Building Intelligent Automation Into Your Organizational Culture

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In order for intelligent automation to be truly successful and produce sustainable results, it can’t be a one-off project that is exclusive to the IT department. It has to be woven into the very culture of the organization and fully embraced across the entire company. But changing corporate culture is much easier said than done. How can you incorporate automation so that it becomes an integral part of the everyday work environment? Here are a few suggestions to get you started.

Get buy-in from top leadership. Cultural changes typically start at the top and trickle downward, so make sure that everyone in a leadership role within your organization understands the benefits of intelligent automation and why it’s so critical that it become a part of the underlying atmosphere of the company as a whole. Once they’re on board, it’s time to start leading by example.

Sell the benefits. If you want your company culture to embrace automation, you have to make everyone in every position aware of how it will benefit them directly. In other words, show what’s in it for them. Otherwise, you will lack the support needed to make the final shift. Remember, intelligent automation isn’t just about those running your help desk. Things like self-service automation also provide enhanced flexibility, autonomy and empowerment to the end-user. Get the message out.

Identify and address obstacles. Change management is challenging, especially when it involves the evolution of an entire corporate culture, but it’s not impossible. You just have to understand what’s standing in the way so you can overcome those obstacles. For instance, if your employees are scared that intelligent automation will make them obsolete, they will resist. You have to address and quell that fear head on by showing them the opportunities it will bring for new roles, such as Automation Engineer, and the ability to do more with less.

Incentivize and reward. Culture change happens much more smoothly and effectively when it’s not shoved down the throats of your employees. Instead of simply telling them and expecting them to adapt, make them a part of your company’s evolution. Not only will this help them better understand the reasons behind the change, but the buy-in will create a much stronger foundation for the shift across the board.

Keep it fluid. The beauty of intelligent automation, and technology as a whole, is that it’s constantly changing and improving. A corporate culture is much the same in that it should be something that can be molded and enhanced as needed. Keep an open mind and make modifications where necessary. As long as you’ve got a solid foundation to work with, the only direction you can go is up.

Have you been successful in weaving intelligent automation into your organizational culture? Please share your insight, advice and tips in the comment section below. And don’t forget to launch your free product demo of our Next Generation Automation & Orchestration platform, powered by AI. It’s something you must experience for yourself!

4 Steps for Selling Intelligent Process Automation to the Masses

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

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

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

Appeal to their self-interest.

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

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

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

Connect your proposal to specific business goals.

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

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

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

Break down your plan into manageable milestones.

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

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

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

Sing your own praises.

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

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

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

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

Want to Innovate? Automate.

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The key to innovation lies in the ability to quickly identify and resolve frictions. Easier said than done? Not necessarily – provided you have the right tools in your corner. That’s where the power of AI, machine learning and intelligent automation come into play. By leveraging these technologies, organizations will be better prepared to pinpoint roadblocks and pivot accordingly to unlock new opportunities. The Process In order to identify issues in workflows, there needs to be a process put in place, and that process must involve mapping out the complete journey of that workflow from start to finish. Hidden within this flow of events is where you’ll find those gaps and imperfections. And chances are, the more detailed the workflow, the greater the number of frictions you will encounter. That being said, the more frictions you find, the greater the opportunities to innovate by solving those issues and streamlining those workflows. Upping the Ante The whole process of laying out a workflow and identifying problems is nothing new. In fact, it’s been employed by top organizations around the world for eons. The problem is, because this process has historically relied on human effort, it’s naturally prone to errors and oversights. Here’s where technology has become a real game-changer. Not only does AI and automation dramatically speed up the process of monitoring workflows and identifying issues, thereby streamlining processes, but because it’s capable of providing users with improved access to knowledge, it’s empowering users to self-serve. The result is a powerful synergy between human and machine which is enabling enterprises to truly up the ante in virtually every area of operation. Automation = Innovation Thanks to rapid advances, not only are we able to use automation technology to explore and identify frictions, but artificial intelligence and machine learning can also present new and expanding solutions to those issues. This capability is becoming one of the most powerful tools for decision-makers, who no longer have to rely on fallible human suggestions, but can instead choose from recommendations derived from real, quantifiable data. In fact, unlike human analysts, AI is capable of sifting through mountains upon mountains of raw data and then convert that data into invaluable insights and actions. With intelligent automation, we are able to gain a new understanding of what’s happening in both the physical as well as the digital arena, as well as the context in which these things are occurring. With these insights, we can then take action, whether it be by informing, alerting or closing the loop. If, in the past, we considered the question, “How can we solve problem A for person B,” intelligent automation changes the game by asking, “How can we automate this process and make it more intelligent?” As such, the solutions we’ll develop will ultimately take us beyond the human user to learn what’s standing in our way, predict and plan next steps and incorporate automated actions whenever and wherever it makes sense. By leveraging the power of intelligent automation, we are essentially shifting responsibilities from human to machine. Putting Ideas into Action It’s easy to write about how AI and intelligent automation has become a game-changer in terms of innovation, but how can organizations actually put this into action? There are two critical questions to ask: • Given your available data and existing assets, which behaviors, activities, processes or environments could be made more intelligent through automation? • What, if any, gaps exist within those physical assets and data? Which devices, tools, applications and analytics capabilities could be added into the mix to capture data more effectively and further the goal of automating? When you incorporate intelligence and automation into your processes and operations, you’ll be able to expand your portfolio of ideas and identify newer and better opportunities as a result. And that’s where true innovation can be found. Ready to get started? Download your free 30-day trial of Ayehu and put the power of AI and intelligent automation to work for you.

The key to innovation lies in the ability to quickly identify and resolve frictions. Easier said than done? Not necessarily – provided you have the right tools in your corner. That’s where the power of AI, machine learning and intelligent automation come into play. By leveraging these technologies, organizations will be better prepared to pinpoint roadblocks and pivot accordingly to unlock new opportunities.

The Process

In order to identify issues in workflows, there needs to be a process put in place, and that process must involve mapping out the complete journey of that workflow from start to finish. Hidden within this flow of events is where you’ll find those gaps and imperfections. And chances are, the more detailed the workflow, the greater the number of frictions you will encounter. That being said, the more frictions you find, the greater the opportunities to innovate by solving those issues and streamlining those workflows.

Upping the Ante

The whole process of laying out a workflow and identifying problems is nothing new. In fact, it’s been employed by top organizations around the world for eons. The problem is, because this process has historically relied on human effort, it’s naturally prone to errors and oversights. Here’s where technology has become a real game-changer.

Not only does AI and automation dramatically speed up the process of monitoring workflows and identifying issues, thereby streamlining processes, but because it’s capable of providing users with improved access to knowledge, it’s empowering users to self-serve. The result is a powerful synergy between human and machine which is enabling enterprises to truly up the ante in virtually every area of operation.

Automation = Innovation

Thanks to rapid advances, not only are we able to use automation technology to explore and identify frictions, but artificial intelligence and machine learning can also present new and expanding solutions to those issues. This capability is becoming one of the most powerful tools for decision-makers, who no longer have to rely on fallible human suggestions, but can instead choose from recommendations derived from real, quantifiable data.  

In fact, unlike human analysts, AI is capable of sifting through mountains upon mountains of raw data and then convert that data into invaluable insights and actions. With intelligent automation, we are able to gain a new understanding of what’s happening in both the physical as well as the digital arena, as well as the context in which these things are occurring. With these insights, we can then take action, whether it be by informing, alerting or closing the loop.

If, in the past, we considered the question, “How can we solve problem A for person B,” intelligent automation changes the game by asking, “How can we automate this process and make it more intelligent?” As such, the solutions we’ll develop will ultimately take us beyond the human user to learn what’s standing in our way, predict and plan next steps and incorporate automated actions whenever and wherever it makes sense. By leveraging the power of intelligent automation, we are essentially shifting responsibilities from human to machine.

Putting Ideas into Action

It’s easy to write about how AI and intelligent automation has become a game-changer in terms of innovation, but how can organizations actually put this into action? There are two critical questions to ask:

  • Given your available data and existing assets, which behaviors, activities, processes or environments could be made more intelligent through automation?
  • What, if any, gaps exist within those physical assets and data? Which devices, tools, applications and analytics capabilities could be added into the mix to capture data more effectively and further the goal of automating?

When you incorporate intelligence and automation into your processes and operations, you’ll be able to expand your portfolio of ideas and identify newer and better opportunities as a result. And that’s where true innovation can be found.

Ready to get started? Download your free 30-day trial of Ayehu and put the power of AI and intelligent automation to work for you.

7 Steps to Creating an Automation Center of Excellence

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The Center of Excellence (CoE) for Automation has become a very hot topic these days, moving from distributed organizations that each own several tools and scripting to one vertical center that provides automation solutions across the enterprise.

In response to this growing demand, Ayehu has established an Automation Academy that will help enterprises to transition and build their own CoE, training people to become Automation Specialists / Engineers. This will allow organizations to better prepare for the future (when machines will do almost everything) and help drive efficiencies via automation with a stronger emphasis on innovation.

Building your own CoE for Automation isn’t necessarily as complicated as you may think. In fact, it can be accomplished by implementing just a few strategic steps. Here’s how.

Step 1: Evaluate and Adopt Automation

The first step in the process of establishing a CoE for Automation is to gain adequate understanding of the various challenges, opportunities and benefits of automation. During this process, project management teams may choose to identify certain “quick wins” that can be automated fast and result in immediate return on investment.

Step 2: Define, Document and Set Up the CoE

Having gained a strong understanding of the challenges surrounding adoption of automation as well as the tremendous, quantifiable opportunities it presents, the next step is actually establishing your Center of Excellence for Automation. This involves selecting the appropriate core team members as well as evangelists who will assist in spreading awareness and advocate for the benefits of automation.

Keep in mind that the ideal core team for a CoE demonstrates a broad spectrum of skill sets. For instance, you’ll need someone who can assess the impact and document the processes, someone who can handle the implementation and integration process and someone else who can monitor and test the automation.

Step 3: Establish Systems and Infrastructure

Your CoE for Automation will only be as effective as the technological foundation upon which it is built. Making wise choices upfront about the systems and infrastructure you establish will set the stage for rapid growth and also help to prevent potential issues from occurring down the road. Invest in enterprise-class automation and architecture that includes robust features. Create and document best practices with a focus on automated processes that are consistent, efficient, accurate and auditable.

Step 4: Train, Educate and Reskill

While automation will inevitably eliminate some jobs, there are opportunities to train and reskill people for new, next generation roles, such as Automation Engineers. Reskilling and redeploying back to work will ultimately create higher value for the organization, its clients and for the employees themselves. Look for training options that are specific to CoE development, like Ayehu Automation Academy.

Step 5: Sustain and Scale

Once your CoE is officially established, the next phase should involve aligning the automation strategy with the strategic objectives of the organization. This typically involves scaling the approach to make it broader. For instance, while the initial goal of automation might have been to reduce costs, the scope should eventually evolve to include such larger goals as creating stronger customer loyalty or driving greater agility.

The entire CoE needs to work on firming a matured process so it can become agile enough to respond to demand and maximize efficiency. This process should have a definition of how the organization should approach the CoE, how the CoE should evaluate and prioritize these requests, how it should develop its internal design to production processes, etc.

Finally, the core CoE team should specifically include analysts who can continuously identify automation opportunities, translate business needs to IT processes, determine potential ROI and create the logic steps necessary for the automation engineers to build and implement the processes. Remember – a CoE isn’t stagnant. It’s something that must change, evolve and improve as time goes by.

Step 6: Incorporate Automation into the Culture of the Enterprise

Ultimately, automation should become a complement of continuous process improvement for the entire organization. The last step of building a CoE for Automation involves changing the overall business mindset to embrace the opportunity automation presents to change and improve how it operates.

Creating the CoE without making a cultural change in the organization simply will not work. The organization (the people) must change their behavior and think about automation as opportunity to live better, to focus on more important things and be freed up for innovation. Embracing automation will allow the CoE to become relevant to an organization that wants to change and automate as much as possible.

Keep in mind that this phase can take a good deal of time to complete. You’ll know you’ve achieved success once automation becomes embedded in every department and function throughout the enterprise.

Step 7: Market the CoE

Once the CoE for Automation is successfully established and the necessary cultural shift has been set in motion, it’s time to start promoting the CoE to outside to end clients. Any client-facing employee should be prepared to sell the innovation and success stories of automation. This will create demand generation and fulfillment and help the organization achieve maximum competitive advantage.

This is clearly a high-level overview of the CoE process, but it should at least provide a framework upon which to build. If you’re considering making a move in this direction, we encourage you to take advantage of our resources and expertise by allowing us to assist you with developing and establishing your Center of Excellence.

Why go it alone when you can rely on a team of experts who can help you every step of the way? To learn more or get started, contact Ayehu today.

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4 Steps for Winning the Battle for AI Talent

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A recent survey revealed that 42% of employers admit they are concerned that they won’t be able to access the talent they need to run their businesses. This worry is compounded for those in IT, a sector notorious for its ongoing staffing shortage. And with the rise of artificial intelligence technology, the demand for top-tier AI talent is far outweighing the supply. The good news is, with the right approach, even smaller organizations can compete for today’s most sought-after candidates.

Create a Purpose Statement

Employee engagement has dipped to a measly 34% in recent years. One way to boost morale is to provide employees with a sense of purpose. Simply put, today’s top AI talent doesn’t want to spend the majority of their time crunching numbers or working on projects that don’t challenge and empower them. They want meaningful work. You can address this desire by developing a compelling AI purpose statement. Ideally, your statement should highlight the unique and exciting opportunities that await candidates.

Focus on “Citizens”

We’re not talking about citizens of a particular locale, but rather the emerging role of citizen data scientists. Unlike data scientists, who focus on wrangling and cleansing data, Gartner defines these individuals as “power users” who are capable of performing both simple as well as moderately sophisticated analytical tasks that would have previously required more expertise. Forward-thinking organizations are already focusing their attention on citizen AI talent as a means to bridge the gap between AI specialists and the rest of the enterprise.

Tap into Universities

Thankfully there has been a recent shift in how much emphasis colleges and universities are placing on equipping students with applicable, employment-ready skills. This includes prepping those studying AI for entering the workplace. A great way to get ahead of the game in terms of recruiting this up-and-coming AI talent is to forge strong relationships with educational institutions, in particular, with academic departments that specialize in artificial intelligence and other related functions.

Reskill / Upskill Existing Staff

It’s been proven time and again that it’s far more cost-effective to retain existing staff than it is to recruit externally. Organizations may be sitting on a gold mine of available AI talent without even realizing it. For instance, the right training can help IT professionals comprehend and cultivate practical skills and gain the fundamental understanding they need to develop into marketable AI talent in the future.

According to a recent Ernst & Young poll, 56% of senior AI professionals said they believed the lack of qualified AI professionals was the single biggest barrier to AI implementation across business operations. You can win this war for AI talent by implementing the four strategies above and implementing the right tools. Experience the Next Generation of intelligent automation and orchestration with your free 30 day trial of Ayehu. Click here to claim yours.

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

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Author: Guy Nadivi

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

It’s that simple.

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

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

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

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

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

Enter AIOps.

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

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

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

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

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

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

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

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

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

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

Gartner concurs with this approach.

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

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

No ambiguity there.

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

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

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

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Your Top Artificial Intelligence Adoption Questions, Answered

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

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

Is a business case necessary for AI?

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

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

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

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

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

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

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

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

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

Practical advice for organizations just getting started with AI:

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

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

A few more considerations:

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

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

5 Things to Avoid for a Successful Intelligent Automation Rollout

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

Focusing on tools and tasks instead of people.

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

Failing to adequately calculate and communicate ROI.

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

Not setting appropriate expectations.

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

Automating broken processes.

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

Not using the right platform.

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

Thinking about trying automation but not sure where to begin?

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

eBook: 10 time consuming tasks you should automate

ITOps: Best practices to improve performance and service quality

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

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

Best Practices for Systems and IT Operations Managers:

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

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

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

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

eBook: 10 time consuming tasks you should automate

The 7 Secrets of Effective Digital Transformation

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

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

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

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

The human element should be front and center.

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

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

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

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

Take the time to really understand your customers.

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

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

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

Establish new teams.

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

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

Cultivate collaboration as you deploy technology.

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

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

Don’t give in to the resistance.

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

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

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

Think like a startup.

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

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

Take a bottoms up approach.

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

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

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

Conclusion

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

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