Have you fallen for these AI myths?

artificial intelligence AI mythsArtificial intelligence has been around for decades, though it just recently became a hot topic in the business world. During this time, many individuals have confused AI with automation, sometimes going as far as using the two terms interchangeably. The reality is, while the general concept may be similar, the two are distinctly different. Furthermore, this confusion has led to a number of other myths and misconceptions. We’d like to clarify a few things, beginning with the difference between AI and automation.

Intelligent automation involves programming technology to perform routine, manual tasks based on a prescribed set of instructions. Artificial intelligence takes this concept several steps further by using intelligent machines which are capable of displaying human behavior, thought and decision processes. Where automation is essentially set in stone (unless manually modified), an AI machine increases its own intelligence and can adapt its actions automatically, based on information it receives.

From a business perspective, artificial intelligence has the power to help organizations make more informed decisions. It can extract valuable information from mountains of data, analyze and organize it in a logical manner and essentially close the gap between insight and action. Given its complexity, however, AI is still often viewed in a negative light. To change this, we’d like to dispel three of the most common misconceptions as follows.

Artificial intelligence is a distant dream.

Many people believe that AI is a technology that won’t be readily available and practically applied until many years into the future. The truth is, widespread adoption of AI, both in our professional and personal lives, is much closer to becoming a reality than you may think. In fact, given that so many organizations across all industries and around the world are already employing automation to some degree, the idea that AI could be worked into the mix isn’t all that far-fetched.

Artificial intelligence isn’t really going to make that much of an impact.

The idea that AI is somehow inapplicable in the business world stems largely from the technologies complexity. People tend to discount things they have difficulty understanding. The reality is that AI is not only practical for business use, but it’s incredibly beneficial. The machine learning component of AI means that computers will have the ability to learn without the need for programming. It also has the capability of mining and analyzing big data to extract valuable insights which can then be put into action to achieve better results. These are things every organization can benefit from.

Artificial intelligence is going to eliminate the need for human workers.

While it’s certainly true that AI will make human workers redundant to some degree (think routine, repetitive tasks like reporting and data entry), this technology will not fully replace humans. This is particularly true in certain fields that require high-touch interactions, like HR, health care and consulting.

Likewise, while intelligent automation will streamline and optimize operations for many organizations, it cannot and will not replace the need for the development and nurturing of customer relationships. AI can, however, leverage data to provide human workers with the insight they need to deliver better, more personalized service.

And because implementing and managing new technology will always require some degree of human input, new roles and responsibilities will naturally evolve, which means that for many, AI will present great opportunities.

Like it or not, AI isn’t going anywhere. In fact, according to IDC research, worldwide spending on artificial intelligence is expected to reach $19.1 billion this year – an increase of more than 54% over last year. By addressing and overcoming the biggest misconceptions about AI, companies can harness the power of intelligent automation to streamline operations and provide competitive advantage.

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Episode #2: Applying Agility to an Entire Enterprise – ISG’s Ola Chowning

Sep 25, 2018    Episodes

Episode #2: Applying Agility to an Entire Enterprise

In today’s episode of Ayehu’s podcast we interview Ola Chowning, Partner at ISG (Information Services Group)

“Agile” has been a highly successful approach to software development since the start of the 21st century, though its conceptual roots can be traced back to 1957.  Are Agile’s principles of adaptive planning, evolutionary development, early delivery, and continual improvement applicable beyond IT to an organization as a whole?  Ola Chowning believes so, and she should know. 

As Head of ISG’s Enterprise Agility practice, Ola is calling on organizations to innovate technologically in order to increase their business responsiveness.  By leveraging emerging technologies like DevOps, cloud, and automation, she helps businesses greatly improve the speed at which they respond to market changes.  The key first step Ola advocates taking towards increasing that velocity though is surprisingly not technical at all.

Guy Nadivi: Welcome everyone, to Intelligent Automation Radio. Our guest today is Ola Chowning from Information Services Group, better known as ISG. ISG is a global technology research and advisory firm, and Ola leads ISG’s enterprise agility practice in the Americas. She is an IT transformational thought leader with over 25 years of leadership experience within various industries helping enterprises make transformation change, and is fluent in enterprise agility, DevOps, cloud, and automation. Ola, welcome to Intelligent Automation Radio.

Ola Chowning: Thanks for having me, Guy.

Guy Nadivi: Ola, I started my career as a technical person before transitioning into more of a business role, and where you claim fluency in a variety of technology concepts, I simply like to say that my ability to translate between English and geek speak makes me bilingual. Maybe one day Rosetta Stone will come out with a training program for technobabble. Ola, you advocate for something called enterprise agility, which calls on organizations to innovate technologically in order to increase their business responsiveness. As a first step, enterprise agility requires adopting a culture of learning, but some IT staffers view innovations like artificial intelligence and machine learning, automation and other technologies, as threats rather than opportunities to learn new things. How do you recommend IT executives overcome that kind of resistance within their organization in order to push forward and become more agile?

Ola Chowning: Well, like any change that we see happen with an organization, organizational change is really twofold. There’s a top-down approach that you need to take which basically says making people comfortable that they have the ability to change, and that you want them to change, and why the change is good for them. From a leadership perspective, a lot of it’s about displaying the willingness to change, showing the employees in the organization that change is a good thing and you indeed expect it to happen. Showing them what good looks like is also a very good idea. As an employee, I think, particularly in technology, we should be relatively comfortable with change. Technology has been expanding exponentially for many, many years, and even for those of us who are new to the industry, we have to stay up on what’s happening from a technological perspective. We should be relatively understanding about how change can help us in our careers, can help us make our jobs more interesting, as well as more successful from a company viewpoint as well. How we look at the business, how we’re helping the business, we should be looking to continually change. Having said that, we have a tendency to make it a long-term goal. We have a tendency to say, “Let’s go take a class.” And we’ll go learn something new as if it’s a big expansive thing that I can just go take a class and learn. What we’re seeing happen with the technology changes, particularly in areas like automation, is that it’s the application of the technology. That’s really what I need to learn. Technology itself, especially for a technologist, is relatively easy to understand and relatively easy to learn. It’s how I apply them that really is the learning that needs to be done. That is going to be very specific to the business and the business value that you’re trying to achieve, which means that I need to quickly learn, in a very iterative fashion, how what I’m doing is actually affecting value, is it affecting it in a good way, is it affecting it in a bad way, and be able to continue on those good paths, and maybe relinquish the ones that aren’t so good. Continuous learning is the key. It’s not about just going off and taking some classes or learning a new tool. It’s about the application of it to affect a business value that I’m trying to affect.

Guy Nadivi: What you said brings to mind an old cliché that in IT the only constant is change, so it’s a very good point you make, that if you’re going to be in this field, you need to be prepared to change. Please tell our audience about the operational model for enterprise agility. How should IT executives organize their teams for change to best facilitate agile success?

Ola Chowning: I think the main concept behind enterprise agility is that not everything you do within your organization needs the same level of agility. I have business functions that are relatively common. They are common business processes. They have relatively similar functions that I need to perform between companies. What I do within HR is very similar to what the company across the street does within HR. There are areas within my business that, while they need technology, and I’m not suggesting that they don’t, the level of agility they need, the level of constant learning, the level of constant change, constant improvement, rapid market response, is really only necessary in those areas that typically are business facing, consumer facing, or business enabling. What I need to do within enterprise agility is recognize the fact that I don’t need to have the same level of agility as everywhere. Where I do need to have the level of agility, though, I need to be able to truly practice the principles of agility. The principles of agility would tell you I need to be in a highly collaborative atmosphere. I need to be able to make decisions in a very rapid way. That’s what drives velocity, and the ability to respond to the market at the speed that the business needs. What drives that velocity is having smaller teams that are enabled to make those decisions, that are focused on a very, let’s call it a smaller scope of business value. Instead of trying to manage or look across the constraints broadly across many, many areas of business function or business value, I’m looking to draw my attention in a smaller group of team members, a smaller amount of capabilities, across a smaller scope so that I can use that smaller team in a highly functional way. What do I mean by that? What I mean is that in a smaller team I have the ability to, against a very small scope within a business function, I can turn my attention on the whole team over to operations when I need to, or I can turn the attention of my whole team over to development if that’s what I need to do. My ability to swarm to what’s necessary and priority is much easier to do when I have a small team that’s focused on a single set of objectives. What has a tendency to happen over time within IT in the past, in our legacy environments, is we’re trying to manage to a long list of priorities that are conflicting against a constraint of a large group of people who are all trying to be used against those same priorities. The real theory, and it’s really not even theory anymore, it’s been proven many times, is that the smaller you get from a teaming perspective, and the more focused you have them on a smaller group of business objectives, the more likely you’re going to get the velocity that you need in order to respond to those objectives and be very, very efficient.

Guy Nadivi: These smaller, more focused, more efficient teams capable of greater velocity, are these what you’ve previously talked about as empowered teams?

Ola Chowning: Correct. Empowerment is an important part of this discussion, and it’s actually probably the most difficult to achieve from a cultural perspective than most legacy organizations. Empowered teams means that the team itself, and they’re usually relatively small … We see teams most often under 20. We’re talking 10, 8, 15 people, and they need to have the decision rights and the authority to do what they need to do in order to meet the objectives. There’s not a command-and-control, a hierarchical structure. We don’t want them to be delayed to make the decision that they need to make in the moment. We’re expecting them to have the accountability with the business, by the way, with the business as part of the team, to make the decision on what is their highest priority today. What is their highest priority in this moment, so that they can go and achieve what they need to achieve against that particular priority, and not have the delay of what are typically hierarchical structures. Now, the culture change comes in in that most of us work in enterprises that are much more hierarchical in how they distribute decision authority. Indeed, IT has been a very control-oriented organization for many years. Part of that is we’re responsible for the security of the environment, and for the applicability of the environment from a business perspective. We are the stewards of data. We’re the stewards of information. That has a very control-oriented atmosphere. Part of what we need to do within these smaller teams is ensure that we have the right guard rail so that those teams know what they’re accountable for. You can’t just have complete anarchy within the team, but what you can do is put in place, as an example, things within your automation criteria that make it impossible for them to take certain decisions without prerequisites being fulfilled. I need to find different ways to ensure that I have the appropriate governance that I need around my environments while still allowing these teams to be highly empowered and very functional in and of themselves, so they don’t have the delay of hierarchical command-and-control. That’s the culture change. The culture change is leadership’s not used to that. Employees aren’t used to that, and a lot of this is about making people comfortable that it will work. We see it work not only just in small organizations, I’ve seen it work in very large enterprises, but it is a big shift in culture.

Guy Nadivi: Ola, can you please talk a little bit about how automation impacts enterprise agility with the cultural change and the empowered teams that you’ve just described?

Ola Chowning: Absolutely. One of the precepts that we see happen within high velocity, particularly those teams that are around IT products that have components, I’ll call them full-stack components, application, infrastructure, all usually moving towards some user interface. That sort of solution, that sort of IT product, today in most organizations that are looking for high velocity in a differentiated product set are using automation. They’re automating their deployment pipeline. They are scripting what they need to achieve during testing. They are not moving code through a person and taking action. They’re moving code into production, as an example, turning features on and off, even setting up infrastructure through code. Because it’s happening through code, it’s happening in a very automated fashion. I’m no longer having to touch things to get them done. I’m sending instructions, if you will. As we see the world as code, which is what’s starting to happen, effectively we’re seeing the ability to automate those things that, in the past, have been pretty manual. If you think about things like our ability to change security protocols within parameters in an environment, through firewalls and those sorts of things, or even stand up a server and have the appropriate storage, and the appropriate operating model there. In highly optimized environments, and particularly in the cloud, what we see are orchestration capabilities that’ll automate. I simply send an instruction and it happens. I now have the environment I need. I now have the storage I need and have the operating system that I need, and I can now move the code in and out. That automation, of course, drives velocity, but that automation also should include those control or governance aspects that need to be there, the security that needs to be there, the auditability that needs to be there, the monitoring capabilities that need to be there. It’s sort of an old adage that says I can also go out and write code that does a very bad thing, or I can write code that does a very good thing. It’s very similar in these new environments. I can use automation to do very good things, or I can use automation and maybe don’t have all the things related to it that I need, but automation is what helps drive us to the velocity that we need. Even within small empowered teams, if I need to take action, I need that action to happen very quickly, and automation is what’s helping us drive that velocity in performing the actions that I need in order to respond to the market.

Guy Nadivi: If you are an IT staffer in an organization adopting the enterprise agility mindset, what new skills or new perspectives should you acquire to best succeed in this paradigm?

Ola Chowning: That’s a great question, Guy. The one thing that most IT folks will have a pretty good sense of is just understanding technology at a conceptual level. But what happens in these smaller teams is that I need more than conceptual. I need to have some hands-on knowledge of how to do not just my usual specialized skill, maybe I’m a tester today, but I have a very specialized skill in testing, but in these smaller teams I need people who have multiple skills. I need them to become much more generalists. I need that tester to start to learn how to also perform functions that are more along the lines of developing, or customer service, or monitoring and operating, responding to events. I need them to be far broader in their knowledge and their capabilities. That rapid learning is something that most of us have somewhere in our tool chest, and we need to really brush it off and be prepared to learn a lot of new things, and be open to taking on different roles within these smaller empowered teams. Having said that, the one thing that is really critical in today’s environment, and particularly in these very rapid, small DevOps teams, is my ability to script. As we see everything as code, and I can direct infrastructure via scripting and via code, and I can direct even down to network bandwidth via code, I need to be able to code. I need to know how to script. Scripting languages, I think, are going to become a really hot commodity. I think they already are, and understanding how to use automation tools would probably be another one, and really any automation tools, because once you understand how automation tools work, going from tool to tool is a relatively simple step.

Guy Nadivi: What one piece of advice would you give CIOs, CTO’s, other IT C-Suite executives that are considering whether or not to dive into enterprise agility?

Ola Chowning: I would give them a couple of suggestions. One is that we have a tendency within IT to think of long transformation programs. We think in a very waterfall manner. It’s just how we’ve been doing it for years. We think of a transformation as a long pole in the tent, and at the end of it there’s going to be some big bang, and everything’s going to be in place. Where we see companies being most successful is when they take a far more agile approach to transforming their organization. That usually starts with teams that are already probably leaning this direction, if not in this space. They’re usually more in the digitized channels within an organization, so if you have, as an example, e-commerce, you’re usually going to have some sort of pilot team, or front runners, or even new capabilities that you need. If you start with those smaller groups of teams, such as in a pilot, what has a tendency to happen is you get practice. You understand how it works within your organization, and for the rest of your organization, they get a really good picture of what good looks like so they can actually see it working. They actually, it’s not some theory. They can actually see it in practice. So, starting small, eventually expanding. By the way, that expansion, in our experience, happens far more rapidly than you think it does. First of all, the business starts to see those pilot teams be successful, and they want the same success, and the same velocity, and internally, in your IT organization, your technologists see that success, and see that velocity, and they want to play a part in that too. You start to see a real expansion happen, not only from the top down, from the business, but also from the bottom up within your IT organization as well.

Guy Nadivi: All right. Looks like that’s all the time we have for on this episode of Intelligent Automation Radio. Ola, thank you very much for joining us today. I’ve really enjoyed our conversation, and thank you again for being our guest.

Ola Chowning: Thank you so much. Have a great day.

Guy Nadivi: Ola Chowning, from Information Services Group, who leads ISG’s enterprise agility practice in the Americas. Thank you for listening everyone, and remember, don’t hesitate. Automate.

Ola Chowning

Partner, ISG

Ola is an IT transformational thought leader with over 25 years of leadership experience within various industries helping enterprises make transformation change.  Ola has advised corporations in the opportunities related to emerging technologies, and is fluent in Enterprise Agility, DevOps, cloud, and automation. She leads ISG’s Enterprise Agility practice globally, where she has guided clients in the development of governance and value assurance models that address decision making from investment through operation, and organizational design and implementation to enable those models. 

Ola can be found at:

E-Mail:               ola.chowning@isg-one.com

Twitter:             @OlaChowning

LinkedIn:           https://www.linkedin.com/in/ola-chowning-797b731/


“As an employee…particularly in technology, we should be relatively comfortable with change.”

"…the main concept behind enterprise agility is that not everything you do within your organization needs the same level of agility " 

“The principles of agility would tell you I need to be in a highly collaborative atmosphere. I need to be able to make decisions in a very rapid way. That's what drives velocity, and the ability to respond to the market at the speed that the business needs.”

“The real theory, and it's really not even theory anymore, it's been proven many times, is that the smaller you get from a teaming perspective, and the more focused you have them on a smaller group of business objectives, the more likely you're going to get the velocity that you need in order to respond to those objectives and be very, very efficient." 

“…rapid learning is something that most of us have somewhere in our tool chest, and we need to really brush it off and be prepared to learn a lot of new things, and be open to taking on different roles within these smaller empowered teams.”

About Ayehu

Ayehu’s IT automation and orchestration platform powered by AI 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.




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Disclaimer Note

Neither the Intelligent Automation Radio Podcast, Ayehu, nor the guest interviewed on the podcast are making any recommendations as to investing in this or any other automation technology. The information in this podcast is for informational and entertainment purposes only. Please do you own due diligence and consult with a professional adviser before making any investment

Adopting Intelligent Automation: Managing Resistance to Change

Intelligent automation offers a variety of benefits to organizations, including improved efficiency, enhanced productivity, greater accuracy and output and an overall cost savings. For companies that have yet to employ this technology, however, the biggest hurdle to overcome in doing so is often resistance from employees. For some, this contention comes from fear of becoming obsolete, while others simply don’t like change. Whatever the reason, having the right strategy in place can help make adopting automation a smooth, painless and positive experience for everyone.

Change Management is About People

On the surface, managing change while implementing a technological advancement such as intelligent automation may seem to be all about the systems and processes being automated. In reality, however, change management is about understanding the fears, needs and desires of your company’s most valuable asset: your people. By addressing the human side of change, you can overcome the roadblocks and obstacles in your way and effectively change the outlook of even the strongest opposition.

Generally speaking, people tend to fall into 3 distinct
categories when it comes to adopting something new:

  • Those who vehemently oppose the proposed change
  • Those who are tentative about change
  • Those who are change champions

When planning your intelligent automation project, your strategy should incorporate the appropriate actions to address those individuals who fall into the first two categories. The ultimate goal is to change how your employees view the new technology being rolled out so that the process becomes a positive initiative that is driven forward by support rather than bogged down by resistance. That said, here are some proven best practices for effectively managing change in the workplace.

Conduct a readiness assessment. This can be done a number of ways, from holding focus groups to conducting a survey amongst all users. The purpose of a needs assessment is to identify the risks, benefits and potential obstacles that you may encounter when rolling out your intelligent automation initiative. It can also be helpful in determining areas of greatest resistance as well as the reasons behind the contention. Remember the old adage, you can’t fix what you don’t know is broken.

Sell the benefits. People aren’t going to jump on your intelligent automation bandwagon unless and until you’ve convinced them that it’s worth their while. They want to know what’s in it for them. Address this by identifying, documenting, communicating and reiterating the specific benefits that adopting automation will have for each individual and team.

Make communication a priority. One of the biggest reasons people resist change is because they don’t understand what is being done or why it’s happening. This lack of knowledge naturally breeds fear, which can derail your intelligent automation initiative. To avoid this, keep the lines of communication open and make sure everyone knows not just what the big picture is, but also their important role in contributing to that big picture goal.

Lead by example. Leadership at every level and in every department should be on-board with adoption of intelligent automation. Excitement and positivity can be very powerful tools in effecting change across an organization. Make sure you have complete buy-in from all executives prior to launch and that they understand the importance of solidarity across the board.

Identify and leverage change champions. These are the individuals who are most excited about the adoption of intelligent automation and the many benefits it will provide. By identifying these key employees, you can begin to leverage them to influence their peers who may be feeling a bit less enthusiastic about the proposed change. These individuals can help bridge the gap between front-line employees and management and become a voice for those directly impacted by change.

Intelligent automation can dramatically improve your organization’s overall performance, but rolling out such an initiative can rarely be achieved without some type of resistance. By taking a proactive approach and developing and implementing an effective change management strategy, the experience will be much more positive for everyone involved.

Nervous about how your intelligent automation project will be received by your employees? Ayehu is designed for fast and seamless implementation, so you can focus your efforts on what’s most important: investing in the happiness of your employees. Experience it for yourself by clicking here.



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5 Ways the Service Desk can Use Chatbots

Pull up any IT related website and you’ll undoubtedly see dozens of headlines dedicated to artificial intelligence, machine learning and chatbots. Some of what’s being published relates to the threat of job replacement. And while that’s certainly true to some degree, AI isn’t something to fear – even for those who work in IT support. To the contrary, the service desk is ripe with opportunity to leverage chatbots to benefit both the end user as well as the support team. Let’s take a look at a few ways this technology can be used in tandem with the service desk.

Human Resource Optimization

Whether it’s resource planning or the redirection of work away from the service desk, AI can be used to make smarter use out of service desk personnel. For instance, machine learning algorithms are capable of analyzing patterns to predict future workload and plan staffing needs accordingly. Routine IT support tasks, such as password resets and remote restarts, can be shifted to chatbots, essentially supplementing (not replacing) help desk workers.

Improved Decision-Making

Decision support is another powerful way AI technology can aid the help desk. From low level automation of workflows to the prediction of future trends in the IT service realm, such as the demand for new or different IT services, the sky is really the limit with how this can be applied. Predictive analytics can even be used to forecast future customer satisfaction levels based on the impact of various factors that occurred in the past.

Self-Service IT Support

There are many different use cases, including intelligent search, through which machine learning algorithms apply meaning and context and draw from previous search successes to deliver more accurate search results. Intelligent autoresponders can provide automated resolution to service tickets without the need for human intervention. And, of course, the use of chatbots to provide a more engaging IT support interface using artificial intelligence and automated solutions. This not only takes much of the heat off of busy IT professionals, but it also empowers end-users and boosts productivity.

Proactive Service Improvement

In addition to identifying and addressing common IT problems that are occurring presently, predictive analytics can also be utilized to project possible high-impact issues that may occur in the future but have not yet been realized. This enables IT support staff to take proactive measures in reducing the risks of those possible future issues, often stopping them before they occur. And thanks to the technology’s advanced learning capabilities, it can improve on its own, getting better over time.

Improved Customer Experience

Each of the four points above contribute to a better customer – or in this case, end-user – experience. From better solutions to faster, more efficient support to self-service options (including chatbots and autoresponders) to proactive problem resolution and more. AI will undoubtedly continue to play a critical role in IT support’s customer experience journey, improving as time goes on.

These are, of course, just a handful of the many ways the service desk can leverage AI technology. There will be many other opportunities to use chatbots in the not-so-distant future. The most important thing to note is that artificial intelligence technology is already here and those in IT support that are currently using it will remain a step ahead of the competition.

Don’t get left behind. Schedule a free product demo of Ayehu today and learn how you can leverage chatbots to bring your service desk to the next level!

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Innovation without Breaking the Bank. Yes, it’s Possible.

Innovation without Breaking the Bank. Yes, it’s Possible.In a recent survey conducted by IDC, 45% of CIOs and senior IT executives stated that one of their top three objectives is to “create competitive advantage for the business.” Today’s IT leaders are, perhaps more than ever before, being asked to deliver more. More reliability. Faster responsiveness. Greater flexibility. They are expected to leverage digital transformation to create disruption. And they are expected to do all of this while keeping financial impact at a minimum.

These IT leaders find themselves facing quite the challenge: finding a way to continuously innovate in the face of increasing budgetary restraints. The good news is, with the right strategy, this is – indeed – possible. Here’s how.

Leverage Your Data

Today’s forward-thinking organizations are increasingly driving business value and seeking to achieve competitive advantage through deeper data insights. This enables business leaders to uncover hidden value in existing services or products. With the right approach, innovation doesn’t have to be expensive. Simply prioritize the results of data discovery in order of those initiatives that require minimal risk and investment but have the potential for high returns.

Ultimately, the key to innovation lies in having a data-oriented mindset. Initiatives that focus on reducing costs and streamlining processes can be successful, but only when the right data is being used the right way to facilitate better decision-making. The ability to rapidly execute on those findings is also important.

Review Risks and Seek Out Greater Efficiency

Another critical key to cost-effective innovation is risk analysis and efficiency planning. Review the risks the business has and assess the impact and likelihood those risks may have on customers and operations. From there, you can prioritize and monetize, making more informed decisions about which areas budget cuts can be made while still meeting business needs.

Budget cuts enable business leaders to focus their efforts on maximizing efficiency. Establishing leaner teams, optimizing process management and improving the effectiveness of certain key elements, such as data storage, can all result in a better product or service at a lower cost. And, of course, adoption of technologies, such as intelligent automation (which we touch on in greater detail below), to help streamline processes and maximize efficiency.

Optimize, Optimize and Optimize Some More

The most innovative organizations – especially those on tight budgets – make a concerted effort to continuously optimize ongoing operations and costs. It’s never a “set it and forget it” kind of thing. It’s a never-ending battle – one for which technology can provide a leg up.

IT leaders must take advantage of targeted applications and cloud opportunities to extend and enhance their core capabilities. By leveraging the power of tools like artificial intelligence and machine learning, operations can be fully optimized. Not only does this create a more productive, efficient work environment, but it also frees up human workers to apply their creative, cognitive skills toward innovative business initiatives.

According to Gartner, 90% of the IT budget is currently consumed by the support of legacy systems. These costs can be dramatically lowered through IT optimization. Adopting the right technologies – preferably ones that seamlessly integrate with legacy systems – can reduce expenditure, improve customer service and provide distinct competitive advantage. At the end of the day, innovation takes funding and resources. IT optimization can free up both.

Looking for a platform that can bring your legacy systems together, improve efficiency and help you harness the power of data for better decision-making support? Ayehu offers all of this and more. Experience it for yourself. Request a live demo today and get your organization on the path to affordable innovation.

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ITSM: The 5 Keys to Intelligent Automation Adoption

ITSM: The 5 Keys to Intelligent Automation AdoptionOne of the biggest hurdles those in IT service management face is the misconception that all the necessary tools and information for success are already available, understood and being put into practice. As such, introducing intelligent automation is often met with resistance that hinders progress and impedes the ability to further improve operations. So, what’s the solution? Well, it starts with good communication. To follow are five steps to help establish a foundation of consistent, open and organization-changing communication that will facilitate positive results.

Don’t take on too much.

You don’t have to solve all of your organizational problems in one sitting, so don’t wear yourself or your team out attempting to do so. Define your goals and set manageable milestones, incorporating intelligent automation into the mix. Then communicate those smaller, incremental objectives to the team via open-plan meetings that keep everyone in the loop. Over time, you’ll begin to figure out what works and what doesn’t so you can improve the meeting process moving forward.

Leave management out of the mix.

Obviously having IT leaders heading up key projects is important, but often times – particularly in a group meeting-type setting – having management present can be intimidating, ultimately hindering progress. The goal is to facilitate more open, honest communication and a more positive relationship with intelligent automation, so it may be wise to hold at least some of the scheduled gatherings sans management representatives. When front-line workers are free to express themselves, it can help to identify new and better ways to improve operations.

Keep things short, simple and focused.

The goal of meetings is to improve communication, but if these gatherings drag on and on without clear focus, they’ll have the opposite effect. Remember, the reason for implementing intelligent automation is to make the lives of IT personnel easier and make operations more efficient. Design meetings with the same purpose in mind. Have an agenda and encourage attendees to arrive with their ideas already prepared. A round-robin type setting where everyone has a set amount of time to share their thoughts and sell their ideas can keep things moving smoothly and on schedule to maximize everyone’s time.

Keep the conversation going.

Ideas, thoughts, feedback and suggestions don’t only arise just prior to or during a meeting, so make sure you’re making it easy and straightforward to keep the conversation going by creating an avenue where people can share and engage with one another any time the need arises. It can also be helpful to have a source of documented ideas to refer back to. Some companies use a Wiki or other open-source forum tool. Others use an enterprise social network that is devoted to all things intelligent automation related. Whatever happens to work for your group, get on it.

Take action.

Ideas and suggestions are great, but they won’t do you or anyone else any good until you actually put them into action. By facilitating open communication and inviting your team members to share their thoughts and feedback, you’ll have a pool of valuable data from which to start building out some intelligent automation initiatives. The best part is, when employees see that their voices are heard and that their opinions make a difference, it will further promote and foster communication going forward.

Support your great communication policy with a powerful intelligent automation tool. Request a product demo of Ayehu today.

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7 Tips for Reducing IT Personnel Turnover

7 Tips for Reducing IT Staff TurnoverAs any IT leader will acknowledge, attracting top talent is only half the battle. It’s keeping them on that’s the real challenge. And with an average employee tenure of only about 3 years, it’s a serious concern for many organizations across the globe. Add in the complex, fast paced and highly stressful role of IT and you’ve got quite the conundrum. So, what’s the secret? How can you keep your talented employees on for the long haul? Here are 7 tips to point you in the right direction.

Keep them challenged. The last thing you want is for your IT personnel to become bored and stagnant in their current positions. Avoid this by investing in ongoing training, setting up mentoring programs, and offering opportunities to learn about and master new strategies and technologies. The more you keep your IT employees engaged and involved, the less likely they’ll be to look elsewhere.

Rotate project time. Being stuck on the same project day in and day out can lead to fatigue and frustration. Consider rotating employees onto various IT projects so that they don’t feel stuck. This will provide exposure to and the opportunity to learn about new skills and also open up the door to be able to approach long-term projects from differing perspectives – both of which can benefit your organization.

Give them the tools they need. These days, keeping up with the onslaught of incoming requests is nothing short of exhausting. Don’t leave your IT personnel out to dry by making them handle this monumental task manually. Arm them with the technology they need to do their jobs better, faster, more efficiently and more effectively, such as IT automation.

Provide a safe place to vent. Without question, the job of keeping an entire organization safe from the potentially devastating financial and reputational damage a successful breach can have is incredibly stressful. Additionally, IT personal often feel tense due to the amount of classified and confidential information they are entrusted with. Provide an opportunity and a secure avenue for these employees to vent their feelings.

Encourage time off. Everybody needs a little down time, but given the fast-paced and highly stressful field of IT, these employees could probably use some time off more than anyone else in your organization. This is where technology can help. By automating a good portion of tasks and leveraging the cloud to embrace more flexibility, your team can take the much needed time off they deserve without the company feeling any negative impact.

Use realistic metrics to measure success. One of the biggest reasons IT professionals find themselves dissatisfied at work is because they feel they aren’t being adequately recognized. This is often due to a lack of clear and specific metrics for success. Management should set realistic expectations, communicate openly and routinely measure progress. Good work should be rewarded and areas of improvement identified and addressed in a positive, productive way.

Empower them. If your employees feel that their only option is to come in every day and put in 10-12 hours of labor, they’re not going to develop any kind of connection or loyalty to your organization. On the other hand, if they know that the work they do plays a direct role in the “big picture” and that their achievements are tied into the company’s overall success, they’ll be much more plugged in, which means they’re more likely to stay on for the long haul. Empower them by inviting ideas and encouraging autonomy.

Are you doing enough to keep your IT personnel satisfied, engaged and plugged in? If not, you could be facing higher turnover, which can negatively impact your company’s bottom line and also leave you more vulnerable. By implementing the above tips, you’ll create a more positive work environment that fosters longevity. Happy employees will work harder to ensure that your organization remains strong, secure and successful.

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Human Learning vs. Machine Learning – What’s the Difference

These days, artificial intelligence is all around us. If you’ve ever used Siri on your iPhone or the live chat feature of a website, you’ve interacted with AI. From a business perspective, the rise of AI can be both exciting and challenging. Furthermore, it’s a concept that isn’t necessarily easy for everyone to grasp. The most common question being asked by individuals who aren’t deeply involved with tech is, “What, exactly, is artificial intelligence?” Perhaps the easiest way to understand AI is to compare it to something that is already widely understood – human intelligence.

How Does Human Intelligence Work?

Generally speaking, human intelligence follows a simple, straightforward and typically predictable pattern. We gather information. We process that information. And we use that processed information to decide what to do next. These three basic steps can be summed up as follows:

Input —> Processing —> Output

Input occurs through sensing and perceiving the things all around us. The senses – eyes, ears, nose, etc. – gather raw input, such as the sight of light or the scent of a flower. The brain then processes that information and uses it to determine what action to take. In the processing stage, knowledge is formed, memories are retrieved, and inferences and decisions are made. Output then occurs in action based on the information processed. For instance, you might hear a siren, see an ambulance in your rear view mirror and subsequently decide to pull over to let it pass.

In order to safely navigate the world in which we live, we must effectively process all of the input we receive. This basic concept is the core of human intelligence, which can further be broken down into three definitive segments:


People gain knowledge through the ingestion of facts (i.e. the Pilgrims landed in 1620) as well as social norms (i.e. saying “Please” or “Excuse me”). Further, memory allows us to recall information from the past and apply it to present situations.


Inferences and decisions are made based on the raw input we receive, combined with our memories and/or built up knowledge. For instance, let’s say you tried a new food a few months ago that turned out to be way too spicy for your taste. The next time you’re offered that food, you politely decline.


There are a number of ways humans can learn, including observation, example and algorithm. With observation, we determine the outcome on our own. With example, we are told the outcome. Learning by algorithm, on the other hand, allows us to complete a task by following a series of steps. A good example of this would be solving a long division problem.

Human Learning vs. Machine Learninghuman learning vs. machine learning

The main aspects of human intelligence are actually quite similar to artificial intelligence. In the same way that humans gather information, process it and determine an output, machines can do this as well.

Of course, because machines do not have physical senses like people do, the way they gather input differs. For instance, rather than sight or smell, artificial intelligence gathers information through things like speech recognition, visual recognition and other data sources. Think about how a self-driving vehicle can sense obstacles in the roadway or how your Amazon Echo listens and recognizes your voice.

The processing piece of the formula also mimics how human intelligence works. Similar to the way people accrue memories and build knowledge, machines are capable of creating representations of knowledge and databases where information is stored. And, just as people draw inferences and make decisions, machines can predict, optimize and determine what the best ‘next steps’ should be in order to accomplish a particular goal.

Similarly, just as humans learn by either observation, example or algorithm, machines can also be “taught.” For instance, supervised machine learning is akin to learning by example: the computer is provided with a data set containing labels that act as answers. Over time the machine can essentially “learn” to differentiate between those labels to produce the correct outcome.

Unsupervised machine learning is like learning by observation. The computer recognizes and identifies certain patterns and subsequently learns how to distinguish groups and patterns on its own. Lastly, learning by algorithm is the process by which a programmer “instructs” the computer precisely what to do, line by line, using a software program. Ideally, the most effective form of artificial intelligence will utilize a combination of the above learning methods.

The output that results sums up how machines interact with the world around them, whether it’s speech generation, navigation, robotics, etc.

Take, for example, the business use case of cybersecurity threat detection. Artificial intelligence can scan enormous amounts of data and monitor an entire infrastructure in real-time. It can then, through a combination of unsupervised and algorithmic learning, pinpoint anomalies that could potentially represent data breaches. It can then use that information to investigate and test, automatically determining what the next steps should be, whether it’s escalation to a human agent or automatic remediation.

The Future is Now

We have, undoubtedly, only seen the tip of the iceberg as it relates to artificial intelligence and its potential impact on our lives – both personal and professional. As technology continues to evolve and improve at a breakneck speed, AI and machine learning capabilities will also evolve. Why wait? Get ahead of the curve and experience the next generation of automation and AI by taking Ayehu for a test drive today.

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The Future of IT Support

Last year, the folks over at Service Desk Institute (SDI) published an in-depth report projecting the future of IT support entitled “Analyst 2.0.” The report spans a number of areas, including the way IT service desk analyst skills and staffing are evolving as well as the growth and impact of automation and self-service technologies. If you’re curious about what the IT support of tomorrow will look like but haven’t got time to dig into the full report, this blog should help by providing a broad overview of its finding, focused primarily in three key areas:

  • Skill requirements of future service desk analysts
  • Artificial intelligence (AI) adoption thus far
  • Service desk challenges as they relate to customer experience

Let’s take a more detailed look at each of these points below.

Skill requirements of future service desk analysts

The SDI report drew contrast between the top three skills that are currently require of service desk analysts and those that will be expected over the next three to five years.

Skills needed in 2017…

Skills needed in 2020 and beyond…

While the increase in analysts’ customer service/empathy skills may not come as much of a surprise, some of the other projected changes may. In particular, the marked jump of tech industry knowledge from 34% to 67% and the ability to be flexible rising from 8% to 27%.

Both of these things are indicative of the need for IT support analysts to know and be capable of performing more complex tasks, particularly in light of the fact that self-service and automation will be eliminating the simpler, more repetitive tasks from their to-do lists.

Further, the increasing need for adaptability and flexibility signify the fact that the service desk will experience frequent and ongoing change.

Artificial intelligence (AI) adoption thus far

Of those surveyed for the SDI report, 27% indicated that they’ve begun the process of researching AI or virtual assistants for their organizations. A smaller percentage is either interacting with potential partners or have already implemented AI to some degree. While trends indicate that these numbers have already risen since the date of the SDI report’s publication, it still appears that the current use of AI in a help desk capacity hasn’t yet caught up with the industry buzz surrounding the technology.

There may be several reasons why this is the case, including the fact that the opportunities for and benefits of AI for IT support haven’t been adequately “sold” to the market (something we are working hard to change here at Ayehu). Other hindrances may include budgetary restraints and fear of change.

A second, perhaps more telling question asked of survey participants was whether or not they felt technology is keeping up with the “hype” surrounding AI and automation. The responses were an even split between yes and no (both at 29%). The rest indicated that they were unsure.

Service desk challenges as they relate to customer experience

The third noteworthy component of the SDI report involved a particular question relating to the customer experience. Specifically, participants were asked: Do you feel pressured to provide the same level of customer service as big businesses?”

The response is demonstrated here:

As you can see, there is significant pressure (whether real or perceived) on corporate IT support teams that extends far beyond the “consumerization of IT.” Service desk leaders need to respond to these pressures accordingly. In particular, adopting self-service and automation can improve the end-user experience and alleviate some of the pressures agents are facing. It will also free up skilled IT staff to focus on more meaningful and fulfilling work, boosting morale in the process.

These are really just a few of the many intriguing points uncovered by the SDI report. You can view the full report here for free.

And if you’re ready to start preparing your support desk for the wave of the future, AI and automation are the place to be. Experience the power of artificial intelligence and machine learning capabilities by taking Ayehu for a test drive today.