Get fast return on your intelligent automation in these 5 areas.

Get fast return on your intelligent automation in these 5 areas.

Without question, intelligent automation has made a significant and permanent impact on the tech world. This technology has essentially provided IT professionals with the ability finally accomplish what they’ve been challenged with for decades: maximizing performance while minimizing costs. Now, routine time-consuming tasks as well as complex workflows can be streamlined and handled almost exclusively without the need for human input. In short, automation has changed the face of IT for the better.

Still not convinced? Let’s take a closer look at five specific areas where intelligent automation can produce the greatest benefit and fastest return, both for your IT department and your enterprise as a whole.

IT Organization – The old way of doing business involved individual silos and separate departments. In today’s digital age, the most successful organizations are the ones that are able to harness technology to break down these barriers and bridge the gap between departments and functions, effectively promoting collaboration and fostering innovation. Intelligent automation can provide the tools necessary to achieve this type of organization.

IT Infrastructure – In order for a business to function cohesively across all departments, there need to be streamlined and standardized procedures and technologies in place. This includes best practices, virtualization, cloud computing and more. When this type of environment is created, intelligent automation can then provide the opportunity for your tech team to leverage their expertise and improve skill levels for the maximum benefit of the department and the company in its entirety.

IT Support – Support that is enhanced with intelligent automation can ensure roll-out, implementation and adoption of best practices. It can also improve processes, such as migrations, ensure more effective enforcement of compliance requirements and ultimately boost service levels across the board.

IT Operations – With more pressure being placed on IT professionals to not only manage their specified workflows, but also possess an in-depth understanding of overarching business practices, these workers must find a way to become better-equipped to meet these demands. Intelligent automation can help by allowing operations the optimal allocation of resources so that personnel can focus on furthering their education and expertise.

General Routine/Repetitive Tasks – Those every day, repetitive manual tasks that your IT department is buried under are inevitably costing your business money. Workflow and self-service automation options can alleviate this concern by taking the burden off the IT team. Not only can this vastly improve efficiency and productivity, but it can also cut costs in the long run.

With demands on IT professionals increasing at a rapid rate, the need for enhanced technological solutions has never been greater. Intelligent automation can help your organization meet these evolving needs so you can remain competitive and achieve ongoing success, both now and well into the future.

Curious to find out how your company could benefit from intelligent automation? Check out the top 10 automated processes below and get started with a free product demo today.

The Secret to Surviving the Tech-Led Revolution

The Secret to Surviving the Tech-Led Revolution

Automation has been at the forefront of the digital revolution for decades, primarily because it maximizes efficiency, reduces costs and accelerates service levels. But the cloud, mobile and other innovative technologies – coupled with an ever-growing volume of raw data – have led to dramatically more complex IT environments.

According to ESG’s IT Spending Intentions Survey from 2018, 68% of those surveyed said their IT infrastructures are significantly more complex than they were just two years ago. Furthermore, 39% of respondents listed automated IT operations as a critical component of survival in today’s digital age.

In response to this increasing complexity, organizations are beginning to make the shift toward the next generation of automation – from basic to intelligent. This new level of automation involves technologies like machine learning and artificial intelligence to orchestrate workflows across a multitude of tools, systems and processes.

In fact, with the right platform, it is now possible to fully automate L2 and L3 tasks – functions which have traditionally required the use of human judgment. Now, those insights lie within the data itself and can be extracted, interpreted and leveraged autonomously by AI.

Embracing intelligent process automation is also enabling enterprises to lay the foundation for AIOps, a focus area that experts predict will boom over the next five years or so.

AI and ML: Augmenting IT Operations

AIOps is helping IT teams manage the increasing challenges created by data and digital disruption, leveraging intelligent process automation and orchestration to gain competitive advantage. Thanks to the powerful processing capabilities of artificial intelligence, IT can sort through mind-boggling amounts of data points to find the proverbial needle in a haystack.

The role of humans in this increasingly tech-driven environment is still present, though it too is evolving. Rather than relying on error-prone employees to handle the bulk of the processing work, human cognition and advanced skillsets are being used to define that proverbial needle.

In response to this, more organizations are focusing their efforts on reskilling and upskilling their existing staff to bring them up to speed on ML and AI technologies.

Making the Switch to Autonomous Operations

Autonomous operations (AO) utilizes advanced AI to deliver unassisted responses to IT incidents across the entire infrastructure. Thanks to the self-learning capabilities of ML algorithms, AO is able to continuously improve its ability to identify patterns and carry out the appropriate actions.

Again, human workers are still needed in an AO-driven environment, but in the role of supervisor as opposed to operator. Yet as the software continues to evolve and improve, and as errors consistently decrease over time, full autonomy and a zero-touch IT operations environment will one day become a very real possibility.

The Role of Data

The key to success with intelligent automation is accurate data, as this enables users to write more impactful rules. There is little to no value in static data. These days, it’s all about dynamic information which comes from things like descriptive metadata as well as relational and behavioral data.

In order to harness this dynamic data and gain adequate insights from it, organizations need to develop software-defined IT environments. Intelligent process automation is about the ability to not only proactively identify anomalies, but to also remediate those issues automatically without causing any business disruption.

The Right Way to Automate Intelligently

In today’s competitive landscape, automation is no longer an option but a necessity. That said, there’s a right way and a wrong way to leverage this game-changing technology. Start by weighing the time, effort, complexity and frequency of a given task and then benchmarking these factors against the cost of transitioning that task to intelligent process automation. From there, create a prioritized list. This will help you maximize ROI and harness the full potential of intelligent IT operations.

Not sure where to start? Why not give intelligent process automation a test drive free for 30 full days? Click here to launch your Ayehu trial today.

5 Ways Intelligent Automation is Shaping the Future of Work

5 Ways Intelligent Automation is Shaping the Future of Work

In terms of disruptive technology, intelligent automation has gained tremendous ground. In fact, according to Statista, more than half of today’s business leaders say they expect to implement automation in the coming years. And for good reason. While technologies like traditional workload automation, cloud computing and Software-as-a-Service (SaaS) reduce costs and provide the flexibility to perform routine tasks and workflows, artificial intelligence (AI) brings these benefits to a whole new level with the capability of performing tasks that normally require human intelligence.

Intelligent automation software enables businesses to perform much more diverse and complex activities without the need for human intervention. Furthermore, thanks to machine learning algorithms, this type of platform is capable of learning and improving entirely on its own based on data from past experience. Artificial intelligence can also provide valuable insight and decision support for management. But how does all of this translate into actual, tangible return on investment? Let’s take a look.

Drastically Saving Time and Money

When a good portion of business processes are shifted from human to machine, the operation runs far more efficiently. Work is performed faster and more accurately, which equates to greater productivity and higher service levels. Fewer man hours results in tremendous savings for the organization. (In one recent case study, one global enterprise slashed man hours by 1,500 in less than a year simply by adopting intelligent automation. That reduction resulted in an overall savings of nearly $500k.)

Distinct Edge Over the Competition

Staying a step ahead of the competition is the key to success – especially in today’s global marketplace. Every company is chasing digital transformation and hoping to claim their spot at the head of the pack in their respective industry. The use of intelligent automation can facilitate this transformation, not only be streamlining processes, but by empowering human workers.

When the mundane tasks and workflows no longer require human input, employees are able to apply their skills, time and effort toward more important business initiatives. The freedom to be creative breeds innovation which can provide the competitive advantage companies are striving for.

Agility and Scalability

The ebb and flow of business has long been a challenge for organizational leaders. Scaling up as needed based on sudden changes in market demand is not only difficult, but it’s also quite costly. Conversely, in situations when finances are lean, such as during economic recessions, the ability to maintain an expected level of production on a limited budget is incredibly problematic.

The deployment of intelligent automation resolves both of these issues by enabling businesses to scale up or down at a moment’s notice. Seasonal or other business influxes can be met seamlessly thanks to the ability of software robots to take on some of the workload. And when it comes time to tighten the belt, automation can help skeleton crews operate as if they were fully staffed. Every business leader understands the importance of agility like this.

Maximizing Uptime

Another way intelligent process automation can deliver tangible benefits to a company is through improved system operability. According to Gartner, the average cost of IT downtime is $5,600 per minute. Due to variations in how businesses operate, experts estimate that on the low end, downtime can cost as much as $140k per hour, while at the high end, can run upwards of $540k per hour.

Regardless of which end of the spectrum a business happens to fall on, system outages can be, without question, downright disastrous. Enter intelligent automation and suddenly there’s an army of robots monitoring the infrastructure 24 hours a day, 7 days a week, 365 days a year. Furthermore, artificial intelligence is capable of identifying threats that could take days, weeks or longer for humans to spot. When incidents can be pinpointed quickly and the platform itself is capable of addressing and remediating those issues, downtime can be dramatically reduced and, in many cases, prevented altogether.

Data-Driven Decision Support

Because intelligent automation is powered by AI and machine learning, it is inherently capable of analyzing massive amounts of data and extracting value. Furthermore, AI-powered automation can then turn that data into actionable insights that can be utilized by business leaders to make better decisions.

Incorporating advanced business automation technology into the mix enables the analysis of overall organizational performance. With these intelligent analytics, business leaders can more effectively identify and implement the right approaches to achieve improved performance over the long-term.

Could your organization benefit from any of the above? If so, adopting intelligent automation should be on your list of priorities for the coming year. Get a jump start by taking Ayehu for a test drive today.

Episode #29: How Applying Darwin’s Theories to AI Could Give Enterprises the Ultimate Competitive Advantage – Cognizant’s Bret Greenstein

November 15 2019    Episodes

Episode #29:  How Applying Darwin’s Theories to AI Could Give Enterprises the Ultimate Competitive Advantage

In today’s episode of Ayehu’s podcast we interview Bret Greenstein –SVP and Global Markets Head of AI & Analytics for Cognizant Technology Solutions. 

Charles Darwin’s Theory of Evolution has long been applied outside biology, to domains such as medicine and psychology.  Evolutionary principles have also found applicability in the realm of artificial intelligence and machine learning via algorithms that have the ability to evolve. Ironically, over 150 years ago, Darwin described himself in almost algorithmic terms when he stated “I am turned into a sort of machine for observing facts and grinding out conclusions.” 

Leveraging Darwinian doctrine to optimize AI outcomes for clients consumes much of the day for Bret Greenstein, VP and Global Head of AI for Cognizant Technology Solutions.  After a 3-decade stint at IBM, Bret joined Cognizant to lead their Evolutionary AI program, which accelerates delivery of those optimal outcomes for a variety of use cases in a broad array of industries. Bret shares with us some finer points about Evolutionary AI’s workings, and the impact it’s having on enterprises today. Along the way we’ll discover why implementing AI & machine learning is going to re-prioritize the agenda for CIOs & CTOs, laying the groundwork for IT to transition from a cost center to an enabler of revenue growth. 



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 Bret Greenstein, Vice President and Global Head of Artificial Intelligence for Cognizant Technology Solutions, a global MSP with 270,000 employees worldwide. Prior to Cognizant, Bret spent over 30 years with IBM where he led their Watson Internet of Things Offerings and with a pedigree like that, and given his deep domain expertise in the fields this podcast focuses on, we absolutely had to have Bret on the show and he was gracious enough to take time out from his very busy schedule to join us today. Bret, welcome to Intelligent Automation Radio.

Bret Greenstein: Thank you very much, Guy. It’s a pleasure to be here.

Guy Nadivi: Bret, you’ve spent most of your career with IBM and then you left Big Blue to join Cognizant Technology Solutions, and Cognizant while being a large global firm with over a quarter million employees, probably isn’t one of the first companies that comes to mind when you think of AI.

Nevertheless, Cognizant is taking an interesting approach to AI by applying Darwinian principles to its machine learning efforts with what it calls Evolutionary AI. Now there are currently three basic machine learning paradigms, Supervised Learning, Unsupervised Learning, and Reinforcement Learning. What are the main principles behind Evolutionary AI and do you think it might become the fourth paradigm of machine learning?

Bret Greenstein: I do. You could think of this as a natural extension of Deep Learning and Reinforcement Learning as well. You can think of it as an extension of that. The key principles are, and we’re able to build models even from almost no data, bootstrapping a model itself, letting AI learn from whatever limited rules or limited data you have to begin to create a surrogate of your business. And then to use the surrogate to explore possible solutions and then improve the model. So this evolutionary technique, genetic algorithms, allow us to take a model that might be reasonably crude at first and then refine the model, improve it over time, and then to use simulations with the model to create optimal decisions.

And you’ll find that most learning techniques usually find strong local optimal decisions, or they can exhaustedly search an entire space for possible solutions, but it can take an almost infinite amount of time depending on the complexity of the space. We use an approach that allows us to do population-based learning. So as we’re exploring the solution space, using our surrogate models, all of the simulation, we can explore every possible opportunity in parallel. And then the population learning allows us to learn from every exploration, stopping ones that are inefficient or suboptimal and putting our focus on ones that are more optimal. If I could just for a second more, the result of that is we can often learn with less data in significantly faster time to get to the same, you know, optimal outcomes, or better.

Guy Nadivi: Interesting. Now, there’s been a lot of hype around AI the past few years and in some quarters even fear, and when I read about the accelerating developments taking place in the field, it reminds me of what the economist Rudiger Dornbusch once said about economics where things take longer to happen than you think they will, and then they happen faster than you thought they could. With that in mind, what do you see as some long promised AI capabilities that will be happening faster now than we thought they could?

Bret Greenstein: So you’re right, it is a combination of them. We’ve all been watching this space for our entire careers, always one day away from the big breakthrough, but it is accelerating and it’s accelerating because we’re finding that instead of AI being generally intelligent and replacing all of us as leaders in whatever we do, it’s actually extremely specialized intelligence and it’s in those specializations that it’s moving very, very fast.

There was only a few years ago where the idea of machine vision could recognize a dog or cat, or hot dog or not a hot dog, these kinds of very simplistic cases, and now it’s already generally recognized that image recognition can provide better diagnostics of x-rays and radiology than a human being, more accurate. It doesn’t replace it. What it does is gives the radiologists new tools to find things they might’ve missed, or to validate things they assumed or figure it out themselves. So in many ways the specialization of AI has progressed much faster than I think everyone thought.

Look at conversational AI. We all saw, Alexa and Google come out and we’re like kind of impressed that you can talk to a computer. That’s been a promise for decades, but you can already see the advancements with Google Duplex for example, where the language is so natural that it’s, you know, can’t be discerned from speaking to a person which is now requiring solutions to identify themselves as AI when you’re talking to them. Who would have ever thought all that discussion about Turing machines and we would actually have to have systems tell you it’s an AI so you wouldn’t confuse it with a person. That’s where we’re at now.

Guy Nadivi: Okay. Now those are some interesting use cases, so I want to go back for a moment to Cognizant’s Evolutionary AI initiative. Can you speak about some of the more interesting use cases your team has applied Evolutionary AI to, and the results you achieved?

Bret Greenstein:Of course. So let me, before I do that, let me just back up on really your opening. When you think of AI, AI has its technology foundation layers. What the APIs and services for natural language processing or image recognition, or other specific cases available through all the cloud providers along with the tools to build up an AI-based system and feed it with the data that’s needed.

These are generally provided through cloud services and cloud providers and the advancements in those are accelerating every day. Amazingly broad movement in technology. Then you’ve got companies like ours which apply AI to business problems. So we take the technologies available there, we apply our own IP use cases, industry knowledge, and then deliver outcomes. So that’s what makes us unique in the space of AI is that we have IP like Evolutionary AI. We work with the technologies from the cloud providers and application providers, and then we build new business processes and transform processes using AI.

So in that light, we’re using Evolutionary AI specifically in two areas. One is for people who’ve built deep learning models, neural networks of any type. They’ve generally been built by people, architected by people, optimized by people, which means you’re constrained by the number of data scientists, PhDs you have to work on a problem.

We can take Evolutionary AI and optimize and improve the architecture of models of neural nets to make significantly more accurate outcomes. So we’ve been using that in conversational AI, in image recognition, in other forms of machine learning patterns to build significantly better, optimize models off of whatever people built manually. So we’re going to take the work of data scientists and improve it. This helps them be more productive and to deliver higher accuracy.

The other thing we’re doing with it is we’re using Evolutionary AI to find optimal outcomes. So in a business process, if you’re trying to decide how to price things or where to put things on shelves or how to staff your business and you have different goals of revenue and profit and customer retention loyalty, we can take all those parameters and all those goals and use Evolutionary AI to find optimal outcomes, which are sometimes counterintuitive outcomes, which are a way for people to take it to the next level. A lot of AI is really good at predictive analytics. We’re raising this to be prescriptive to help business decision makers know what to do to get the very best possible outcome.

Guy Nadivi: So, with the idea of prescriptive AI in mind, right, what do you think are going to be some of the biggest disruptions we’ll see in three, five, or 10 years from now with respect to automation, AI, and machine learning?

Bret Greenstein: I think it’s going to be the access of AI to business decision makers on a broad scale. While the technologies themselves will get faster, we’re going to see continued improvements in performance in cloud based on a GPU and CPU performance and virtualization capabilities. You’re going to see significantly better performance for raw execution of models. But the faster pace, and of course you’re going to see improvements in data science and the tools to accelerate the creation of models, the pipelining of data, the scaling out of AI services. But we’re going to move from an era where a small number of people, data scientists, data engineers, other experts are creating AI, to 10 or a hundred times or a thousand times more people who will be using AI, using AI to make real business decisions every day.

So, we worked with quite a few analysts and they consistently predict more than 100% growth every year of the number of projects each client is working on in AI. And that’s because it starts out as one project and then you realize I can apply this more generally and it gets broader and broader, but you’re also seeing it more accessible.

So today it’s still a lot of work to build, going back to that retail example, a store optimization engine using evolutionary AI. It’s still a fair amount of work involved in that, but these things will be packaged up and become more repeatable at which point any store manager anywhere should have access to this. Being able to feed it the data, their historic data, their supply data, their staffing and resource and cost data, and local data and get optimal models. So I think in the next few years we’re going to see AI being useful by business leaders the way that spreadsheets became useful, it’s just going to happen significantly faster.

Guy Nadivi:And so with that growing ubiquity, whenever we have an AI expert like yourself on the show, I always like to ask them the following question. Over the long term, do you think that AI and machine learning will ultimately augment more people or replace more people?

Bret Greenstein:So I think it’s going to create more jobs because it creates more value and as long as human beings run businesses and create value in market, which means grow the economy, it creates more opportunity, new jobs, new style of jobs. Now there’s definitely going to be individual jobs which will be able to be done by AI, some augmented by AI and some that can’t be replaced. And so I think for individuals looking at constant education is really important. We built a data science academy within Cognizant so that all of our employees have access to training and skills so they can continue to learn new skills and grow as AI becomes a part of how we all do our jobs. So, and I think a lot of companies are going to have to consider how they do in continuous education of their workforce as they embrace AI.

It also creates all kinds of new jobs. There was never a time long ago where you had people cleaning up data to the degree we’re going to be. Managing data, its integrity, cleaning it up, bringing new insights, creating data marketplaces, this is all new stuff. As we move from data warehouses to data lakes and modern data architectures, this is all because of AI and then the roles in AI. For example, we have conversational designers in our AI team. That was not a career anyone could choose in college. That didn’t exist a few years ago, but now we have people who design amazing customer experiences using conversational AI and they’re like the web masters of natural language and it brings all kinds of new design skills and social and psychological skills in building great user experience and doing it with conversation.

Guy Nadivi: Okay, now you just spoke a lot about the data driving AI and machine learning, but the underlying models AI solutions are based on are built upon algorithms, and late last year a Harvard Business School published an article calling for the auditing of algorithms the same way companies are required to issue audited financial statements, public companies anyways. Given that AI developers can incorporate their own biases into algorithms, even unintentionally or unconsciously, what do you think about the need for algorithm auditing?

Bret Greenstein: So I think it’s a little more subtle than what you described and that is when people do auditing of anything, they audit results. They don’t always audit the algorithms, although they can. I think in the case of AI you’re looking at systems where … I don’t think you should be looking at neural network designs to figure out is it fair or not, but I think you should be accountable for the output generated and whether it’s providing biased outputs and biased results.

There’s already technologies which some we’ve built, some of the cloud providers have built that are for bias detection and remediation where you can compare the output of an algorithm to the expected distribution and assess whether it’s created some inherent bias. There’s also more subtle forms of bias that can be incorporated that are harder to detect, but I don’t think this is something where you crawl through the code or crawl through the algorithm the same way you might think. I think this is one where you have to look more at the output and the behavior of systems rather than the code itself. The algorithm, the data that feeds it, the fine tuning the parameters, all can influence the bias of an overall system. The same way that human beings can be inherently biased. You can hire someone who seems brilliant, great resume, and they’re your recruiter for you, and they just have a bias you couldn’t see.

You’ll know it if you look at their output, but you wouldn’t know it if you tried to inspect their brain. So inspecting the brain of an AI is probably not the best approach, but looking at the output, understanding how the system is behaving, having testing criteria and design in principles around how you manage it and govern it over time is probably the best way for us to recognize and reduce bias. In addition, introducing a diversity among the people who create systems. Also, looking at the time frames of data. A lot of times people are training systems on very old data, which is itself inherently biased because people were more biased years ago. And so you have to really look at a few of these principles when you’re designing systems to make sure you’re looking at it with fresh eyes.

Guy Nadivi: Interesting perspective. There’s a lot of excitement, Bret, about AI for young people entering the workforce and even for more established professionals thinking about a career change. I’m curious, what kinds of skills does Cognizant covet the most when hiring talent for automation, AI, & machine learning?

Bret Greenstein: So there’s the core skills around the ability to create algorithms and models and to operate businesses using AI as well as the data engineering in order to make data available on the forms needed for AI. So those skills are all extremely hard. Those are the creators. But increasingly we’re looking for people who are AI aware with business point of view because ultimately we’re transforming business processes, call centers, supply chains, retail store operations, diagnostics in healthcare, insurance underwriting, reconciliation and banking. Those are not data science skills. They require data science, but they also require a subject matter and domain expertise in those processes. So I’m very excited to start it, to see universities and online education and graduate degrees beginning to be aimed at, I’ll call it AI awareness, AI understanding for business people, not just for technologists. And so as we become algorithmic thinkers, people who can recognize the value in the use of algorithms and data for your business, that skill is going to become extremely important.

And I don’t think most companies should delegate their AI transformation to data scientists or technologists. It’s really going to come down to business leaders who understand the value of this and how it works and what it means. I’ll give you a metaphor. When the internet came out, most CMOs knew they had to do something, so they took their catalog and they stuck it on the web and they called it a day. And those companies are mostly out of business. There were other people, webmasters, HTML geeks and others who recognized that the web could be the front end of business and they created companies like Amazon and others that wouldn’t have existed otherwise.

And so those people saw the web for what it was, which is a form of interaction, information sharing, and engagement and they transform business around it. So we need people who see business in terms of the data that make up the business and what algorithms could do with it. Those people will transform every business process.

Guy Nadivi: So there are concerns cropping up about the misuse of AI machine learning. And I’m curious, Bret, if you see any economic, legal, or political headwinds that could slow adoption of these advanced technologies, or is the genie out of the bottle at this point to an extent that they just can’t be stopped and perhaps not even effectively regulated?

Bret Greenstein: There are really strong lessons to learn from the past on this. And I think the idea of saying the genie’s out of the bottle, it can’t be stopped is irresponsible. And a lot of that happened in the web and it led to companies taking advantage of or abusing public trust on the web, which led to privacy and other implications and now a lot of backlash. I think all of us need to learn from how technology can get ahead of policy and good judgment, and I think we’re already seeing that in terms of AI where there’s significantly more talk around data privacy, on ethical and responsible AI, and a lot of talk at the government level. We’ve been involved with the World Economic Forum for example, as well as we recently speaking at, I spoke at a conference for Politico specifically around responsible and ethical AI and it’s implication on the workforce.

I think it’s getting discussed because I think we’re all smarter than we were and I think we recognize collectively that this is not something you just unleashed and see what happens. These are specialized skills and capabilities. They transform business. There’s implications to it and we have to go ahead in recognizing this needs to be regulated, it needs to be managed. Companies need to be responsible and how they manage it. We have a, for example, a council for responsible AI at Cognizant specifically to make sure we have cross functional leadership looking at the projects we do, the projects we don’t do, and then we’re thinking about how we use it to help and support our brand.

And if there’s anything to be learned from recent history, it’s that brands rely on how they’re perceived by the public. The use of AI can enhance your brand or hurt it, just like the use of any other technology. And so we’ve been spending a lot of time with clients discussing that and helping them to see their way through it.

Guy Nadivi: Overall, Bret, given your high-level perspective, what makes you most optimistic about AI & machine learning?

Bret Greenstein: I think the people who work on it are so much less hype than the marketing. The people who actually do the work are very grounded in what it’s good at and what it’s not. You’ll often hear media or other people talk about general intelligence and robot apocalypse and all that stuff. And that’s fun to talk about because it’s pop culture kind of stuff, but when you get down to it and talk to real data scientists, they’re not worried about that.

They’re focused on accuracy of algorithms, improving data, getting more forms of data to work with, how they build ecosystems of insights, obviously regulations. They’re thinking in very grounded terms and I think business leaders are embracing this. I haven’t met a business yet who doesn’t have some degree of investment, and they’re all trying to figure out how do they responsibly get into this? How do they do projects that deliver business outcomes, not just experiment?

And that feels very different to me than the dotcom bubbles and hype that came around from “everyone must be on the web”. I don’t think people generally believe everyone must use AI. They’re trying to figure out where it applies, where it delivers value and they’re focused on that. So we don’t get asked to do a lot of frivolous projects. We get asked to do things that deliver real outcomes, and I think projects grounded in outcomes are going to be the way that all companies embrace this responsibly.

Guy Nadivi: Bret for the CIOs, CTOs, and other IT executives listening in, what is the one big must have piece of advice you’d like them to take away from our discussion with regards to deploying AI & machine learning at their organizations?

Bret Greenstein: While data is something that’s important to be managed, almost all the customers of IT have told us they need access to data, and not just the data that runs the business, but all the data about the customers, supply chain, environments they operate in. So local data, geospatial data, social data, IOT data, all the other stuff that CIOs may not have had to manage historically are now relevant to AI-based systems for business.

And so embrace the fact that data accessibility and the forms of data is going to be a never ending agenda for you now, and we can’t just manage it and protect it. We’ve got to really unlock it, in ways that reach the business needs. And then the business leaders themselves are going to continue to ask for more, more help on data science, more help on governance, more help on access to data. And this is part of the new normal. I don’t think it can be controlled under one person. I have not seen that pattern happen very often. It seems to be pockets all over companies and we have to figure out ways to help them, govern them, steer them, enable them, without limiting them.

Guy Nadivi: I think that’s going to be a real eye opener for a lot of the CIOs and CTOs listening in.

Bret Greenstein: I think so. If I could just for a second more … So many CIOs are transitioning and pivoting to this already. So, they’re driving agendas for data modernization, creating a much more modern architecture for access to data across their enterprise. And so that’s being driven from CIOs as well as Chief Data Officers, which are sometimes in the CIO office. I think that’s great. I think it shows the connectedness between the CIO and some of the business buyers who are driving an AI agenda. That kind of teaming moves IT from being a cost to an enabler of revenue and growth. And I think that’s probably the best thing that could happen to the world of IT.

Guy Nadivi: All right. Well, it looks like that’s all the time we have for on this episode of Intelligent Automation Radio. Bret, it’s always great interviewing someone with the kind of deep domain expertise you possess, because that usually means we’re going to learn something intriguing and you definitely didn’t disappoint today. Thank you very much for coming on the show. It’s been great having you on.

Bret Greenstein: Thank you so much, Guy. It was a pleasure for myself as well.

Guy Nadivi: Bret Greenstein, Vice President and Global Head of Artificial Intelligence for Cognizant Technology Solutions. Thank you for listening everyone, and remember, don’t hesitate, automate.



Bret Greenstein

SVP and Global Markets Head of AI & Analytics for Cognizant Technology Solutions. 

Bret Greenstein is Global Vice-President and Head of Cognizant’s Digital Business AI Practice, focusing on technology and business strategy, go-to-market and innovation helping clients realize their potential through digital transformation.  

Prior to Cognizant, Bret led IBM Watson’s Internet of Things Offerings, establishing new IoT products and services for the Industrial Internet of Things. He built his career in technology and business leadership across a range of roles throughout IBM in software, services, consulting, strategy and marketing, and served as IBM’s CIO for Asia-Pacific. He has worked globally in these roles, including living in China for five years, working with clients and transforming IBM’s IT environment.  

Bret holds patents in the area of collaboration systems. He holds a bachelor’s degree in electrical engineering and a master’s degree in manufacturing systems engineering from Rensselaer Polytechnic Institute.  

Bret can be reached at: 

Email:                              bretgreenstein@gmail.com

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

Twitter:                            https://twitter.com/bretgreenstein

Quotes

“We've all been watching this space for our entire careers, always one day away from the big breakthrough, but it is accelerating and it's accelerating because we're finding that instead of AI being generally intelligent and replacing all of us as leaders in whatever we do, it's actually extremely specialized intelligence and it's in those specializations that it's moving very, very fast.” 

"There was only a few years ago where the idea of machine vision could recognize a dog or cat, or hot dog or not a hot dog, these kinds of very simplistic cases, and now it's already generally recognized that image recognition can provide better diagnostics of x-rays and radiology than a human being, more accurate. " 

“Look at conversational AI. We all saw, Alexa and Google come out and we're like kind of impressed that you can talk to a computer. That's been a promise for decades, but you can already see the advancements with Google Duplex for example, where the language is so natural that it's, you know, can't be discerned from speaking to a person which is now requiring solutions to identify themselves as AI when you're talking to them. Who would have ever thought all that discussion about Turing machines and we would actually have to have systems tell you it's an AI so you wouldn't confuse it with a person. That's where we're at now.” 

“…we're going to move from an era where a small number of people, data scientists, data engineers, other experts are creating AI, to 10 or a hundred times or a thousand times more people who will be using AI, using AI to make real business decisions every day.” 

“…we need people who see business in terms of the data that make up the business and what algorithms could do with it. Those people will transform every business process.” 

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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Ayehu Automation Academy is Now Available

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
Episode #11: Key Metrics that Justify Automation Projects & Win Budget Approvals
Episode #12: How Cognitive Digital Twins May Soon Impact Everything
Episode #13: The Gold Rush Being Created By Conversational AI
Episode #14: How Automation Can Reduce the Risks of Cyber Security Threats
Episode #15: Leveraging Predictive Analytics to Transform IT from Reactive to Proactive
Episode #16: How the Coming Tsunami of AI & Automation Will Impact Every Aspect of Enterprise Operations
Episode #17: Back to the Future of AI & Machine Learning
Episode #18: Implementing Automation From A Small Company Perspective
Episode #19: Why Embracing Consumerization is Key To Delivering Enterprise-Scale Automation
Episode #20: Applying Ancient Greek Wisdom to 21st Century Emerging Technologies
Episode #21: Powering Up Energy & Utilities Providers’ Digital Transformation with Intelligent Automation & Ai
Episode #22: A Prominent VC’s Advice for AI & Automation Entrepreneurs
Episode #23: How Automation Digitally Transformed British Law Enforcement
Episode #24: Should Enterprises Use AI & Machine Learning Just Because They Can?
Episode #25: Why Being A Better Human Is The Best Skill to Have in the Age of AI & Automation
Episode #26: How To Run A Successful Digital Transformation
Episode #27: Why Enterprises Should Have A Chief Automation Officer
Episode #28: How AIOps Tames Systems Complexity & Overcomes Talent Shortages

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Twitter: twitter.com/ayehu_eyeshare

LinkedIn: linkedin.com/company/ayehu-software-technologies-ltd-/

Facebook: facebook.com/ayehu

YouTube: https://www.youtube.com/user/ayehusoftware

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

How AI Can Reduce Service Desk Ticket Costs from $20 to $4 [Webinar Recap]

Author: Guy Nadivi

It’s the End of the IT Service Desk as We Know it (and We Feel Fine)

If you’ve been paying attention the last few years, you know Digital Transformation is a concept that’s sweeping through many organizations, and fundamentally changing how they operate and deliver value to customers.

There’s some very cool, but still somewhat emerging technologies underpinning this disruption, and you’re no doubt familiar with them. Things such as:

  • Data Science
  • Machine Learning
  • Artificial Intelligence

But in the last couple years, the emerging technology that seems to have garnered the most mindshare faster than any of them is chatbots. That’s right! Chatbots are the coolest kids on the digital transformation block, because they assimilate many of the benefits from data science, machine learning, and artificial intelligence into a form that can be used today, and deliver value to your organization and customers right now. As a result, chatbots have emerged as perhaps the most familiar digital transformation experience for end users.

BTW – There isn’t any consensus yet on a single definition of “Digital Transformation”. One thing just about everyone can agree upon though is that shifting more of the laborious, repetitive tasks that people shouldn’t be doing in the first place over to chatbots is a good idea. This becomes especially true when you look at some numbers.

A the 2017 HDI show, Jeff Rumburg, Co-Founder and Managing Partner of MetricNet, an IT research and  advisory practice, delivered a presentation on the results of his research into the costs of different service desk access and communication channels. He discovered some amazing disparities.

Jeff found that incidents requiring Vendor Support cost on average a whopping $599 per incident.

If you needed to get IT Support involved (that’s level 3 support), the average cost was $104 per incident.

Desktop Support (level 2) was cheaper, but still relatively expensive at $69 per incident.

Incidents going through the Service Desk, your level 1 support tier, cost $20 per incident. Since level 1 tickets comprise by far the highest volume at most service desks, that’s a logical place to start applying chatbots.

If you can push out incident resolution for level 1 tickets to your end users, enabling them to initiate and remediate their own incidents with chatbots, the cost of support drops down to a very economical $4 per incident. Yeah, wow!

At this point, some more skeptical people in IT might be asking – are chatbots a passing fad or are they here to stay? Let’s look at the objective data on that, and see what direction the numbers point to.

Earlier this year, Salesforce.com released a major report entitled the “State of Service”. Nearly a quarter of their respondents (23%) said they currently use AI chatbots and nearly another third (31%) said they plan to use them within 18 months.

That represents a projected growth rate of 136% in the use of AI chatbots over the next year and a half. By any definition, that’s a viral trajectory.

Spiceworks published a report not long called “AI Chatbots and Intelligent Assistants in the Workplace”.

One question their survey asked was about utilization of intelligent assistants and chatbots by department. Guess which department uses chatbots more than any other? That’s right – IT.

Another question in that Spiceworks survey specifically asked IT professionals if they agree or strongly agree with a number of different statements. The statement IT professionals overwhelmingly agreed with more than any other was that AI will automate mundane tasks and enable more time to focus on strategic IT initiatives.

Those IT professionals Spiceworks surveyed were right. One of the biggest benefits of chatbots is that they automate many of the robotic, laborious tasks that humans shouldn’t be doing anyway. That frees up those IT professionals to work on more strategic and far more valuable IT initiatives. Which in turn makes those professionals more valuable to their organizations.

Why is offloading that tedious work from IT staff so important? Because Gartner has shown that the biggest budget item for IT Service Desks is personnel. Between 2012 and 2016, the average percentage of a service desk’s budget allocated to labor ranged from 84% – 88%. With digital transformations driving up the demand for IT support, there’s simply no way an organization can hire their way out of this situation, even if they wanted to.

The reality is that quality service desk personnel simply cost too much, and no matter how good those personnel are, they can only keep up with so much volume. At some point the laws of physics reassert themselves, reminding everyone that people simply don’t scale very well. Chatbots though, have infinite scalability.

That limited human capacity to scale, combined with the increased volume of requests for service desk support, is degrading end user experiences.

A 2016 Harvard Business Review Webinar titled “How to Fix Customer Service” revealed that:

  • 81% of consumers say it takes too long to reach a support agent.
  • 43% of customers try to self-serve before calling a contact center.

What that tells you is that waiting for human support has gotten so insufferable, end users are increasingly willing to remediate their own issues. All they need is for IT to enable a channel for them to do that.

What kinds of requests are keeping IT service desks so busy?

Well if you’ve attended any of our previous webinars you might’ve heard us cite a well-quoted statistic from Gartner that as much as 40% of an IT service desk’s call volume is nothing but password resets. 40%!

Another big drain on your service desk? Requests for ticket status updates. Those can comprise as much as 10% of a service desk’s call volume, and we’re citing ourselves (Ayehu) as the source on that.

How do we know? Well, Ayehu knows because our clients tell us which workflows have the biggest impact on reducing call volume to their service desks.

Therefore, if you can use a chatbot to automate just these two processes – password resets and ticket status updates – you could cut call volume to your service desk in half! That’s huge, and it will go a long way towards reducing your service desk ticket costs dramatically.

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The future of work is here. Using intelligent automation to work SMARTER, not harder.

intelligent automation - work smarter not harder

Process automation is most often leveraged as a tool to replace the need for humans to perform routine, repetitive manual tasks. Beyond this, however, intelligent automation powered by machine learning capabilities can be leveraged to continuously optimize work and improve business outcomes. Imagine what your organization could do if all the simple but necessary daily tasks were shifted from humans workers to software robots. Now imagine how you could scale up or down instantly based on workflow demands, without the expense or hassle of adjusting personnel numbers.

Regardless of where you stand on the concept of intelligent automation, it is widely acknowledged by experts across every industry to be a central component of the future of work. Business leaders and decision makers view intelligent automation as highly beneficial due to its ability to maximize output while also minimizing costs. On the other side of the coin, however, many front-line workers are resistant to automation as they view artificial intelligence as a threat to their future and livelihood.

The biggest question that plagues businesses still remains whether or not intelligent automation will eliminate the need for human workers altogether. There is no easy or straightforward answer to this question. In reality, some jobs will inevitably be eliminated. In fact, there are certain industries, such as banking, in which AI has already had a significant impact on staffing levels. The fact is, job elimination is not the goal of automation. Rather than replace humans, this technology should be viewed as a tool for augmenting them (more on that here).

The goal of intelligent automation is to create a better user experience at every level. From an employment standpoint, it’s about leaving the grunt work for the computer to handle and leveraging artificial intelligence to analyze and use data for improving operations. Increased internal efficiency leads to cost savings and ultimately a higher level of employee satisfaction. Externally, tools like virtual support agents, proactive problem solving and faster resolution will dramatically improve customer engagement and happiness.

For now and well into the foreseeable future, intelligent automation should not be seen as a threat, but rather something that will free up intelligent and experienced workers to be able to apply their creativity and problem-solving skilled to more important issues that cannot (yet) be handled by technology alone. Beyond this, the increased adoption of automation will also open up new roles that did not exist 10 or even 5 years ago. So while some jobs will become obsolete, other opportunities will replace them.

In today’s highly-competitive world, organizations of every shape, size and industry are battling to stay ahead of the curve and remain profitable. Intelligent automation provides the solution, allowing businesses and their employees to work smarter, thereby improving their chances of achieving continued success well into the future.

Is your staff working as smart as they could be? Learn how automation can be the key differentiator. Launch your free product demo and experience the power of artificial intelligence for yourself today.

Ayehu

Ayehu’s New Advanced Features in NG v1.5 [Webinar Recap]

Author: Guy Nadivi

In response to growing user requests to add more flexibility to the Ayehu NG automation platform, Ayehu has released NG v1.5. This release will significantly expand the scope of what you can automate in your environment, all from a single pane of glass, and we think that makes it a real game changer in the IT orchestration and automation market.

If you’re an existing user of Ayehu NG, or even if you’re just thinking about trying us on for size, you probably know that one of the core strengths of our solution is how easy and quickly you can plug Ayehu into various ITSM platforms, cyber security tools, operating systems, messaging and notification tools, and increasingly chatbots and AI services. Almost all of these integrations can be activated seamlessly without writing a single line of code.

And the purpose of providing you with all these pre-built integrations and connectors that make up our ever-expanding ecosystem, is to simplify your ability to orchestrate automation across any platform in your environment. All from a single pane of glass!

So, here’s what’s really exciting about this new version of NG. We’ve added a “Do It Yourself” capability to allow you to build your own platform-specific activities without the need for Ayehu to do it for you.

From the feedback we’ve received, that’s really going to appeal to those of you who aren’t afraid to roll up your sleeves, do a little coding, and craft your own specific intelligent IT automation activities.

In fact, when you see how easy we’ve made it to build your own activities, we think some of you non-coders might even be tempted to take a crack at it yourself and perhaps fulfill some aspirations on your personal automation wish list.

Without further ado then, let’s dive into what’s new in our latest release of NG, v1.5:

  • Activity Designer – This is the big one. It’s a new feature designed to give users the option to build their own activities, which marks the first time they’re not relying on us to build an activity. You already know we provide an Out-Of-The-Box library of more than 500 no-code, pre-built activities. With the Activity Designer though, customers can now independently develop or modify existing activities in Python, C# or .Net to extract further value through customization that meets specific needs.
  • GitHub Community Repository – Ayehu now has a new community on GitHub that contains more than 100 of Ayehu’s workflow templates, as well as source code for built-in activities. Customers can use this in conjunction with the Activity Designer to create custom activities based on existing pre-built workflows. The GitHub Community Repository also provides free access to other peer-developed workflow templates and activities which have already been created and contributed to the community. 
  • Ayehu Academy Advanced Courses – We now have two new Ayehu Automation Academy courses – Activity Designer Essentials and Advanced Activity Designer. Together, these courses help train and certify developers in creating new activities using the Activity Designer. The Academy has already certified nearly 1,000 IT automation engineers since its inception earlier this year.

Let’s talk a bit more about the Activity Designer.

Typically, when building a workflow you simply drag and drop activities onto a canvas, and position them in the order you want them to execute. There’s no coding, scripting, or programming of any kind required. All you have to do is configure any particular activity by entering some parameters into a popup window, as shown in the image below:

With the new Activity Designer, you can build your own activities from scratch, in Python, C#, or .NET. We believe this will typically be for a system we haven’t integrated yet, perhaps some home-grown in-house application. But it can also be used to create new custom activities for an existing integration, like ServiceNow or SolarWinds. This is a big deal because now organizations will be able to take previously unintegrated systems and incorporate them into enterprise-wide orchestration and automation via Ayehu’s single pane of glass. The Activity Designer interface is shown in the image below:

Ayehu’s GitHub Community Repository marks an expansion of our presence on GitHub’s open-source community, and can be seen at this link: – https://github.com/Ayehu

At the repository, you’ll find:

  • 100+ Ayehu workflow templates
  • Source code for built-in activities

There are many benefits to our users from this new repository, including:

  • Shorter time to value through reuse of existing, pre-built workflows
  • Shorter time to value thru customization of open source activities
  • Free access to peer-developed workflow templates and activities

Here’s an example. If we want to see what kinds of workflow templates are already available for Cisco devices, we can just click on the Cisco category, and drill down to all the workflow templates you can access that are Cisco-specific, as seen in the image below:

These new features are also accompanied by new advanced courses created for the Ayehu Academy, which can be found on our website ayehu.com under the Customers menu.

The two new Ayehu Automation Academy courses are:

  • Activity Designer Essentials
  • Advanced Activity Designer

Together, these courses help train and certify developers in creating new activities using the Activity Designer. 

Ayehu recommends getting certified because your new knowledge will enhance 2 areas of interest:

  • Your organization’s automation capabilities
  • Your own personal professional standing.

Furthermore, as this market continues to grow, we anticipate new income opportunities will be created for Certified Activity Designers. The Academy has already certified about 1,000 IT automation engineers despite only opening earlier this year. That’s a reflection of the growing interest in automation, and if you’re one of those IT automation engineers, you’ve positioned yourself very nicely for the growth curve ahead.

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How Automation Levels Up AIOps

automation levels up AIOps

In today’s increasingly complex digital environment, the ability to pinpoint, resolve and mitigate potential IT problems has never been more critical. And with a hybrid blend of public and private cloud, on-premises and virtual servers, a growing variety of mobile devices and a skyrocketing volume of network and application traffic, it’s also never been more challenging. To address this significant concern, organizations are turning to artificial intelligence for IT operations – or AIOps for short.

The term AIOps encompasses the use of advanced data analytics technologies, such as AI and machine learning, to automate the process of identifying and remediating performance issues. AIOps leverages the colossal volume of data generated by IT services and systems to proactively monitor the infrastructure and gain complete visibility over all system and application dependencies. These advanced capabilities enable AIOps to manage and address potential problems, often before they occur.

Organizations put AIOps in place to gather and analyze all IT operational data and simultaneously automate all main IT operations. The AIOps system then organizes and prioritizes that data, presenting it to IT managers so they can react accordingly. In short, AIOps provides IT decision-makers with the insight they need to stay a step ahead of IT operations. Gartner predicts that by 2023, the use of AIOps will increase from 5% to 30%.

The Key is Automation

The most critical component to a smooth and efficiently run AIOps is automation. This technology helps AIOps to perform ongoing monitoring while adhering to predetermined policies and dependency mapping and quickly and effectively carry out the steps necessary to resolve events or failures.

With all of these technologies operating in tandem, and automation at the center, AIOps can ultimately help to reduce the volume of potentially damaging events, provide proactive alerts to issues that could cause an outage, pinpoint the root cause of those issues and apply intelligent process automation to autonomously remediate.

AIOps is capable of increasing the effectiveness of infrastructure resources, streamlining and expediting service requests and problem resolution, and ultimately generating consistent, measurable value from its ability to support current and future business initiatives.

The Benefits of AIOps

Harnessing the power of automation in combination with AIOps delivers a multitude of benefits for IT. Firstly, it can dramatically enhance and improve the effectiveness of existing tools and services. And since it saves time while also increasing efficiency and productivity, organizations employing AIOps can also realize a decrease in overall expenditure.

Likewise, AIOps can also reduce the amount of time and effort currently required to manage service requests and remediate performance issues and outages. All of this adds up to improved service levels, a significant reduction in risk, and a quicker time-to-market for new initiatives.

Automated AIOps runs on a 3-phrase approach:

  • Identify
  • Analyze
  • Respond

In other words, it monitors the environment to detect any potential anomalies or concerns, then analyzes, validates and prioritizes those potential events before finally determining the best course of action to take to address the issue at hand. While this last step may involve escalation to a human decision-maker, in most cases, these steps can all be carried out without the need for human intervention. Therein lies the true value of AIOps.

To learn firsthand how AIOps can help position your organization for future stability and sustainable success, try it yourself for 30 days. Click here to start your full-feature trial of Ayehu NG today.

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Episode #28: How AIOps Tames Systems Complexity & Overcomes Talent Shortages – LogicMonitor’s Gadi Oren

November 1 2019    Episodes

Episode#28: How AIOps Tames Systems Complexity & Overcomes Talent Shortages 

In today’s episode of Ayehu’s podcast we interview Gadi Oren – Vice President of Technology Evangelism for LogicMonitor

It’s been said that the enemy of execution is complexity.  Nowhere has this rung truer than corporate data centers, where growing systems complexity threatens the execution of digital transformation strategies.  How can enterprises successfully transition to this new digital business paradigm, without succumbing to the hazards that can jeopardize increasingly complicated IT environments?  According to one expert, the answer is AIOps.   

In this episode, we speak with Gadi Oren, Vice President of Technology Evangelism for LogicMonitor.  His company recently conducted a global survey of 300 senior IT executives and found that 90% of them had experienced a serious outage in the past 3 years.  A shocking figure which LogicMonitor directly attributes to the rising complexity of IT infrastructures.  Gadi shares with us how the emerging field of AIOps can counteract this negative trend, mitigate industry talent shortages, and ultimately reshape the future of data centers. 



Guy Nadivi: Welcome, everyone. My name is Guy Nadivi and I’m the host of Intelligent Automation Radio. Our guest today is Gadi Oren, Vice President of Technology Evangelism for LogicMonitor, a SaaS-based performance monitoring vendor that serves up 70 billion metrics per day to an extensive list of global clients. All that enterprise-grade monitoring also produces massive data sets that are fueling automation, artificial intelligence, and machine learning in the emerging field of AIOps. Since digital transformations often start in IT, many organizations increasingly view AIOps as a foundational component for their digital transformation strategy. Given Gadi’s extensive expertise in this domain, we invited him to come on the show and help us better understand how AIOps can add value to an enterprise and perhaps also make them more competitive. Gadi, welcome to Intelligent Automation Radio.

Gadi Oren: Guy, thank you for having me.

Guy Nadivi: Gadi, the first question I think our listeners would want me to ask you is, how do you define AIOps?

Gadi Oren: So that’s a great question. It’s actually a very large one. There’s multiple definitions to what AIOps is. We used to work, at the beginning, with a definition that’s saying “…any advanced algorithm that can be used to create a sustainable and stable value for the customer, in terms of making their operations work a lot better.” Any one of those algorithms is relevant. That includes also artificial intelligence and machine learning type of algorithms. However, there’s a lot of people that just don’t understand what that means. So more recently, we’re trying to explain what we think about AIOps is by just trying to tell a story about what is it that we’re building? What it is that we do and use terms that help customers understand in a more simplified way.

So we tend to look at AIOps … certainly one of the main things that it will do is, with LogicMonitor is something we call an early warning system. So an early warning system is something that can understand when something is about to happen, something that may have a negative impact, and letting you know about it and giving you really strong information about what’s going to happen while you can still do something about it and prevent it or improve the situation. So the early warning system, we kind of break it into roughly three parts. There’s three parts to it. One is understanding what’s going on and sifting out the noise from the signal, if you will. The signal could be anything like metrics. It could be logs. It could be anything, but understanding the things that are important for you to understand versus the things that just are not material right now. Then these things will create alerts or incidents or things you need to know about.

Then the second part of it is making those alerts into more actionable type of information. When something is more actionable, it could be that you know enough in order to do something about it or it could be the system will propose what you can do in order to solve it or giving you contextual information that will help you understand what does it all mean. So that’s the second piece of it.

The last part of it is remediation or prevention. So that part is, once you know what’s going on and you have actionable information about it, what could this early warning system … What could it do in order to change it or prevent it from happening enough time in advance to make a difference? So when we … We tend to … We get some good responses on the notion of early warning system. It’s something that people tend to understand much better than, a list of buzz words and descriptions, very technical descriptions.

Guy Nadivi: I imagine having a high quality signal to noise ratio requires sifting through a lot of data. So I’m curious, how much data, from logs and elsewhere, does an AIOps tool need to ingest and analyze before it can start churning out meaningful predictions?

Gadi Oren: Right, so that’s a great question. It’s almost a question of, if I buy into that, how quickly will I get the value? Well, there’s not one answer to that. It actually depends, because the field of AIOps is composed of so many discrete problems that are being solved that there’s multiple answers to that. Some of the problems where AIOps or the implementation of AIOps will start giving you value, will happen very, very quickly. For example, part of what we do with AIOps is we build topology of the IT and relationships between different objects. That is being done actually very, very quickly. It would take minutes to discover that and learn what are those relationships are, probably less than 15 minutes and you will start seeing certain types of value as soon as that, within minutes to, let’s say 30 minutes.

Other types of problems, let’s say things that relates to abnormal behavior or some people call it dynamic thresholds. Those are things that will take longer to get into value and for the system to learn, because the system needs to look at the IT for a day or two in order to understand what is normal for those two days, and then in the third day maybe will be able to tell you, “Look, this is not what’s happening. This is significantly different than what happened in the last two days.” So there’s a whole category of problems that the more data we see, the better the predictions will be. You will probably get a reasonable prediction after a day or two, but after a week or after a month, those predictions will go and become better. You also have to add on top of that the fact that some cycles, especially certain business cycles, are just very long. You have … The weekend backup happens over the weekend, so in order to tell you something intelligent about that, we have to wait until at least one or two weekends. The same goes for different business cycles that take … some take 24 hours, some take a week, some take a month. So certainly different types of timeframes to start seeing the value, but some of them will be very short, definitely minutes to hours, and then the rest of them will come through over time.

Guy Nadivi: So there’s been a lot of buzz about the numerous areas where AIOps can provide benefits to organizations, but I’d like to hear what you think is the single biggest benefit AIOps delivers.

Gadi Oren: So, AIOps … So you’re right. It’s multiple things. The challenge here is to choose, what is the one thing? What is the biggest thing that will make the difference? I think that the value is, for organizations that choose to do that and choose to adopt it, is that the organizations will be able to be a lot more proactive. So they will have … Again, looking at using this analogy of an early warning system is very applicable here. They will know about problems well in advance, comparing to other companies, and hopefully will be able to act on it and prevent them. The business value of such a capability is very, very high because you will have opportunity to avoid issues. You will probably be able to use less people to manage more IT and more complexity if you will be more proactive than you are today. That is the biggest value, I think, that AIOps will deliver organizations.

Guy Nadivi: Okay. So can you please talk about some particularly interesting use cases where AIOps acted as an early warning system and made predictions that mitigated problems, big problems?

Gadi Oren: Yeah. I have a number of examples. One of them is obviously a simple situation of a signal that was measuring behavior of a certain system within the IT. That system was basically starting to behave differently. That was different than the previous seven or eight days, so that company, they were looking at what could be the causes to that and they found that one of the hardware pieces, there was some redundancy in the system and one of the hardware pieces that was part of this redundancy was failing. That was just a much bigger … It was sort of a brown out situation that had the potential to become into a blackout, which would cost a lot of money because the company was running trading over the network, which means that they would lose a lot of money per every hour of down time. So that really prevented a huge financial loss. So that was definitely one example.

The other example that we had was a situation where there was … with a customer that’s an MSP, so it’s a service provider. The service provider have customers and they have service level agreement between them and their customers. So one of the customers all of a sudden had what they called an alert storm, where there’s just not a single alert, but it’s coming as 3,500 alerts are coming in at the same time. Now, that becomes a big issue because now people just don’t know where to start. Which one of the 3,500… Are they all problems? It’s very unlikely. Usually they’re all interconnected somehow, but finding what caused this is like looking for a needle in a haystack, right? 3,500 alerts, size of a haystack. So what LogicMonitor does in that situation is we’re able to categorize the alerts based on their dependency.

I mentioned before that we have topology and we map topology between devices. So we can extrapolate from that the dependency between alerts. In that type of a situation, there’s usually one or two or three root cause alerts, if you will. So we put them … We pull them aside and say, “These are the two or three things you need to look at right now and all the rest are likely to be an artifacts of the first.” What that is doing is that it allows you to not waste any more time, but just focus on the problem. That in turn means that the MSP is not breaking their SLA with their customers, so there’s actual financial incentive to use such a system, because otherwise it’s just everybody is suffering and there’s business implications to that.

Guy Nadivi: So those are definitely interesting predictions, but like many trending buzzwords, we hear a lot of hype about AIOps, including its implication for the future of data centers. Do you think AIOps is the beginning of the end for data centers?

Gadi Oren: No, I do not. I think that AIOps will actually enable … Well, first of all, you have to ask yourself, what is a data center? Is a data center, cloud, is what we used to call a data center on premise? I don’t know, it really depends on how you define that, but I think that, if anything, what AIOps and different implementations of it, what they will do is they will allow you to manage more complexity and more devices and more network with the same amount of people. So this is not the end of the data center. I think it’s the beginning of what data center will be in the future. It will be different, bigger, more complex, and do a lot more for you. I don’t know if it will be exactly the same shape and form that it is today, but this is the beginning of the new data center.

Guy Nadivi: When I read about AIOps, it’s primarily positioned as being for IT operations, but I’m curious if it has, in your opinion, applicability in other non-IT areas of a business?

Gadi Oren: Right, so I think there’s definitely a crossover between IT and other parts of a business. First of all, it’s obvious that more and more companies and their IT, their various … The threshold between what IT is and what is the other parts of the business getting more and more gray and they’re all blending together because the business is so dependent on IT working correctly for the normal operations. There’s also different types of activities that were used to be considered strictly more on the business side, things like capacity management and planning. When you are running out of equipment or running out of a certain capability and that capability is either very expensive or it has a long lead time, right? So we can say that about, let’s say, storage systems, right? If it takes you a long time to buy the storage and bring it on premise or it’s just very, very expensive and you need to manage it, then there is some activity of capacity planning, looking ahead and trying to understand how the business needs are aligned with all those things that you need to bring into the organization.

So I think that AIOps is definitely spilling out from strictly low-level IT, which is speeds and feeds and making sure that things are working, into other parts of the business and into aligning your just general business activities with how they map into what you have in your IT.

Guy Nadivi: So AIOps is heavily dependent on AI and machine learning and the entire value proposition of AI and machine learning being able to get you to the point where you can predict failures before they happen and mitigate them in advance is all predicated on one particular skill, data science. If you don’t have the data scientist to build the algorithms to generate the predictions, then you can’t leverage AIOps. Right now there is a very big shortage of data scientists. The August 2018 LinkedIn Workforce Report stated that there was a nationwide deficit of over 150,000 data scientists. How will companies like Logic Monitor overcome this staggering talent shortage?

Gadi Oren: Yeah, that’s a great question. So it’s a very real problem. There are a couple things that I can tell you about that. First of all, it’s true that a lot of the machine learning side requires a lot of data and may require data scientists, but a lot of those systems have other parts to it. So first of all, we create a lot of value also without strictly the data science side. So we definitely need data scientists, but it’s not a black and white situation where if you need 10 data scientists and you have only eight, then everything is coming to a screeching halt because there are other types of things that you have to put together in order to have a really great product. So it’s not just data scientists. Some algorithms are really not about data scientists, but having said that, we do need data scientists and have them. When we had a shortage, then we used external companies that provided us with those capabilities, which, again, is confirming your statement. It’s not the solution, but so far we were able to hire the people we need, as well as augment it with external companies.

I think also that the one thing I always like to think is that people really want to work on very interesting problems. So I believe that, so far, the fact that we were able to hire anybody that … All the different parts, whether it’s computer science or data scientists or other positions, we were able to hire and sustain and have very rapid growth to the company because, once people understand what we’re doing and they look under the hood, it’s very, very interesting. That is one of the things that help us with hiring people. So I guess one answer would be, make sure you’re working on interesting things and your problems will probably still be there, but not as difficult as other companies.

Guy Nadivi: Gadi, for the CIOs, CTOs, and other IT executives listening in, what is your one big must-have piece of advice you’d like them to take away from our discussion with regards to implementing AIOps for their IT operation?

Gadi Oren: Right, so I think that it’s about understanding your goal. I think that the goal of implementing AIOps, you should really think about what are you trying to achieve by that? Maybe five, six years ago, there used to be companies that decided they’d move to the cloud. Moving to the cloud is not a solution. It’s a type of an activity that you do in order to achieve a certain goal. So understand what your goals are when you’re implementing AIOps. Try and have them measurable. I believe I can suggest a goal and that is really to make your organization a lot more proactive than it is today. That’s something that you can measure. You can measure the results of that. You’ll have less brown outs and less outages. I think this is a very important type of a goal that will help you get value from your AIOps.

We also recently published a study that we did, and I don’t remember the exact numbers, but we did it across around the world with 300 people and roughly CIO, CTO, or VP of Infrastructure type of level. 90% of them said that they experienced some serious outage in the recent three years. This is a very, very high number. So because the complexity is constantly increasing, the size of IT that you have is constantly increasing, and usually the size of the team is not, then having AIOps will really help you go through this digital transformation and make sure that you are proactive in preventing issues rather than being constantly in a firefighting mode.

Guy Nadivi: I think you’ve just given any CIOs or CTOs that have experienced outages a great reason to consider deploying AIOps. All right, looks like that’s all the time we have for on this episode of Intelligent Automation Radio. Gadi, thank you so much for joining us today and providing your expert opinion on the value and business benefits of AIOps. We’ve really enjoyed having you on.

Gadi Oren: Thank you, Guy, for having me on the show.

Guy Nadivi: Gadi Oren, Vice President of Technology Evangelism for LogicMonitor. Thank you for listening, everyone, and remember, don’t hesitate, automate.



Gadi Oren 

Vice President of Technology Evangelism for LogicMonitor

In his role as Vice President of Technology Evangelism, Gadi is responsible for the company's strategic vision and product initiatives. Previously, Gadi was the CEO and Co-Founder of ITculate, where he was responsible for developing world-class technology and product that created contextual monitoring by discovering and leveraging application topology. With over 18 years of industry experience from IT operations management and monitoring to storage and cloud. 

Gadi previously served as the CTO and Co-founder of Cloudoscope. Prior to Cloudoscope, Gadi was head of Product Management for analytics and manageability BU at NetApp.  He served in leadership roles for some of the fastest growing and industry-transforming companies. Gadi has a management degree from Sloan MIT and B.Sc. Eng. in computer science and electrical engineering from the Technion in Israel. 

Gadi has been featured in a number of articles: 

Gadi can be reached at: 

Website:                             https://www.logicmonitor.com/

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

Twitter:                            https://twitter.com/gadioren

Quotes

“There's multiple definitions to what AIOps is. We used to work, at the beginning, with a definition that's saying ‘…any advanced algorithm that can be used to create a sustainable and stable value for the customer, in terms of making their operations work a lot better’.” 

"You will probably be able to use less people to manage more IT and more complexity if you will be more proactive than you are today. That is the biggest value, I think, that AIOps will deliver organizations." 

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Episode #5: Why your organization should aim to become a Digital Master (DTI) report
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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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Episode #12: How Cognitive Digital Twins May Soon Impact Everything
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Episode #20: Applying Ancient Greek Wisdom to 21st Century Emerging Technologies
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