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Episode #59: Why 2021 Is The Year Organizations Will Start Widely Trusting AI – Mist Systems’ Bob Friday

February 16, 2021    Episodes

Episode #59:  Why 2021 Is The Year Organizations Will Start Widely Trusting AI

In today’s episode of Ayehu’s podcast, we interview Bob Friday, Vice President, CTO, and Co-Founder of Mist Systems, a Juniper Company. 

If a parallel could be drawn between the history of Artificial Intelligence and a professional star athlete who rehabilitated his career, it might go something like this.  As a rookie, AI flashed occasional signs of brilliance that enthralled a million minds with the promise of greater possibilities.  Then it floundered, getting sent down to the minor leagues to overhaul & revamp itself.  Eventually it worked its way back up to the big leagues, and began fulfilling the expectations of greatness many had predicted.  Now that AI is delivering consistent superstar results, organizations seeking their own operational victories want to sign it to a long-term contract.  Has AI finally redeemed itself enough to gain everyone’s trust though? 

That’s a topic of particular interest to Bob Friday, Vice President, CTO, and Co-Founder of Mist Systems, a Juniper Company.  As a pioneer in smart wireless networking, Bob has seen a lot in his storied Silicon Valley career.  He stops by to share with us why 2014 was a watershed year for AI, why adoption of AI is accelerating for enterprises with complex networks, and the risks for companies who don’t develop an AI for IT strategy in the coming year. 



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 Bob Friday, Vice President, CTO and Co-Founder of Mist Systems, a Juniper Company. Now for those not familiar, Mist Systems is in the business of providing wireless networks with AI built in, to provide automated incident remediation. We haven’t spoken much on this podcast about that kind of intelligence being baked into an enterprise’s network, so we decided to bring Bob onto the show to gain further insights on how AI-powered networks might be part of an AIops-driven future. Bob, welcome to Intelligent Automation Radio.

Bob Friday: Guy, thank you for having me. This is a topic dear to my heart, so happy to be here.

Guy Nadivi: Bob, let’s start then with asking you if you could just share with us a bit about what path you took that led you to co-found Mist Systems and the AI work that you do.

Bob Friday: For me personally, this path to Mist and AI really started almost all the way back into, for people who remember, the eighties. When the FCC first came out with this unlicensed spectrum rules, that’s when I really got into the wireless space. For those who remember back then, I was doing Metricom Ricochet. This is building a nationwide packet wide network, and that’s where I really got my first taste of wireless. From there I really went off and co-founded a company called Airespace and that was back in the early 2000’s. And for those who remember back then, that’s when wifi was just becoming, going from a nice-to-have to a must-have. And that’s when I started really working with enterprise customers and really started trying to help them figure out how to manage these wireless networks that were coming into the enterprise space. From there, I actually sold that company to Cisco and it was really at Cisco where I became the CTO of mobility that I really started working with some very large enterprise customers. And there we saw wifi and wireless kind of go from a nice-to-have to must-have to really becoming a business critical. And that’s where I really saw the paradigm shift from enterprise customers wanting help managing these network elements, to where they really wanted help managing the end-to-end user experience. It wasn’t good enough to tell them that the EP or the switch is up and running, but they really wanted to know if they were going to put an app on that consumer device, that they can ensure that that consumer really had great internet connectivity. And that’s how I got into AI. You know, from there, it happened to be the convergence with AI was becoming popular. We finally had the technology. When I started Mist back in 2014, that’s really when AI kind of went from a marketing thing to a reality thing. We really had the compute and storage that we could actually use to solve interesting problems. So that’s how I got to Mist, that’s how I got to AI and that’s how I got to here today.

Guy Nadivi: 2020 was a challenging year for so many organizations. Bob, when it comes to AI for IT, what are the biggest lessons that enterprises learned in 2020?

Bob Friday: Like I said, I think one of the big lessons that they’ve learned in 2020 is that really AI is really becoming more than just marketing hype. I think for a lot of IT departments and enterprise businesses, AI has been kind of a marketing thing and not a reality thing. And I think when we look back on why is AI becoming real now? I think it really kind of started, like I said, back in the 2014. 20 years ago when I did my masters, I actually did neural networks and masters. But 20 years ago, the problem was we really couldn’t build neural networks that were big enough to do interesting things. Somewhere around 2014, we got this perfect storm of compute storage costs going low enough, datasets getting big enough, open stores, where we find we’re able to start building AI that can actually solve real problems. And I think that that is one of the things that they learned in 2020. I think the other thing they’ve learned is really a difference between AI versus ML. We’ve been using ML and machine learning to solve problems throughout most of my engineering career. Really in 2020, people have started to learn that AI is really doing something on par with a human. Whether it’s learning to drive a car, interpreting a medical MRI or x-ray, and really in networking it’s really about can we really build something that can do something on par with a network domain expert? And that was one of the inspirations for Mist was really for those who remember Watson playing Jeopardy. When I started Mist, when I saw Watson playing Jeopardy, I was like, if they can build something that can play championship-level Jeopardy, we really should be able to build something that can actually play networking Jeopardy. Do something on par with real network domain experts.

Guy Nadivi: Okay. So with 2020 thankfully behind us, can you talk about the role of AI and AIOps in the future of work?

Bob Friday: Yeah. So I think what we’re going to see in the future of enterprise IT work, we’re going to start to see these AI assistants actually start to become part of the IT team, right? And so I think what we’re going to start to see is IT administrators and businesses start to free up their IT teams to do more strategic things in the business. Case in point is right now we’ve got to the point where we can actually build these systems that can actually detect bad ethernet cables. That’s a very hard thing for a person to go detect. That’s an easy thing for an AI assistant with machine learning to find bad ethernet cables. So now you don’t have your IT team busy trying to basically go track down that bad cable. You can have AI assistants join the team. So I think what we’re going to start to see coming forward is really around IT starting to adopt these AI assistants onto their team as kind of a trusted member and start bringing that and start training those AI assistants like an employee coming on to their team.

Guy Nadivi: There are different shades of AIOps, but Bob, can you explain what the difference is between domain agnostic and domain specific AIOps?

Bob Friday: Guy, I think you always hear people talk about kind of narrow AI or general AI. And usually when they use those terms, they’re talking about kind of “Terminator AI”. And I think most people agree today, most AI is narrow. We’re teaching AI to drive cars, interpret medical records, images. When we talk about domain agnostic, domain specific, specifically we’re talking about in the IT space about different platforms that were designed specifically to solve a specific IT problem. And when you look at AI, it really starts with a question. You got to kind of answer, what questions do you want your AI assistants? So like when I started Mist, the questions we wanted to answer was really around why is the user having a poor internet experience? Why is the user having a poor connectivity experience? And that is really around trying to figure out what data you need. So when you think about domain specific, it’s really starting with that specific question or that specific problem. Domain agnostic is more about we’re going to take a platform, a generic platform, and try to train it to actually solve a problem. And right now I think if you look at the Gartner who kind of came up with these terms, the general consensus is that most enterprise businesses will get to an ROI quicker if they start with a domain specific platform versus a domain agnostic platform. When you look at having to solve these AI problems, there’s a lot more than just putting the data into the platform. It turns out, after doing this for the last six years, a lot of work goes right into the feature engineering. Even after you know the question you want to answer, you may spend months, weeks, making sure you have the right features you need to solve the problem. And that’s one reason when I started Mist, that’s the reason why I built my own access point. It wasn’t because I thought that the world needed another access point. It’s because I wanted to make sure I could actually get the data I need to actually answer that specific question of why that user experience is poor. Why are they having a poor internet experience? So when you look at domain agnostics and domain specific right now, I think most enterprise businesses are going to find that they will get to a quicker ROI with the domain specific platform where all the feature engineering has been done for them. Where the data has been cleaned up, they don’t have to worry about preprocessing the data. There is a specific solution that’s actually processed that data and has that solution up and running to answer a very specific question in cloud. As opposed to kind of do it yourself. So you kind of break it down to domain agnostic is kind of the do it yourself approach. Domain specific is you’re actually finding a solution, it actually solves a specific problem that needs to be solved.

Guy Nadivi: What market transitions, if any, have you seen Bob that are driving business and IT to adopt AIOps?

Bob Friday: As we kind of started the discussion earlier, when I started Mist, I was the Mobility CTO of Cisco and I was working for some very large retail customers. Some very large enterprises customers. And back then I was building these controllers with these kind of embedded software architectures. And what I heard from these customers was, you know Bob, before I put any of your stuff on the network, I need to make sure that your controllers don’t crash. I really need to make sure that you can keep up with my mobile development cycle. They were developing mobile code every week, right? The mobile developers had these very agile development environments. Whereas we were releasing code maybe once or twice a year type thing. And then as I mentioned before, they were really wanting to make sure they had end-to-end visibility. So I think one of the big market transitions is really going from this networking becoming business critical, where they were putting some sort of critical applications, whether it’s a robot in a distribution center or a consumer app on top of a consumer device, it was really around going to that business critical, networking was becoming business critical at that point of view. So that was one of the key market transitions is business going from that paradigm of managing network elements to really managing the end-to-end user experience.

Guy Nadivi: What are the biggest motivators and barriers that you’re seeing for AI adoption?

Bob Friday: I think as we mentioned, the biggest motivator is really these networks are becoming very much more complex. I mean the other big transition we’re seeing out in networking right now is watching the workflows move from on-prem, behind the firewall to public clouds. So if you look at Salesforce, Microsoft Office 365, we have people in workflows working from home now in addition to working from the office. We have workflows scattered all the way across from the private data center inside the enterprise to the AWS, the Googles and the Azures out there. So that is one of the biggest motivators is really how do you handle the complexity? And we’ve gotten to a point now where when we transitioned from CLIs to dashboards, dashboards were kind of the one way to deal with that complexity. Now to the point where there’s just too many dashboards. We’re getting to the point where a network IT person cannot deal with the amount of information and log files that need to be dealt with. And that is why we’re starting to see them move to more of these AI assistants. Because AI assistants and these conversational interfaces are what’s really helping our IT departments be able to get the data quicker. Instead of having to remember the hundred different dashboards you need to find something, you can now go to your AI assistants and basically just simply ask, “Please tell me, why do I have unhappy users right now?” And the AI assistant can do the work of basically aggregating the data necessary to answer that question. So that is kind of the motivator. The barriers is really around, as we mentioned before, the adoption of AI. When you look at these AI assistants, whether you head down the domain agnostic path, there’s barriers there, and that’s why it’s hard to get to the ROI quickly. If you do it yourself, you find that there’s a lot of feature engineering. Getting the data you need to answer the question is a barrier in itself. And that’s why we’re seeing more enterprises move towards these domain specific, where the domain expert is actually helping them bring a solution to this table that can actually solve an immediate problem. So I think from a motivator point of view, it’s complexity that’s driving people to adopt these AI solutions. From a barrier point of view, it’s basically a knowledge base. We’re starting to ask our IT departments, we’re asking a lot out of the enterprise IT department nowadays. First of all, we asked them to move from the CLI paradigm into these dashboard paradigms. And then we’re asking them to become Python programmers. We built all these cloud APIs for them to help them automate and get data out of their networks quicker and easier. And now we’re asking our enterprise IT departments to start to wrap their heads around the data science and the AIs. We’re asking them to become a little bit of a data science expert enough so they can evaluate all the different options out there. So that’s probably the biggest barrier right now is this knowledge. Bringing our enterprise IT departments and educating them around the different data science options that are out there to help them solve their problems.

Guy Nadivi: Bob, with 2020 behind us, what do you expect AI for networking adoption will look like in 2021?

Bob Friday: I think the one word I would use here is acceleration. If anything, what we’ve learned over the last hundred years is the adoption of technology seems to be accelerating faster and faster. And I think this is going to be totally true with what we’re seeing with the adoption of AI. It’s becoming very clear that AI is going to be valuable in helping enterprise businesses basically deal with these complex networks going forward. I think we’re going to see the adoption accelerate. We’re going to see the technology definitely accelerate. We’re definitely seeing AI become, we’re in that kind of exponential hockey point of view of the AI adoption thing. That started back in 2014. Every year we’re starting to see more and more AI solutions show up in the marketplace. We’re starting to see more and more open source AI solutions on top of which we can build. So this is becoming easier and easier for startups like Mist to actually add value, because we’re building on the shoulders of giants right now. The mountain of AI open source code is just accelerating faster and faster every year, making it easier for us to actually bring value to customers. So acceleration is the word I would stick with for 2021.

Guy Nadivi: So with adoption of AI accelerating, as you’re seeing, what’s at risk for companies who don’t develop an AI for IT strategy in the coming year?

Bob Friday: I mean, interestingly, I think there’s two risks. There’s kind of the ultimate customer experience. And this is what we’re seeing with our big B2C customer, hospitality, retail. Anywhere where there’s a business-to-consumer experience, that’s the first thing. The risk is, hey, if you’re in that business and you’re providing experiences to your consumer or your employees, you are going to need AI to start to manage this end-to-end client-to-cloud connectivity experience. The more implicit type of risk is it’s really subtle that unless you’ve been doing it is really what I call the “vendor to customer support”. The interesting thing as we start to move to a cloud AI paradigm, your big, large networking vendors, they now actually have the data to do much more proactive, even on your support models, your networking support models. And this is the one thing I learned at Mist interestingly is what we did organizationally. There was the technical, architectural issues of building real-time pipelines for AI, but there’s actually an organizational component here really around combining customer support with the engineering team at Mist. And that was the key to success, to really bringing a new support model into the enterprise. Where we as a vendor now can actually help our enterprise customers proactively send broken hardware now. If there’s a broken piece of hardware or software in a network, the customer doesn’t have to send us an RMA ticket, we know it. So we can be very proactive on helping, saying, “Hey, we know there’s a broken AP out there, a broken switch out there. There’s one in the mail for you.” That’s a total paradigm shift of what they’ve had to deal with in the past of arguing with their vendors about networking problems. Which usually turns into multi-day, multi-week discussions about sending log files back and forth to each other. So that’s probably the other big thing that we’re going to start to see around the risks for companies who don’t start to really wrap their arms around, and their heads, and start to internalize where AI can help their businesses.

Guy Nadivi: Interesting. Staying with 2021, what are your biggest 2021 AI for IT predictions that you’re most excited about?

Bob Friday: Yeah, for me personally right now, my big focus in prediction for 2020 is trust. I think it’s become, I think people are starting to become aware of AI/ML. You know, they’re starting to understand that AI/ML is more than just marketing hype. They’re starting to see it actually solve real problems that are relevant to their businesses. I think 2021 is a year of trust. How does an AI assistant earn the trust of the IT department to be a trusted member of that team? So when I look at 2021 and AI, where we are in the journey right now, it’s about conversational interfaces and bringing that AI assistant into AI as a trusted member into the IT team.

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

Bob Friday: As the saying goes, every journey starts with the first step. So my words of wisdom for a CIO, a CTO who hasn’t started the journey is to take that first step. It seems daunting sometimes, and probably that first step really starts around the question and the data. If you’re just starting this journey, first start with the question you want to be answered. What do you want to leverage AI to do? What human characteristic, what human task do you really want AI to take on? And that’s back to the point of really what is the difference between AI and ML? And I try to highlight to people sometimes, AI is really about building solutions and software that actually does something on par with the human. So for that first step is really thinking about what are you asking AI to do on par with human? For me personally at Mist, it was really about building a solution that really was on par with networking IT domain experts. Can we build a solution that really can answer questions and manage networks on par of network domain experts? So I would say that’s the first step I would recommend to CIOs, CTOs is really look at the question, what human behavior are you trying to mimic in your business that you think AI can help you with, and then move on to the data. Once you’ve got that figured out then start working on the data, making sure you have the data that kind of answer that question.

Guy Nadivi: Interesting. A new way of thinking for IT executives. All right. Looks like that’s all the time we have for on this episode of Intelligent Automation Radio. Bob, it’s very interesting to hear about AI-powered networks when we mostly only hear about AI as an add-on to an existing network. So I’m very much looking forward to seeing how your approach to AIOps plays out in the near future. Thank you so much for coming onto the podcast.

Bob Friday: Guy, thank you for having me and it’s been an honor.

Guy Nadivi: Bob Friday, Vice President, CTO and Co-Founder of Mist Systems, a Juniper Company. Thank you for listening everyone. And remember, don’t hesitate, automate.



Bob Friday

Vice President, CTO, and Co-Founder of Mist Systems, a Juniper Company. 

Bob Friday is VP/CTO of the AI-Driven Enterprise at Juniper Networks and co-founder of Mist, a Juniper Company. Bob started his career in wireless at Metricom (Ricochet wireless network) developing and deploying wireless mesh networks across the country to connect the first generation of Internet browsers. Following Metricom, Bob co-founded Airespace, a start-up focused on helping enterprises manage the flood of employees bringing unlicensed Wi-Fi technology into their businesses. Following Cisco’s acquisition of Airespace in 2005, Bob became the VP/CTO of Cisco enterprise mobility and drove mobility strategy and investments in the wireless business (e.g. Navini, Cognio, ThinkSmart, Wilocity, Meraki). He also drove industry standards such as Hot Spot 2.0 and market efforts such as Cisco’s Connected Mobile Experience. He holds more than 15 patents. 

Bob can be reached at: 

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

Email: bfriday@juniper.net 

Twitter: https://twitter.com/WirelessBob 

Quotes

“When I started Mist back in 2014, that's really when AI kind of went from a marketing thing to a reality thing. We really had the compute and storage that we could actually use to solve interesting problems.” 

“…right now we've got to the point where we can actually build these systems that can actually detect bad ethernet cables. That's a very hard thing for a person to go detect. That's an easy thing for an AI assistant with machine learning to find..." 

“…the general consensus is that most enterprise businesses will get to an ROI quicker if they start with a domain specific platform versus a domain agnostic platform.” 

“If anything, what we've learned over the last hundred years is the adoption of technology seems to be accelerating faster and faster. And I think this is going to be totally true with what we're seeing with the adoption of AI.” 

“Every year we're starting to see more and more AI solutions show up in the marketplace. We're starting to see more and more open source AI solutions on top of which we can build. So this is becoming easier and easier for startups like Mist to actually add value, because we're building on the shoulders of giants right now.” 

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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
Episode #17: Back to the Future of AI & Machine Learning
Episode #18: Implementing Automation From A Small Company Perspective
Episode #19: Why Embracing Consumerization is Key To Delivering Enterprise-Scale Automation
Episode #20: Applying Ancient Greek Wisdom to 21st Century Emerging Technologies
Episode #21: Powering Up Energy & Utilities Providers’ Digital Transformation with Intelligent Automation & Ai
Episode #22: A Prominent VC’s Advice for AI & Automation Entrepreneurs
Episode #23: How Automation Digitally Transformed British Law Enforcement
Episode #24: Should Enterprises Use AI & Machine Learning Just Because They Can?
Episode #25: Why Being A Better Human Is The Best Skill to Have in the Age of AI & Automation
Episode #26: How To Run A Successful Digital Transformation
Episode #27: Why Enterprises Should Have A Chief Automation Officer
Episode #28: How AIOps Tames Systems Complexity & Overcomes Talent Shortages
Episode #29: How Applying Darwin’s Theories To Ai Could Give Enterprises The Ultimate Competitive Advantage
Episode #30: How AIOps Will Hasten The Digital Transformation Of Data Centers
Episode #31: Could Implementing New Learning Models Be Key To Sustaining Competitive Advantages Generated By Digital Transformation?
Episode #32: How To Upscale Automation, And Leave Your Competition Behind
Episode #33: How To Upscale Automation, And Leave Your Competition Behind
Episode #34: What Large Enterprises Can Learn From Automation In SMB’s
Episode #35: The Critical Steps You Must Take To Avoid The High Failure Rates Endemic To Digital Transformation
Episode #36: Why Baking Ethics Into An AI Project Isn't Just Good Practice, It's Good Business
Episode #37: From Witnessing Poland’s Transformation After Communism’s Collapse To Leading Digital Transformation For Global Enterprises
Episode #38: Why Mastering Automation Will Determine Which MSPs Succeed Or Disappear
Episode #39: Accelerating Enterprise Digital Transformation Could Be IT’s Best Response To The Coronavirus Pandemic
Episode #40: Key Insights Gained From Overseeing 1,200 Automation Projects That Saved Over $250 Million
Episode #41: How A Healthcare Organization Confronted COVID-19 With Automation & AI
Episode #42: Why Chatbot Conversation Architects Might Be The Unheralded Heroes Of Digital Transformation
Episode #43: How Automation, AI, & Other Technologies Are Advancing Post-Modern Enterprises In The Lands Of The Midnight Sun
Episode #44: Sifting Facts From Hype About Actual AIOps Capabilities Today & Future Potential Tomorrow
Episode #45: Why Focusing On Trust Is Key To Delivering Successful AI
Episode #46: Why Chatbots Are Critical For Tapping Into The Most Lucrative Demographics
Episode #47: Telling It Like It Is: A 7-Time Silicon Valley CIO Explains How IT’s Role Will Radically Change Over The Next Decade
Episode #48: How Microsoft Will Change The World (Again) Via Automation
Episode #49: How One Man’s Automation Journey Took Him From Accidental CIO To Unconventional VC
Episode #50: How Automation Helped LPL Financial Grow Into The Largest Independent Broker Dealer In The US
Episode #51: Why Cognitive Architecture Might Be An Early Glimpse Of A Future With Artificial General Intelligence
Episode #52: Chatbots Aren’t Human, So Don’t Expect People To Pretend They Are
Episode #53: Why End User Experience May Be A Better Measure Of Automation Success Than ROI
Episode #54: How Digital Dexterity Will Generate Competitive Advantage For Agile Enterprises
Episode #55: Is It Time To Start Hiring Digital Coworkers So Human Staff Can Spend More Time With Customers?
Episode #56: How Intelligent Automation Will Empower People, Transform Organizations, & Improve Our World
Episode #57: Can The World’s Largest ITSM Vendor Innovate Fast Enough To Maintain Its Meteoric Growth?
Episode #58: What Works? A Senior Partner From Bain Articulates The Keys To Automation Success

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Using Self-Service ITSM Automation to Supercharge IT Support

Using Self-Service ITSM Automation Tools to Supercharge IT Support

In today’s digital landscape, organizations are facing increasing demands to do more with less, keeping expenditure at a minimum and efficient output at a maximum. In response, more and more enterprises are turning to artificial intelligence to bridge the gap.

But what about internal customers? Couldn’t they, too, benefit from AI technology? The fact is that the IT help desk has become an indispensable component of business success. With increasing pressures to cut costs and a growing demand to drive efficiency, however, IT technicians and administrators often find themselves struggling to keep their heads above water. As a result, delays and bottlenecks impact end-user productivity, and IT talent is wasted.

Self-service ITSM automation has the potential to revolutionize the way the service desk is run, transforming inefficient, manual-laden workflows into a streamlined, self-driving operation.

What is ITSM automation?

Self-service ITSM automation tools, such as intelligent chatbots, have the ability to extract data from various sources, such as best practices and existing user guides to help end users resolve issues quickly without having to open help desk tickets.

The Role of Automation IT Service Management (ITSM)

Some experts estimate that anywhere from 30% to 50% of all Level 1 help desk support functions are repetitive in nature. Not only are these tasks time consuming and monotonous, but they are also quite costly from a human resource perspective.

Paying skilled IT personnel to perform laborious elemental work day in and day out isn’t just a waste of money. It’s a tremendous waste of talent. And when the work isn’t meaningful, the risk of employee turnover also goes up.

Meanwhile, from an end-user perspective, sending help desk tickets and waiting for responses impedes productivity. So, not only are IT agents bogged down by tedious requests, but the entire workforce can potentially be impacted.

A Better End-User Experience

Introducing automation into the IT service management process enables organizations to shift the regular and repetitive tasks and workflows away from human agents and toward AI-powered software. Self-service ITSM automation tools are capable of answering simple user inquiries, troubleshooting issues and providing self-service remediation options.

When an end user has a problem that they need IT’s assistance to solve, they can get their answer or resolution via a quick chat using a conversational electronic interface — just as customers do when using a live chat.

As a result, end users no longer have to wait for resolution. In fact, in many cases, employees can be empowered to use self-service options to resolve issues entirely on their own.

Cutting Costs

Simple requests, like password resets, may be necessary, but they’re also time-consuming and costly. Consider the time it takes for the end user to get locked out, open a ticket to the help desk and wait, as well as the time it takes the IT agent to manually process the request. Surely there are better ways for talented IT professionals to spend their time and energy.

Shifting simple but essential tasks like this from human to ITSM automation can save tremendously, both in time and in end-user productivity levels. And this is just one example. Take into account the aforementioned 30% to 50% of other repetitive Level 1 help desk functions, and you’ve got something you can really take to the bank.

Finally, though equally as important, introducing self-service ITSM automation can take much unnecessary pressure off of IT personnel. According to Gartner, artificial intelligence and machine learning can free up to 30% of support capacity for IT service desks. Rather than wasting time and energy on mundane, tiresome tasks, IT workers can use their creativity and cognitive abilities to perform work that interests and challenges them.

Getting Started With AI And Chatbots

If your organization decides to invest in self-service automation, maximize your investment by looking for quick wins that solve specific ITSM issues, or tasks that can be automatically performed by a bot. These are typically relatively easy to automate but will produce a fast and measurable return on investment.

A good place to start is with a simple virtual support agent (VSA) that can create and assign tickets, escalate tickets to real agents, assist end users with questions and provide important updates on critical incident IT and security.

Intelligent self-service ITSM automation can take that a step further. Here are a few good places to start:

• Ticket handling: Categorization, prioritization and assignment of tickets.

• Level 0 support: Leveraging artificial intelligence to provide 24/7, self-service support.

• AIOps: Use of advanced analytics technologies to proactively detect, diagnose and address problems.

• Decision support: Utilization of the predictive capabilities of machine learning algorithms to make better, more data-driven decisions.

Simply put, ITSM automation has the potential to supercharge the IT help desk, skyrocketing the productivity of both the support agents as well as end users. This ultimately results in greater efficiency, lower operational costs, improved retention and the opportunity to innovate at a much faster rate.

And in today’s digital age, this is what will separate the success stories from the failures.

How AI Can Transform the Decision-Making Process

How AI Can Transform the Decision-Making Process

Fifty years ago, businesses relied almost exclusively on human judgment for key decision-making. While some data existed, it was professionals and their intuitions, honed over years of experience, who were central to the process of determining good vs. bad and safe vs. risky. Not exactly the most ideal solution.

From there, we moved to data-supported decision making. Thanks to the growing number of connected devices, business leaders were able to access unimaginable volumes of data – every transaction, every customer interaction, every macro and micro-economic indicator – all available to make more informed decisions.

Unfortunately, even this approach had its limitations. For one thing, leveraging such a massive amount of data wasn’t feasible, which left a summarized version. This often obscured many of the patterns, insights and relationships that existed in the original data set. Further, cognitive bias from humans still existed.

Enter stage three: AI-powered decision support. Artificial intelligence is already ahead of the game because, provided the data being used is accurate, it’s not prone to cognitive bias. Therefore, it is more objective in its decisions. Furthermore, AI is better capable of leveraging not just mountains of data, but also all the information contained within that data, allowing for a much higher degree of consistency and accuracy.

As a real-world example, decision support that is powered by artificial intelligence can determine with much more certainty what the optimal inventory levels are, which ad creative would be most effective and which financial investments would be most lucrative.

While humans are essentially removed from the workflow, however, the purpose of introducing AI into the mix is to enhance and enable better decisions than what humans are capable of achieving on their own. In other words, the ideal scenario would involve both humans and AI working in tandem to leverage the inherent value of both for the benefit of the organization. In fact, there are many instances in which business decisions depend on more than mere data alone.

Take, for example, inventory control. While AI may be leveraged initially to objectively determine the appropriate inventory levels for maximum profitability, other information that is inaccessible to AI but incredibly relevant to business decisions may also come into play. For instance, if the organization is operating in a highly competitive industry or environment, human decision makers may opt for higher inventory levels in order to ensure a positive customer experience.

Or, let’s say the AI workflow indicates that investing more in marketing will generate the highest ROI. That company may decide, instead, that it’s more important to focus on areas other than growth for the time being, such as improving quality standards.

So, where artificial intelligence offers consistency, accuracy and objective rationality, other information that is available to humans in terms of values, strategy and marketing conditions may merit a change of direction. In these cases, AI can essentially generate a number of different possibilities from which human decision makers may select the best course of action based on the whole picture at hand.

The key takeaway is that humans are no longer interacting directly with data, but rather the insights produced by artificial intelligence’s processing of that data. Culture, strategy and values still remain a critical component of the decision-making process. AI is basically a bridge to marry them with the objective rationality that cannot be achieved through human cognition. Essentially, it’s a “best of both worlds” situation. By leveraging both humans and AI together, organizations can reach better decisions than they ever could using either one alone.

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4 Ways Intelligent Technology is Transforming IT Support

The IT help desk, as it once existed, has forever changed. Driving this revolution has been the changing demands and expectations of digital customers. Simply put, intelligent technology is revolutionizing the world of IT support and service. These newer and more complex requirements of digital customers (which include employees) are causing IT teams to re-evaluate what they have to offer in terms of support and capabilities. If your organization or team is at a similar crossroads, here are four key areas on which to focus. 

IT Support Strategies

Regardless of whether your IT service desk happens to support a company of ten or a multi-location, enterprise level organization, the time to start thinking about intelligent technology is now. A great place to begin is by uncovering how employees have evolved into so-called “digital consumers” and, more significantly, how this evolution has changed the expectations they have of IT support.

To do this, evaluate the gap between your current situation and those changing expectations. In particular, look at your current channels of support. Poll employees to determine whether they feel the current channels they are using to contact IT support are sufficient and effective. Figure out what channels they might prefer. Also, examine common customer use cases and needs. Then, use this information to develop a strategy that incorporates newer, more innovative support channels (like self-service chatbots and virtual support agents).

Operating Models

How does your IT service desk engage with customers? The focus here should be more on this approach as opposed to best practices and ITSM processes. To bring your operating models in line with intelligent technology, ask yourself and your team the following questions:

  • Is your approach to IT support adequately in line with your realistic business needs and expectations? For example, what is the overall goal? Cutting IT support costs? Minimizing lost time and revenue at a business level? Understand your objectives and align your strategy accordingly.
  • Do your IT support agents understand “personas” of its customers (i.e. the common characteristics and behaviors they share)? Do your operational practices accurately reflect these personas?
  • How does your IT support desk measure success? Is it primarily related to how the IT service desk has helped and/or improved customer and business operations?

Once you’ve answered these questions, use the data you’ve gathered to identify any and all disconnects between IT support status quo and the actual needs and desires of both the customer as well as the business as a whole. These gaps are where changes must be made.

IT Support Tools

Without question, the future of IT support will rely heavily on automation. In fact, intelligent technology has already made it possible for organizations to augment their human workforce by leveraging ever-improving AI capabilities. With these advanced technologies deployed in the right areas, IT support teams are able to more effectively deliver on the increasing demands of digital customers.

Whether your service desk is already leveraging virtual support agents or is planning to in the near future, it’s important to ask the right questions. In particular:

  • Are your virtual agents being used to their fullest potential?
  • Are your virtual support agents being employed at the right points during the customer journey?
  • Do end-users feel that the VSAs improve their support experience?
  • Have you established a robust and accurate knowledge-base from which the VSAs can draw?

This last point is key, as virtual IT support will only be as good as the data behind it. That being said, creating an environment that blends high-tech automation with the human touch of IT support agents will position your organization for optimal success.

IT Support Staff

The question of whether human service desk agents will be assisted, augmented and possibly even replaced by intelligent technology is no longer an “if,” but rather a “when.” Getting employees onboard with the concept of virtual support isn’t always easy, especially those L1 agents who view automation as a threat to their livelihood. But it’s essential for an organization of today to remain competitive tomorrow.

Educate your IT support team on the value and benefits that AI has to offer. Make it about them and not just the company – how AI will make their lives easier, enable them to perform more meaningful work, provide an opportunity to learn new skills and make themselves more marketable, etc.. And start investing in your current workforce. Identify champions of the cause and reskill them so they’ll be ready to face the digital future with confidence. Get them excited about the possibilities that lie ahead!

There is no longer any doubt. IT support as we know it today is changing. Only those organizations that are willing to adapt and evolve their strategies, models, technologies and people alongside those changes will make it through unscathed.

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Using AI to Level Up the CIO Role

Using AI to Level Up the CIO Role

Today’s executive leaders are being challenged to identify ways to help their organizations work smarter and adapt to a rapidly evolving landscape, and all of this while keeping expenditure as low as possible. Perhaps no role is under as much pressure to achieve these things than that of the CIO. As the individual most frequently leading the charge for digital transformation, CIOs are tasked with not only overseeing IT initiatives, but also take on additional responsibilities, like market positioning and revenue generation.

Thankfully, there is a not-so-secret weapon that can be leveraged to help executives in this role not only meet the growing demands that they are facing, but exceed those expectations to reach even higher levels along their career path.

A Shift in Perspective

CIOs have transitioned from being viewed primarily as a service provider to becoming an integral business partner. This, of course, requires an alignment between the overarching business objectives and the organization’s IT capabilities, including the help desk, network and infrastructure.

Conventional responses are simply no match for the ever-growing demands of the cloud, big data and IoT. As such, CIOs must take appropriate measures to ensure that ITOps does not interfere with or limit the digital transformation journey.

Introducing AI into the mix – a process now formally referred to as AIOps – has become a go-to solution for forward-thinking IT executives. And while early adopters may have focused on monitoring data and automating remediation to reduce downtime, more experienced leaders have begun to embrace and employ the technology’s advanced capabilities, such as chatbots and virtual support agents (VSAs).

AIOps has proven to be effective in optimizing time and resource allocation for the IT department, primarily by automating both routine as well as complex and multi-faceted processes. This thereby reduces MTTR, eliminates unnecessary work and boosts productivity, improving service levels across the board.

Achieving Self-Funded Status

When applied aptly and broadly, AI has the potential to become self-funded. That is – its overall, long-term and sustainable value far outweighs its upfront and/or ongoing investment. This is the ideal scenario, especially for the CIO who is under immense pressure to minimize costs without sacrificing quality and quantity of output.

Once self-funded status is successfully established, the results can then be lined up with the various key metrics that business stakeholders are targeting.

To start, CIOs and their teams should identify tasks, processes and workflows that are not only able to be quickly and easily automated, but will also produce rapid and quantifiable ROI. These will typically include repetitive, manual activities that are tedious but necessary to continuous business operations.

When these basic, “low-hanging fruit” are identified and successfully automated, the next step for the CIO is to ask questions and uncover areas or segments where AI can be applied to make processes and experiences more intelligent. Looking for repeatable patterns is a good place to begin.

The third step should include the creation of a formal inventory which lists these various business opportunities for automation and AI, and prioritizes them based on impact and feasibility. Again, the focus should be on achieving the ultimate end-result of a fully (or at least largely) self-funded operation.

Like it or not, the role of CIO is rapidly evolving. With so many critical hats to be worn, it can easily begin to feel like an overwhelming and unachievable task. Thankfully, with tools like AI and intelligent automation, those in this leadership position can boldly push forward, not only achieving the goals set forth for them, but exceeding them at every turn.

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Episode #55: Is It Time To Start Hiring Digital Coworkers So Human Staff Can Spend More Time With Customers? – Roots Automation’s Chaz Perera

December 15, 2020    Episodes

Episode #55: Is It Time To Start Hiring Digital Coworkers So Human Staff Can Spend More Time With Customers?

In today’s episode of Ayehu’s podcast, we interview Chaz Perera, Co-Founder & CEO of Roots Automation.

Bots.  They’re everywhere, proliferating fast, and evolving their capabilities.  Most of us are familiar with them in the form of chatbots, crawlers, and of course RPA bots, but what about an emerging class of autonomous software programs called Digital Coworkers?  They’re not just next.  They’re now, and are already impacting the future of work in verticals such as the insurance industry. 

To learn more about Digital Coworkers and how they’ll interact with their human colleagues, we talk with Chaz Perera of Roots Automation.  As the former Chief Transformation Officer of AIG (America’s 4th largest insurer by assets, as of 2019), he sought a better way to deploy robotic automation in enterprise operations.  Chaz explains to us why Digital Coworkers succeeded where other bots failed.  Along the way we’ll learn what the magic number is of automatable processes organizations need to have in order to justify establishing their own Center of Excellence, why a bot’s greatest value might be freeing up staff so they can spend more time with customers, and what a future with Digital Coworkers might look like. 



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 Chaz Perera, Co-Founder and CEO of Roots Automation, which claims to have the world’s first self-learning, zero integration Digital Coworker bots. There’s lots of debate about whether automation in the form of bots will replace more people or augment more people, and that’s one of the subjects we want to hear about directly from an expert. So we’ve invited Chaz to join us today and share his thoughts with our audience. Chaz, welcome to Intelligent Automation Radio.

Chaz Perera: Hi, Guy. Thanks for inviting me on. I’m excited to share what I’ve been learning over these last few years in the automation space, and yeah, hopefully your listeners will take a few bits and bites from it, and help them improve their businesses.

Guy Nadivi: Well, fantastic. I think the first thing, Chaz, that everybody would like to know is what was the path that led you to start Roots Automation and your focus on Digital Coworkers?

Chaz Perera: Sure. I, at one time was the Chief Transformation Officer of AIG, a large multi-national insurance company. We were, at the time, we had been leveraging RPA technologies for roughly five years. It struck me as odd that we were spending, in our case then, many millions of dollars running an RPA program, and we were sort of challenged to get to value. I started to ask people, my peers at other companies how they were sort of dealing with that challenge, and I was coming across the same sort of problems, which is that the traditional RPA deployment methods, which really mean you need to spend a lot of money on a variety of different skill sets, you need to spend a lot of money on different technologies, and you need to spend a lot of time, typically many months, building a bot, and that is different from what RPA technologies typically promise, which is speed, and short time to value, and so I wanted to come up with a different way to deploy bots in a working environment. The second thing that struck me and the reason that we are focused so much on Digital Coworkers, as opposed to just bots, is at one time, I also ran shared services for the company. I noticed that when we were running shared services, often, people here in the U.S. or the U.K., for example, would often complain that the folks in the Philippines or in India, wherever our shared services entities may have been didn’t understand the business. They weren’t helpful, and so people would rather just keep the work onshore and do it themselves. I noticed that when we started to introduce bots into the operating environment, people had the same responses that the bots weren’t doing the job properly. They weren’t as complete as they needed to be, et cetera, and so it struck me that we needed to create bots that had more human-like qualities that, where humans could feel almost a fellowship between themselves and bots. Otherwise companies would be making huge investments in robotic technologies, automation technologies, but not getting value from them because people were essentially opting out of leveraging these technologies.

Guy Nadivi: Now, you just mentioned shared services, so I want to ask about an article you recently posted about the importance of standardization for automating processes. Before automating a process, in addition to it being standardized, it’s also a well-accepted best practice that it should be optimized, as well as thoroughly documented, so I’m curious, Chaz, what percentage of business processes are you seeing that are standardized, optimized, and documented before you begin to deploy your automation?

Chaz Perera: It’s a really good question, Guy. I think the reality is when you start to talk to a prospective customer, you find more often than not, and by more often, I mean, 70, 80% of the time, their processes are not well-documented. Their processes aren’t optimized. Their processes may be standardized in their mind, but not necessarily the way we, in the world of sort of process improvement, think of what a standard process looks like. All that being said, one of the things that we see at Roots Automation, and this is the advantage of sitting outside of a company and seeing the business processes of lots of similar companies, what we find is that the processes at one company are often similar to the same process at another company. A very simple example would be the accounts payable process within a finance team at one insurance company, is often very similar to the accounts payable process at another insurance carrier. In fact, what we’re finding is that for those back office processes, the similarities amongst those processes are closer to 80%, so what we end up doing is thinking about that remaining 20% and how we can build our Digital Coworkers in such a way that that 20% becomes something you can configure as opposed to having to always build from scratch. Similarly, as we move into the front office of insurance or the middle office of insurance, so underwriting and claims, we find similar things that the underwriting processes have a lot of commonality, insurance company to insurance company. The claims processes similarly have a lot of commonality, insurance company to insurance company. I suppose a long-winded answer to your question, even though these companies may not have standardized processes in their mind or they may not have documented these processes to the degree that we would like, we see enough of these processes to be able to say that there is standardization, there is commonality amongst the processes that we can leverage and take advantage of.

Guy Nadivi: In another recent article you posted about automation and return on investment, you touched upon the topic of automation centers of excellence, which generally speaking, are highly recommended for enterprises as part of their automation journey. However, you state that, “Statistically speaking, only about 4% of COEs deliver a positive ROI.” Is everyone wrong then about the need for centers of excellence, or is there a better path to take in order for them to successfully deliver an appropriate return on investment?

Chaz Perera: It’s a really good question. I would say there is no one size fits all. I think if you are a very large enterprise, having an automation COE makes a ton of sense, because you want to standardize on a certain subset of technologies. You want to be able to take advantage of the scale, that leveraging those same technologies will bring you, but if you’re a medium-sized company or a smaller business, the requirements around talent, the requirements around the technologies you need to bring forward are so vast that a COE is not an affordable exercise for most companies. Just to give a very simple example, if you want a bot to be much more human-like, and you think about how processes often begin today, they often start with an email, you need to think about Natural Language Processing, and so you need to bring some AI, machine learning expertise to the table. How many companies have people with NLP as a specialty, and maybe you need to now start to think about OCR as well, because sometimes you need to actually digitize these documents before you can run them through your NLP, and that OCR expertise and the computer vision that’s required to get OCR to work at a very high degree of accuracy is very expensive, and so that’s why I say the COE model doesn’t make sense, unless you’re a large enterprise. Excuse me, what I would add to just provide additional color, some of the largest enterprises in the world don’t need to have a singular COE. They could afford to have COEs by function as long as those COEs are operating as a federation, where the sharing, the learning, leveraging the same technologies so you can take advantage of your scale is still sort of core to that federated model. For everyone else, the best bet is to partner with a third-party advisory house like EY or KPMG to consider systems integrators, or look for a company like Roots Automation, which provides Digital Coworkers as a service, where you physically don’t have to worry about anything other than, “Here’s my business process. Can I leverage one of your bots to run it?”

The last thing I’d say, Guy, on that particular topic, what we found in our research is that the magic number for needing to have your own COE is if you believe you can automate 35 processes at your company, then it makes sense for you to have a COE because you can get to break even on that investment over the course of a few, short years.

Guy Nadivi: Very interesting. I’ve never heard that specific number. Chaz, you write a lot of really good articles, and in another of your recently published ones, you talk about the importance of enterprises upskilling their employees. That’s actually a topic we’ve spoken about quite a bit on this podcast. Let’s say I’m one of those employees, and let’s also assume that I want to acquire the skills needed to ride this growing automation wave that’s digitally transforming everything. Regardless of whether my organization pays for my upskilling or I have to pay for it myself, what are the top skills I should acquire to take advantage of job opportunities in automation?

Chaz Perera: I think it is wise for everyone, regardless of the industry they work in to try to learn to code in some language. There’s a mindset, a way of thinking through logic that coding brings forward, and I think that’s important. If you’ve never coded before, learn to code in some language, and it doesn’t have to be deep understanding of it, but just understanding the basics. If you want to be in the world of automation and more specifically in the world of RPA, it makes a ton of sense to try to learn using one of the base, more common RPA platforms like a UiPath, or a Blue Prism, or an Automation Anywhere. I believe all the RPA software providers out there for the most part, at least, provide a community edition or some sort of free edition that you can use to learn. Then, I would say that, thinking about my answer to an earlier question, Guy, around machine learning, and specifically computer vision and NLP, I’m not saying you need to become a data scientist, but I do think it’s important you understand what the art and the science of data science actually is, so that you can speak to it with some level of understanding so that when you are sitting with a data scientist and trying to solve some of these complex problems, you’re able to work off of a single dictionary, and you’re not sort of two or three steps behind. I’ll quickly add one more thing that sort of comes to mind, Guy. In the world of automation, it’s really important to remember there are humans at the end of every single transaction, and so change management has to be a skill that we all have. Automation technologies should be scary. They’re not intended to be scary, but the way you can make them scary is by not being transparent, by not talking about how these technologies can really help to improve the experience for customers, help to advance business objectives. If you introduce this stuff without a little bit of fanfare, without the right change management, that’s when people start to worry about their jobs, and so I would say also focus heavily on change management.

Guy Nadivi: Okay, so let’s talk about the humans in those automation transactions. Your company, Roots Automation specializes in Digital Coworkers, and yet, that’s a term I imagine might create some anxiety in people, and perhaps even trigger some resistance from employees fearing job loss, or radical changes to their job. On this podcast, we refer to that kind of resistance as robophobia, and it’s been known to create friction for enterprises deploying automation. Chaz, what would you say to someone experiencing robophobia at the thought of working alongside a Digital Coworker?

Chaz Perera: We were very careful when we thought about what Roots Automation’s product would be and how we would offer it to the world. The reason we chose the term, Digital Coworker, the emphasis on “Co”, is because we wanted people to recognize that our bots are not simply there to take over their work. Our bots are there to be one, an extension of their team, two, to essentially step in and do the types of work that people typically don’t enjoy doing, don’t get much satisfaction from, and by being able to engage in that more mundane and rote work are, you as a team member of this Digital Coworker, you should now have the ability to engage with customers, to work on projects, to do things that we all hope will create more value for the company than more of the transactional work that bots or Digital Coworkers are just great at. All that being said, transparency is really important, and so when we are talking to customers about implementing one of our Digital Coworkers, we think it’s really important that they talk about where these coworkers fit in on the team, the types of work that the Digital Coworkers are going to be doing, the types of work that the humans will now start to do. Fundamentally, in our platform, Guy, you talked about how we provide this self-learning bot. We don’t pretend as though our bots on day one will be able to do all the work that a human does today, and we also expect that they will be imperfect in that exercise, and so what we encourage is an interactivity between humans and bots, and in our product, that means that as the bots come across things they’re not sure about. If they’ve come across data that they’re not sure about or data that’s missing, or they can’t triangulate data, what they will do is they’ll stop, and they’ll actually ask one of their teammates, the human on the team, a set of questions. Through that interactivity, the bots are starting to learn. What we found is that our customer’s employees, the people that are teammates to these Digital Coworkers, really enjoy that experience. What it’s allowed us to do is to dial down very naturally some of that fear, some of that trepidation that people have had.

When our Digital Coworkers are introduced in one part of an organization and people start to get excited and start to feel like these bots are really part of a team, they naturally start to talk to other parts of the organization about the experiences they’re having, the excitement they’re having, and it helps to, again, dial down some of those fears, and so nothing is better than one employee talking to another to say, “Hey, it’s not what you think it is.”

Guy Nadivi: Chaz, can you share with us some of the outcomes, and particularly the ones that created that excitement from Digital Coworker deployments you’ve overseen?

Chaz Perera: Yeah, absolutely. Typically, what we are able to do for our customer, because we provide these pre-trained Digital Coworkers, what we’re able to provide is a coworker that’s ready to work in a customer’s environment typically between three to six weeks. Because of their ability to learn and engage, they get to productivity quite quickly, and as a result, our customers are seeing a break-even on that investment in as little as five months. If you extrapolate the benefit, because one of our Digital Coworkers is as effective as four to eight people at a company or four to eight people on a team, what we’re seeing is our Digital Coworkers will get a company to about a 250% ROI over the course of a five-year cost benefit analysis. What’s also interesting, Guy, what we’re seeing that’s less to do with the financials and much more to do with the feeling on the ground at a company, our customer’s employees really do endear themselves to our bots. They give them names, they give them personalities, they talk about them as though they’re real people. In fact, we regularly get emails from customers saying, “Hey, any chance Roxy could do this? Any chance Claire could do that?” That is probably the best indicator of success, that we know we can make the CFO happy by getting them to the financial value, but making sure that the line staff and the line managers are just as excited is really what we’re striving to do.

Guy Nadivi: Personalization is very interesting. Chaz, given some of the radical changes to the way people have worked since the start of the COVID-19 pandemic, what role do you envision Digital Coworkers can play going forward?

Chaz Perera: I hope that because of the pandemic, companies recognize how critical their human workers are to keeping customers apprised of what’s happening at the company, keeping customers highly engaged, keeping people loyal to brands and how important employees are to solving those substantive problems that customers often have. And so really, I hope that Digital Coworkers are enabling that by continuing to leverage these bots to not just handle your low complexity and mundane tasks, but because in the world of intelligent automation bots are starting to learn that they can move beyond the more rote tasks that companies often use RPA to do, and start to move towards the tasks that are slightly less sort of defined and structured, because then, you really can free your people to focus almost entirely on engaging with your customer, having conversations with them. Ultimately, that’s the thing that will allow you to build your brand and keep customers on your books in perpetuity.

Guy Nadivi: Chaz, as you know, the pace of innovation in our field can leave your head spinning, given some of the advances in automation, AI, and other digitally transforming technologies, but I’ll ask you this question anyways. What do you envision will be some of the biggest disruptions we’ll see in the next one to three years with respect to Digital Coworkers?

Chaz Perera: I think a couple things. The first, in the context of process mining, I think a lot of the process mining technology we have today is very good at developing a solution designed to document, providing you with that process map, but I imagine that over the next few years, we will be able to go from an exercise in process mining to an actual working bot, this concept of no code actually occurring in the world of bots, and so that would be a huge leap in terms of getting to value quickly, simplifying the exercise of developing and maintaining what are quite complex technologies. I think that would be a fantastic shift in something I do see coming. The second thing would be GPT-3, starting to get bots to not just be able to converse more naturally, but read more naturally. That will allow these bots, as I was sort of talking to earlier, start to move beyond the more mundane, rote, standardized tasks, and start to move into those tasks that require more and more judgment. Then lastly, and this is something we pride ourselves on at Roots, when we talk about Digital Coworkers, we want our coworkers to have very human-like qualities, and so today, our bots learn, our bots communicate, and they can do that on Slack, they can do that on email, whatever it might be, and our bots have this ability to sort of anticipate things. In our parlance, two examples of that might be that our bots, Guy, might recognize that you have your cup of coffee at 9:15 every morning, so that’s not a good time to bother you, come ask me questions at 10:00 instead because you’re more likely to provide a good answer. That’s an example of how we see bots being more human-like. What I hope to see over the next few years is that bots, our Digital Coworkers start to anticipate more. Imagine a Digital Coworker participating in your daily huddle, listening in on the conversation, hearing that the manager who’s leading that team saying things like, “These are the 10 things that are going to be the priority for the day.” “Everything else comes second,” and the Digital Coworkers actively reprioritizing work as a result of what it’s hearing. That’s how we get to a more cohesive office environment that has this happy balance between humans and Digital Coworkers, and certainly that’s what we’re striving for.

Guy Nadivi: Intriguing. Chaz, for the CIOs, CTOs and other IT executives listening in, what is the one big must-have piece of advice you’d like them to take away from our discussion with regards to implementing Digital Coworkers at their organization?

Chaz Perera: It’s interesting, I’ve heard a lot of IT leaders at companies of all sizes talk about agile, and that being the way they deploy technology quickly and create value quickly, but what’s often missing in those conversations are business people, so the IT part of the organization has adopted agile, is trying to move fast, but they’re not doing as good a job educating their business counterparts about how they need to operate, they need to contribute, how they need to learn in this agile-operating environment. You can’t have a true agile environment unless all parties are at the table with an equal understanding and an equal ability to contribute, so I would say that is something, thematically speaking, I see often that some people are left behind in that conversation. Then, the other thing I’d sort of throw out there, bots don’t fail gracefully, and so I would encourage IT leaders across an organization to think long and hard about the interactivity layer that needs to exist between humans and bots so that you don’t leave a human stranded. The thing that, going back to that original question, Guy, you asked about, “Why Roots Automation?,” one of the things that absolutely drove me nuts about the deployments of robotics at AIG, we would give our business users an Excel spreadsheet at the end of the day that said, “Here are all the things the bots did. Here are all the things the bots couldn’t do.” “For whatever reason, you need to pick it up.” That is not a great user experience. That is not a great customer experience, and so I would say spend time thinking about that interactivity layer and how you can create a better human experience for people that now have to work with bots.

Guy Nadivi: Setting realistic expectations, always a good idea. All right. Looks like that’s all the time we have for on this episode of Intelligent Automation Radio. Chaz, we love having automation innovators on the show, and it was great hearing your thoughts about the innovative role Digital Coworkers will play in the future of work. Thank you so much for coming onto the podcast.

Chaz Perera: Guy, it was a pleasure, and again, thank you for the opportunity to share my two cents at least with your audience.

Guy Nadivi: Chaz Perera, Co-Founder and CEO of Roots Automation. Thank you for listening, everyone, and remember, don’t hesitate, automate.



Chaz Perera

Co-Founder & CEO of Roots Automation

Chaz builds AI-powered, fully-functional digital coworkers that empower business owners to intelligently automate any process, leaving the time and resources of their human team to focus on strategic tasks they’re passionate about. As a result, businesses experience a 400-800% ROI and have happier, more productive team members.  

As a senior executive with 15 years of global operating experience leading successful transformation across multiple global businesses and functions, Chaz leverages data science, behavioral science, robotics, and AI technologies to build products, drive profitable growth, and reduce operating costs. He has a proven track record of building and leading high performing teams of up to 7,000 with a diverse range of skills, capabilities, cultures, and geographies. Chaz is recognized for his ability to develop and execute a strategic vision with sustained change, while being an agile leader with strong influencing skills who drives change with stakeholders from front-line teams to the board. 

Chaz can be reached at: 

Email: chaz@rootsautomation.com 

Phone:(973)713-3585 

Quotes

“I think the reality is when you start to talk to a prospective customer, you find more often than not, and by more often, I mean, 70, 80% of the time, their processes are not well-documented. Their processes aren't optimized. Their processes may be standardized in their mind, but not necessarily the way we, in the world of sort of process improvement, think of what a standard process looks like.” 

“…if you want a bot to be much more human-like, and you think about how processes often begin today, they often start with an email, you need to think about Natural Language Processing, and so you need to bring some AI, machine learning expertise to the table." 

“In the world of automation, it's really important to remember there are humans at the end of every single transaction, and so change management has to be a skill that we all have.” 

“…transparency is really important, and so when we are talking to customers about implementing one of our Digital Coworkers, we think it's really important that they talk about where these coworkers fit in on the team, the types of work that the Digital Coworkers are going to be doing, the types of work that the humans will now start to do.” 

“…bots don't fail gracefully, and so I would encourage IT leaders across an organization to think long and hard about the interactivity layer that needs to exist between humans and bots so that you don't leave a human stranded.” 

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
Episode #17: Back to the Future of AI & Machine Learning
Episode #18: Implementing Automation From A Small Company Perspective
Episode #19: Why Embracing Consumerization is Key To Delivering Enterprise-Scale Automation
Episode #20: Applying Ancient Greek Wisdom to 21st Century Emerging Technologies
Episode #21: Powering Up Energy & Utilities Providers’ Digital Transformation with Intelligent Automation & Ai
Episode #22: A Prominent VC’s Advice for AI & Automation Entrepreneurs
Episode #23: How Automation Digitally Transformed British Law Enforcement
Episode #24: Should Enterprises Use AI & Machine Learning Just Because They Can?
Episode #25: Why Being A Better Human Is The Best Skill to Have in the Age of AI & Automation
Episode #26: How To Run A Successful Digital Transformation
Episode #27: Why Enterprises Should Have A Chief Automation Officer
Episode #28: How AIOps Tames Systems Complexity & Overcomes Talent Shortages
Episode #29: How Applying Darwin’s Theories To Ai Could Give Enterprises The Ultimate Competitive Advantage
Episode #30: How AIOps Will Hasten The Digital Transformation Of Data Centers
Episode #31: Could Implementing New Learning Models Be Key To Sustaining Competitive Advantages Generated By Digital Transformation?
Episode #32: How To Upscale Automation, And Leave Your Competition Behind
Episode #33: How To Upscale Automation, And Leave Your Competition Behind
Episode #34: What Large Enterprises Can Learn From Automation In SMB’s
Episode #35: The Critical Steps You Must Take To Avoid The High Failure Rates Endemic To Digital Transformation
Episode #36: Why Baking Ethics Into An AI Project Isn't Just Good Practice, It's Good Business
Episode #37: From Witnessing Poland’s Transformation After Communism’s Collapse To Leading Digital Transformation For Global Enterprises
Episode #38: Why Mastering Automation Will Determine Which MSPs Succeed Or Disappear
Episode #39: Accelerating Enterprise Digital Transformation Could Be IT’s Best Response To The Coronavirus Pandemic
Episode #40: Key Insights Gained From Overseeing 1,200 Automation Projects That Saved Over $250 Million
Episode #41: How A Healthcare Organization Confronted COVID-19 With Automation & AI
Episode #42: Why Chatbot Conversation Architects Might Be The Unheralded Heroes Of Digital Transformation
Episode #43: How Automation, AI, & Other Technologies Are Advancing Post-Modern Enterprises In The Lands Of The Midnight Sun
Episode #44: Sifting Facts From Hype About Actual AIOps Capabilities Today & Future Potential Tomorrow
Episode #45: Why Focusing On Trust Is Key To Delivering Successful AI
Episode #46: Why Chatbots Are Critical For Tapping Into The Most Lucrative Demographics
Episode #47: Telling It Like It Is: A 7-Time Silicon Valley CIO Explains How IT’s Role Will Radically Change Over The Next Decade
Episode #48: How Microsoft Will Change The World (Again) Via Automation
Episode #49: How One Man’s Automation Journey Took Him From Accidental CIO To Unconventional VC
Episode #50: How Automation Helped LPL Financial Grow Into The Largest Independent Broker Dealer In The US
Episode #51: Why Cognitive Architecture Might Be An Early Glimpse Of A Future With Artificial General Intelligence
Episode #52: Chatbots Aren’t Human, So Don’t Expect People To Pretend They Are
Episode #53: Why End User Experience May Be A Better Measure Of Automation Success Than ROI
Episode #54: How Digital Dexterity Will Generate Competitive Advantage For Agile Enterprises

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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