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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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Making the Case for Artificial Intelligence in Your Organization

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

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

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

Illustrate success through real-life case studies.

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

Demonstrate AI’s decision-making support.

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

Position AI as the cornerstone to successful digital transformation.

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

Link AI with the power to innovate.

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

Reinforce the power of AI for optimizing client experience.

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

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

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

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

Pros and Cons of IT Chatbots

When IT chatbots were first introduced, admittedly, they were less than impressive. They were slow, clunky and in many cases, it was painfully obvious that a robot was on the other end. Advances in artificial intelligence technology, however, have addressed these concerns and the virtual agents of today are becoming more human-like by the minute. And their popularity is growing, with Gartner predicting that by next year, 50% of medium-to-large enterprises will be using chatbots.

Should you be one of them? Let’s take a look at a few of the ups and downs of using virtual support.

Pros of IT Chatbots

Frees Up Humans Resources – The first and most obvious benefit to deploying IT chatbots for the helpdesk is that it shifts a significant portion of the workload from human to machine. This frees up human agents to be able to focus their high-level skills and cognitive talents on more complex and important business initiatives. This is a much more optimal allocation of resources.

Enables 24/7 Support – Most organizations can’t afford to pay for round-the-clock IT support, and for those that do have the budget, justifying it can be a challenge. IT chatbots are available 24/7, which means if a problem arises at 2am, there’s a good chance it can be automatically addressed and resolved without human intervention. A higher level of support without having to pay live agents? Yes, please.

Advanced Interaction – With the right software solution, IT chatbots can be fully customized. Furthermore, thanks to advanced AI technologies like machine learning and natural language processing, virtual agents can be so “intelligent” that the end-user doesn’t even realize they’re interacting with a robot and not a fellow human.

Cons of IT Chatbots

Volume – The overarching goal of any IT chatbot implementation is to automate routine, manual and recurring tasks. Logically, if the volume of recurring activities for your IT team is low, the benefits of introducing virtual agents may not outweigh the cost and effort it takes to implement.

Time/Resources – While it’s true that chatbots free up existing IT staff, the technology isn’t something you simply plug and play. It requires oversight and maintenance by skilled human workers who can make sure the software has all the information it needs, can add new services, applications and processes for the end-user as needed and that the bots are properly tested.

Fear/Resistance – Lastly, as with most automation technology, chatbots often elicit feelings of fear and resistance from human workers who may be concerned that they are being replaced. Additionally, if the technology is not up-to-par, the end user may push back against the idea of working with virtual agents vs. human help desk support.

These issues can be overcome, provided the right people, technologies and policies are in place. In fact, if you take your time to fine-tune your chatbot platform, it can easily become an effective and realistic channel for supporting end-users, making it well worth the time and investment.

Get started today by laying a strong foundation. Try Ayehu FREE for 30 days. Click here to download.

Ayehu Launches Automation Academy, Propelling Technology Innovation in Artificial Intelligence

The AI-Powered Automation Future Creates New Opportunities and Demands New Skills

San Jose, CA –- March 14, 2019 Ayehu, a leader in intelligent automation, today launched its Automation Academy to provide IT and security technology professionals with up-to-the-minute knowledge, experience and tools necessary to compete in the rapidly transforming and increasingly AI-driven world.

According to industry leading analyst firm Forrester Research, while 16% of jobs will be lost over the next decade as a result of artificial intelligence and technology, 13.6 million new jobs will be created during that time. Another recent study by Deloitte revealed that while 800,000 low-skill jobs were eliminated by AI and automation technologies, 3.5 million new, higher paying jobs were created.

“Automation and AI are impacting our world every day, and companies are realizing that if they don’t act now they will quickly be left behind,” said Peter Lee, Vice President Customer Experience, Ayehu. “Our dedication to intelligent automation has earned us a reputation as an expert, so we created the Automation Academy to support all those that want to re-educate and re-invent themselves for the future.”

Ayehu’s Automation Academy demystifies automation and gives companies the power to transform, advance and compete in this new environment where traditional approaches to IT and business operations no longer suffice.

The Automation Academy offers an Essentials Package, Advanced Package and online certifications, depending on business needs. Courses are designed to help IT professionals comprehend and cultivate practical automation skills through a variety of interactive learning activities. Training options are flexible to help students get started on the journey from basic to breakthrough.

Ayehu Academy will offer the following courses:

Essential Training

  • Introductory training designed for beginner-level users starting to explore and use automation.
  • Self-led, online module guides students through the fundamentals of Ayehu’s Next Generation Intelligent Automation and Orchestration Platform.
  • Explore theoretical concepts and participate in hands-on exercises to build a foundational understanding of automation technology.

Advanced Training (includes Essentials)

  • In-depth training on how to become the future of intelligent operations, including the ability to plan and build automated workflows.
  • Receive expert instruction either online or on-site for small or large audiences.
  • Formal certification awarded upon successful course completion.

Online Exams

  • Exams to test automation technology knowledge, assess in-depth awareness and verify student skill level.
  • Flexible online availability for anywhere, anytime testing.

Ayehu’s AI-powered Next Generation Intelligent Automation Platform is the cornerstone for the academy. The platform incorporates artificial intelligence to augment human ingenuity, in order to enable the creation of the next generation of intelligent applications. It delivers no-code, automated workflows that help enterprises save significant time on manual and repetitive tasks, accelerate mean time to resolution, and maintain greater control over IT infrastructure.

“We’re excited about the overwhelmingly positive response we’ve had already. Many tell us they ready to go all in on training, and some are planning to establish internal automation centers of excellence. Embracing automation will create opportunities for promotions and new positions, and most importantly give people the freedom to be true innovators,” concluded Lee.

To learn more about the Ayehu Automation Academy and the company’s Next Generation Automation and Orchestration Platform powered by AI, click here.

About Ayehu

Ayehu’s AI-powered automation and orchestration platform is a force multiplier for IT and security operations, helping enterprises save time on manual and repetitive tasks, accelerate mean time to resolution, and maintain greater control over IT infrastructure. Trusted by hundreds of major enterprises and leading technology solution and service partners, Ayehu supports thousands of automated processes across the globe. For more information, please visit www.ayehu.com and the company blogFollow Ayehu on Twitter and LinkedIn.

Intelligent ITSM Automation – Your Secret Formula for Success

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

Widespread Benefits of Intelligent ITSM Automation

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

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

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

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

Aligning Intelligent ITSM Automation with Business Goals

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

Obstacles to Intelligent ITSM Automation

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

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

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

IT Process Automation Survival Guide

How to Build an AI Team

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

Learn what successful AI looks like.

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

Recruit/train (and retain) top talent.

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

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

Tap freelancers.

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

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

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

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