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

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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The changing role of CIO and intelligent automation’s impact.

With the ever-increasing volume and complexity of data coming in (thanks in large part to trends like the IoT, BYOD and, of course, Big Data), the role of the CIO has also begun to rapidly evolve over the past decade or so. These individuals are now facing pressures to keep infrastructure updated as well as analyze and leverage the data available to them for the benefit of the organization, and all while keeping costs down and internal networks, systems, applications and information secure. This is no easy feat, but thanks to intelligent automation, it is entirely achievable.

Due to the heavy volume of data being shared today, integrating automated workflows and processes has become increasingly necessary in order to analyze and derive value from that data, and in a way that is as cost-effective as possible. If IT departments are to remain relevant, drive efficiency and support a profitable operation, it is imperative that they employ the use of intelligent automation, and with the CIO as the key decision maker, it’s up to him or her to ensure that the right resources are in place.

As recently as just a few short years ago, the general public was becoming aware of the IoT, but today organizations of every size and industry are capturing insight and achieving real, sustainable ROI from this advanced (and ever-evolving) technology. Furthermore, intelligent automation is virtually revolutionizing everything from the SOC and NOC to the service desk and data center. Intuitive technology and artificial intelligence are being utilized to proactively monitor systems and devices, gather and evaluate complex data, remediate incidents and resolve issues – in many cases before any human worker is even made aware.

As a result of all of these changes, more basic requests, like password resets and system refreshes, which used to be handled almost exclusively by L1 support professionals are now being shifted to intelligent automation technology. Self-service chatbots are empowering the end-user like never before while simultaneously alleviating IT personnel of the heavy burden associated with these routine, repetitive (but necessary) tasks.

Of course, this hasn’t necessarily made life perfect for IT professionals. Increased consumerization of IT has resulted in the services of many IT departments being compared and contrasted against that of external service providers. Expectations of faster service and the demand to take on more while also minimizing costs as much as possible continue to rise, subsequently increasing the pressures on top IT personnel. Perhaps no one is feeling the pressures of these demands more than the CIO. Embracing intelligent automation is no longer an option, but a critical requirement.

At the same time, the IT world is witnessing a significant change in responsibilities for the CIO, shifting from the old way of the maintenance and provision of physical infrastructure and devices to more of a data management role with an emphasis on innovating and creating value. Digitalization is now the focus, with CIOs playing a lead role in developing and implementing it throughout the entire enterprise. Paradoxically, these high-level IT professionals are being forced to orient and align themselves more with value creation than the efficiency that once defined them.

Data analytics is now being hailed as one of the primary contributors to driving this value, particularly given the ever-increasing pool of available information. It’s important to point out, however, that CIOs and other top IT managers must take the time necessary to understand what data is available to them, what that data equates to and, most importantly, how they can best leverage that information to improve operations across all functions of the organization. Savvy CIOs will leverage intelligent automation to obtain key insights that will support current and future business goals as well as identify new insight and make data-driven decisions that will give the company competitive advantage.

Finally, the evolving role of the CIO will involve more engagement, inspiration and education of others than ever before. To fulfill these duties, it’s absolutely essential that the CIO develops into a strong visionary and consistent innovator for the organization. Through better data analysis and the more widespread use of intelligent automation, those in this important role will begin to morph into the position of strategic advisor, driving the business onward and upward toward increasing and sustainable success well into the future.

Are you a CIO that is struggling to adapt to your changing role? Intelligent automation, powered by AI and machine learning, could provide the foundation upon which you can continue to build your career and your legacy.

Experience the power of Next-Gen Intelligent Automation today!

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Still holding out on IT automation? Here are 4 signs the time has come.

stop resisting IT automation

IT automation is certainly not a new concept. In fact, it’s been in use to some degree for over a century. Yet, there are still a great number of enterprise-level organizations that are on the fence about whether this advanced technology is really worth investing in. If you are one of these late bloomers and are still unsure of whether or not you should take the plunge and employ intelligent IT automation in your company, here are four signs that will let you know it’s time.

Your IT department is struggling to deliver services in a timely, efficient manner.

When a ticket gets opened to IT, how long does it take to achieve satisfactory resolution? In today’s fast-paced business environment, regardless of what industry you are in, agility and efficiency are absolutely critical to ongoing success and future growth. If the demands of your workforce are becoming too much for your skilled IT personnel to handle, the time to leverage technology has come. Not only will IT automation alleviate the burden of many of the day-to-day repetitive tasks, but it will also free up your talented technicians to apply their valuable skills in a more resourceful and profitable manner.

You have way too many staff members on hand just to handle those peak cycles.

Optimized resource allocation is the key to running a lean, profitable operation. If you have far too many IT employees on the payroll just so you can ensure smooth workflow during peak cycles, you are undoubtedly wasting money the rest of the year. Conversely, if your current IT department becomes completely overwhelmed during those peak cycles, your capacity is too low and you’re likely to see higher employee turnover rates. IT automation provides the ability to scale up or down as needed without having to make any changes to your human workforce.

Your employees are wasting an incredible amount of time and effort on repetitive tasks.

Even if you feel that your operation is being managed at the appropriate capacity and the turnaround time of your IT department is acceptable, if your IT team is spending the majority of their day completing manual tasks and processes, you’re wasting money and missing out on opportunity. You’re also facing a much higher risk of costly human error. Why not let artificial intelligence handle these simple, routine tasks? That way you’ll be paying an appropriate salary to workers who are able to better utilize their valuable skillset and the work will be completed faster and more accurately.

Your legacy systems and applications are operating independently.

Of course it doesn’t make sense to invest in an entire system overhaul, but what kind of operation are you running if every application you’ve got in place is functioning in its own silo. The problem many organizations face is the fact that legacy systems which offer useful benefits individually don’t have the capability of working together. This leads to tremendous inefficiency. The beauty of most modern IT automation and orchestration platforms is that they are designed to integrate existing systems, platforms and applications to create a more cohesive and streamlined infrastructure. This allows the organization to avail itself of all the benefits of each legacy system as they work in tandem, complementing and enhancing each other’s capabilities.

If you can relate to any of the four challenges listed above, the time to consider adopting intelligent IT automation is now. Get started today with your free 30 day trial and see for yourself what you’ve been missing out on.

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Transform Your Organization with AI in 5 Steps

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!

What is AI-Powered Decision Support?

Fifty years ago, businesses relied almost exclusively on human judgment for key decision-making. While some data existed, it was professionals and their intuitions, honed over years of experience, who were central to the process of determining good vs. bad and safe vs. risky. Not exactly the most ideal solution.

From there, we moved to data-supported decision making. Thanks to the growing number of connected devices, business leaders were able to access unimaginable volumes of data – every transaction, every customer interaction, every macro and microeconomic indicator – all available to make more informed decisions.

Unfortunately, even this approach had its limitations. For one thing, leveraging such a massive amount of data wasn’t feasible, which left a summarized version. This often obscured many of the patterns, insights and relationships that existed in the original data set. Further, cognitive bias from humans still existed.

Enter stage three: AI-powered decision support. Artificial intelligence is already ahead of the game because, provided the data being used is accurate, it’s not prone to cognitive bias. Therefore, it is more objective in its decisions. Furthermore, AI is better capable of leveraging not just mountains of data, but also all the information contained within that data, allowing for a much higher degree of consistency and accuracy.

As a real-world example, decision support that is powered by artificial intelligence can determine with much more certainty what the optimal inventory levels are, which ad creative would be most effective and which financial investments would be most lucrative.

While humans are essentially removed from the workflow, however, the purpose of introducing AI into the mix is to enhance and enable better decisions that what humans are capable of achieving on their own. In other words, the ideal scenario would involve both humans and AI working in tandem to leverage the inherent value of both for the benefit of the organization. In fact, there are many instances in which business decisions depend on more than mere data alone.

Take, for example, inventory control. While AI may be leveraged initially to objectively determine the appropriate inventory levels for maximum profitability, other information that is inaccessible to AI but incredibly relevant to business decisions may also come into play. For instance, if the organization is operating in a highly competitive industry or environment, human decision makers may opt for higher inventory levels in order to ensure a positive customer experience.

Or, let’s say the AI workflow indicates that investing more in marketing will generate the highest ROI. That company may decide, instead, that it’s more important to focus on areas other than growth for the time being, such as improving quality standards.

So, where artificial intelligence offers consistency, accuracy and objective rationality, other information that is available to humans in terms of values, strategy and marketing conditions may merit a change of direction. In these cases, AI can essentially generate a number of different possibilities from which human decision makers may select the best course of action based on the whole picture at hand.

The key takeaway is that humans are no longer interacting directly with data, but rather the insights produced by artificial intelligence’s processing of that data. Culture, strategy and values still remain a critical component of the decision-making process. AI is basically a bridge to marry them with the objective rationality that cannot be achieved through human cognition. Essentially, it’s a “best of both worlds” situation. By leveraging both humans and AI together, organizations can reach better decisions than they ever could using either one alone.

Want to experience the power of AI to create a force-multiplier for your business decisions? Try Ayehu NG absolutely FREE for 30 days. Click here to start your trial.

Hybrid AI in the Future of Work

 Hybrid AI in the Future of Work - ITOps Guest Post
This article was originally posted on ITOps Times

Due to ongoing improvements in artificial intelligence and machine learning technologies, we are on the cusp of an entirely new era in automation. Not only are software robots adept at performing routine, repetitive tasks on behalf of humans, but they are now capable of carrying out activities that rely on cognitive abilities, such as those requiring the use of judgment and emotion. One only needs to look at the cars we drive to recognize just how far automation technology has come.

Does this mean that there will be no place for humans in the future? The answer – at least for the foreseeable future – is a resounding no. That’s because, despite the growing list of benefits, there are also a number of drawbacks to having a system that is entirely autonomous. That’s where hybrid AI comes into play.

The concept behind hybrid AI is remarkably simple, even if the actual technologies and strategies driving it are incredibly complex. In basic terms, a hybrid model integrates humans throughout the automation process, but uses advanced technologies like deep learning and natural language processing to make automation systems even smarter.

AI needs humans
Beyond the hype, the truth is that artificial intelligence technology is simply not yet ready to replace humans – particularly when it comes to mission-critical applications. Take, for example, Tesla’s autopilot feature. While the vehicle itself is equipped with the capability to drive on its own, the driver behind the wheel is still required to remain alert and attentive to ensure his or her safety. In other words, AI is capable of running unassisted, but when it comes to mission-critical functions, it still needs humans, not only to train it, but to make sure everything stays on track.

The truth is, when artificial intelligence gets things right, everything is peachy. But when it doesn’t, the outcome can be disastrous – especially for larger organizations. And while modern AI may have some impressive cognitive capabilities, at the end of the day, it’s still just as its name indicates: artificial. Keeping humans in the mix ensures that the nuances of communication are present and that the output is accurate and relevant.

Humans need AI
On the other side of the coin, humans can benefit tremendously from artificial intelligence technology. And with 37% of organizations having already implemented AI to some degree, it’s clear that people and machines working side by side is becoming the norm rather than the exception. The reason being, artificial intelligence is like a force multiplier for human workers.

For example, data mining can be handled far faster and in much more massive volumes than any human being is capable of. Using AI, organizations can more effectively turn data into insights that can then be used to assist in human decision-making. This thereby drives innovation and competitive advantage.

Bringing it all together
As we progress toward a more automated future, a hybrid approach to integrating AI can help organizations figure out how to get from point A to point B with as little business disruption as possible. One way executives are handling the shift is to create automation centers of excellence (COE) that are dedicated to proliferating automation throughout the organization. Taking a structured approach like this helps to reduce confusion and limit friction.

Members of the COE are responsible for planning, ongoing testing and continuous oversight of the enterprise automation strategy. Typically, this group is made up of individuals who possess a mix of critical IT and business skills, such as developers, operations specialists and business analysts. Additionally, an entirely new role of automation engineer is being created to support the COE.

CIOs may choose to create their COEs with existing employees who are reskilled or newly hired team members. Regardless, COEs represent a strategic approach that is designed to drive adoption across the enterprise while delivering key results in support of company goals.

Ultimately, choosing a hybrid approach that includes a combination of humans and artificial intelligence, is simply the logical evolution of any disruptive technology. It safeguards against the risks of early-stage gaps and helps organizations get the most out of new solutions every step of the way. Done right, technology enables humans to focus on mission-critical applications while using AI to streamline operations and identify the best opportunities and strategies for ongoing organizational success.

AI is not an either/or proposition. It’s up to each organization to determine the right mix of humans and technology that makes sense. As new capabilities and options emerge, that mix will inevitably evolve. And the IT leaders that fully embrace their increasingly strategic value will know how to create the balance that will continually optimize and elevate staff, technology and the entire future of work.

This article was originally posted as a guest piece on ITOps Times. Click here to redirect to the official publication.

4 Tips for Successful Adoption of AI at Scale

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

Start small.

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

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

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

Focus on augmentation vs. replacement.

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

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

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

Prepare for knowledge transfer.

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

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

Seek transparent solutions.

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

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

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

Your Top Artificial Intelligence Adoption Questions, Answered

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

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

Is a business case necessary for AI?

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

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

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

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

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

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

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

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

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

Practical advice for organizations just getting started with AI:

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

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

A few more considerations:

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

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

The Rise of Artificially Intelligent Service Management (AISM)

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

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

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

What is Artificially Intelligent Service Management?

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

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

How does AISM work?

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

Support Request Management

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

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

Incident Management

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

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

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

Problem Management

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

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

AISM – From Reactive to Proactive and Beyond

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

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

Conclusion

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

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

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Smart CIOs know AIOps is the key to maximizing efficiency

In today’s volatile marketplace, businesses in every industry are focusing on cutting costs. Unfortunately, some folks still view IT as an expense and an area in which the metaphorical belt can be tightened. What they don’t realize, and what an increasing number of CIO’s are embracing, is that implementing AIOps can actually result in reduced expenditure overall.

CIO’s that are concentrating on IT as a force of operational automation, integration and control are losing ground to executives who see technology as a business amplifier and a source of innovation. Ongoing advances in technology are now providing forward-thinking CIO’s with a much broader spectrum with which to work in terms of cutting costs across the entire organizational platform.

It has nothing to do with cutting IT capability, but rather finding ways to make IT operations more efficient. This is primarily achieved through intelligent automation, which significantly reduces the time and resources needed to run both routine tasks as well as complex workflows. When these tasks and workflows are automated, IT personnel are freed up to focus on other, more critical matters, thereby improving the overall performance of the department and subsequently the company as a whole.

Another way that CIO’s are leveraging AIOps for the benefit of their organizations is through improvement of incident management and mean time to resolution (MTTR). Critical system errors are costly and can have a significant impact on an organization’s bottom line. AI-powered intelligent automation is allowing businesses to manage incidents and downtime scenarios more efficiently and in a much timelier manner, which means less risk of negative impact, both on the business and on the end user.

AIOps isn’t just becoming a tool for cutting costs, either. It’s also significantly improving business performance, which plays a key role in increasing revenue. According to a recent survey conducted by Gartner, the main focus of CIO’s in the current climate is growth. They want to attract new customers and effectively retain their current ones. Intelligent automation helps to improve service levels, thereby improving the customer experience.

In a time when budgets are at the forefront of every manager’s mind, from the top down to those on the front line, finding areas to improve service and lower expenditure has become a necessity. The concept of AIOps has opened up a number of opportunities for streamlining operations and improving efficiency, which ultimately achieves the goal of reducing costs and boosting enterprise growth. By applying technology as an amplifier to business operations, rather than as simply an individual component, organizations that are embracing artificial intelligence and automation are already reaping the benefits and are poised for ongoing success as we move toward the future.

Ready to join these forward-thinking business leaders? Download your free trial of Ayehu and start building your AIOps strategy today.
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