied upon by huge user communities, MTTRs of that duration are simply unacceptable.

But it gets worse.

What happens when the complexities of today’s hybrid infrastructures lead to an overwhelming number of alerts, many of them flooding in close together?

You know exactly what happens.

You get something known as an alert storm. And when alert storms occur, MTTRs degrade even further because they overwhelm people in the data center who are already working at a furious pace just to keep the lights on.

If data center personnel are overwhelmed by alert storms, it’s going to affect their ability to do other things.

That inability to do other things due to alert storms is very important, especially if customer satisfaction is one of your IT department’s major KPI’s, as it is for many IT departments these days.

Take a look at the results of a survey Gartner conducted less than a year ago, asking respondents what they considered the most important characteristic of an excellent internal IT department.

If an IT department performed dependably and accurately, 40% of respondents considered them to be excellent.

If an IT department offered prompt help and service, 25% of respondents considered them to be excellent.

So if your IT department can deliver on those 2 characteristics, about 2/3 of your users will be very happy with you.

But here’s the rub. When your IT department is flooded with alert storms generated by incidents that have to be remediated manually, then that’s taking you away from providing your users with dependability and accuracy in a prompt manner. However, if you can provide that level of service regardless of alert storms, then nearly 2/3 of your users will consider you to be an excellent IT department.

One proven way to achieve that level of excellence is by automating manual incident remediation processes, which in some cases can reduce MTTRs from hours down to seconds.

Here’s how that would work. It involves using the Ayehu platform as an integration hub in your environment. Ayehu would then connect to every system that needs to be interacted with when remediating an incident.

So for example, if your environment has a monitoring system like LogicMonitor, that’s where an incident will be detected first. And LogicMonitor, now integrated with Ayehu, will generate an alert which Ayehu will instantaneously intercept.

Ayehu will then parse that alert to determine what the underlying incident is, and launch an automated workflow to remediate that specific underlying incident.

As a first step in our workflow we’re going to automatically create a ticket in ServiceNow, BMC Remedy, JIRA, or any ITSM platform you prefer. Here again is where automation really shines over taking the manual approach, because letting the workflow handle the documentation will ensure that it gets done in a timely manner, in fact in real-time. Automation also ensures that documentation gets done thoroughly. Service Desk staff often don’t have the time or the patience to document every aspect of a resolution properly because they’re under such a heavy workload.

The next step, and actually this can be at any step within that workflow, is pausing its execution to notify and seek human approval for continuation. Just to illustrate why you might do this, let’s say that a workflow got triggered because LogicMonitor generated an alert that a server dropped below 10% free disk space. The workflow could then go and delete a bunch of temp files to free up space, it could compress a bunch of log files and move them somewhere else, and do all sorts of other things to free up space, but before it does any of that, the workflow can be configured to require human approval for any of those steps.

The human can either grant or deny approval so the workflow can continue on, and that decision can be delivered by laptop, smartphone, email, Instant Messenger, or even via a regular telephone. However, note that this notification/approval phase is entirely optional. You can also choose to put the workflow on autopilot and proceed without any human intervention. It’s all up to you, and either option is easy to implement.

Then the workflow can begin remediating the incident which triggered the alert.

As the remediation is taking place, Ayehu can update the service desk ticket in real-time by documenting every step of the incident remediation process.

Once the incident remediation is completed, Ayehu can automatically close the ticket.

And finally, it can go back into LogicMonitor and automatically dismiss the alert that triggered this entire process. This is how you can leverage intelligent automation to better manage alert storms, as well as simultaneously eliminating the potential for human error that can lead to outages in your environment.

Gartner concurs with this approach.

In a recently refreshed paper they published (ID G00336149 – April 11, 2019) one of their Vice-Presidents wrote that “The intricacy of access layer network decisions and the aggravation of end-user downtime are more than IT organizations can handle. Infrastructure and operations leaders must implement automation and artificial intelligence solutions to reduce mundane tasks and lost productivity.”

No ambiguity there.

Ayehu

Preparing for the Future of Work: Transitioning to Intelligent Automation

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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 Ways Digital Transformation will Impact IT Support

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The IT help desk, as it once existed, has changed. Driving that evolution has been the changing demands and expectations of digital customers. Simply put, digital is revolutionizing the world of IT support and service. These newer and more complex requirements of digital customers (which include employees) are causing IT teams to re-evaluate what they have to offer in terms of support and capabilities. If your organization or team is at a similar crossroads, here are four key areas on which to focus. 

IT Support Strategies

Regardless of whether your IT service desk happens to support a company of ten or a multi-location, enterprise level organization, the time to start thinking digital is now. A great place to begin is by uncovering how employees have evolved into so-called “digital consumers” and, more significantly, how this evolution has changed the expectations they have of IT support.

To do this, evaluate the gap between your current situation and those changing expectations. In particular, look at your current channels of support. Poll employees to determine whether they feel the current channels they are using to contact IT support are sufficient and effective. Figure out what channels they might prefer. Also, examine common customer use cases and needs. Then, use this information to develop a strategy that incorporates newer, more innovative support channels (like self-service chatbots and virtual support agents).

Operating Models

How does your IT service desk engage with customers? The focus here should be more on this approach as opposed to best practices and ITSM processes. To bring your operating models in line with digital transformation, ask yourself and your team the following questions:

  • Is your approach to IT support adequately in line with your realistic business needs and expectations? For example, what is the overall goal? Cutting IT support costs? Minimizing lost time and revenue at a business level? Understand your objectives and align your strategy accordingly.
  • Do your IT support agents understand “personas” of its customers (i.e. the common characteristics and behaviors they share)? Do your operational practices accurately reflect these personas?
  • How does your IT support desk measure success? Is it primarily related to how the IT service desk has helped and/or improved customer and business operations?

Once you’ve answered these questions, use the data you’ve gathered to identify any and all disconnects between IT support status quo and the actual needs and desires of both the customer as well as the business as a whole. These gaps are where changes must be made.

IT Support Technologies

Without question, the future of IT support will rely heavily on automation. In fact, newer technologies have already made it possible for organizations to augment their human workforce by leveraging ever-improving artificial intelligence capabilities. With these advanced technologies deployed in the right areas, IT support teams are able to more effectively deliver on the increasing demands of digital customers.

Whether your service desk is already leveraging virtual support agents or is planning to in the near future, it’s important to ask the right questions. In particular:

  • Are your virtual agents being used to their fullest potential?
  • Are your virtual support agents being employed at the right points during the customer journey?
  • Do end-users feel that the VSAs improve their support experience?
  • Have you established a robust and accurate knowledge-base from which the VSAs can draw?

This last point is key, as virtual IT support will only be as good as the data behind it. That being said, creating an environment that blends high-tech automation with the human touch of IT support agents will position your organization for greater success.

IT Support Staff

The question of whether human service desk agents will be assisted, augmented and possibly even replaced by virtual support agents is no longer an “if,” but rather a “when.” Getting employees onboard with the concept of artificial intelligence isn’t always easy, especially those L1 agents who view automation as a threat to their livelihood. But it’s essential for an organization of today to remain competitive tomorrow.

Educate your IT support team on the value and benefits that AI has to offer. Make it about them – how AI will make their lives easier, enable them to perform more meaningful work, provide an opportunity to learn new skills and make themselves more marketable, etc. – not just about the company. And start investing in your current workforce. Identify champions of the cause and reskill them so they’ll be ready to face the digital future with confidence. Get them excited about the possibilities that lie ahead!

There is no longer any doubt. IT support as we know it today is changing. Only those organizations that are willing to adapt and evolve their strategies, models, technologies and people alongside those changes will make it through unscathed.

Want to experience the power of artificial intelligence for your IT support team? Try Ayehu NG absolutely free for 30 full days. Click here to download your free trial.