How to Achieve Quick ROI for your Intelligent Automation Project

The most important component of any business decision, particularly in terms of IT, is being able to measure the results as quickly as possible. Ongoing success depends heavily on whether or not the money you’re spending is truly getting you the type of return that warrants continued investment; otherwise you may end up wasting cash and resources. Obviously, the sooner you can identify what is and isn’t working, the better. If you’ve recently launched an intelligent automation project, or are about to do so soon, here are five simple ways you can quickly show results.

Identify quick wins or pain points that can show quick value.

What was it that ultimately drove you to make the decision to adopt intelligent automation for your organization? What pain points were you hoping to address with this technology? Perhaps you identified areas where your team was being bogged down by time-consuming manual tasks, which was killing their productivity. Or maybe you realized that writing scripts was becoming a huge waste of valuable time and resources. Whatever the reasons, when you clearly identify them, you can quickly match the appropriate solution with each one, making it much easier to see the results.

Clearly define your outcomes.

Know ahead of time what your desired outcomes are and how you can anticipate achieving them. Then, calculate the potential savings you will realize by automating these steps. This gives you a clear picture of future savings and ROI that you can use as a benchmark to measure against as you work further into the process of shifting to automation.

Choose the right tool.

Understand that not all intelligent automation tools are created equal. Be careful and diligent when evaluating your available options and know ahead of time what to look for in a quality automation platform. For instance, some of the criteria you should be using includes determining whether the product is easy to use, modular, offers any type of pre-designed templates, and if so, what kind of customization is available. You should also be particularly aware of the 80/20 rule – that is, avoid tools that require 80% of your time, but that you will only benefit from 20% of the time.

Get the right buy-in and team engaged in the process.

Make sure that you’ve got the right team in place to help see this process through to fruition. Not only do you need buy-in from decision makers and those high level team members that will lead the project, but you’ll also need to ensure that everyone involved remains focused and motivated to achieve the end result. This will help you stick to your timeline for implementation, avoiding costly delays that can affect your overall ROI.

Measure results of this project and identify other areas for expansion.

Intelligent automation isn’t something that only presents solutions for the here and now – it’s a long term solution that can help streamline your operations, improving ongoing efficiency and productivity, and enhancing your bottom line over the long haul. Don’t just focus on the short term benefits. Measure results on a regular basis. This will help you determine your long term ROI as well as identify other areas for expansion that could further benefit your firm.

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

Creating an Intelligent Virtual Support Agent for your ITSM

Author: Guy Nadivi, Sr. Director of Marketing, Ayehu

A topic that’s really getting a lot of buzz these days is Virtual Support Agents.  Virtual Support Agents – or VSAs – are the next logical evolution of a chatbot, because where chatbots are primarily conversational, a VSA is both conversational and actionable, making them much more valuable to an enterprise, and particularly to a service desk using an ITSM platform.

In order to better understand why VSAs are so top-of-mind, it can be helpful to take a step back and understand what’s happening right now in IT operations at enterprises around the world, particularly with all the data they’re dealing with.

Do you know what a Zettabyte is? NO GOOGLING!

A Zettabyte is a trillion Gigabytes. That’s a “1” followed by 21 zeroes.

As humans, it can be hard for us to wrap our minds around numbers that large, so let’s use a visual metaphor to help provide a frame of reference for how much data 1 Zettabyte represents.

The visual metaphor we’d like you to envision should be a familiar one – grains of rice. For the sake of this visual comparison, let’s say one grain of rice is equal to one byte of data.

That makes a kilobyte, a thousand grains, equal to about a CUP of rice.

A megabyte of data would then be the equivalent of 8 BAGS of rice.

1 gigabyte of data in terms of rice is equal to 3 container TRUCKS.

A terabyte of data would then be the equivalent of 2 CONTAINER SHIPS full of nothing but rice.

Now get this – an exabyte of data in terms of rice, would cover all of MANHATTAN.

A petabyte of data, would just about cover all of TURKEY. (BTW – turkey & rice is a great combo!)

Finally, here’s what a Zettabyte of data in terms of rice would do.  It would fill up the PACIFIC OCEAN! 

Now, this last visual using the Pacific Ocean is very relevant, especially if you work in IT operations.  That’s because you literally feel like an entire ocean’s worth of data is inundating you these days, thanks to all the systems you’re maintaining that create, store, access and deliver data for your employees, customers, partners, etc.  Life in IT operations is a relentless tsunami of incidents, events, thresholds being crossed, etc., and it’s only getting worse.

How much worse?

In 2017 The Economist published a chart produced by IDC in conjunction with Bloomberg estimating the size of data comprising “The Digital Universe”. In 2013 there were 4.4 Zettabytes of data worldwide, but by 2020 there will be 44 Zettabytes. That’s an astounding CAGR of 47%. Don’t expect things to slow down though, because it’s estimated that by 2025 there will be 175 zettabytes of data worldwide!

Interestingly, IDC/Bloomberg discovered what appears to be a correlation between the exponential growth of data and an increase in the number of times companies are mentioning “artificial intelligence” in their earnings calls. This is probably not a coincidence, and it underscores something we say over and over at Ayehu – people don’t scale very well. 

Even the very best data center workers can only do so much. At some point, and that point is pretty much right now, end user self-service in concert with automation has got to take on a greater share of the service desk tasks all this growth in data is necessitating.

So when does it make the most sense for your end users to interface with Virtual Support Agents instead of live operators at the service desk? Well, one obvious answer to that might be as your organization’s first point of contact for L1 support issues.

We’re talking things like password resets, which Gartner estimates are responsible for as much as 40% of your service desk’s call volume.

Onboarding employees, and its closely related counterpart task offboarding employees, are also excellent tasks for a VSA.

How about VM provisioning and VM resizing?  These tasks lend themselves very well to a VSA interface.

Another good one is service restarts.  It makes a lot of sense to empower end-users to be able to do this themselves via a VSA.

And there are many, many more L1 support issues that would be great candidates for Virtual Support Agents.

But then what about your service desk?  What should your human agents do once the Virtual Support Agent has taken on all these tasks? 


Well, there are still probably a number of L1 support issues that are not well-suited for a VSA (at least not yet). The service desk can continue handling these.

Of course most, if not all of your L2 and L3 support issues are still best handled by a human service desk, for now.

And finally, vendor support issues are also probably still best managed by the service desk.

Still, deploying a Virtual Support Agent will clearly shift a lot of tedious, laborious tasks off of the service desk and free them up to do other things. But what kind of impact does this have on the cost structure of a service desk?

The average industry cost of handling an L1 support ticket is $22. In comparison, the average cost of a ticket handled by a Virtual Support Agent is just $2. Using these figures to do a back-of-the-envelope calculation of how many tickets can be off-loaded from the service desk to the VSA, will likely yield a significant reduction in support costs. What’s more, issues handled by VSAs often gets remediated much faster, resulting in greater end-user satisfaction than going through the service desk.

And if end users are going through a VSA instead of the service desk, then that means the VSA can reroute a trainload of L1 incident tickets away from the service desk, freeing up that staff to focus on more important, and more strategic things.  This is a huge value proposition!

What about the benefits VSAs provide to end-users?

Here’s an obvious one. Ask any end-user how much they love waiting on hold when they need an issue resolved, but no one’s available to help them. Waiting on hold can really degrade the user’s experience and perception of the service desk. With Virtual Support Agents, on the other hand, no one ever gets put on hold because the VSA is available 24/7/365. VSAs never take a break, or call in sick, or get temperamental due to mood swings. They’re always available and ready to perform on-demand.

Then there’s the mean-time-to-resolution metric, better known as MTTR. As every service desk knows, the speediness of their incident remediation outcomes is one of the major KPI’s they’re judged on.  Well when it comes to speedy MTTR, a VSA should be faster than a human being just about every single time.

Finally, does your enterprise have younger employees, particularly from the millennial generation? Of course it does! Just about every organization does, and some have them in far greater percentages than others. Well, guess what? This generation, raised on Facebook and mobile apps, generally prefers interfacing with technology as opposed to people. And that’s something we all know empirically because we’ve seen it for ourselves.

In addition to that visual proof, a survey conducted in 2018 by Acquire.io found that 40% of millennials said that they chat with chatbots on a daily basis! So, providing VSAs that empower younger workers with self-service capabilities might just give your organization a competitive advantage in attracting the best and brightest young talent to your company.

Questions & Answers

Q:          What are the pros and cons of using general purpose bot engines compared to your solution?

A:           General purpose bot engines won’t actually perform the actions on your infrastructure, devices, monitoring tools, business applications, etc. All they could really do is ingest a request. By contrast, Ayehu could not only ingest a request, but actually execute the necessary actions needed to fulfill that request. This adds a virtual operator to your environment that’s available 24x7x365. Additionally, Ayehu is a vendor-agnostic tool that is capable of interfacing with MS-Teams, Skype, etc. to provide a general purpose chat tool with intelligent automation capabilities.

Q:          Do you have on-premise solution?

A:           Yes.  Ayehu can be installed on-premise, on a public or private cloud, or in a hybrid combination of all three.

Q:          Do you have voice integration?

A:           Ayehu integrates with Amazon Alexa, and now also offers Angie™, a voice-enabled Intelligent Virtual Support Agent specifically designed for IT Service Desks.

Q:          If a user selects a wrong choice (clicks the wrong button) how does he or she fix it?

A:           It depends on how the workflow is designed. Breakpoints can be inserted in the workflow to ask the endpoint user to confirm their selection, or go back to reselect.  Ayehu also offers error-handling mechanisms within the workflow itself.

Q:          Does Ayehu provide orchestration capabilities or do you rely on a 3rd party orchestration tool?

A:           Ayehu IS an enterprise-grade orchestration tool, offering over 500 pre-built, platform-specific activities that allow you to orchestrate multi-platform workflows from a single pane of glass.

Q:          Can you please share the slide on IVA vs. ServiceDesk and elaborate a bit on the use cases?

A:           The entire PowerPoint file presented in this webinar can be found on SlideShare.

Q:          Can you explain in a bit more detail on intent-based interactions?

A:           Intent is just that: what the user’s intent is when interacting with the Virtual Support Agent (VSA). For example, if a user types “change my password”, the intent could be categorized as “password reset”. That would then automatically trigger the “password reset” workflow.

Q:          Can we use machine learning from an external source, train our model, and let Ayehu query our external source for additional information?

A:           Yes. Ayehu can integrate with any external source or application, especially when it has an API for us to interface with.

Q:          Can I create new automations to my in-house applications?

A:           Yes. Ayehu can integrate with any application bi-directionally. Once integrated with your in-house applications, Ayehu can execute automated actions upon them.

Q:          Is there an auto form-filling feature that can fill in a form in an existing web application?

A:           Yes. Ayehu provides a self-service capability that will enable this.

Q:          How can I improve or check how my workflows are working and helping my employees to resolve their issues?

A:           Ayehu provides an audit trail and reporting that provides visibility into workflow performance. Additionally, reports are available on time saved, ROI, MTTR, etc. that can quantify the benefits of those workflows.

Q:          What happens when your VSA cannot help the end-user?

A:           The workflow behind the VSA can be configured to escalate to a live support agent.

Q:          If there is a long list of choices, what options do you have? Dropdown?

A:           In addition to the buttons, dropdowns will be provided soon in Slack as well.

Q:          Did I understand correctly, an admin will need to create the questions and button responses? If so, is this a scripted Virtual Agent to manage routine questions?

A:           Ayehu is scriptless and codeless. The workflow behind the VSA is configured to mimic the actions of a live support agent, which requires you to pre-configure the questions and expected answers in a deterministic manner.

Q:          is NLP/NLU dependent on an IBM Watson to understand intent?

A:           Yes, and soon Ayehu will be providing its own NLP/NLU services.

Q:          Are you using machine learning for creating the conversations? Or do we have to use intents and entities along with the dialogs?

A:           Yes, you currently have to use intents and entities, but our road map includes using machine learning to provide suggestions that will improve the dialogs.

Q:          What are the other platforms from which I can deploy the VSA, apart from Slack?

A:           Microsoft Teams, Amazon Alexa, ServiceNow, ConnectNow, LogMeIn, and any other chatbot using APIs.

Missed the live Webinar? Watch it on-demand and see the above in action by clicking here.

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5 Non-Tech Skills to Look for in an Intelligent Automation Engineer

Everyone talks about the changes in the IT world – the increased complexity, the pressures to improve efficiency, and the need for tighter links between IT and business. But how does this influence your IT personnel and skill set requirements?

It goes without saying that there’s a need for individuals who have backgrounds in areas like data center operations, systems integration, virtualization, etc. Yet the demand for closer links between IT technologists and business operations implies an entirely new set of skills.

Particularly with intelligent automation becoming an essential element, IT engineers need new skills beyond familiarity with technologies. Where past requirements focused solely on technical expertise and you were only looking for scripting wizards and troubleshooting superheroes, today’s IT teams need a much wider set of abilities.

You need intelligent automation engineers who are able to understand the needs and processes of the business, translate those needs into IT activities, and prioritize and implement them in the most efficient, productive way possible. So what are the additional skills an intelligent automation engineer needs? Here are 5 that we believe top the list.

  1. Business perspective. A business/financial state of mind that enables the consideration and application of non-technical data inputs. For instance, figuring out key KPI’s affecting IT projects, measuring return on investment (ROI), and optimizing project implementation to achieve overarching financial goals.
  2. Process analysis. The ability to define and implement processes such as incident management, change management, operations, information security, business continuity and disaster recovery, as well as business service management.
  3. Project management. Skilled project managers who can not only oversee and monitor projects, but also identify business users’ needs and translate them into IT requirements. Intelligent automation engineers that can clearly justify how a business may increase its staff productivity and efficiency using different processes and tools.
  4. Process implementation. The new intelligent automation engineer has to be able to comprehend end-to-end processes, have a deep understanding of workflows and the ability to create them in a dynamically automated environment.
  5. Interpersonal skills. The need for strong communication with business managers requires solid interpersonal skills. This involves the ability to communicate effectively with a wide range of people outside the IT domain, understanding business peoples’ needs, concerns and different points of view, and that rare ability to negotiate and make compromises on both sides of the aisle.

Of course, not all businesses have the capacity or recruit an intelligent automation engineer. For instance, those organizations operating on leaner staffing budgets or ones that already have large IT teams to tap into may benefit greatly by investing in the reskilling of existing personnel.

Ayehu’s Automation Academy offers expertly developed courses are designed to help IT professionals comprehend and cultivate practical skills through a variety of interactive learning activities. Multiple, flexible training options are available. Give your team the knowledge it needs to turn your organization into a powerful, self-driving enterprise. Click here to get started.

Why “Free” Automation Software Isn’t Really Free

Who doesn’t love getting something for nothing – especially when you’re working with a limited budget and have to find creative ways to keep your business running efficiently, such as with intelligent automation tools, without breaking the bank? While some of the products out there that are being offered for free are great, and can serve a purpose for your business, some of aren’t quite so beneficial after all. In fact, turning to the wrong type of “freebie” can actually end up costing you more money in the long run. Here’s how.

First of all, free products typically focus on providing a single-use solution. In other words, they’re designed to address one or maybe two problems that you or your team may be facing, but they aren’t all-encompassing. This is all fine and good until you step back and realize you’ve got a dozen different tools running at the same time, and none of them are actually working together. In some cases, they may even be working against each other. The result of this fragmented approach is decreased efficiency and increased chance of error, both of which can be costly to your business.

Secondly, free products are not usually designed to support a long term strategy, but rather to fix a present need. Any successful business person knows that in order to continue to be profitable, there must be a strategy in place that will account for both the present and the future. Without an effective long term plan, you’ll only be able to realize short term goals, and you may find your tools falling short over time. Intelligent automation tools that you invest in, on the other hand, are much more robust and can be figured into the big picture and help you achieve long term goals.

Another way that free intelligent automation products may end up costing you in the long run is that they typically lack real substance. When they’re built to be free, they’re usually missing several key components that could really take your business to the next level. This means your automation won’t be running on all cylinders and will ultimately fall short. As a result, you may find yourself with no other choice but to purchase the full product in the end, just to avoid having to start over again. Then, if the full version isn’t much better than the free one, you’ve wasted valuable money that could have been spent on a better product.

Finally, there’s the age-old theory that if something looks too good to be true, it probably is. If you’re going to place your trust in a free product, you’d better make sure you understand the motive behind the giveaway. These businesses are in it to make money too, and giving everything away isn’t going to do them any good….unless they can somehow rope you in via their free product to have to pay for other things, such as ongoing support or add-ons. Before you know it, that “free” tool has suddenly cost you more than it would have to simply purchase a quality product outright.

In short, while availing yourself of free products may seem to make smart business sense on the surface, in the long run it may end up costing you more. Do you really want to take chances on a half-rate product that could end up doing the exact opposite of what it was designed for by actually costing money and hindering productivity and efficiency? The wisest choice, at least when it comes to intelligent automation, is to do your homework and select a quality tool that isn’t free, but will produce the benefits your business needs over the long haul.

At Ayehu, we don’t offer a free version. We do, however, allow users to try our full platform completely free for 30 full days. Experience intelligent automation powered by AI and see for yourself what an investment in technology can do for your business. You won’t be disappointed!

Episode #17: Back to the Future of AI & Machine Learning – SRI International’s Manish Kothari

May 16 2019    Episodes

Episode #17: Back to the Future of AI & Machine Learning

In today’s episode of Ayehu’s podcast we interview Manish Kothari – President of SRI International.

What do you think of when you hear the phrase “artificial intelligence”?  Do your thoughts veer more towards Terminator or Robby the Robot (Will Robinson’s guardian from “Lost in Space”)?  Do you envision future machines being more like Hal from “2001: A Space Odyssey” or Samantha from the 2013 movie “Her”?  Nobody knows for sure how real-world AI will eventually evolve, but one person has a better idea than most, and that’s led him to predict where AI, machine learning, & automation will have the biggest impacts.

Manish Kothari, President of SRI International leads an organization that’s world-renowned for its innovations in a broad spectrum of focus areas.  He joins our show to share his insights on the areas AI, machine learning, & automation are disrupting the most.  Along the way we’ll learn whether AI will augment more people or replace more people, whether AI algorithms should be audited like financial statements, and which industry stands to benefit the most from automation, AI, and machine learning.



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 Manish Kothari, President of SRI International. SRI, formerly the Stanford Research Institute, was established in 1946 and has become one of the world’s leading R&D labs for government agencies, commercial businesses, and private foundations.

SRI has a staggering number of focus areas they’ve raised the bar in, including biomedical sciences, chemistry, computing, earth and space systems, and much, much more. And they’ve got the results to prove it. To date, they’ve received more than 4,000 patents and patent applications worldwide, including my favorite one, patent number 3541541 filed in 1967 for the first mouse prototype. So everybody listening today has benefited from SRI’s R&D. And with credentials like that, we thought it only fitting that we bring their President on the show to talk about some of the really exciting stuff they’re doing with automation, AI, and machine learning. By the way, in the interest of disclosure for our audience, SRI is a design partner with Ayehu, the sponsor of this podcast.

Manish, welcome to Intelligent Automation Radio.

Manish Kothari: Thanks, Guy.

Guy Nadivi: Manish, I imagine that you’re involved with so many amazing new technologies that it must be impossible to pick just one that you’re most excited about. So instead, I want to ask you to tell us what you think are going to be some of the biggest disruptions we’ll see in 3, 5, or 10 years from now, with respect to automation, AI, machine learning, et cetera.

Manish Kothari: Yeah, that’s great, Guy. First of all, thanks for bringing me on the podcast. Pleased to be here. And thanks for that great introduction about SRI.

Regarding disruptions, disruptions are happening from many different areas. I’ll focus on the ones around automation, AI, and machine learning, as you said. There are disruptions happening and tremendous advances happening in other areas, such as biology, synthetic biology, which also involves AI, but let’s focus on a few that are very near to the future around AI and automation.

Automation, you can really think that there is really a convergence happening between the physical world and the cyber world in a way. That convergence is enabling us to do more and more things through the use of software, versus through the use of purely hardware.

AI in particular has been around for a while. It started back in the ’60s and ’70s as a rules-based system. It then lost popularity, as many people are aware of, and then it sort of regained popularity over the last 5 to 10 years, as neural nets came about as an alternative way to think through these problems. AI has been now moving and maturing from the application of neural nets for pretty much everything, primarily classification, to much more sophisticated ways of using it.

One of the real areas of disruption in innovation we’re seeing are the areas where AI can become much more explainable. Today neural nets are largely a black box. You put the data in and you build a model, and then you apply it to get an answer. You don’t exactly know why the answer is what it was, why it was feasible. This has traditionally been challenging for specialists to accept who like to understand the logic or the role model behind it.

The next generation of AI systems are starting, just starting, to go into the areas of explain-ability, and even beyond, into the areas of common sense reasoning. There are DARPA programs happening in all of those areas. We should anticipate to see, within three to five years, that these techniques are going to have a profound impact on AI, and even as importantly in the dispersal of AI within the common working ecosystem, because once you have those techniques it really does transform things.

I’ll make one more comment here, which is, one of the challenges with artificial intelligence has been, do you have a appropriate twin of the environment you’re working in so you can model AI appropriately? Do you understand the objective functions? The math around how to handle multiple objective functions, how to deal with those elements, are all coming to fruition right now as well.

And maybe I’ll end by saying there’s a Back To the Future moment taking place as well. We used to do everything on the edge. The last 15 to 20 years has been all about moving to the cloud, and as AI is maturing and becoming faster, lighter, quicker, we’re talking about moving back to the edge. Enabling decisions to be made using your own nets and AI methodologies, and machine learning methodologies, but do it back at the edge. And that is something that is happening quite rapidly right now, and in three years or five years from now we should really … We won’t be talking about just the cloud, we’ll be talking about both a hybrid-edge cloud-based approaches to AI.

Guy Nadivi: Given the advanced level of automation, AI, and machine learning that you work with at SRI, do you think that these technologies will augment more people, or replace more people?

Manish Kothari: That’s a great question Guy, and the answer is really both, with augmentation being, I think, the greater majority of the cases than automation, if you will, complete automation. And the real reason is, there’s two reasons, one, AI is not yet very strong in elements like common sense reasoning, and common sense reasoning is a fundamental portion of how we live our day-to-day and make business decisions. So there will be an element of humans-in-the-loop that will be required to develop these areas. There you’re going to end up with a lot of augmentation. Now augmentation is terrific, because what it does is, it relies on the AI to do some of the very complex tasks that human beings typically have challenges doing. When you have multiple independent inputs into making a decision, after a certain number – humans have a challenge in processing that dataset. AI can handle there. Making the decisions out of that will be an augmentation methodology, and that will be very important.

I will say one comment here though. When you do think about augmentation and automation, it’s important to think about that early. When you’re automating, human factors are not that relevant, so therefore you can design the AI systems to do many other things. When you augment, you have to think the human-in-the-loop and how that interaction is going to take place. And that, if you do that correctly, your productivity boost from AI can be tremendous. I’ll maybe go into this into a little bit more detail.

Let’s take an example right now of a physician. A physician is standing in front of a patient, let’s say she’s 85 years old, and the physician prescribes an ultrasound for an exam. You go ahead and you use your AI system, and the AI system says, “This person should get an MRI.” The physician looks back at the system and says, “I don’t think, I know that the right answer is an MRI. However this person’s frail, they’re old, the MRI is six weeks away, and there’s a 20% chance that the ultrasound will give me an answer. I’m going to go that route first, knowing it’s not the better route.” This is a perfect example as, if the AI system were intended to automate this process, they may actually come up with the wrong decision for the patient. But if it is intended to augment, they may indeed come up with the correct decision for this patient.

Another example of this is the idea of context. If you have an X-ray and you’re analyzing it using your own machine learning to evaluate whether there’s lung cancer, or a nodule or not, you may find a nodule. A physician is typically going to ask, “Where did you visit over the last five years?” And if you had visited, let’s say, southeast Asia, they will do a further exam to evaluate whether it’s tuberculosis or not. If you don’t ask that question you will probably conclude it is a cancer symptom and move forward. This is an example of an area where context becomes important, and augmentation is a really handy way to add the context to the system. The math around AI today is not very strong from context evaluation. It is a work in progress, and as I said in my earlier question, 5, 10 years from now the answer may be different, but as of now, the augmentation is really where it’s at.

Even in IT, I’ll end with an example. With the proper use of machine-learning based system(s), you could, instead of having a person who has 10 years experience having to handle any critical problem, have somebody with one year of experience, augmented by the AI system, which is allowing them to take the expert knowledge and make that person with one year of experience as valuable, or as effective, as somebody with 10 years of experience. So thinking about how you implement this, AI is actually a way to really democratize the ability of many, many different groups to achieve things that were previously possible by only being able to hire hundreds and hundreds of people. That doesn’t mean it’s replacing those people, it’s really democratizing and making those techniques available to groups that couldn’t have it previously. So it’s actually quite, very strong on the augmentation front.

Guy Nadivi: In 2017, Vladimir Putin said, “Artificial intelligence is the future, not only for Russia, but for all humankind. It comes with colossal opportunities, but also threats that are difficult to predict. Whoever becomes the leader in this sphere will become the ruler of the world.” Do you agree with that?

Manish Kothari: I think there’s no doubt that artificial intelligence is going to have a major impact into the future of what humans do and how they perform their productivity, what they are able to achieve. It is by democratizing things that were previously available only to a select few extremely high-caliber people, we have now enabled a large amount of humanity to think and achieve things that previously … So think of, instead of 10 inventions, we are now going to have 1,000 great inventions. So there is no question that artificial intelligence is enabling a sea change already, and will enable stronger, more colossal opportunities. And it’s important to remember, this is not just going to be on the IT side or on others, it’s going to be in medicine, it’s going to be in healthcare, it’s going to be in biology. That’s absolutely true.

It is also true that, if you treat neural nets as the black box they can be, then it becomes difficult to predict what the outcomes are going to come from there, and if you give them a degree of control without managing it, then there’s a risk that there are threats that are difficult to predict that can come about. If somebody looking at a radar signal has to decide whether this is an airplane or a missile, and you’re relying completely on an AI system, that could be quite threatening for humanity. That I think is true. Whoever becomes a leader in this field will be the ruler of the world.

I think the good news is, the AI community is a very robust community that is providing a lot of its algorithms and developments in an open source environment, so I actually think very positively about how this technology is permeating the world. I think that there will be people who are strong at it, will have significant advantages, but I think there will be a lot of room for a lot of people to become strong at it. So as a result of that, yes it’s really important, yes there are some threats that are difficult to predict, but I feel really confident and positive of the long-term impact to society from AI.

Guy Nadivi: We hear a lot about AI and machine learning, but we hear much less about the underlying models they’re based on, whether they’re decision trees, neural networks, random forests, or something else. And these models are built upon algorithms. Late last year, Harvard Business School published an article calling for the auditing of algorithms, the same way companies are required to issue audited financial statements. Now given that SRI develops many proprietary and even classified AI and machine learning algorithms, what do you think about the need for algorithm auditing?

Manish Kothari: That’s a really interesting question, and I think algorithm auditing … First of all, let’s start by first saying a lot of the algorithms that are being developed are being put into open source, and the open source community is very active in building them. So there is a form of openness towards evaluating algorithms. That said, all companies are using their own mixes of those algorithms, their proprietary nature of developing those algorithms, and we don’t know exactly which way they are being used.

I think, two comments I would make. One, auditing algorithms is actually going to be very challenging. Even when you ask a few computer scientists to evaluate code written by another computer scientist, it’s not as trivial as it sounds. You would have to ask for a very high degree of standardization across multiple industries to be able to enable that form of auditing. It’s non-trivial.

The second comment is probably as important, if not more important, and it is, to some extent, the models that are underlying models that are built are really built on the training data that is put into these systems. The training data, while large, is a finite set of data that could be used, or audited if you will, to ensure, for example, there’s no bias. There isn’t some inherent racism that is introduced, or sexism that is introduced. So there are certain sort of biases that can be audited in a relatively manageable fashion.

So I think it would not surprise me if AI systems suddenly become responsible, for example, for setting the sentencing of potential criminals. Then you will need to be auditing that data that builds the underlying models, because you may find that, by using historical data, it ends up providing longer sentences for people of color than for others. And so therefore an auditing mechanism of the data itself would be something interesting to consider.

I think that, as the field evolves, it’ll be interesting to see where those decisions happen. I think a lot of whether the need is required or not will depend on just how much autonomy we’re giving these systems. Are they systems that enhance the intellect of an individual to make a decision, versus systems that start making decisions on their own, either because they’re programmed to do that or because the individuals start relying on them so much that they allow them to do that? Depending on that circumstance, one may need more formality in these areas.

Guy Nadivi: What kinds of skills does SRI covet the most when hiring engineers for automation, AI, & machine learning?

Manish Kothari: It’s a good question. I think you know the current race in the world is for, anybody, for engineers with a background in computer science, in machine learning. Today an undergrad coming out of college is getting both those basic skillsets. I think it depends, whether you’re talking about … AI and machine learning have many different steps. It has the very creative step of new algorithm creation, which is where SRI traditionally focuses on, so we focus a lot on the areas of hiring engineers with a high degree of creativity, and often Ph.D.’s in the background or Master’s in these areas, which enable them to create new algorithms, or conceive of new algorithms.

For the bulk of AI hiring today, there is a real migration taking place. Over the last few years all the industries, too, were looking for AI engineers and data scientists, because you have to put those both hand-in-hand, which had that very creative skillset. That is evolving. As AI itself is maturing, it is moving from it being needing for these extremely creative AI engineers, to execution-driven AI engineers, and those are a different set. That’s actually a very nice evolution taking place in the marketplace as well.

Maybe I’ll elaborate on this a little bit more with a little anecdote. So when my great uncle was involved back almost 50, 75 years ago, CNC machines, or computer numerically controlled machines, were starting to replace mills and lathes in manufacturing. So these are machines that you could program in that could do high-throughput manufacturing with extreme precision without the need for somebody who had had 20 years of lathe experience to be there. In the beginning, everybody who ran these machines needed to be a Ph.D. in this area of manufacturing, mechanical engineering for computer CNC manufacturing. My uncle was one of them. Fast-forward 20 years from then, it went down to an undergrad level. Fast-forward to today, somebody with a high school diploma, a high school degree, and an appropriate one to two year apprenticeship in these areas can run a CNC machine very, very well. In fact better than somebody with a Ph.D.

So I think we’re seeing a similar evolution in what’s required for AI. The first ticket on seeing this is seeing what’s happening in colleges. It used to be that Stanfords, MITs, were the places where all the AI work was happening. It’s migrated now to every college offering this. It’s now migrated to undergrads being able to offer this service in the college. And finally, I would not be surprised if things like data processing and data entry to build the underlying model, at some point we will even see technical degrees in that area.

Guy Nadivi: Which industries, Manish, do you think stand to be disrupted the most from automation, AI, and machine learning?

Manish Kothari: Look, all industries are going to face the impact of it, and it’s going to be … So all of them are in the process of being disrupted, already if not counting the disruption. So when you see things such as shopping and Amazon, as today Amazon is using artificial intelligence-based techniques and machine learning based techniques in everything they do, from giving you suggestions of what can be used in the marketplace to thinking through supply delivery areas. I think it’s important to, when you think about automation and AI, to also broaden the thought to think about IoT and distributed sensing.

Once you start having distributed sensing taking place, and distributed computing, and machine learning being thrown in, pretty much every industry, from warehousing, logistics, and manufacturing, just in time manufacturing, and education, all of them. The place which stands to have the most disruption from these areas is really healthcare. Because if you look at areas that have not adopted automation significantly, healthcare is one in which there’s still a lot of room for automation. If you take something as in contrast, let’s say farming corn. Farming corn has been automated for a long time. One person can handle thousands of acres. I don’t think AI or automation is going to really disrupt that in a tremendous way, however healthcare is maybe a few percent automated today. We should expect to see that.

In the immediate future, areas like cybersecurity, network operations, security operations, these are all being transformed today. And like I said, the democratization of capabilities so that more and more companies can adopt better and better security policies, network operation policies, taking to advantage this democratization, those are all great opportunities today.

Guy Nadivi: Do you see any economic, legal, or political headwinds that could slow adoption of these advanced technologies, or are automation, AI, machine learning, and others basically runaway trains that can’t be stopped at this point?

Manish Kothari: They are moving very quickly, but I do think we have a strong ethical responsibility to think how these techniques can be used. And many groups, Harvard has a responsible AI program, a couple of other universities do too. SRI has a design program that’s thinking about how to adopt these techniques; all of these techniques, because we’re going to be working in symbiosis or symbiotically with these methods, we’re going to have to put these frameworks in place. Some of these frameworks are already being established. I think there’s a lot of risk here too, in the sense that, how do you apply these techniques? Even the same solution, when applied to a country like India, or sub-Saharan Africa, versus to Japan or Germany, are very different.

And maybe I’ll give an example here. Let’s say you have an automated robotic welding system that uses artificial intelligence to think through how to do an optimal weld. If you’re in a place like Germany or Japan you’re looking to automate these processes, because you have a shortage of manpower. If you’re in India or sub-Saharan Africa, you are actually looking to use these techniques to train new workers because you have a surplus of manpower, and the economic argument is probably suited to you using that approach, and democratize the quality that’s available to humans.

So there really is different objective functions in different locations, and one needs to think about it. Just from a historical context, I spent half my childhood in India and half my childhood in Australia, and if you want to think about it, Australia is a country that was, is, and almost certainly always will, have a labor shortage. India is a country that had, has, and almost certainly in the near future will have, a labor surplus. So you have to apply these productivity techniques differently, and you have to apply the economic, legal, and political approaches appropriately.

That said, the productivity benefits, if done right, from AI are so strong that I think while there may be some headwinds adopting this, businesses will be quick to adopt these techniques.

Guy Nadivi: Manish, for the IT executives out there 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 implementing automation, AI, and machine learning?

Manish Kothari: Yeah, that’s a great question. We have seen now, in SRI, through its own research and through partnerships with other companies, have seen that in many places we’ve seen initial adoption, rapid adoption of AI system, but poor stickiness of these techniques in that place. Partly it’s because AI technology itself is evolving and getting better, but part of it is, there is a lack of understanding of, that design is playing a very, very big role. If you go back to the early days of IDEO, IDEO walked in and really established a really de facto approach to user centered design that has permeated through the entire ecosystem, and today no piece of conventional software, or good piece of conventional software, is designed without the user-centered approach in mind. AI is bringing about a new set of challenges that cannot be addressed just simply with the original user centered design approach.

If you’re an executive out there, you should be asking yourself a question of, not just how do I adopt these techniques, but how do I really get stickiness of these techniques in my workforce? For that I would say the one thing you want to think about is getting a design team together, not just an engineering team, and focusing in on it, and understanding what the unique aspects of design requirements are that enable successful implementation of AI systems. SRI has always been at the forefront of design, back from the mouse example, technical design, the example of the mouse that you gave, to Doug Engelbart’s Mother of All Demos with user interfaces, over 50 years ago now. This is something that we feel very passionate about, and the data really supports, that if you don’t think about the unique aspects of design related to AI, you are likely to have some challenges, not necessarily in adoption, but stickiness. Get that right, and I think you’ve got a chance of both democratizing the abilities that you have within your organization, and enhancing productivity significantly.

Guy Nadivi: All right. Well looks like that’s all the time we have for on this episode of Intelligent Automation Radio. Manish, it’s really been a treat having you on the show so we could learn from a genuine expert with deep insight about the state of AI and machine learning today.

Manish Kothari: Thanks a lot Guy, it was a pleasure to be here.

Guy Nadivi: Manish Kothari, President of SRI International. Thank you for listening everyone, and remember, don’t hesitate, automate.



Manish Kothari

President of SRI International

As president of SRI International, Manish Kothari, Ph.D., jointly develops SRI strategy with the CEO and ensures that corporate resources and talent are aligned with supporting SRI’s research needs. In this role, he oversees SRI Ventures, Global Partnerships, Marketing and Communications, and SRI’s Japan office. He holds multiple patents and is the author or co-author of several peer-reviewed publications and book chapters.

Kothari joined SRI in 2013 as a business development consultant and entrepreneur-in-residence. He became a program director in the Robotics Program in 2013. In 2014, he moved to SRI Ventures as director of commercial ventures and licensing, with an emphasis on healthcare, engineering, and physical sciences. He was named president of SRI Ventures in 2015 and has been leading the venture creation process from concept through market development, team building, technology transition, seed funding, partner and investor identification, and launch.

Manish Kothari can be found at:

E-Mail:         manish.kothari@sri.com

LinkedIn:     https://www.linkedin.com/in/manish-kothari-5458453/

Quotes

“AI in particular has been around for a while. It started back in the '60s and '70s as a rules-based system. It then lost popularity, as many people are aware of, and then it sort of regained popularity over the last 5 to 10 years, as neural nets came about as an alternative way to think through these problems. AI has been now moving and maturing from the application of neural nets for pretty much everything, primarily classification, to much more sophisticated ways of using it.”

"The next generation of AI systems are starting, just starting, to go into the areas of explain-ability, and even beyond, into the areas of common sense reasoning.”

“With the proper use of machine-learning based system(s), you could, instead of having a person who has 10 years experience having to handle any critical problem, have somebody with one year of experience, augmented by the AI system, which is allowing them to take the expert knowledge and make that person with one year of experience as valuable, or as effective, as somebody with 10 years of experience.”

“AI is actually a way to really democratize the ability of many, many different groups to achieve things that were previously possible by only being able to hire hundreds and hundreds of people.“

“This is something that we feel very passionate about, and the data really supports, that if you don't think about the unique aspects of design related to AI, you are likely to have some challenges, not necessarily in adoption, but stickiness. Get that right, and I think you've got a chance of both democratizing the abilities that you have within your organization, and enhancing productivity significantly.”

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
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

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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

ITOps: Best practices to improve performance and service quality

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

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

Best Practices for Systems and IT Operations Managers:

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

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

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

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

eBook: 10 time consuming tasks you should automate

The Rise of Artificially Intelligent Service Management (AISM)

It’s been said that the best way to serve customers is to anticipate their needs, whether it’s a restaurant concierge offering to walk patrons to their vehicles with an umbrella overhead on rainy evenings or rolling out an update on a software product. The same concept can be applied in the IT realm, specifically in IT service management (ITSM).

The fact is, with today’s technology, it’s entirely possible to predict that certain situations will occur, from simple password reset requests to servers crashing. It’s not really a matter of if these things will happen, but rather when. And if you know what’s coming, you can be prepared to respond and, in many cases, even head problems off at the pass.

That’s where artificial intelligence comes into play. Thanks to AI and machine learning technologies, ITSM professionals can now predict potential problems faster and with a much higher degree of accuracy. As a result, the end user (or “customer”) enjoys a much more positive experience. In other words, everybody wins.

What is Artificially Intelligent Service Management?

The core principles of ITSM remain sound. The introduction of AI into the mix doesn’t change this. Instead, it enhances it. AISM simply takes the fundamental concepts and processes of ITSM – incident response, service request management, etc. – and leverages newer and better technologies to make them even more effective. In the context of IT service management, AI can be applied to improve, simulate and/or replace the work of a human agent.

You may be asking yourself, “Isn’t this really just automation?” The answer isn’t necessarily cut and dry. The truth is, we’ve been automating processes and workflows for decades, and ITSM is no stranger to this technology. The difference is that with AI, these processes and workflows become more intelligent and independent. Rather than just carrying out predefined or scripted instructions, AI is capable of identifying and carrying out required actions all on its own.

How does AISM work?

Now, let’s take a look at how AI can enhance the execution of ITSM activities.

Support Request Management

The basics of ITSM: an end user needs assistance. They either pick up the phone to call the help desk, send an email request, submit a support ticket or browse the self-service options (if available). The steps necessary to fulfill that incoming request are then followed and the user receives his or her desired outcome. The problem is, that outcome could potentially take hours, days, weeks or even longer.

Now, let’s look at that scenario with AISM at the helm. The end user initiates contact and immediately receives two-way support from an intelligent bot. They request what they need and the bot – relying on underlying technologies of machine learning, deep learning, neural networks and natural language processing – understands the request and responds accordingly. Rather than waiting for a human to take action, AISM can produce results for the end user within seconds.

Incident Management

The ability to react, respond to and correct an incident is one of the most basic components of ITSM. Traditionally, a form would be filled out. Perhaps the analyst might do a little research. Ultimately, the task is assigned to a team. There it might sit untouched for a while before it is either rejected, resolved or possibly even assigned to another team altogether. In the end, the incident is resolved, but after much back and forth and passing of the torch.

Enter AISM. The end user reports a problem via his or her self-service portal and an incident is immediately created. Thanks to artificial intelligence, however, that same end user may instantly be prompted with various suggestions that are pulled from the underlying knowledge base. This may result in resolution right away.

If not, it is turned over to a support analyst who is automatically provided with suggested resolution methods. The AI can even advise who the incident should be assigned to, what relevant implications may exist, the scope of the situation and more.

Problem Management

In a traditional ITSM setting, problem management would often involve a person taking the time to review prior incident patterns and trends and develop possible resolutions. Along the way, however, many twists, turns, delays and bottlenecks exist. For instance, let’s say a support agent grows weary of addressing the same incidents over and over. The problem may be investigated further. Perhaps some knowledge may be created and a change is even identified. But, given the chaotic nature of the ITSM environment, time passes and nothing really gets done.

Now, take that same scenario in the context of AISM. Instead of a frustrated human agent taking the initiative to identify and resolve problems, machine learning technology continuously scans patterns of data to pinpoint and present potential issues that should be investigated. What’s more, thanks to data processing and learning across multiple patterns of work, AI is even capable of proposing a solution, backed by data-driven risk and impact analyses. In other words, it takes the guess-work out of decisions.

AISM – From Reactive to Proactive and Beyond

Getting back to our original point – that the best customer experiences are anticipatory in nature – AISM enhances service management by facilitating the shift from reactive (meeting needs when they occur) to proactive (predicting and preventing issues from happening in the first place). There are three key ways AISM can do this:

  • Guidance – The end user has a need and AISM uses a connection with endpoint tools to identify and make suggestions based on that need.
  • Learning – Building a knowledge base used to be a hassle. Not with AISM. Thanks to machine learning and AI tracking systems, the knowledge base can naturally grow based on issues encountered over time.
  • Strategy – AISM is capable of identifying and recommending both changes to existing core services as well as new innovations to improve for the future.

Conclusion

As you can see, AISM follows many of the same principles, processes and best practices of ITSM. It’s just faster and more accurate. And with AI being leveraged to intelligently automate complex tasks at just about every operations level, IT professionals will be freed up to spend more time innovating and evolving to help achieve business goals.

Buckle up folks, because AISM is poised to be a true game-changer.

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The real secret to success for modern businesses

Intelligent automation has become one of the most talked about areas of enterprise technology over the past several years. The virtual workforce which was once just a concept has quickly become an integral component of modern business structure.

The technologies behind intelligent automation – AI and machine learning – have already begun delivering significant returns on investment for forward-thinking organizations that have taken a chance and chosen machine over outsourcing. Instead, those companies that want to remain a step ahead of the competition, operating efficiently and at maximum productivity, have found a way to complement and augment their human workforce with a virtual one.

On the flip side, employees that are wise enough to recognize the value that intelligent automation brings to their lives and to the success of their organization are thriving in this melting pot environment. They are freed up to focus their expertise on more complex and challenging projects while their robotic counterparts handle the day-to-day menial work and repetitive, manual administrative tasks on their behalf.

Widespread adoption of intelligent automation is also bringing to fruition an entire new class of jobs, just as experts predicted it would. Those roles that are being eliminated are making way for newer opportunities to reskill existing workers. After all, someone has to oversee the automation, not to mention identify additional entry points, plan for future deployments, etc. There’s plenty of room for robots and humans to work side by side in the enterprise of the future.

For organizations still on the fence, it’s important to realize that intelligent automation is the fastest and easiest way to digitize, something that’s going to be inevitable for success in tomorrow’s landscape. And automation is no longer something that is simply governed by IT. Today, this technology is capable of assisting with everything from employee onboarding to compliance and cybersecurity. To be fully utilized, automation must be viewed not as software, but as a capability across the entire organization.

Just as the industrial revolution redefined the way businesses operated a hundred years ago, the digital revolution is now upon us, offering a similar transformation. In many ways, this is a defining moment for business leaders and key decision makers. The way humans and technology interact is evolving yet again, facilitating far more than just quick wins, but sustainable and highly scalable success. In some instances, intelligent automation is enabling firms to bring offshore resources back in-house.

The key is striking the right balance between the human and virtual workforce with the goal of maximizing the use of technology while also focusing on retraining and redeployment of human resources. Once that balance is achieved, the sky is the limit for the organization.

If you’d like to see what intelligent automation can do for your business, simply request a free product demo and we’ll show you around! Or better yet, experience it for yourself with a free 30-day trial.

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7 Steps for Getting Started with AIOps

Today’s IT teams are dealing with a growing mountain of data. What’s more, they’re finding themselves having to use a multitude of tools in order to monitor and manage that data. In situations of technical outages, this can make it incredibly difficult and time-intensive to identify and resolve underlying issues. Anyone in business knows that even just a tiny amount of down-time can have a serious and costly impact on the bottom line. And it’s the IT team that bears the brunt of the burden.

Take, for example, the two largest supermarket chains in Australia. Last year, both experienced severe technical issues which forced them to shut down several stores while they worked on fixing the problem. Not only did those companies lose revenue during the shutdown, but they also suffered a serious blow to their reputation. In other words, customers were not happy.

To better and more quickly identify, resolve and prevent outages and other problems, organizations are turning to artificial intelligence for IT operations (AIOps) – the long-term impact of which will be nothing short of transformational.

What is AIOps

In simplest of terms, AIOps combines data science and machine learning functionality to enhance and/or replace the majority of IT operations functions. This includes performance and availability monitoring, event analysis and correlation, ITSM and automation. To put it even more simply, AIOps platforms gather and analyze all of the data produced by IT to extract what’s of value and present meaningful insights.

How to Get Started with AIOps

Step 1: Don’t put it on the back burner.

If you really want to reap the benefits of AI for your IT operations, the time to jump on the AIOps bandwagon is now. Don’t make this an afterthought or push it out as some far-off future initiative. Even if the actual deployment isn’t imminent, start preparing yourself and others within your organization by becoming familiar with artificial intelligence and machine learning capabilities today. This way, in the event that priorities shift and you need to implement sooner, you’ll already be a few steps ahead of the game.

Step 2: Be careful when choosing your initial test case.

The concept of AIOps at scale may seem overwhelming, but keep in mind that truly transformative initiatives almost always start small. Focus first on capturing knowledge, testing frequently and iterating as needed. You don’t need to be an expert right out of the gate, and not every project you spearhead will be a resounding success. Just be mindful of what you’re starting with and work your way up from there.

Step 3: Work on developing and demonstrating your proficiency.

If you are leading the AIOps charge in your organization, you’ll inevitably be the go-to subject matter expert, at least initially. It will be up to you to communicate and convey the value of the technology to your colleagues and others in leadership. Wear your role with pride and start assembling a team of others who can champion the cause alongside you. Start by identifying gaps that exist in skills and experience, and then create a plan to address those gaps together.

Step 4: Don’t be afraid to experiment.

There are already many AIOps platforms on the market that are incredibly complex and subsequently cost-prohibitive. As with any tech product or solution, it’s wise do experiment and test the waters. Keep in mind that more features doesn’t necessarily equate to a better product. Your organization may not need all those bells and whistles. If possible, take advantage of product demos and free trials. This will enable you to evaluate AIOps uses and applications specific to your business needs without having to invest too heavily or commit to one particular solution.  

Step 5: Expand your vision beyond the IT department.

Data management is a massive component of AIOps. Take a step back and examine your organization. Chances are very high that your existing teams are already skilled in this area and that there are data and analytics tools already present within your organization. Resist the urge to reinvent the wheel and be willing to expand your vision to look beyond the IT department. It could save you tremendous time, effort and money.

Step 6: Standardize whenever possible and modernize wherever it makes sense.

You can prepare your existing infrastructure so that it is capable of supporting an AIOps implementation in the future by developing a consistent automation architecture, immutable infrastructure patterns and infrastructure as code (IaC).

Step 7: Consider build-vs-buy.

Understand that there are a number of variables involved in making a shift to AIOps. Likewise, the platforms available on the market today will continue to evolve, as will the infrastructure and applications for which you are responsible currently. Be mindful of this as you weigh whether to purchase a solution or build one of your own. Ideally, the best answer will likely be a combination of the two, so be prepared to figure out which approach best applies where and by how much.

Over the past few years, AIOps has developed from an emerging category to an IT necessity. Successful companies are beginning to leverage AIOps to automate and improve IT operations by applying machine learning to their data. Furthermore, forward-thinking organizations will use AIOps to draw valuable insights from their IT data that will help drive strategic business decisions.

If AIOps is on your to-do list (and it certainly should be), the steps outlined above should help you to, at the very least, lay the groundwork so that when the time comes to implement, the process will go faster and much more smoothly.

Why wait? Experience the next generation of IT automation, powered by machine learning and artificial intelligence and get started on the fast track to successful AIOps deployment. Start your free 30 day trial of Ayehu today!

Episode #16: How the Coming Tsunami of AI & Automation Will Impact Every Aspect of Enterprise Operations – Capgemini’s Tom Ivory

May 1 2019    Episodes

Episode #16: How the Coming Tsunami of AI & Automation Will Impact Every Aspect of Enterprise Operations

In today’s episode of Ayehu’s podcast we interview Tom Ivory – Head of Intelligent Automation, Digital Operations, North America for Capgemini.

Thomas Preston-Werner, the American billionaire founder and former CEO of GitHub, once famously said “You’re either the one that creates the automation or you’re getting automated.”  Corporations around the globe are clearly heeding this admonition.  The current frenzy of POC’s, pilots, and other projects underway to automate various aspects of enterprise operations using AI & Machine Learning is staggering.  What about the results though?  Are organizations seeing the kinds of returns on automation that justify the investment?  And what about the impact to employees whose jobs are affected by automation?

To answer these and other questions, we turn to Tom Ivory, Head of Intelligent Automation, Digital Operations, North America for Capgemini.  Tom’s role affords him a global perspective on the digital transformation enterprises are undergoing as a result of these automation projects.  In this episode, we’ll learn that whatever jobs are lost due to digital transformation will be more than replenished by new positions including many that don’t currently exist, why every business that wants to remain viable must become a digital business, and how a college student studying art history might be just as qualified to become an AI expert as a computer science major.




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 Tom Ivory, Head of Intelligent Automation/Digital Operations North America for Capgemini and according to consulting.com, Capgemini which is headquartered in Paris, France and has over 200,000 employees in more than 40 countries, is the world’s second largest consulting firm by revenue. So Tom brings a truly global perspective on intelligent automation to our show.

Guy Nadivi: Tom, welcome to Intelligent Automation Radio.

Tom Ivory: Hi Guy. Thank you for having me on the show. I’m excited to talk with you today.

Guy Nadivi: Tom, even though our show is focused on intelligent automation, that term can be a bit ambiguous in some people’s minds. So my first question for you is, how do you define intelligent automation?

Tom Ivory: Right. Yeah. It is an ambiguous term. I agree. When we look at automation, we’ve been doing automation across the technology and services industry for decades now. But we’ve added this adjective “intelligent” to it. I think that separates from what we’ve done in the past, to what we’re achieving to do now as a result of using new emerging technologies, like the cloud. Like machine learning. The ability to analyze unstructured data in a more rapid rate. So adding intelligent to automation is enabling us to do things that we weren’t able to do when you say “business process management” for example.

Tom Ivory: You know that’s been around for awhile and we’ve looked at trying to automate processes but it was a very rules-based process where we looked at how to take existing patterns and use technology in order to create workflows. But now with intelligent automation we’re moving beyond just basic process automation, to harvesting data and automating that unstructured data in a way that can achieve business outcomes for us that we weren’t able to do in the past.

Tom Ivory: So when Capgemini looks at intelligent automation, we’re looking at the spectrum of robotic process automation, chat bots, virtual assistants, adding machine learning to give the ability to organizations, our clients, to teach software how to think for itself and reach this Nirvana that we’re all trying to achieve, this holy grail of artificial intelligence, where we can mimic human thinking, human behavior, without human intervention.

Guy Nadivi: So you’ve mentioned artificial intelligence and you’ve stated that “AI has quickly evolved from simply a tool to reduce costs to a strategic solution that enables our clients to re-imagine their business for accelerated growth”. Can you share with us some quantified examples of the accelerated growth you’ve seen AI provide?

Tom Ivory: Yeah absolutely. Capgemini has done a number of studies recently on intelligent automation including the journey to artificial intelligence. We’re seeing a huge impact with regards to enhancing customer service for example. We’ve looked at engagements where we’ve rolled out intelligent automation, maybe even implemented conversational commerce. The ability for their consumers, their clients, to engage with the organization has increased exponentially. We’re looking at upwards of 80 to 100% gains on their ability to leverage customer interactions for revenue opportunities which is a massive opportunity I think for organizations across the board. You know but even in the smaller engagements, where our clients are just starting out on their road map for intelligent automation.

Tom Ivory: Starting with robotic process automation, we’ve worked with a life sciences company in the last couple of years, who adopted RPA. We helped set their Center of Excellence up for them, and looked at what processes we can optimize and automate across their enterprise. Running from finance to procurement, to supply chain and more specific life sciences based processes around compliance and the GXP regulations that they need to adhere to. We were able to take out several hundred, and I think the number was actually 320 FTEs as a result of them adopting RPA and enabling them to take on this technology, which was equivalent to around $7 to $8 million dollars in savings.

Tom Ivory: The funny thing is they didn’t just fire all of those employees. They were actually able to redeploy them on more strategic projects, where they were able to use a lot of their skill set that they were doing with more finance transformation or HR transformation. They knew the processes, they knew the manual as is state. And they were able to get “trained up” and start working with some of these more sophisticated AI technologies that the company is adopting in order to help the company transform. So we’re seeing a re-skilling, even with some of the savings that are going on. We can talk about that in more depth throughout the conversation.

Guy Nadivi: Given the broad perspective that you have on AI projects, what have you seen are some of the most unrealistic expectations currently plaguing AI?

Tom Ivory: We’ve seen a number of unrealistic ones. You know this market is obviously attracting a lot of marketing, advertising, and investment dollars, so with that always comes a lot of hype as we’ve seen with popular trends in the past, within the technology industry. So it’s no different. Honestly I think AI is probably one of the more hyped technology trends that I’ve seen in my 20 plus year career. And I think a lot of people have the impression that it’s just going to come out of the box and work like magic. And that’s not the case at all. The technology is getting better out there, but at the end of the day, it really all comes down to understanding the processes and the operations within your organization, and how you can apply robotic process automation and artificial intelligence to those processes and operations.

Tom Ivory: Understanding your business, working with partners that understand your business, at that industry-specific level in order to re-imagine and re-engineer how you’re currently doing business in order to look at opportunities for growth, look at opportunities to impact customer service, how to leverage savings for research and development, and really go into this constant state, this continuous state of embracing innovation. And the tools, the technologies that are out there in the market and there are new vendors popping up every single day that we’re exploring and wrangling through in order to educate our customers to not necessarily just work out of the box and provide this magic road map to achieving artificial intelligence.

Tom Ivory: There’s a lot of preparation that goes with that and I think that’s why there is such a vast ecosystem of software vendors, service providers like us, consultancies, business advisors, and so on in order to make sense of all of this.

Tom Ivory: As it matures, that preparation will probably decrease like you’ve seen with ERP, BPM, and other technology trends that have come along the way, but right now we’re early on and everybody is trying to make sense of what’s available right now in order to put together the blueprint for an organization.

Guy Nadivi: You mentioned a few different areas where automation and AI are applied and that made me curious. If management has to choose one top priority for which to leverage automation and AI, should it be to improve the customer experience or improve internal operations?

Tom Ivory: That’s a great question and one that I’ve been asked many times by our customers. And at the end of the day I think it comes down to your internal operations because you need to get your house in order in order to serve your external partners and customers. If you can transform your finance organization, your IT organization, and all these various functions within the organization, then you can then transform the external world that you’re interacting with, right? But if you’re still doing manual processes and you’re living in ancient history with the way that you’re going about servicing your customers, then you’re not going to be able to achieve digital transformation.

Tom Ivory: So a lot of our customers right now are focused on back office processes. They’re looking at how to apply RPA and AI, use technologies like OCR, image recognition, voice recognition, speech to text. And look at how they can start transforming their HR, their finance, their IT organizations by automating and becoming more lean and nimble and seeing how they can become a “digital core” is what we’re referring to and then be able to take that nervous system and apply that to the external world.

Guy Nadivi: You know one of America’s legendary businessmen Ray Kroc who built McDonald’s into a global empire, used to tell people McDonald’s wasn’t really in the food business like most people thought. They were actually in the real estate business. And a CFO once clarified that by saying “the only reason we sell 15 cent hamburgers is because they’re the greatest producer of revenue from which our tenants can pay us rent.” Now that was a real aha moment for a lot of business people the first time they heard that.

Guy Nadivi: McDonald’s thought of themselves as a real estate company not a fast food chain. I bring this up because you’ve stated that “eventually all companies will become digital organizations that harness the exponentially growing volume of data inside and outside of their companies.” I think that quote can have an even bigger aha potential than the McDonald’s revelation because as you point out, becoming a digital organization applies to just about all companies. So my question for you Tom is – do you think most companies, regardless of industry have had the epiphany yet and realized, the opportunity digital transformation offers them.

Tom Ivory: I would even go further to say that we’re really at the point in time where digital is becoming meaningless because it’s become so pervasive and adopted throughout the industry. So yes I do think we’ve reached that tipping point where organizations have all had the epiphany for the most part. And if they haven’t then they are most likely facing extinction. We talk about Darwin in the corporate world and how those same principles are applied to survival of the fittest of organizations, and you look at the landscape of the Fortune 500 list, just in the last 10 years. I believe it’s more than 50% of any Fortune 500 company that’s on that list did not even exist or is a new company that was added to the list in the last 10 years.

Tom Ivory: I think most industry experts believe there to be even more catastrophic change in that Fortune 500 list over the next 10 years. So, when we look at that, I think it’s as a result of digital transformation. I think you see organizations that we always pick on like the Ubers and the Netflixes that didn’t exist a decade or so ago, and now they’re digital companies. They are operating in a completely different manner than the Blockbusters did years ago, with cloud based technology, using automation, becoming more intelligent through the use of machine learning, algorithms, and new advanced software technologies that are coming out into the market.

Tom Ivory: So I think the writing is on the wall for a lot of organizations that aren’t. We’re working with blue chip companies that have been around for 100 years. Manufacturers, retail organizations, grocery store chains, healthcare companies that are all achieving digital transformation right now because as I mentioned earlier, we work with a life sciences company that has adopted robotic process automation and their charter these days is no longer to be a medical device manufacturer. They want to be a digital health organization as new technologies emerge, with your iWatch for example that can monitor your heart rate and provide more detailed data that’s coming out on the market such as arrhythmia or risks of heart attacks.

Tom Ivory: We see advances coming right now in terms of diabetics being monitored and linked to their doctor’s office in real time. This type of thing is changing the health care industry by becoming more digital, working with data, and no longer about just going to a doctor’s appointment and taking a few tests and hearing a couple weeks later how everything goes. Everything is more instantaneous. More real time. More data focused. And that’s happening in every industry, not just healthcare. Like I said in manufacturing & elsewhere.

Tom Ivory: Industries also where you don’t even think sometimes, that shock and surprise me with how they are leveraging digital technologies. We see it in the agricultural business. For example, we’ve got firms that you know work with farmers. Clients that have basic business models that have been around for years that are now using digital technologies in order to isolate where they should apply fertilizer and how to avoid particular plants over weeds when they are harvesting crops and some really amazing things on how they are using digital technology. So we see this as definitely the epiphany as being reached, has been reached, I think it will only become a way of doing business here on out.

Guy Nadivi: Not long ago when Capgemini surveyed senior executives from nearly 1,000 organizations around the world that are already implementing AI, some pretty surprising results came back and I’ll just cherry pick a few of them for our audience. Three fourths of your respondents attributed a rise in sales of 10% or more as a result of their AI deployment. Two thirds reported there had been no reduction in overall jobs due to AI, and perhaps what would be the most surprising finding of all for most listeners, four out of five of the organizations surveyed reported that AI had created jobs.

Guy Nadivi: Tom with results like these, why does AI still frighten so many people?

Tom Ivory: It’s a great question. I think again it goes back to my comment earlier about the hype and the confusion around exactly what AI is. A lot of people, even you know consultants, technology professionals, business leaders, have a hard time defining artificial intelligence and don’t really understand the difference between machine learning, deep learning, artificial intelligence, and how to make sense of all of this and then you have a lot of industry pundits, authors with dystopian views that “All of our jobs are going to be eliminated”. Most conferences I go to, there is someone on stage that has just published a book that’s pretty much the end of times in terms of being employed and our economy is going to massively shift in terms of having government paying salaries from here on out.

Tom Ivory: I think that’s pretty extreme. I don’t really believe in that philosophy. I get frustrated every time I see the Terminator or a robot in the opening slides of a PowerPoint presentation. At the end of the day, this is process automation and it can be structured or it can be unstructured, and robotic process automation is the solution for automating repetitive, manual, task-based processes, which nobody wants to do in the first place. Right? So if the does eliminate a job, I do see that as an opportunity for growth, for all organizations, and us as a human race. And that’s an ability for us to do more meaningful things with our careers and I look forward to automation taking away a lot of those mundane activities that nobody really wants to do and enrich their lives as they go to work and get excited about the day ahead.

Tom Ivory: When it comes to artificial intelligence, that’s all about automating unstructured data and I think that’s where we can see some true, really amazing things that we’ve alluded to here so far in the conversation on industry-specific outcomes that we didn’t even dream of that we’ll be able to do that will lead to curing diseases and doing things that we wouldn’t have been able to do because of time or just trying to find a needle in a haystack or correlating decisions or data that we weren’t able to wrangle before.

Tom Ivory: I think people are scared because of the marketing projection out there, but in the reality we may take a few steps back with eliminating jobs that are only focused on repetitive manual tasks, but in the long run I think it’s going to be a huge opportunity to create jobs that never existed before.

Tom Ivory: I even look at our own organization. So I’ve been with Capgemini for over four years now and when I first came on board, the initial charter I was given was take a look at automation, we want to make sense of this and become a leader in the industry and make it pervasive in all the work that we do for our clients. We want to drink our own champagne so to speak. So for all of our managed services engagements, we wanted to put automation in there for productivity gains, for our customers, make us more competitive in the market, whether it was application managed services or BPO managed services or infrastructure managed services and at first, there was a lot of fear in our organization that a lot of jobs would be eliminated and there wouldn’t be room any longer for a lot of our employees.

Tom Ivory: But what’s actually happened is yes, we’ve been able to eliminate jobs, make our operations, our managed services capability more streamlined and efficient for our customers as we’re managing their ERP and apps portfolio or their business processes around finance and HR. But we’ve created new centers, here in the US and Columbia, South Carolina as well as offshore in India. There are new roles that never existed before. Robotic process automation architects. Machine learning experts. RPA developers. Heads of intelligent automation. Those roles didn’t exist before and I think this is creating just a new pool of talent that will now be able to do a lot more interesting things that will lead to significant business outcomes.

Tom Ivory: We’ll be able to reimagine business and IT processes that will be directly linked to new business ventures and initiatives such as enhancing the customer experience through chat bots and virtual assistants. So I downplay the hysteria out there, because I’m a firm believer in, just like with the internet and technologies before, that it will create more opportunities and enhance our lives more effectively than people realize.

Guy Nadivi: As AI has grown in importance, you’ve been a big advocate for the re-skilling of people and you’ve stated that “the heart of re-skilling is improving the way employees can leverage data to improve customer service, accelerate research and development, and innovate.” So my question for you is what are some of the top skills organizations should be re-skilling their people with to prepare for this paradigm shift?

Tom Ivory: Absolutely. I think communicating with executives is a big thing in our firm, I think that translates to across the board. I think the days of just showing up at work and taking orders and doing repetitive tasks is over. This new wave of robotics, automation, AI, the ability to replace human labor for those repetitive tasks is the new era that we’re in right now. The new skills will require communication, imagination, reinventing, not looking at everything just as it seems but using strategic thinking in order to be competitive and look at how we can apply new technologies to solve those business problems.

Tom Ivory: I always talk about how my kids will adopt to the new world. I have a 10-year old and an 8-year old. Two boys. In the last 10 to 20 years it’s all been about software coding and learning programming languages and I think that is still a very important skill set, but I think the liberal arts education has been downplayed to some extent, and now I feel we’re seeing a resurgence of that mindset, where being exposed to a lot of different things whether it’s learning foreign languages or studying art history, reading novels and classic literature and so forth. Having a well-rounded mind in order to communicate with a lot of different people in this global economic ecosystem, and coming up with new ideas, requires a background like that. So as much as software programming will continue to be important, I think we’ll also see here a rise in the liberal arts education again in order to bring critical strategic thinking that can be applied in a vast ecosystem of initiatives that they may be facing across the organization.

Tom Ivory: So yeah I think this paradigm shift that we’re seeing will make employees more empowered with the different skill sets that they can bring to the table, and I’m already seeing that with some of the college hires that we’re bringing into the organization right now, where they are not just mathematics majors or computer science majors but they may have studied art history in school and now they are learning on how to apply artificial intelligence to business initiatives with our clients and I think that’s very exciting because you need new schools of thought. The corporations really right now are very hungry for new thinking and re-imagining the way that they’re doing business.

Tom Ivory: We’re doing that right now with a number of customers, from McDonald’s to Coca Cola and bringing digital transformation through new thinking and not just the same old way of doing things and applying the technology and just throwing technology at something, but really using strategic thinking to help them get to that next level.

Guy Nadivi: I’m sure the parents of those art history majors who may have been concerned about their kids’ employment prospects upon graduation, will be overjoyed to hear that there’s opportunities for them in the high-paying field of artificial intelligence.

Tom Ivory: I think you’re right. I think they will. And again it’s not to diminish any of the technology majors out there or the people that are very skilled in software programming but as you see, I mean a lot of the new software that’s coming out on the market, even with robotic process automation, it’s low code or even no code right? So even the software development process is getting automated to quite an extent and DevOps and Agile has advanced that quite a bit. So the emphasis is not just on the coding and nothing against coders out there, but it’s how do we take this software and apply it. I think people in our industry tend to focus too much on the technology sometimes and not as much on how the technology is an enabler for business outcomes.

Guy Nadivi: Tom, what should enterprise executives who have never dealt with AI know before deploying it?

Tom Ivory: Well I think for one, they should know that they need to do it. They need to get on this train. I read a quote by Gartner the other day that just within the next couple of years, I believe 40% of enterprises will deploy RPA software, and that’s up from 10% today. So we are on a big wave of adoption here, over the next 24 months and I see it, being in my seat, demand is through the roof right now for robotic process automation services. And it’s in all phases of maturity, from just downloading an RPA tool and understanding and getting training on how this all works and getting educated on it, all the way through “hey we’ve already deployed 200 to 300 bots and we want more help on how to scale even more”. Maybe they hit a wall and everything in between.

Tom Ivory: So I think it’s important for executives to know that this is real. There are real ROI savings. It’s not easy all the time. You need to work with people that have been in the trenches, that understand all the pitfalls and the obstacles along the way. But this is something that is certainly here and has arrived. I would also tell them that, and I think it follows some of the points that I’ve made in this conversation so far, that it’s not just about the technology, right? So the IT team is very important in understanding how the software will fit in their technology landscape and ecosystem, and things like security and the integrity of bots that are developed and how to ensure that there are no failures because they are going to rely on these bots like they’ve relied on people to show up to work and to be productive and try not to make any mistakes, right?

Tom Ivory: So that’s important to have a technology team that delivers the support and backbone to get RPA and AI initiatives off the ground. But at the same time, the business is instrumental in accelerating and helping scale the adoption of RPA and AI across the enterprise. So we for example work with organizations on process intake as a critical component of this operation. We bring in stakeholders from Supply Chain, Customer Service, Finance, IT, together. You want a program that will bring process intake and their request for automation across the organization and they need to have that seat at the table. Business leaders are instrumental.

Tom Ivory: I think that really supports why the RPA software, the big three vendors for example are receiving such lofty evaluations and investments from venture capitalists right now, because it reminds investors quite a bit, like a Salesforce, where business users are downloading, they’re installing that software, using it, and that’s a different mindset than some other technologies that have been out there where only an IT professional can get access to it. But with these tools, you can just go online and start using it, creating a bot. Like I said before, a lot of it is zero code, or very low code and it’s meant for a business user to be able to adopt it. I think that’s why we’re seeing such huge acceleration here. So my advice again to corporate executives is to bring in those business users that are now looking at this because you’re going to have to get a hold of this and streamline it throughout the organization instead of just having all these islands of automation going on throughout your organization.

Tom Ivory: That’s a different animal that you’re dealing with when you have business users starting to independently look at software to help them achieve some of the efficiencies in their daily jobs.

Guy Nadivi: All right. Well looks like that’s all the time we have for on this episode of Intelligent Automation Radio. Tom, thank you for very much for joining us today and providing some fascinating insights about the market for AI and automation. It’s been a pleasure having you as our guest.

Tom Ivory: Thank you, Guy. This has been fun and I really appreciate you having me on the show. Thank you very much.

Guy Nadivi: Tom Ivory head of Intelligent Automation Digital Operations North America for Capgemini. Thank you for listening everyone and remember, don’t hesitate, automate!



Tom Ivory

Head of Intelligent Automation, Digital Operations, North America for Capgemini.

As Head of Intelligent Automation for North America at Capgemini, Tom Ivory leads a practice that enables clients to digitally transform with Robotic Process Automation (RPA) and Artificial Intelligence. The Capgemini Intelligent Automation practice leverages RPA and AI to fuel next generation business operating models with the creation of a Digital Workforce. With over 20 years of experience in the technology services industry, Tom is a trusted advisor on how emerging technologies can drive growth for clients.

Prior to Capgemini, Tom held the position of Chief Operating Officer at HfS Research, a global research & advisory firm dedicated to how innovation impacts global business operations and was a pioneer in research on Robotic Process Automation in the global services industry.  Earlier in his career, Tom held senior roles with the Corporate Executive Board and software companies OpenText and TIBCO.  Tom holds a B.A. from the University of North Carolina at Chapel Hill and lives in Dallas, Texas.

Tom Ivory can be found at:

E-Mail:         tom.ivory@capgemini.com

Twitter:        @tivory

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

Quotes

“Honestly I think AI is probably one of the more hyped technology trends that I've seen in my 20 plus year career. And I think a lot of people have the impression that it's just going to come out of the box and work like magic. And that's not the case at all. The technology is getting better out there, but at the end of the day, it really all comes down to understanding the processes and the operations within your organization, and how you can apply robotic process automation and artificial intelligence to those processes and operations.”

"So a lot of our customers right now are focused on back office processes. They’re looking at how to apply RPA and AI, use technologies like OCR, image recognition, voice recognition, speech to text. And look at how they can start transforming their HR, their finance, their IT organizations by automating and becoming more lean and nimble and seeing how they can become a “digital core” is what we're referring to and then be able to take that nervous system and apply that to the external world.”

“A lot of people, even you know consultants, technology professionals, business leaders, have a hard time defining artificial intelligence and don't really understand the difference between machine learning, deep learning, artificial intelligence, and how to make sense of all of this and then you have a lot of industry pundits, authors with dystopian views that ‘All of our jobs are going to be eliminated’.”

“There are new roles that never existed before. Robotic process automation architects. Machine learning experts. RPA developers. Heads of intelligent automation. Those roles didn't exist before and I think this is creating just a new pool of talent that will now be able to do a lot more interesting things that will lead to significant business outcomes.“

“I think this paradigm shift that we're seeing will make employees more empowered with the different skill sets that they can bring to the table, and I'm already seeing that with some of the college hires that we're bringing into the organization right now, where they are not just mathematics majors or computer science majors but they may have studied art history in school and now they are learning on how to apply artificial intelligence to business initiatives with our clients and I think that's very exciting because you need new schools of thought.”

“I think people in our industry tend to focus too much on the technology sometimes and not as much on how the technology is an enabler for business outcomes.”

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
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

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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