Episode #12: How Cognitive Digital Twins May Soon Impact Everything – Accenture’s Dr. Ahmed El Adl

Feb 28, 2019    Episodes

Episode #12: How Cognitive Digital Twins May Soon Impact Everything

In today’s episode of Ayehu’s podcast we interview Dr. Ahmed El Adl – Principal Director of AI Consulting & Intelligent Solutions for Accenture.

“May you live in interesting times” (a 19th-century English expression often mistaken as being of Chinese provenance), aptly describes today’s technology environment.  Artificial intelligence, machine learning, data science, and the internet of things were already interesting enough.  It turns out though that those were just prologue to the rise of something called a “Cognitive Digital Twin”.

To shed light on this and other advances, we turn to Dr. Ahmed El Adl, Principal Director of AI Consulting & Intelligent Solutions at Accenture.  He shares his far-reaching insights with us, and clarifies whether AI will ultimately have a bigger impact as a tool for automation or worker augmentation, whether machine learning will ultimately eliminate the need for data science altogether, and the most important background to consider when hiring an AI professional.




Guy Nadivi: Welcome everyone! Our guest today on Intelligent Automation Radio is Dr. Ahmed El Adl, Principle Director of AI Consulting and Intelligent Solutions at Accenture. Dr. El Adl holds a Ph.D. in artificial intelligence and robotics and does a lot of public speaking about AI, machine learning, the technologies and standards surrounding the internet of things and something very intriguing called Cognitive Digital Twins. And with a background like that, we’re hoping he can help us make sense of the AI market’s direction for these innovations.

Dr. El Adl, welcome to Intelligent Automation Radio.

Dr. El Adl: Thank you, Guy. Thank you for having me.

Guy Nadivi: Let’s dive into it. Dr. El Adl, do you think artificial intelligence will ultimately have a bigger impact as a tool for automation or a tool for worker augmentation?

Dr. El Adl: Great question, Guy. I think it will play a major roll on both fronts. So, from the automation perspective, let us talk about two major categories, the process automation and machine or industrial automation. We see today one of the major areas of adopting even simple AI technologies is intelligent process automation. This is already going on and showing a lot of business values. The other one, which has maybe the number of implementation maybe is less than industrial automation but the impact is huge. On the other side what we call human worker with augmentation, so the humans will stay in the equation, will stay in everything we are doing. Maybe we will change. What we are doing will change. How we are doing it will change, but we’ll stay a measure part of the equation. Therefore I don’t see that one of them is going away, both, but we see the impact today.

Honestly it is equally, from the adoption perspective it is equal. If you see today the human worker augmentation take for example, call centers. They take virtual agents, customer interaction like chat bots and all of those. Adoption is very hard and it is rising every day. On both fronts, automation, whether process or machine automation, human augmentation, or also say not augmentation only, but also to replace humans in some areas that humans can make the best out of our biological intelligence, which is very valuable. So I see the adoption on both sides, Guy. It depends on the functional areas and the industry as well.

Guy Nadivi: So let’s talk about industry. You’ve stated that in your role at Accenture you are “driving the application of artificial intelligence into every industry, globally”. Given that lofty objective, from your vantage point, what industries and functional areas have you seen experience the biggest transformational impact from deployment of AI?

Dr. El Adl: Yes, just a small correction. My focus within Accenture is mainly on the fossil energy, renewable energy, utilities, chemicals, mining, which I will come back to this later on. So, three major areas I see the adoption is from the number of implementations and the added values to the business. Within a very short time there are three major areas. One, which is customer interaction. We know that one of the most mature areas in machine learning is natural language processing, natural language understanding, and we see all of those intelligent assistants, Alexa, Cortana, Siri and others. The adoption of those technologies is very, very high in customer interaction and customer understanding also.

This is one of the major fronts I see nearly across the board. Any industry which has intensive customer interaction, mainly B2C kind of interaction, I see major adoption whether it is virtual agents, chat bots, or software bots automating a lot of work, which takes a lot of time from humans while they are interacting with customers. The second area is, which has in my opinion the highest values – industries with critical physical assets. Take oil & gas industries, chemical industry, automotive industries, aviation industry. All of those industries have used machine learning or other AI technologies since actually a long, long time. Since I was myself in academia, you could mainly say aviation industries maybe automotive industrial machinery using embedded intelligence or embedded machine learning algorithms. Those industries today in my opinion, are leading the serious adoption of complex AI capabilities from the simple statistical machine learning all the way up to very sophisticated deep learning or deep artificial neural networks and knowledge representation capabilities.

The third area, which is R&D. Take for example pharmaceutical industry. Here I am sitting today in Boston. Across the street is MIT and Harvard. They are using very sophisticated machine learning approaches and other AI technologies and capabilities for drug discovery and showing results. It is showing real results especially when you combine sophisticated machine learning algorithms with quantum computing, delivering the required computing capabilities. This is a game changer. On the other side, even we at Accenture are working this field but also we have what we call digital chemists. It is exactly the application of complex AI or sophisticated machine learning and reference to help R&D people to discover in pharmaceutical, new drugs and chemicals or specialty chemicals, discover new chemical products, new materials, and so on. So those are the major three areas, Guy, where I see serious adoption. Not only in terms of the revenue but also in terms of the values which the business leaders are looking for.

Guy Nadivi: Now that serious adoption you refer to is driving up the demand for AI professionals, and given the critical talent shortage in AI, machine learning, data science, etc., should organizations rely on in-house staff or outsource when planning for their AI deployment?

Dr. El Adl: Yes, so I think to answer this question, Guy, I think let’s take a step back and now we have AI projects on the industry side, not anymore on the academic side. On the industry side, the structure of an AI project is as follows. You have data engineering team, you have data science team, you have machine learning team, and you have around them the IT infrastructure teams, UR/UX teams and all of those subgroups. One of the major misunderstandings we see today is you wanted to start an AI project, you have to hire 10 academics, have a Ph.D. from MIT or Stanford in AI. This is misunderstanding, this is not true. Actually if you have 10 people in your AI team maybe you need one or two good AI experts with strong AI academic background. For me personally, I like to hire for the machine learning part, I like to hire people with strong academic AI background even if they never applied machine learning to solve industry problems. But in this case, can every company hire those people? No. They don’t exist. Even if they exist they have some preference around the employer and so on, so what I see today, Guy, is those guys work mainly for consulting companies. I believe that this is the right approach, and consulting companies share those people across different projects. It doesn’t harm if you are a large company to hire one, two, three, or five people, but I think it should be a hybrid approach. Those very experts in AI, I think I hear some numbers around 10,000, 12,000 people across the globe, which is 1% of what we need today. What I want to say is let us use a hybrid approach where we can share those rare sources among different projects, and the 80% will come from your existing workforce.


Guy Nadivi: Welcome everyone! Our guest today on Intelligent Automation Radio is Dr. Ahmed El Adl, Principle Director of AI Consulting and Intelligent Solutions at Accenture. Dr. El Adl holds a Ph.D. in artificial intelligence and robotics and does a lot of public speaking about AI, machine learning, the technologies and standards surrounding the internet of things and something very intriguing called Cognitive Digital Twins. And with a background like that, we’re hoping he can help us make sense of the AI market’s direction for these innovations.

Dr. El Adl, welcome to Intelligent Automation Radio.

Dr. El Adl: Thank you, Guy. Thank you for having me.

Guy Nadivi: Let’s dive into it. Dr. El Adl, do you think artificial intelligence will ultimately have a bigger impact as a tool for automation or a tool for worker augmentation?

Dr. El Adl: Great question, Guy. I think it will play a major roll on both fronts. So, from the automation perspective, let us talk about two major categories, the process automation and machine or industrial automation. We see today one of the major areas of adopting even simple AI technologies is intelligent process automation. This is already going on and showing a lot of business values. The other one, which has maybe the number of implementation maybe is less than industrial automation but the impact is huge. On the other side what we call human worker with augmentation, so the humans will stay in the equation, will stay in everything we are doing. Maybe we will change. What we are doing will change. How we are doing it will change, but we’ll stay a measure part of the equation. Therefore I don’t see that one of them is going away, both, but we see the impact today.

Honestly it is equally, from the adoption perspective it is equal. If you see today the human worker augmentation take for example, call centers. They take virtual agents, customer interaction like chat bots and all of those. Adoption is very hard and it is rising every day. On both fronts, automation, whether process or machine automation, human augmentation, or also say not augmentation only, but also to replace humans in some areas that humans can make the best out of our biological intelligence, which is very valuable. So I see the adoption on both sides, Guy. It depends on the functional areas and the industry as well.

Guy Nadivi: So let’s talk about industry. You’ve stated that in your role at Accenture you are “driving the application of artificial intelligence into every industry, globally”. Given that lofty objective, from your vantage point, what industries and functional areas have you seen experience the biggest transformational impact from deployment of AI?

Dr. El Adl: Yes, just a small correction. My focus within Accenture is mainly on the fossil energy, renewable energy, utilities, chemicals, mining, which I will come back to this later on. So, three major areas I see the adoption is from the number of implementations and the added values to the business. Within a very short time there are three major areas. One, which is customer interaction. We know that one of the most mature areas in machine learning is natural language processing, natural language understanding, and we see all of those intelligent assistants, Alexa, Cortana, Siri and others. The adoption of those technologies is very, very high in customer interaction and customer understanding also.

This is one of the major fronts I see nearly across the board. Any industry which has intensive customer interaction, mainly B2C kind of interaction, I see major adoption whether it is virtual agents, chat bots, or software bots automating a lot of work, which takes a lot of time from humans while they are interacting with customers. The second area is, which has in my opinion the highest values – industries with critical physical assets. Take oil & gas industries, chemical industry, automotive industries, aviation industry. All of those industries have used machine learning or other AI technologies since actually a long, long time. Since I was myself in academia, you could mainly say aviation industries maybe automotive industrial machinery using embedded intelligence or embedded machine learning algorithms. Those industries today in my opinion, are leading the serious adoption of complex AI capabilities from the simple statistical machine learning all the way up to very sophisticated deep learning or deep artificial neural networks and knowledge representation capabilities.

The third area, which is R&D. Take for example pharmaceutical industry. Here I am sitting today in Boston. Across the street is MIT and Harvard. They are using very sophisticated machine learning approaches and other AI technologies and capabilities for drug discovery and showing results. It is showing real results especially when you combine sophisticated machine learning algorithms with quantum computing, delivering the required computing capabilities. This is a game changer. On the other side, even we at Accenture are working this field but also we have what we call digital chemists. It is exactly the application of complex AI or sophisticated machine learning and reference to help R&D people to discover in pharmaceutical, new drugs and chemicals or specialty chemicals, discover new chemical products, new materials, and so on. So those are the major three areas, Guy, where I see serious adoption. Not only in terms of the revenue but also in terms of the values which the business leaders are looking for.

Guy Nadivi: Now that serious adoption you refer to is driving up the demand for AI professionals, and given the critical talent shortage in AI, machine learning, data science, etc., should organizations rely on in-house staff or outsource when planning for their AI deployment?

Dr. El Adl: Yes, so I think to answer this question, Guy, I think let’s take a step back and now we have AI projects on the industry side, not anymore on the academic side. On the industry side, the structure of an AI project is as follows. You have data engineering team, you have data science team, you have machine learning team, and you have around them the IT infrastructure teams, UR/UX teams and all of those subgroups. One of the major misunderstandings we see today is you wanted to start an AI project, you have to hire 10 academics, have a Ph.D. from MIT or Stanford in AI. This is misunderstanding, this is not true. Actually if you have 10 people in your AI team maybe you need one or two good AI experts with strong AI academic background. For me personally, I like to hire for the machine learning part, I like to hire people with strong academic AI background even if they never applied machine learning to solve industry problems. But in this case, can every company hire those people? No. They don’t exist. Even if they exist they have some preference around the employer and so on, so what I see today, Guy, is those guys work mainly for consulting companies. I believe that this is the right approach, and consulting companies share those people across different projects. It doesn’t harm if you are a large company to hire one, two, three, or five people, but I think it should be a hybrid approach. Those very experts in AI, I think I hear some numbers around 10,000, 12,000 people across the globe, which is 1% of what we need today. What I want to say is let us use a hybrid approach where we can share those rare sources among different projects, and the 80% will come from your existing workforce.

Guy Nadivi: And so if I’m an IT executive thinking of doing an AI deployment and I’m gonna hire some people in addition to maybe outsourcing some work, what do you think would summarize the most important qualities an AI expert should have that I should look for?

Dr. El Adl: Yes, this is a very, very, very good and very important question, Guy, here. Two things I look for. One is strong academic background in machine learning. What I mean by that is, we see a lot of data scientists coming from data analytics background pretending to be machine learning experts. This is not true and this is one of the major reasons some POC or AI POC projects fail today, because we know that machine deep learning main goal is to eliminate data science. So if my job is to eliminate your job, you cannot take my job because you don’t have the qualification for that.

Let us go deeper a little bit, Guy. The mathematical background of machine learning is not or doesn’t exist on the data science or data analytic part, and when you are faced by real world problems you don’t have only to take the standard algorithms we have if we were the open source or platforms and just use it as it is. You will have to understand the industrial problem and mathematically tweak your algorithms to bring the best and most reliable results out of those algorithms. So this is the first quality. It is academic background.

Secondly at least you should be aware or familiar with at least one open source library and one enterprise-grade AI today. Take Google Cloud AI platform/TensorFlow, open source libraries, Azure, AWS, you name it, but those are the major two qualities. The academic part and the practical part in terms of libraries or enterprise AI platforms.

Guy Nadivi: Let’s switch gears a little bit Dr. El Adl. In late 2016, you coined the term “Cognitive Digital Twin”. Can you please explain to our audience, what is a cognitive digital twin and how will it potentially impact their organization?

Dr. El Adl: The digital twin? OK. Yes, I do believe, Guy, and maybe you read my article I wrote in 2016 as a response to a lot of noise at the time around the term “digital twin”. To explain the digital twin I also like to take a step back. What is the ultimate goal of digital twin today? Beyond the goals NASA had in late 90’s. In the late 90’s, NASA created the first digital twin for simulation for the spaceships, for the engineering and physics-driven simulations. Today actually the real rise of AI, the progresses we have on different areas like IOT, sensing, mobile communication network, and all of those technologies together actually give us a hope and I am one of the hopeful people that someday we’ll create machines independent like our human body. They can sense. They have edge intelligence. They have central intelligence. They have distributed intelligence. They can take smart decisions. They can take smart actions.

We have enough from those, sorry, stupid machines we have today. So the ultimate goal is to have a physical twin and digital twin first separated and the digital twin is not only a representation of the physical twin, this is not the right definition. It is partial representation, partial augmentation, and companion for its physical twin. Once you have the baseline of digital representation, you continue updating this new creature in the digital world, which will continuously represent the physical twin, continuously augment and also extend the capabilities of the physical twin and combine it across the life cycle.

One of the major problems, Guy, which unfortunately one of the major reasons that many digital twin initiatives failed, POCs failed, why? The initial initiatives to create digital twin mainly was kind of engineering digital twins. Some vendors connected the physical assets, retrofitted them with maybe some sensors, collected data, and you have real time data coming in, in a data lake, and these data lakes are growing by minute. You are dumping a lot of data in those data lakes and we know from the data science perspective, even from the data management perspective, this could be a nightmare. And I told some of my friends and colleagues in this industry after one or two years, you will drown in your data lakes and you will not be able to manage anything and unfortunately, without mentioning names, it happened to some major vendors in this field.

Why I coined the term “Cognitive Digital Twin”? It is exactly as I mentioned, biologically inspired. When you ask me a question now, I do not go and read everything I read before to answer your question because I read, I practiced, I memorized some parts, but other parts are knowledge represented somewhere, I don’t know how, in our human brain. The same here. I want that we don’t only collect data and have large data lakes or data oceans, I want that we continuously – the digital twin, have the capabilities to continuously convert the data to knowledge, which you need when you want to take a decision, whether the decision will be made by the digital twin or the physical twin or by a human, but if you just collect data this is not going anywhere and this is why I said, Guy, let us use AI capabilities like machine learning, knowledge representation, the simple reasoning capabilities we have today, to add a brain to the digital twin and instead of adding a large, large data lake underneath it where it will drown. This is the way I define and I hope will implement cognitive digital twins rather than just digital twins.

Guy Nadivi: What you said just now reminded me of a demonstration last year by Google’s CEO of something called Google Duplex and there was a video about it that quickly went viral. And for those in the audience that don’t remember, Google Duplex is a virtual assistant that can make phone calls on your behalf to schedule appointments, make reservations at restaurants, etc. Dr. El Adl, would Google Duplex be considered a type of cognitive digital twin?

Dr. El Adl: It is a basic component of a digital twin of a human and in my paper again in 2016 I had different categories of digital twin. One of them is a cognitive digital twin for a human, for us, and I am happy to see that some start-ups even here in Boston area started to create businesses or start-ups around creating a digital twin for humans. For me, for you, for everyone, collecting all the vital health data and other data and monitoring, continuously monitoring our health to of course have a better life.

Google Duplex, why though the video went viral? It was because the sound quality. The real-time intelligence behind Google Duplex, and this is why every one of us liked it. It looks real. It sounds real. It feels real. Everything is real and in real-time, and this is exactly, Guy, what I hope for the overall cognitive digital twin concept is to add real intelligence rather than data, and of course in real-time. By the way, one of the major promising areas of the concept of cognitive digital twin is in healthcare, and I am predicting now, I am not in the business of prediction now, but I am predicting that within three or five years you will see the rise and major investments in creating healthcare services based on the concept of cognitive digital twin. Maybe Google will use the core technology they implemented for the Google Duplex, maybe, but it will be a beautiful piece of technology to have it underneath the cognitive digital twin for a human.

Guy Nadivi: We’re gonna come back to some more predictions after the next question but first I wanted to ask you Dr. El Adl, a little bit about the internet of things or IOT. You’ve written that when it comes to IOT initiatives, many “failed miserably or were put on hold mainly because organizations underestimated the effect of the existing IT and OT infrastructure”. What are organizations underestimating about their infrastructure that’s derailing IOT initiatives?

Dr. El Adl: It’s exactly the same reasons we see today on the AI adoption site. IOT, when we started to talk about IOT or Internet of Everything in all of those terms. People rushed, “Oh IOT’s about connection”. Maybe, but it is not. IOT is about the solution, which can have the right data in the right time to take the right decision on time. This is the ultimate goal of IOT initiatives. When some clients started to implement IOT projects they discovered that they have all kinds of problems from the sensing. Sensing itself you need a lot of progress on the sensing technology side. Chemistry, biology, physics, all of those is the sensing hardware on all of this stuff.

You need it. You have a lot of problems around the communication and the network, whether mobile or line based, and again as you might remember a couple of years ago we didn’t have standards, protocols. We had to do a lot of work on the standards side. Standards and protocols and we see a lot of consortiums, like the industrial internet consortium here in Boston and similar started to work on even private standards or global standards to get things done.

Also again on the other side, this was on the infrastructure side, but also on the skills side. Again, having an IOT solution needs a lot of technology experience but actually like on AI side, you need to understand the problem you want to solve and in IOT sometimes you have to solve a problem with machines, nuclear power plants, chemical plants, manufacturing, factories. You have to understand a lot of things are on the process, the machines, the technology, the IT infrastructure. This was too much, and again as you know, a lot of bloggers promising a lot, over-promising everything and of course this resulted in failures, but the good side here, Guy, is we learned a lot. The standards and protocols people learned a lot. The hardware companies learned a lot. We as consulting companies learned lot, and I think I see now the number of failures is going down, number of successes is going up mainly because we learned, we failed, we learned, and I think we are doing better now.

Guy Nadivi: Let’s get back to predictions. In addition to your prediction for healthcare services, what are some of your other predictions for AI over the next three to five years?

Dr. El Adl: I would say I am still 30%-40% academic and the rest is maybe mainly industry and software guy. It is not honest, not professionally correct to predict. Why? Especially on the AI side. If you take this from the academic side, Guy, early 2000 Jeff Heaton published his first paper around “Convolutional Neural Network” or CNN. CNN was mathematically not very sophisticated work. It was results of a lot of work. I personally was part of it, but CNN alone enabled computer vision which we see today from simple computer vision, autonomous cars, robotics, video analytics, and so on. So one invention, one mathematical model solved the problem which opened the gates for a large number of AI applications.

Will we have similar inventions like CNN? Yes. It could happen today, it could happen tomorrow. Once such simple mathematical models or inventions happen on the academic side, you will see a lot of adoption. Therefore I wouldn’t predict anything, but I predict one thing, Guy, today. AI is here and here to stay and I always say, even if the academia ceased to deliver any new breakthrough inventions, what we have today in our open sources machine learning libraries or enterprise-grade platforms like Google, Microsoft, AWS, we have more than enough to deliver a lot of business values, solve problems, implement smart products for the next five years and we don’t need anything new from the academia at all. On the industrial side, I am predicting a second prediction, the industrial adoption of AI will rise like the IOT today. Once the business leaders and I’m not talking about IT leaders, the business leaders understood the real capabilities of AI, what AI can do, what AI cannot do, why AI can do this, why AI cannot do that, once they understand this, adoption will grow exponentially on the industrial side. Of course I’m predicting a lot of good things on the academic side as well.

Guy Nadivi: So getting back to today, what should enterprise IT managers who have never dealt with AI know before deploying it?

Dr. El Adl: First message I say always, some IT managers, their job is to take care of the IT infrastructure. Those guys will continue to be very, very important in the era of AI. We need them, whether you are SAP administrator or Oracle administrator, we need you guys. So therefore, but because we need you I want you to start reading about real AI. Don’t read books and all of this stuff, read serious materials, not mathematically sophisticated about AI. Inform yourself that you can help us create the new IT organization, which will build the intelligent business solutions and also service intelligent products. If you are today an IT organization of automotive company, tomorrow you will be servicing connected cars. Smart cars like Tesla. Tesla IT guys are not just taking care of salesforce platform or SAP platform. They are servicing the connected cars of Tesla.

The same applies to any IT organization virtually in any industry. So my message, inform yourself about AI and help us, help you to help us and stay relevant in the era of what I call intelligent enterprise.

Guy Nadivi: Sounds like some prudent advice. Alright, looks like that’s all the time we have for on this episode of Intelligent Automation Radio. Dr. El Adl, thank you very much for joining us today and sharing your thoughts about the current state of AI. It’s been great having you as our guest.

Dr. El Adl: Thank you, Guy, for having me. It was a pleasure talking to you. Thank you.

Guy Nadivi: Dr. Ahmed El Adl, principle director of AI consulting and intelligent solutions at Accenture. Be sure to visit their website to learn more about their services. Thank you for listening everyone and remember, don’t hesitate, automate.



Dr. Ahmed El Adl

Principal Director of AI Consulting & Intelligent Solutions for Accenture.

Dr. Ahmed El Adl is Principal Director with Accenture, leading their AI Technology Consulting & Intelligent Solutions with a focus on understanding the recent advances in AI on the academic side, and applying them to solve real world industry problems. Dr. El Adl finished his Ph.D. and Post Grad in AI and mobile Robotics. He witnessed the last winter of AI, and is trying to avoid another one through a pragmatic and practical approach.

Dr. El Adl also published one of the most comprehensive architecture frameworks and overall visions for what he calls a "Cognitive Digital Twin", and how it will influence everything we do soon.

Dr. El Adl can be found at:

E-Mail:           eladl.ahmed@gmail.com

Twitter:         @aeladl

LinkedIn:       https://www.linkedin.com/in/ahmedeladl/

Quotes

“Any industry which has intensive customer interaction, mainly B2C kind of interaction, I see major adoption whether it is virtual agents, chat bots, or software bots automating a lot of work, which takes a lot of time from humans while they are interacting with customers.”

"One of the major misunderstandings we see today is you wanted to start an AI project, you have to hire 10 academics, have a Ph.D. from MIT or Stanford in AI."

“Those very experts in AI, I think I hear some numbers around 10,000, 12,000 people across the globe, which is 1% of what we need today.”

“The mathematical background of machine learning is not or doesn't exist on the data science or data analytic part, and when you are faced by real world problems you don't have only to take the standard algorithms we have if we were the open source or platforms and just use it as it is. You will have to understand the industrial problem and mathematically tweak your algorithms to bring the best and most reliable results out of those algorithms."

“AI is here and here to stay and I always say, even if the academia ceased to deliver any new breakthrough inventions, what we have today in our open sources machine learning libraries or enterprise-grade platforms like Google, Microsoft, AWS, we have more than enough to deliver a lot of business values, solve problems, implement smart products for the next five years and we don't need anything new from the academia at all.”

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Episode #5: Why your organization should aim to become a Digital Master (DTI) report
Episode #6: Insights from IBM: Digital Workforce and a Software-Based Labor Model
Episode #7: Developments Influencing the Automation Standards of the Future
Episode #8: A Critical Analysis of AI’s Future Potential & Current Breakthroughs
Episode #9: How Automation and AI are Disrupting Healthcare Information Technology
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How to Stay Relevant in the Changing World of ITSM

When it comes to continual improvement, the focus often lands on systems, processes, applications and policies. What it tends to neglect is the importance of keeping human skills up to date. The fact is, the world around us is changing and the skills and abilities that were once considered valuable are not at risk of becoming obsolete. Today’s ITSM professionals must be diligent about developing and honing the skills and knowledge that will be needed in the future. Here’s a bit of advice on how to do just that.

Technology has evolved over the years. Whereas in the past an IT tech may have required in-depth experience using an oscilloscope and soldering iron to diagnose and repair computer problems, today an entirely different set of trouble-shooting skills are needed. Those who continue to realize success in ITSM are the ones that recognize and adapt to the changes as they are occurring. If one were to try and make a living using an oscilloscope in this day and age, they simply wouldn’t be marketable.

This same concept applies to just about any skill that one has developed over the years. If you became versed in ITSM a decade ago, chances are you learned all about the importance of having well-planned and documented processes or that defining metrics and reporting them in SLAs was critical. These things aren’t necessarily obsolete (yet). It’s just that, they’re simply no longer enough. It seems that every day we’re uncovering newer and better ways to manage incidents, deal with problems and engage with customers.

To remain relevant and continue to deliver value as an ITSM specialist, there are three key areas you should be concentrating on, as follows.

Focus on Agile Principles

A shift has occurred between delivering software and/or service to delivering value. Agile service management, in simplest of terms, means working smarter and watching solutions evolve through strategic collaboration. These days, customers want instant gratification. Launching massive projects that take months or years before they produce value are no longer acceptable. Instead, ITSM professionals should take an agile approach – one which involves continuous experimentation, ongoing learning and rapid adaptation.  

In short, focus on the following:

  • Pinpoint what could be improved upon
  • Develop a hypothesis around what’s standing in the way of progress
  • Establish a plan for fail-safe experimentation
  • Experiment, measure results and proceed accordingly

Go Lean

If you’re unfamiliar, the concept of “Lean” focuses on creating and delivering maximum value to the customer while minimizing waste. In terms of ITSM, the most relevant facets of Lean include:

  • Identifying end-to-end value chains
  • Mapping out specific steps to ensure full understanding of the work
  • Eliminating waste in every area to create maximum value
  • Ensuring that every action is value-added

Emulate DevOps

Many individuals involved in ITSM are under the false impression that DevOps is solely about development. As such, they miss a tremendous opportunity to (you guessed it) create value for the customer. There are five key DevOps characteristics ITSM teams can mirror:

  • Culture
  • Intelligent Automation
  • Lean Policies
  • Continual Process Improvement
  • Collaboration

These qualities can just as easily be applied to IT service management as they are in development.

Closing Thoughts…

The previous approaches to ITSM aren’t necessarily wrong. We still need to deal with change, manage incidents and resolve problems. But it’s how we go about doing these things efficiently and effectively that has to evolve. There are many new approaches that, if adopted, can help ITSM professionals do their jobs better and create more value, both for their employers as well as their customers. Those who are willing to adapt their skillset accordingly will be the ones who win the opportunities of tomorrow.

And remember – having an agile tool in your corner can’t hurt either. Ayehu’s Next Generation Automation platform is designed to streamline ITSM while freeing up agents to focus on honing and applying their evolving skills most effectively. Now you can try it free for 30 days by clicking here.

ITSM Automation Explained

IT Service Management is the lifeblood of an organization. But if the people, processes and technology that are in place aren’t adequately optimized, the very function of ITSM simply cannot add value. Automation can pull all of this together and streamline operations for maximum efficiency and service levels. But what, exactly, is ITSM automation? More importantly, what can it do for your business? Let’s take a look.

There are several different levels of ITSM automation, each offering a certain degree of functionality. The most basic form of automation used in the context of IT service management is that of ticketing workflow. This involves automating the following tasks:

  • Opening tickets based on the service catalog
  • Updating ticket SLA based on priority
  • Setting ticket attributes based on category
  • Initiating ticketing status workflow

The next level of ITSM automation is capable of repeatedly opening tickets based on a particular schedule. For instance, the IT team can schedule weekly or monthly maintenance workflows for network equipment.

Beyond this is advanced ITSM automation, which is intelligent and intuitive and subsequently adds the most value. With this type of AI-powered automation, tickets can be moved from creation all the way through resolution without the need for any human intervention. A sample workflow using this type of automation might be as follows:

  • Monitoring software detects low disk space on a specific server
  • Monitoring software opens a ticket in the ITSM automation platform
  • The automation platform automatically assesses the ticket request and categorizes it accordingly
  • ITSM platform initiates the automated workflow for disk space remediation (i.e. deleting temp or large files that are no longer needed)
  • Upon completion of the designated workflow and a follow-up disk space analysis, the ITSM platform automatically updates the ticket and marks it as resolved

Of course, this is just one example of the myriad of tasks, workflows and processes that can be fully automated. Perhaps the most important takeaway here is that an advanced ITSM automation platform can dramatically streamline the way IT operates, shifting most or all of the system administration tasks from human to machine. And because the automated processes are recorded, management can review these events to determine if there are areas where further improvement can and should be made.

Not only does ITSM automation save the enterprise a significant amount of money in terms of operating costs, but it also frees up service desk agents to focus their efforts on more mission-critical tasks and projects, such as those pertaining to increasing customer satisfaction rates.

In seeing the functionality and benefits of ITSM automation, the question then becomes not if an organization should adopt this technology, but how soon can they do so.

Take our ITSM automation platform for a test drive today and experience it for yourself free for 30 days!

Free eBook! Get Your Own Copy Today

The Secret to MSP Success…

Anyone familiar the role of a managed service provider understands all-too-well the pressure of finding a way to do more with less. It’s really all about efficiency and scalability. The more effective your performance, the more success you’ll achieve. The problem is, achieving this isn’t always easy, particularly when there are tight budgetary constraints involved, because for many organizations, hiring additional staff simply isn’t feasible. The solution? Intelligent automation, and leveraging it to win the race and position yourself firmly at the head of the pack.

Most MSPs agree that when it comes to intense competition and budgetary restrictions, the best and only solution is to invest in intelligent automation. In fact, this is even the case with enterprises that do not struggle with cash flow issues. Even those that can afford to hire additional staff, doing so will inevitably impact the bottom line. By taking a step back and allowing technology to do the heavy lifting, on the other hand, scalability is achieved without the added expenditure of increasing personnel.

Outsourcing or relocating operation centers to other areas of the world may provide a slight advantage in terms of overall cost, but it won’t necessarily level the economic playing field. The ultimate goal of every MSP is to successfully deliver as many quality services as possible while simultaneously utilizing the fewest resources…hence, doing more with less. Tapping into foreign markets and moving your IT department will not achieve this. Only automation will.

That said, it’s also critically important that key decision makers perform their due diligence when choosing an intelligent automation platform to employ. Those who have been most successful in this so-called “survival of the fittest” contest have provided some insight into what to consider during the selection process. Most importantly, keep the concept of scalability in mind. Make sure you choose a product that will be able to bridge the past with the future, integrating with legacy systems while also adapting with your changing business needs as they occur. Flexibility is key.

It’s also incredibly important that the automation platform you choose is robust but easy to implement and use. Remember, you’re going to be relying on this system to handle not just simple, repetitive tasks but also complex workflows that are critical to the success of your entire organization. Do your homework and make sure the product you ultimately select is comprehensive enough to handle anything and everything your business may need, not just today but in the future as well.

There’s no question that intelligent automation is fast becoming the central part of many successful MSP operations, and only those organizations that embrace and act on this will continue to remain competitive. Don’t get left behind! Get on track to a profitable future by investing in AI-powered automation.

Get started today by launching your free 30 day trial of Ayehu. 

Episode #11: Key Metrics that Justify Automation Projects & Win Budget Approvals – Dave Robbins, former CIO of Ellie Mae

Feb 14, 2019    Episodes

Episode #11: Key Metrics that Justify Automation Projects & Win Budget Approvals

In today’s episode of Ayehu’s podcast we interview Dave Robbins – former CIO of Ellie Mae, former CTO of NetApp, and currently an advisor to a number of companies including BGV.

Automating IT operations is mainstream today, and in fact a strategic priority for many enterprises, but this wasn’t always so.  In an earlier era, when automation tools were still embryonic and difficult to use, IT executives had to not only grapple with technical challenges, but also budget justifications, organizational complexities, and not least of all the dynamics of cultural resistance to radical technological change.

As a senior IT executive with market leaders such as Capgemini, Ellie Mae, & NetApp, Dave Robbins was a pioneer in many bleeding edge automation initiatives for these organizations.  He shares his insights with us on what worked, what didn’t, and why advice from Henry Ford might be your most powerful tool for succeeding with automation in your enterprise.




Guy Nadivi: Welcome everyone, my name is Guy Nadivi and I’m the host of Intelligent Automation Radio. Our guest on today’s episode is Dave Robbins, formerly the CIO of Ellie Mae, a software company that processes 35% of all US mortgage applications. Prior to Ellie Mae, Dave was the CTO of NetApp, a Fortune 500 cloud data services company well known to many people in the IT industry. Dave currently serves as an advisor to a number of companies including Benhamou Global Ventures better known as BGV, a leading Silicon Valley VC, and in these leadership roles Dave has had an extraordinary amount of first-hand involvement with automation. So we asked him on the show today to share some of his experiences and more importantly the insights he’s gained as a result. Dave, welcome to Intelligent Automation Radio.

Dave Robbins: Well Guy, thanks for having me. It’s a pleasure to be here.

Guy Nadivi: Dave, you were involved with IT automation at a fairly early stage during your career at Capgemini. Can you please tell us what it was like automating IT operations back then and what takeaways you learned from that experience?

Dave Robbins: Sure, you know back in those days it was … you know, we were in the outsourcing business in North America and you know, Capgemini is a global company and running various types of outsourcing in different places around North America and in Europe and a lot of it was fledgling. I worked 15 years at EDS and comparing the two at that point in time was … you know, EDS had been in for much longer, it was much more mature, so a lot of this was emerging and the outsourcing type deals that they were doing were … you know. You take over somebody’s infrastructure, you move it to your data center, you optimize process, and you try to drive costs down and make some money that way.

So while we were attempting to increase the capabilities of that out of our Kansas City office, we worked with IBM as a key partner to implement an SAP On Demand, an infrastructure, and in those days people were talking about being able to do that but not many had achieved it, if any, and we used Tivoli Provisioning Manager. IBM actually donated a bunch of equipment, you know servers, storage, software, everything was gratis, and the team there went through a pretty lengthy process of planning. They were a highly committed team in Kansas City that worked on that. And we, at the time created I think, a completely state of the art system where it was automatically provisioned, it had client segregation, it had dynamic capacity management so that if thresholds were surpassed in certain areas of the database or in the app server layers, servers were added. Capacity was added, and then they were decommissioned as the demand came down.

So it was really a great project, if you will, and we created a real nice environment. Of course IBM’s objective at the end of this project was to sell us the kit, that they had actually worked hard with us to create, so that we could then provide that as a service through our outsourcing organization to clients. So we were very happy with the outcome, but the project in the end didn’t turn out quite the way we wanted it to because there’s a lot of things when you automate, or when you create an environment such as that, they have to be absolutely in lock step. When you’re going along that process, a lot of things look aligned, but I think you have to check in very deeply to find out are they really aligned, and are you really going to be able to use what you create in the end.

In the end, the sales team didn’t know how to sell it. They knew how to sell, “I’ll take your kit and move it into our data center and then we’ll optimize it.” They didn’t know how to sell, “I’ll take your SAP installation and host it for you.”

And so we really got tangled up in the end when it came time to utilize this thing, this great thing that we created, and in the end it kind of fell by the wayside and nobody used it. It was a great activity, great lab experiment if you will, and a great learning experience to really feed into future projects because while we did a lot, it really didn’t bring the value in the end that we wanted it to. But people learned a lot through the process. That was the good outcome from it.

Guy Nadivi: And I’m sure that was applicable down the road to a lot of other projects.

Dave Robbins: Sure. I think it helped Capgemini in that outsourcing area in North America, really think, because everybody was aligned pretty well, I mean we thought they were. But they didn’t really know what it was. It sounded great to have a dynamic infrastructure, run it at a lower cost, higher leverage of assets. Those are all beautiful words, and everybody’s gonna say yeah I want that. But how do you actually deploy it, sell it, run it and gain revenue from it.

Guy Nadivi: When you were CTO of NetApp, a Fortune 500 company with annual revenues at the time of over a billion dollars a year, your growth rate was close to 30% year-over-year for four consecutive years. And it was during that stretch you grappled with how to scale up server capacity and other infrastructure while maintaining a reasonable headcount. How did you leverage automation to achieve that kind of balanced upscaling?

Dave Robbins: Yeah, that was a really tough time, it was exciting, and you know, a company like NetApp even today is very dynamic and growing and kind of the comeback kids if you will of that market. What we ran into was the situation, the more capacity they demanded, essentially the only answer that the team in place had was to add more people to support the incidents that would come out of that or the configuration of deployment requirements that would come out of the growth. Driving more server deployments.

Of course the infrastructure that was there that this company was growing on wasn’t brand new. It was in a state of atrophy really. Some of the leaders there told me when I met with them, we’re sorry we did this to you, because we starved G&A spending for years after the market bottomed out in 2001. The comeback was a long ride and they starved G&A, so a lot of the investment that would’ve normally been done to refresh the capital, refresh infrastructure and put more modern things in place really wasn’t there, so everything kind of had to be done at once.

So we looked at other tools at the time, we looked at Opsware, and we made a pretty big commitment to that product, and it covered configuration management, so it had the CMDB component, had the network components, and server provisioning automation. It even had some ties into incident management, at that time we were using Remedy, so it had some hooks to be able to create managed tickets automatically through the product. And it was an interesting project, like I said it spanned the configuration, the network, and the server, and as we deployed that, it clearly was, we found that the most motivated team was the network team, and part of that motivation was they had evolved or devolved, if you will, to this global network that was essentially a point to point network.

It had grown so large, there were “Sev Ones” 10 to 15 times a month, where network engineers were getting paged because a routing table got updated, a router flapped, a network went down, and they had to do something to fix that. So they were very motivated, but they were stalled because they couldn’t figure out how they could even inventory effectively what was there to put the plan together to normalize the IOS levels, patch levels, and then be able to go from there to migrate to newer assets and even a newer network.

They were point to point, it was a time where we were looking at pretty rapidly going to MPLS to support the scale of the company. We were in 100 countries at the time, so it was a pretty big effort to manage all those assets. So they were very motivated and very quickly they saw things they could never see before with that network tool. It basically gave them the configuration of every asset in the network so they knew everything right away, and instead of manually find the asset, log into it, determine what was there, they were able to do that inventory…we found out we had over 55 versions of IOS at various patch levels. I don’t know what the actual permutation was of variable there, but it was a lot.

That product allowed us to very, very quickly, that automation allowed us to get a hold of the configuration, get a hold of change, because when changes were made, they were alerted to the key people in that organization. So they knew, it actually surprised one network engineer, that his boss called him the next day and said, “Did you have a change control for that change you made last night?” And the fellow was like, “Wait a minute, how did you know I made that change?”

So it really put kind of a very nice magnifying glass on that environment to make sure you knew what was going on, and it also gave us some tools to update the IOS. It updated the IOS down to, I think it was eleven versions of IOS, and the only reason there was that many in the initial pass was that the diversity of the model numbers of the switches and routers that were out there drove a certain IOS configuration. But to take it down by that many that quickly within about a 3 month time period was pretty amazing. And 3 months later we were fully upgraded to an MPLS network and there were zero callouts per month to the network engineers. There were no network incidents after that.

They saw, they believed it would help them, they knew they needed it, they drove the implementation from themselves, they had the business support to get the availability up. So they really achieved their objectives very quickly, and they had a much better quality of life after that, from the standpoint of not getting woken up in the middle of the night. And even change management after that, where we had to grow and add things was a breeze. They were able to push a button and deploy an asset basically.

So that piece of it worked fantastically. On the other hand, the server deployments component and the overall company-wide CMDB kind of flailed a bit. If I had to boil it down, I’d say the human factors here, if you think about the Capgemini case, the human factors there were all positive. The team was committed to create something new. They were motivated by new technology, not being stuck in these old school customer migrations that they were doing, just moving assets and managing old technology. So at the end of the day, this team, the network team was highly motivated because they didn’t want to get those SevOne calls anymore, and they knew the business impact was critical, and they knew they had to get a hold of this configuration that was such a mess, they had to do something.

The server guys didn’t quite believe they needed it. And they didn’t know exactly what would happen to them if this tool did their job. I think their belief in the benefit was low, their fear of change was high. The thing about belief is a key factor in anything. If you don’t believe that it’s going to work, or if you don’t believe that it’s going to make your life better, then your commitment is going to be pretty low. So in the end we got a lot less value out of an area that, probably in the end would have had a lot more potential for value, than the network piece, just because the organic growth of the company was driving server demands and storage demands and so on, that would’ve been clearly met by that part of the product.

Guy Nadivi: During your tenure at NetApp, you were involved with an initiative called Cloupia, that was eventually acquired by Cisco. What interesting insights can you share with us about that venture?

Dave Robbins: Well again, you get down to motivation and belief and commitment. You know Cloupia was an interesting product because when they came out, they were a product, order of magnitude that was selling in the $100,000+ kind of category for a license. And they were competing with companies like Opsware that were millions of dollars to procure and deploy. And the feature set that Cloupia had really had a huge overlay between monitoring, automation and a number of other pieces where you’d have three or four tools in place to do those things, and they had interfaces to change management systems as well.

So when I became aware of Cloupia, I met the CTO and CEO and we chatted about a number of things, I believe I met them first at a Gartner event perhaps, but anyway, we brought them back into NetApp and it turned out they had been working with the product side of the organization as well a little bit, and at that point I was CTO and I had a lab that was a model for how you manage NetApp assets in a typical kind of IT environment and we needed a way to rapidly provision and deprovision assets in that lab and I said why don’t we go ahead and look at Cloupia and see how it works in that environment. The lab team was highly motivated to do this because they had to break down configurations and redo them all the time.

Cloupia, we put them in, and I’d say by the end of day two we had a product that was valuable. So the time to value with them was enormous, not only was their feature set great, but their time to value was really great. And so, at that point I was doing a lot of customer interaction and demos and showing how things work, and I actually demoed some provisioning, at one of my last runs at customer events over in Germany I think it was…anyway, I demonstrated the ability to do that type of provisioning and I had people rush the stage at the end, you know “I gotta have that.”

It was really kind of interesting, again the dynamics of the project were very different, it was you had a small team that was in high demand, that was very resource constrained, that believed they needed this kind of a tool with automation to really give them the ability to respond to that demand without killing themselves to get it done. It really turned out quite well, and we did a lot of joint presentations actually with Cisco, who ended up buying Cloupia, and called it UCS at the end of the day, that was their tag for it.

It ultimately became one of the only tools that was certified in the NetApp labs as full end-to-end management and automation for the whole FlexPod environment that NetApp and Cisco came up with. That was a great experience and from a lab perspective it worked out perfect, and gave us a lot of value very quickly. It ended up actually getting presented at a quarterly all hands meeting and its very rare to see an IT project that’s kind of infrastructural-based get exposure at that level but even the business side was very proud of what we had done.

Guy Nadivi: Back in that earlier era of IT automation, ten plus years ago, I think a lot of our listeners would be interested in knowing what kind of key metrics were used to persuade management that an automation project was justified?

Dave Robbins: Well, the classic metrics that were used then were, I’m not saying they were right, but the classic metrics were avoidance of staffing or reduction in effort to allow for growth. So if I had ten people and I could put some automation in, those ten people could configure ten servers a day today, with automation they could do a hundred. So that would allow me to scale my capabilities in a way that wasn’t linear to the business growth.

Those were the typical arguments that were made, and it was really the cost offset of those things. I think the failure in those days and maybe today as well, is what metrics make sense today, I think not only do those metrics make sense, but I think it also makes sense to say, “how many servers am I provisioning per day, how many incidents did I avoid, could I do automatic resolutions on certain things. When I saw a capacity issue, could I automatically raise a ticket, could I automatically do the provisioning and could I automatically close a ticket?”

So those sorts of metrics, I don’t think at the time we saw a way to easily instrument that kind of data, so we focus more on the avoidance of staffing as the metric to get the project approved. At the end of the day, those numbers aren’t really…once you get the project approved, nobody goes back and looks at those numbers. So I think you’ve really hit on kind of a failure, of our ability to articulate the value of automation and how it actually meets that value over time. And it’s not about staff avoidance, it’s more about what does the automation actually achieve for you.

Guy Nadivi: So for an IT executive listening to this episode of our podcast, what are the…are their specific key metrics that you would suggest they use to justify an automation project today?

Dave Robbins: I think that what you have to do with an automation project, is you have to look at the full business stack and you have to think about what are the current metrics; how long does it take to do certain things? How long does it take to implement a change? What’s your QA process like? When you provision and deprovision servers or when you manage network assets, what are the things that relate to that whole business stack that you’re working on and then what’s the constraint that’s there today?

I think what you could do, is if you could analyze a full stack and say what are my constraints today, and then make those your automation targets and then make sure that those are your key metrics. So if your constraint was it takes me 6 weeks to deploy a certain size of a change in a business app because of all the coordination required to cross the different organizations and the time to get your test kits redeployed if there are errors during testing, those sorts of things.

So think about the full stack and think about what does it take you today and make sure…and you may not have those metrics, you may have it just takes me that long. So you gotta get into, how’s automation going to impact that, or how’s some intelligence going to help me deal with that better, some insight that automation or AI might be able to bring. And then make sure you’re targeting the metrics that mean something to the business.

Guy Nadivi: Sometimes announcements of automation projects can trigger various forms of resistance among staff; psychological, emotional, even on an organization level. Dave, what’s your prescription for IT executives to get buy-in from staff on automation initiatives?

Dave Robbins: That’s a great question. I think a lot of it depends on the culture of your organization and you have to be in touch with that. So what motivates them? What creates the belief that you want to create? Because I think “belief” is the key word in this thing from my perspective.

Dan Warmenhoven, was the CEO at NetApp for many years, used to quote Henry Ford and he’d say, “Believe you can or believe you can’t. Either way you’re probably right.”

I think that’s a great quote to remember, because the human factor and whether they believe, and you gotta think about what do they believe, do they believe that this particular automation is the right one. Do they believe that it will help them? Do they believe it will fix the problem we’re after? Do they believe they have the right resources to get it done? Do they believe they’re being asked to do something reasonable given their current work load?

It’s kind of like, you’re usually asking somebody to do this that has a day job, and that has demands that are overwhelming for them in some cases, so how do they get through not doing the same thing they’re doing now over and over, and changing that cycle, and believe that this is the right thing to do.

And I think a lot of that depends on culture, and an organization that’s clearly aligned with the business and they’re motivated by business outcomes and they can see how the business outcomes actually make their lives better; that they might be more inclined to do that. Where you’ve got a larger organization, and their people are coming in, doing their jobs, not necessarily in touch with the details of the outcomes and their particular impact on the outcomes. They may think more about what it is to them. So I think you need to figure out where your culture is and then figure out how to market and sell this to that group of people, which is key, to get your deployment done and achieve the objectives you’re after because…and they need to be engaged and involved in it too. But again, you’ve got this kind of cycle in most organizations that happens that says, these resources are too busy to change, and so to get them to buy in to something takes a break from what they’re doing and some clean whiteboard thinking. And that’s hard for people.

And change is threatening to people too, and change equals problems they have to solve because nothing you change, most things you change, aren’t perfect the first time. So there’s a lot of problems in the mind that go against the belief and I think understanding your environment, your culture, where this person is in the organization. Are you a small organization, you’re tightly aligned with the business. Or you’re a big organization, these guys are in the bowels of the organization. How do you deal with those different environments, so it’s very situational. But I think it comes down to the human buy-in can be either a great thing, like in the Capgemini example, or it can be a challenge like it was on the server side of the NetApp example.

Guy Nadivi: Last question for you, what advice do you have for IT executives who want to dive into, or scale up, their automation initiatives?

Dave Robbins: I think I would start with a clear objective. That could be a particular project. I like to think green field, you know, if you can create a scope that is not encumbered by the thought that’s we’ve always done it this way, or I’m not sure how that’s going to change my life. Then you say, wait a minute, this is a new thing, it’s a new box, there’s nothing in it and we’re going to create this project. And if you can isolate the thought process within, okay we’re not thinking about what happened yesterday, we’re thinking about what’s going to happen tomorrow.

Get your best team on it, put clear objectives, and get back to those business metrics, whatever they are to your organization, and I mean business metrics not server metrics. How many servers you provisioned? How many incidents you might have avoided? What the avoided downtime was? How much more quickly did you get a release deployed and with how many fewer errors? Whatever the metrics are for your business but really not infrastructure and not business app, end-to-end. If you can create that team, kind of like an office pilot or POC, whatever you might want to call it, and focus on those metrics…

I think what’s an interesting sideline of human behavior is people will watch that. So if you take people out of one team and you put them on this dedicated team to achieve this result, they still go to the same lunch room together, they still talk about things. I think that the drag you get from a success, you know what the outcomes are, where somebody’s like, “well I don’t have to be up at night anymore doing that stuff” or “I don’t do that work anymore, I just push a button.”

And you see that that person is still gainfully employed, they’re still having fun, they’re still challenged. And the project met its objectives and you create a drag that’s like, well I want some of that too, hopefully. And then you create another project. But I don’t think big bangs work. I think you have to carve out, what is it that you can isolate to verify, validate, and prove to the whole organization that this isn’t just the next thing out of a CIO magazine, or whatever, it’s not just a fancy new product.

It actually, these things together, with this business app, create a different dynamic for managing your application.

Guy Nadivi: Sounds like sage advice. Alright, looks like that’s all the time we have for in this episode of Intelligent Automation Radio. Dave, thank you so much for joining us today and providing some great first-hand accounts about the role of automation in IT operations. It’s been good having you as our guest.

Dave Robbins: Well thanks for having me, I’ve enjoyed it.

Guy Nadivi: Dave Robbins, former CIO of Ellie Mae, former CTO of NetApp, and currently an advisor to a number of companies including BGV. Thank you for listening everyone, and remember, don’t hesitate, automate.



Dave Robbins

Former CIO of Ellie Mae, former CTO of NetApp, and currently an advisor to a number of companies including BGV

Dave Robbins has over 30 years of management and directorship experience, including the executive leadership roles of CTO of NetApp and CIO/Sr. VP of Ellie Mae, respectively.  During his four years with leading mortgage industry technology provider Ellie Mae, Mr. Robbins played a direct role in helping the company transition from 70% self-hosted business model to more than 80% SaaS or Hosted Services. As CTO of IT at NetApp, Mr. Robbins spearheaded the development, implementation and enforcement of a three year technology roadmap and adoption strategy for IT systems, networks and storage. Prior to this role, Mr. Robbins served as the company’s Global Infrastructure VP, during which time he was responsible for leading a global team of more than 170 professionals, managing annual OpEx spend of more than $80M and a capital spend of $20-40M. He is highly respected in the industry and has published several articles in both national and global publications, including CIO Magazine and Network Solutions.

Ritu can be found at:

E-Mail:          d.r.robbins@icloud.com

Twitter:         @darobbin

LinkedIn:        https://www.linkedin.com/in/robbinsdave/

Quotes

“It sounded great to have a dynamic infrastructure, run it at a lower cost, higher leverage of assets. Those are all beautiful words, and everybody's gonna say yeah I want that. But how do you actually deploy it, sell it, run it and gain revenue from it.”

"I think their belief in the benefit was low, their fear of change was high. The thing about belief is a key factor in anything. If you don't believe that it’s going to work, or if you don't believe that it's going to make your life better, then your commitment is going to be pretty low ""

“And it’s not about staff avoidance, it’s more about what does the automation actually achieve for you.””

“I think that what you have to do with an automation project, is you have to look at the full business stack and you have to think about what are the current metrics; how long does it take to do certain things? How long does it take to implement a change? What's your QA process like? When you provision and deprovision servers or when you manage network assets, what are the things that relate to that whole business stack that you're working on and then what's the constraint that's there today?"

“So you gotta get into, how's automation going to impact that, or how's some intelligence going to help me deal with that better, some insight that automation or AI might be able to bring. And then make sure you're targeting the metrics that mean something to the business.”

“Get your best team on it, put clear objectives, and get back to those business metrics, whatever they are to your organization, and I mean business metrics not server metrics.”

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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Links

Episode #1: Automation and the Future of Work
Episode #2: Applying Agility to an Entire Enterprise
Episode #3: Enabling Positive Disruption with AI, Automation and the Future of Work
Episode #4: How to Manage the Increasingly Complicated Nature of IT Operations
Episode #5: Why your organization should aim to become a Digital Master (DTI) report
Episode #6: Insights from IBM: Digital Workforce and a Software-Based Labor Model
Episode #7: Developments Influencing the Automation Standards of the Future
Episode #8: A Critical Analysis of AI’s Future Potential & Current Breakthroughs
Episode #9: How Automation and AI are Disrupting Healthcare Information Technology
Episode #10: Key Findings From Researching the AI Market & How They Impact IT

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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

Intelligent ITSM Automation – Your Secret Formula for Success

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

Widespread Benefits of Intelligent ITSM Automation

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

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

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

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

Aligning Intelligent ITSM Automation with Business Goals

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

Obstacles to Intelligent ITSM Automation

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

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

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

IT Process Automation Survival Guide

7 Key Business Benefits of Intelligent Automation

Intelligent automation is being adopted and implemented in businesses of every industry. Still, there are some decision makers who are on the fence about whether it’s worth the investment. If you are among those who aren’t 100% certain, let’s take a look at a few of the quantifiable benefits AI-powered automation can provide to your organization.

Cost Savings

One of the biggest advantages of intelligent automation is the immediate and significant reduction in expenditure it can deliver. When work is automated, not only is it completed faster, but it also can be performed round-the-clock at a much lower rate. So, you get greater output for less, which results in a better bottom line.

Quality, Accurate Work

Let’s face it. Even the most careful human can and will make an occasional mistake. Multiply those errors by the number of people you have performing routine tasks for your company, and you could be looking at a pretty costly problem. With artificial intelligence, the work is performed error-free. Better quality means higher satisfaction rates, which – again – is good for your company’s profitability.

Enhanced Cycle Time

How long does it take a human worker to perform a given task such as completing a web form? Even if it’s mere minutes, an intelligent robot could shave that time down to just a few seconds. Over time and multiplied by dozens of tasks and several staff members, this savings really begins to add up.

Employee Empowerment

Intelligent automation does not require any special technical skills. That’s why it’s an ideal application for the end-user. The ability to deploy robots to perform certain tasks without having to enlist the help of someone from IT empowers the end-user to get their jobs done more efficiently and effectively. Meanwhile, it frees up IT to focus on more important tasks and projects.

Simplicity and Flexibility

Automating tasks and workflows through Ayehu’s AI-powered automation does not require coding or script-writing. That means even complex processes can be transferred from human to machine with little effort. The faster these tasks and workflows can be automated, the sooner your organization will begin reaping the benefits. In other words, intelligent automation delivers quick returns.

Better Control

Many companies choose to outsource so-called busy work to external parties. This, of course, comes with inherent risk. Intelligent automation can provide a better solution and since the work remains in-house, the business maintains maximum possession, control and visibility.

Insights and Analytics

Learning from the past can help your business leaders make better decisions for the future. Machine learning algorithms offer the ability to gather, organize, track, analyze, report on and store valuable data. That information can then be utilized to improve on current operations, address and correct issues in a timelier manner, accurately forecast and develop best practices.

Still not convinced that intelligent automation is a beneficial solution for your business needs? Why not try it for yourself? Click here to start your free trial of Ayehu NG today.  

Free eBook! Get Your Own Copy Today

Attn: MSPs – Want to Maximize Efficiency and Gain Competitive Advantage? Here’s how.

In years past, businesses had little choice when it came to managing their own internal IT operations. For many, the only feasible option was outsourcing. Today, with so many advancements in technology, the concept of outsourcing has shifted and more companies are bringing these services in-house. So, how can Managed Service Providers compete and stay profitable? The answer is simple. MSPs must find a way to maximize efficiency while delivering the highest value proposition to their customers. And here’s how.

Do More with Less

The first step in staying successful as an MSP is finding a way to get the most out of your employees while also keeping expenditure down. While hiring more staff may seem like the logical solution to staying on top of tasks and delivering exceptional client service, doing so would be too cost prohibitive for most firms – especially in today’s highly competitive landscape.

As an alternative, MSPs can turn to technology and leverage the various tools available to them to help boost productivity without the need to bring on additional employees. Intelligent automation, for example, an excellent solution to this dilemma, as it can eliminat many of the day to day tasks from your team’s to-do list. Not only will this allow you to keep staffing lean, but it also frees up those who would normally handle those repetitive workflows to focus on other, more meaningful business initiatives.

By automating activities such as incident management, the program itself can handle much of the process without the need for human intervention. This improves response time and efficiency levels, which ultimately enhances SLA to the clients.

Make an Offer They Can’t Refuse

One of the reasons the managed service provider industry is considered so risky is because tech advancements, such as cloud hosting, have made managing operations in-house easier and more affordable. To counteract this, MSPs must make what they have to offer too valuable to refuse. Beyond just taking on the task of handling IT operations for a company in real-time, these firms must remain one step ahead. This means taking a proactive approach and identifying and addressing issues even before they occur.

The best tool for this task is again automation. By leveraging a robust intelligent automation product, preferably one that can be integrated with other existing systems. That way, things like alert management can be streamlined to maximum efficiency. This reduces human error and allows problems to be corrected at a much faster rate. When you can effectively demonstrate to a company that is considering hosting IT in-house that your services are much more valuable, you’ll win the battle for survival.

Ayehu for MSPs

To stay afloat in an increasingly volatile, cut-throat environment and disprove the impending “outsourcing is dead” theory, MSPs must continue to adapt. Performance will need to be at its highest with cost at its lowest. The only logical solution to this is intelligent automation. This will provide the edge you need to remain ahead of the pack in the months and years to come.

Are you an Managed Service Provider that is feeling the pressure to perform better? Are you ready to experience what the right automation platform can do for your business? Download your free 30-day trial of Ayehu today!
eBook: 10 time consuming tasks you should automate

Episode #10: Key Findings From Researching the AI Market & How They Impact IT – IDC’s Ritu Jyoti

Feb 1, 2019    Episodes

Episode #10: Key Findings From Researching the AI Market & How They Impact IT

In today’s episode of Ayehu’s podcast we interview Ritu Jyoti – Program Vice President, Systems Infrastructure Research Portfolio for IDC.

What does the speed of eye blinking have to do with detecting credit card fraud, and how is that relevant to the average lifespan of companies on the S&P 500 dropping from 61 to 18 years?  The answer in both cases is digital transformation, which thanks to spectacular advances in artificial intelligence, machine learning, and automation is reshaping the global business landscape.

We discovered these insights from talking with Ritu Jyoti, Program Vice President of IDC, an international market intelligence & advisory firm.  Ritu shares some intriguing observations from her research, including:

• the surprisingly straightforward things IT executives can do today to start preparing their organizations for the inevitability of artificial intelligence in their environments
• the #1 objective organizations are targeting for AI that’s more important to them than increasing product revenue, acquiring new customers, or reducing costs
• why “time to insight” might be the biggest value proposition artificial intelligence offers




Guy Nadivi: Welcome everyone! My name is Guy Nadivi and I’m the host of Intelligent Automation Radio. Our guest on today’s episode is Ritu Jyoti from IDC. For those of you who are unfamiliar, IDC is a global provider of intelligence and advisory services for the IT, Telecom and Consumer Technology markets and they employ over 1100 analysts worldwide to figure out trends and opportunities in over 110 countries. Now, Ritu is a program Vice President at IDC and she recently co-authored a report which caught our attention because it examines the use of artificial intelligence in digital transformations.

So we’ve invited Ritu to come on the show and share with our audience some of the very interesting insights she and her colleagues published in that report. Ritu, welcome to Intelligent Automation Radio.

Ritu Jyoti: Thank you Guy, it’s my utmost pleasure to join the session today.

Guy Nadivi: Ritu, you’ve talked about the speed at which artificial intelligence functions and that it’s faster than the blink of an eye. And you actually point out that the time it typically takes to blink your eye, on average, is 300 milliseconds. While the time it takes for AI to detect fraudulent credit card activity is only 40 to 60 milliseconds. I think this might be a good example of what you’ve described as AI’s ability to “accelerate time to insight.”

Can you please elaborate about accelerating time to insight and why that’s something that should make IT executives and business leaders want to deploy AI?

Ritu Jyoti: Yeah, absolutely. It’s a fun trivia in terms of the difference between 300 milliseconds of the blink of an eye and a 40 to 60. I just enjoy that data point. So, it’s basically a guide of a digital transformation.

Digital destruction is real. If you look at the average company life span on the S&P 500 index, in the third platform which we characterize by cloud, social, mobile, and digital technologies, starting in 2005 is 18 years. But when you compare it to the first platform which is characterized by the mainframe, the average life span was 61 years. So, you can see the contrast and the differences there.

IDC has done the research and it shows that organizations across every industry are under threat, and the average percentage of traditional revenues that are at the risk of destruction and digital transformation it varies from about 11% for hospitality to about 29% for utilities, and as we all know data underpins digital transformation and people are using the digital transformation to balance the business objectives between tactical and strategic objectives, whether it is improvement in operational efficiencies, reducing risk & penalties, increasing existing product revenue, and if you look into all of this, timely access to insights is crucial to enable all these business objectives.

In today’s world, if you see, data is being increasingly distributed. It’s stored at the edge. It’s on- premises. It’s on the cloud. It’s very dynamic in nature and also diverse. Gone are the days when we just had structured data. Now we have a lot of unstructured content. We have semi-structured data, and with such huge volumes and distributed data sets, it’s humanly impossible for us to go through files of data to gain timely insight. In leveraging AI, one can address the time to insights need.

Also, data quality is important for AI algorithms’ trustworthiness, and AI algorithms are being used to dynamically create data validation checks and improve the quality. I’ll give you a very small example, we [that was] just recently – Microsoft Azure capability was announced. If you see there are 800 million people who use mobile applications today, and they have an increasing amount of reliance on the mobile banking app. And with this rate of mobile banking adoption, mobile device fraud has inevitably increased as well. To detect the fraudulent transactions have become all the more important, and just two weeks back Microsoft developed a mobile banking fraud detection architecture, which actually uses artificial intelligence to spot fraudulent transactions. And it is using a combination of Azure Cloud Services. So, it’s a long and very important juncture that we are at, where getting timely insight can help us in meeting all the business objectives.

Guy Nadivi: We live in the age of the algorithm and thanks to sophisticated algorithms, pervasiveness of data, GPUs and accelerators, and of course the infinite scalability of cloud computing, you’ve predicted that by 2019, just a few months away, 40% of digital transformation initiatives will use AI services and that by 2021, 75% of commercial enterprise apps will use AI. That’s a pretty dramatic uptake for this technology. So Ritu what should CIO’s, CTO’s, and other IT executives do right now to start preparing for this landmark shift in the way that IT will be providing its services?

Ritu Jyoti: Yeah that’s a great question, Guy. We get this question from everyone. So, our advice to the leading executives, CIO’s, CTO’s, and all is to have – establish a change management organization – to proactively address any human concerns and retaliations. [Retaliations] is a strong word, but real. To embrace AI-infused infrastructure or technology to help them gain and meet the business objectives.

Look for ways for retooling their IT staff to support those strategic initiatives and also fill in the skills gap. They could look for augmenting their in-house staff with external consultants who have already done this, and have some experience. They should look into, you know, a lot of public cloud service providers. I just gave the example of Microsoft Azure. They help us to jump start their adoption of AI. They make it very easy to use with some pre-installed, pre-configured templates so, you know, I would suggest that they should start their journey by embracing public cloud services, and then look for evolving an approach to adopt AI technology and AI-enabled infrastructure.

As a first step, they could use it for predictive analytics and once they have gained some valuable insight, then they can slowly save in [integrate] the automations once the trustworthiness and the quality of data is established, and the comfort level is also improved.

Guy Nadivi: At IDC, you conducted a survey and found that IT automation is the top use case for AI and machine learning with 65% of your respondents saying they currently use AI & Machine Learning for IT automation. Ritu, what do you think makes AI and machine learning so compelling for IT automation as opposed to some of the other use cases like workforce management, or CRM, or supply chain and logistics?

Ritu Jyoti: Yeah, you know, I mean in fact we were a little bit surprised. We were thinking that it’s mainly used in the front office functions. But when you kind of sit through and think through as to why this is so important – because remember I just talked about how the digital transformation’s business objective is to improve operational efficiencies. If you think about, you know that there’s significant amount of operational inefficiencies in the IT organizations right now. So when we looked into – in addition to that the skills gap, there are folks trying to figure out how to use the AI algorithms and kind of bring efficiency in delivering the need, or the IT needs to support the business objectives.

So, you know, when we kind of talked through, it’s basically because if you start from the back office functions, there is already a lot of computer to computer interactions in the IT and finance and accounting in the back office function. Especially there the IT automation can help. So there is much more easier [to get the] buy-in. I’m not saying that the front office functions like CRM, supply chain, and all cannot benefit, but the reason folks are starting from the back office is because it’s easier. The return on operational efficiency is crucial to make even the front office functions succeed and again value all the AI algorithms.

Guy Nadivi: In that same survey that IDC conducted, you found that improving operational efficiencies was the number one business objective for AI & machine learning projects. Improving operational efficiencies was even more important to your survey respondents than increasing existing product revenue, acquiring new customers, or reducing costs. What do you think makes artificial intelligence and machine learning so compelling as a tool for improving operational efficiencies?

Ritu Jyoti: Yeah, as I just answered in the previous question there’s a close tie up in the two situations, and if you look into it when they ask them what are your big challenges in adopting AI, skills shortage was rated to be one of the top challenge. Organizations have tight budgets between investing in innovation versus investing in OpEx spend. Any routine repetitive task can easily be automated. You know if you think about organizations are spending significant amounts just to keep the lights on. Let’s take the example of the infrastructure. There’s a lot of telemetry data, there’s logs, there’s stats available, where ML algorithms can easily be used. For example, IT organizations can reduce or eliminate call centers by using ML to process and integrate incoming help desk calls routed to the right person for problem resolution within a single call. They can, you know, look for using machine learning algorithms to optimize software deployment strategies and reduce the failure rate.

So these are very simple examples that not a lot of extra overhead or intelligence that is needed and manually it’s impossible for folks to kind of go through heaps of log files and predict server failures or outages. So it becomes very, very simple, and there’s not a lot of dispute about the guidance or the results that they get from the ML algorithms usage. It’s a simple starter, easy to use, decent trustworthiness here unlike the other examples that we talked about where there could be some ethical biases concerns and stuff. Improvement in operational efficiency is the number one place where the ML algorithms can be used and the IT automation – very, very direct correlation with IT automation being the number one case and with the business objective being operational efficiency and how the two tie together to support that objective.

Guy Nadivi: Ritu do you think there’s any specific industries that stand to benefit the most from deploying artificial intelligence & machine learning for IT automation?

Ritu Jyoti: Actually I think it benefits all the industries, but if you have to think, you know, a few examples. Retail and health care jump a little bit because you know these are the places where a lot of edge processing will be key. For example, in the case of retail at the point of sale areas, AI could be used to do automated out-of-stock detection on the aisles. In the health care, AI algorithms could be used to route the inquiry to the right physician or there could be virtual radiologist checks done, and in all these edge locations, you don’t have the IT staff. You don’t have that much of bandwidth or IT hand holding sort of thing, and in those cases, you switch to AI algorithms to scale to support the need is all the more highlighted, but at the meta level it can help across all different industries.

Guy Nadivi: IDC interacts with thousands of organizations around the world. Can you share with us an example organization or two that made a successful digital transformation thanks to artificial intelligence?

Ritu Jyoti: Yeah, I mean if I have to take an example – payment services you know the company PayPal? They are using GPU-accelerated deep learning algorithms for fraud protection. There’s another company consulting firm Accenture’s R&D arm & other businesses, they are using to detect internet security threats. Drive.ai is using the AI algorithms preparing to offer a self-driving car service for public use very soon. It spans different industries and currently you see most of the people are using it in the fraud detection or large scale, web-scale examples, internet security threats, but the key examples are growing rapidly.

Guy Nadivi: If I’m a CIO, CTO, or other IT executive, what are the critical KPI’s or success metrics I should focus on at different stages of the digital transformation journey?

Ritu Jyoti: Yeah that’s again a very good question, Guy. This is something that we think in and out in all our interactions with the C-level executives. You know I think transformation is integral. Without the IT transformation, without the improvement in operational efficiency in IT automation, there’s no way that the digital transformation can succeed. So, when we think about in the past, most of the IT organizations just focused on total cost of ownership, or cost metrics, or ROI investment. But today people are looking into a little bit more business specific-KPI’s. They’re looking into – to make the judgment call that how much of IT spend was on newer business initiatives, how much of that is used to grow the business, transform the business. They are using the metrics to see present stage of their spend that is spent on customer-facing initiatives. They are tracking their customer satisfaction scores for business-facing initiatives, and it’s all about looking from a business eye than the traditional IT efficiency metric. There’s a huge transformation happening in terms of the KPIs.

One very interesting thing that we see more and more executives doing these days is that there’s a lot of projects that are funded by IT and there’s more direct linkage of how these IT projects line up with the business objectives. Now there is a concept of program governance where every IT initiative is linked to a business objective and there are tools which are supporting this tie up. So that’s another very upcoming metric that a lot of customers are trying to tap into.

Guy Nadivi: What do you think are some of the biggest misconceptions in IT departments about artificial intelligence and machine learning?

Ritu Jyoti: Yeah this is interesting because when we talk to some folks they think, you know, I mean it’s become like a buzz word right. Everybody thinks that people use this term and for everything very loosely. It’s become like AI is a silver bullet and that it will solve everything. So, there’s some players who actually believe that, and there’s some players who actually are very – the nay-sayers and they think that, it’s too premature, it cannot be used, there are ethical biases, and skill shortage. So, I would say that it being a silver bullet is a big misconception and also that it’s too early and I have the time and the bandwidth to get on to this. The reason I say this is that because the rate at which we kind of say that this is a slow-motion explosion, the way it is kind of evolving every day, the innovations are happening at a rapid pace. So they don’t really have the time. They really have to kind of get on to it and embrace it.

Guy Nadivi: You stated that the top three challenges for adopting intelligent infrastructure are data volume and quality, advanced data management, and the skills gap. Can you please elaborate a bit on how leading organizations are addressing these challenges today?

Ritu Jyoti: Yeah sure. So, poor data quality has a direct correlation to bias and inaccurate model build-out and ensuring data quality with large volumes of time makes the most distributed data set. It’s very difficult, and it is hard for developers to know, correct, and accurately check for validation. You know in the past people just had structured data sets. They had it within their own boundaries and within their data centers. The diversity of the data wasn’t there and there was not a lot of unknown factors. Today, the data is distributed, it’s dynamic, it’s varied sources, internal and external, and it’s humanly impossible for coders to develop all the checks and validations. And to address these challenges, enterprises desire an autonomous data quality and validation solution. So such a solution could automatically learn data’s expected behavior, create thousands of data validation checks on the fly without the need for coding. You can update and maintain the checks over time and eliminate both anticipated and unanticipated data quality errors so that you can make the data more trustable and usable.

So, when we talk about that enterprises have – what are their top three challenges, data volume & quality is important, but also skills gap. There are, talents meaning both AI engineers and data scientists. They’ll be needed to support the growing segment of AI-dependent digital transformation initiatives. But when you think about it, the IT organizations are still grappling with a shortage of these professionals.

For example, it’s not just also having enough data scientists or data engineers, but even the data scientist they haven’t done something like this. Leave aside that they are fewer in number, they haven’t done something like this and they have a high learning curve in building, and optimizing, and training model skillsets a lot of data scientists don’t have. They really need to improve their productivity and some of these, if you look into the life cycle of building-out of an AI model, a training stage is very iterative. It can take millions of hours, days, weeks – and the toolset supporting that requirement is not there. There are some organizations who are helping out with that.

So, you know, how the training stage is happening, and it’s still in the middle of the process, you cannot sit & wait for the entire process to complete over 36 hours and then revisit it. People need to have an instant support of something has failed and we can stop there and restart it. So those tools are evolving and essentially and I’m talking about if you notice, AI itself is coming to the rescue of solving these challenges that they are having. It’s an interdependent kind of relationship. They are leading to AI problems because they don’t have the skillset, or the data is distributed and dynamic, but then again, they have to use AI to solve those problems and it becomes a very, very integrated problem that folks are – there’s no way. They have to embrace AI and continue to use it to solve the challenges and escape.

Guy Nadivi: Ritu if you can offer one piece of advice to CIO’s, CTO’s, and other IT executives that are thinking about taking the plunge with AI for IT automation, what would it be?

Ritu Jyoti: Embrace it. It’s not optional. As I was just saying at the start of the conversation that there are companies who are thinking in the IT industry that it is a magic bullet or that we have to stop and wait. Companies that are not embracing strategic innovations with artificial intelligence and analytics, they’ll fall behind and they’ll lose their competitive advantage. So embrace it, set up your change management organization, speak to your people. Augment it with external help but, embrace it fully and run with it.

Guy Nadivi: Prudent advice. Alright, looks like that’s all the time we have for on this episode of Intelligent Automation Radio. Ritu thank you so much for joining us today and providing some unique insights about the role of AI in IT automation. It’s been a real pleasure having you as our guest.

Ritu Jyoti: Same here, likewise. Thank you Guy such a pleasure to share the insights that we gather from the broad industry and look forward to future collaboration. Thank you.

Guy Nadivi: Ritu Jyoti – Program Vice President at IDC. Thank you for listening everyone and remember don’t hesitate, automate.



Ritu Jyoti

Program Vice President, Systems Infrastructure Research Portfolio for IDC

Ritu Jyoti is Program Vice President for IDC's Cloud IaaS, Enterprise Storage and Server team, which includes research offerings, quarterly trackers as well as advisory services and consulting programs.  Ms. Jyoti is responsible for managing the systems infrastructure research portfolio spanning topics such as cloud computing, software-defined infrastructure, cloud data management and protection, Infrastructure for artificial Intelligence, acceleration and automation, Big data analytics workloads infrastructure needs, and Digital Transformation-IT transformation infrastructure strategies. She oversees a team of analysts responsible for identifying and analyzing vendor strategies, emerging technology and market and customer trends affecting IT buyers and datacenters worldwide.

Ritu can be found at:

E-Mail:          rjyoti@idc.com

Twitter:         @ritujyoti

LinkedIn:       https://www.linkedin.com/in/ritujyoti/

Ritu’s published research can be found at:

Digital Transformation Use of AI Services: IT Role and Impact

  • This IDC Web Conference examines digital transformation's (DX's) use of AI services. By 2019, 40% of digital transformation initiatives will be supported by cognitive/AI capabilities, providing timely critical insights for new operating ...
  • May 2018 | Doc #WC20180517 | Web Conference: Tech Supplier

GPUs and GTC 2018: NVIDIA's Love Letter to Graphics, AI, and Deep Learning

  • This IDC Market Perspective examines and provides an overview of the NVIDIA 2018 GPU Technology Conference held in San Jose, California. The event highlighted a range of new GPU acceleration products and services by NVIDIA and its partners ...
  • Apr 2018 | Doc #US43744418 | Market Perspective

What Is the Preferred Deployment Location for AI/ML/DL Workloads?

  • This IDC Survey Spotlight provides analysis of the deployment location/computing model used for AI/ML/DL workloads. Specifically, this Survey Spotlight highlights the broad use of public cloud and expected increase of private cloud and edge ...
  • Apr 2018 | Doc #US43731818 | IDC Survey Spotlight

Worldwide Storage for Cognitive/AI Workloads Forecast, 2018–2022

  • This IDC study presents the worldwide 2018–2022 forecast for storage hardware and software for cognitive/AI workloads.
  • "Cognitive/AI is poised to transform next-generation IT. Machine learning and deep learning require huge amounts ...
  • Apr 2018 | Doc #US43707918 | Market Forecast

What Type of Storage Architecture Will Be Used for On-Premises Run of AI/ML/DL Workloads?

  • This IDC Survey Spotlight provides analysis of the storage architecture currently used/will be used for on-premises run of AI/ML/DL workloads. Specifically, this Survey Spotlight highlights the expected increased use of software-defined ...
  • Feb 2018 | Doc #US43587818 | IDC Survey Spotlight

Are You Ready for Intelligent Infrastructure in Enterprise Datacenters?

  • These event proceedings were presented at the IDC Directions conferences in Santa Clara and Boston in February and March 2018.
  • Cognitive is poised to transform next-generation datacenters, improving productivity, managing risks, and ...
  • Feb 2018 | Doc #DR2018_T5_RJ | Conference: Tech Supplier

Quotes

“IDC has done the research and it shows that organizations across every industry are under threat, and the average percentage of traditional revenues that are at the risk of destruction and digital transformation it varies from about 11% for hospitality to about 29% for utilities…”

"Gone are the days when we just had structured data. Now we have a lot of unstructured content. We have semi-structured data, and with such huge volumes and distributed data sets, it's humanly impossible for us to go through files of data to gain timely insight. In leveraging AI, one can address the time to insights need. "

“…IT organizations can reduce or eliminate call centers by using ML to process and integrate incoming help desk calls routed to the right person for problem resolution within a single call.”

“…I think transformation is integral. Without the IT transformation, without the improvement in operational efficiency in IT automation, there's no way that the digital transformation can succeed. "

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.

GET STARTED WITH AYEHU INTELLIGENT AUTOMATION & ORCHESTRATION  PLATFORM:

News

Ayehu NG Trial is Now Available
SRI International and Ayehu Team Up on Artificial Intelligence Innovation to Deliver Enterprise Intelligent Process Automation
Ayehu Launches Global Partner Program to Support Increasing Demand for Intelligent Automation
Ayehu wins Stevie award in 2018 international Business Award

Links

Episode #1: Automation and the Future of Work
Episode #2: Applying Agility to an Entire Enterprise
Episode #3: Enabling Positive Disruption with AI, Automation and the Future of Work
Episode #4: How to Manage the Increasingly Complicated Nature of IT Operations
Episode #5: Why your organization should aim to become a Digital Master (DTI) report
Episode #6: Insights from IBM: Digital Workforce and a Software-Based Labor Model
Episode #7: Developments Influencing the Automation Standards of the Future
Episode #8: A Critical Analysis of AI’s Future Potential & Current Breakthroughs
Episode #9: How Automation and AI are Disrupting Healthcare Information Technology

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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