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Episode #60: Why Lasting Automation Success and Competitive Advantage Require Process Excellence – FireStart’s Robert Hutter

March 1, 2021    Episodes

Episode #60:  Why Lasting Automation Success and Competitive Advantage Require Process Excellence

In today’s episode of Ayehu’s podcast, we interview Robert Hutter, Founder and CEO of FireStart. 

“If you can’t describe what you are doing as a process, you don’t know what you’re doing.” That quote was coined by William Edwards Deming, the famous 20th-century management consultant, who viewed process excellence as a sine qua non to performance excellence.  When it comes to digital transformation, process excellence is very much foundational to automation excellence.  Yet surprisingly, many organizations can’t fully describe what they’re doing as a process, suggesting that Deming was more prescient than many realize.

One man on a mission to bring process excellence to the mid-size market is Robert Hutter, Founder and CEO of FireStart. His firm focuses on human-centric process modeling, documentation, and enterprise workflow automation, resulting in holistic digital transformation.  We chat with Robert to learn how enterprise workflow automation differs from robotic process automation, the natural limitations facing citizen developers, and why you should value process as the most relevant asset in your company. 



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 Robert Hutter, Founder and CEO of FireStart. FireStart, for those not familiar, focuses on process modeling, documentation, and enterprise workflow automation in the mid-size market. This sort of holistic approach to automation is something we haven’t really dived into yet on the podcast and we don’t get a chance to talk much about the mid-sized market either, which is expected to be booming for automation. So having Robert on gives us a chance to kill two birds with one stone and hopefully learn some new things about both of these areas. Robert, welcome to Intelligent Automation Radio.

Robert Hutter: Hello everyone. Thanks for the invite.

Guy Nadivi: Robert, can you please share with us a bit about the path you took that led you to launch FireStart, and how you came up with that name?

Robert Hutter: Yeah, sure. So I originally started FireStart back in 2008, together with a colleague of mine. And from my professional background, I’m a software engineer, so I started software engineering and started building applications in the markets. And whenever we came across customer problems and we tried to fix them in the applications we were building, the process was always a very key element of the architecture and the fulfillment of the requirements in the end. But it always was a very hard topic to tackle. And since I was always a big fan of a model-driven approach and how to tackle problems and we have a fast track to building solutions, that was kind of the core idea why we started FireStart, to really have a platform that on the one hand can help you keep the governance on your process development on the business side, but makes it a lot easier in the translation to the automation and the IT architecture involvement. And with that in mind, how did we come up with the name? So we wanted to have something with emotional attachment and fire is a very emotional element that everybody knows, and usually has a very positive attitude towards, and it also has a little destructive element in it, which at some point you need, because very often you have to tear down old-fashioned mindsets and silos in order to let new processes and agility grow. And that’s also a major part in the name. And the third part, it’s like the “Start” is like, we want to get in production quickly. So it’s not about beating around the bush with the process, but the process is a core element, but you want to deliver results in a very fast fashion. And that’s like getting up to speed very quickly that also reflects in the name.

Guy Nadivi: So process architecture and governance are big parts of what you do at FireStart. Why are those such important elements in building a lasting automation strategy?

Robert Hutter: In general, whenever you want to build something, you should have a plan before you start building. Just like as an example, if you want to build a house you’re not starting right away building it and then see where you end up, but you would want to have a decent plan before you start building that gives you an idea how it looks, that gives you an idea how expensive it will be, or will it fall apart? And that’s a major element in efficiency on delivering the right results because if you have a plan before you start building it, you also have a better expectation about the results. And then once you have a plan, you can repeat the process. You can build multiple houses on the same plan that you have initially designed, and then it’s worth spending more time on the plan, so the better the execution, you can just get better and better and faster and faster. And this is why very often have the feeling that when you don’t have a proper process governance and process strategy in place, you very often end up in dead ends and you are kind of reinventing the wheel each time and you have nothing you can build upon. And processes are very heavily dependent on usually, so they’re not living side-by-side. But you build processes on top of other processes and therefore, you also need to manage the governance and translation between process changes, which is very essential if you want to make business process automation a part of your digitalization strategy. And it’s about all the segregation of duties. So you might have somebody doing the design, you have somebody else doing the cost calculations. You have somebody else doing the actual building the house. So process architecture is also about having segregation of duties, but that at the end of the day, everybody can collaborate on working together as a team, creating the results. And this is also what process management and workflow automation, it’s about getting people together so that they actively can work as a team in the whole engineering process.

Guy Nadivi: So you’re talking about a process-first approach, which of course makes a lot of sense, but I’m curious, what other approaches have you seen in the market?

Robert Hutter: Well, in my opinion, process is pretty much the most critical asset that you should start, where you want to build a design upon. And from the other approaches I’ve frequently seen in the last years, either they are very application-centric, let’s put it that way. So usually you start trying to squeeze an architecture into one application. And you always start with the boundaries of your application in your process design, which is…since a process is mostly a natural element that lives beyond a single department or beyond a single application, a very bad approach, because it already constrains you in the way, the creativity you can imagine about how this process could look and feel in the end. Another approach is very, let’s say data-centric or document form-centric, where everything is around the document that you’re processing and that is like the key artifact that you’re managing in the process. But then it’s very often hard to imagine parts of the process where it’s just about communication, where it’s just about collaboration, where not a single document is involved at all. And so I would always try to also start with a people-centric approach because when you do process design and you do automation, it’s mostly about making the day-to-day work of people easier to complete and to accomplish and to get satisfied in the work they do. And so I would really enforce a people-centric approach and start with the process design first, like a process-first approach and then on the second-hand, talk about data integration, talk about application design. But the process should be the core asset to start in the architecture and design.

Guy Nadivi: We’re hearing more and more about process mining and other AI-based discovery platforms being deployed as part of digital transformations. Robert, what role do you foresee process mining will play in enterprise workflow automation.

Robert Hutter: It’s definitely a topic that is very much picking up and it’s just like a technology that you can use to answer a very simple question, which is what is the current status in my day-to-day business, with the processes that we already have in place and that we already run on specific applications? And as easy as that question might sound, hardly any company is really familiar with what’s really going on in their applications. And either you bring in a lot of external consultancy or you have internal development teams that start analyzing to find out what the status quo is. And that, of course, takes a lot of time and is also very resource intense and costly, just to find out what’s going on in my processes. And process mining at the heart, is technology that can really shorten the time from, to understand what the current status quo of your processes is and to really raise the full visibility on the processes. And not like the PowerPoint. Nice to have visibility, but really like the brutal hard day-to-day facts about which variations you have in place, you never imagined that they could be in place. And it’s just like a good starting point to get the full truth of your process, so that you actually find in a quicker and more efficient way, where to tackle the problems. So you might find some variations of the process that you don’t want to have because they would violate maybe data privacy rights. And then you can start working with RPA and workflows to get rid of this unguided processes and bring in more structured and guided processes in place, so that at the end of the day, from the many thousand variations you might have, you can come down to the dozens of design variations that you want proactively establish at your company. And process mining just makes the fast ramp up for this first discovery phase – what’s going on in my process world today.

Guy Nadivi: Now, speaking of RPA, what is the difference between enterprise workflow automation and robotic process automation?

Robert Hutter: Yeah, it’s a term that very often gets mixed up in the market from my experience and they sound similar, but they’re heavily different topics, in order what problem do we tackle with them? So when we talk about enterprise workflow automation, we come from a organization or company perspective. And the goal here is to create process standards where all the applications and users are aligned to the corporate guidelines, so that the process runs as smooth and efficient as possible and also fulfills the enterprise requirements. The product process automation starts from the end user to tackle automation problems from a personnel perspective. So you would want to… Robert is always doing the same repeating tasks, so we try to build a software robot that can help Robert doing his job better. But it works in this scope from a individual perspective and it usually also works in the governance of the individual user. So I’m personally responsible for building the robot, for maintaining the robot, for phasing out the robot, if not needed anymore. So it’s in my personal governance. If we talk about enterprise workflows, the whole governance is a lot more difficult and complex because you need to have multilayer architecture. You need to have staging in place. You need to deal with data access and security. So you might end up in a conflict because the company wants to have standardized processes, and the user would maybe want to have highly individual requirements fulfilled with his robots. So in a ideal world, these should originally work hand-to-hand together and so that you can have the individual flavor and the enterprise standards combined in one automation strategy. And think of it like if you compare Excel sheets with SQL server. The one is like holding the governance on the corporate data, making the access available for the users, but it’s one single point of truth, where people can go and pick up the data in a governed and maintained way. Excel sheets, at the other hand, I would like to have my personal calculations and work with the standardized data, but with my personal pie charts that I just like, and nobody else. So I can change the Excel look and feel in my personal flavor, but I would work with the corporate data and standards that the SQL server provides me. And this is how enterprise workflow and RPA should be seen as technologies that just fulfill different requirements that automation needs.

Guy Nadivi: Robert, there’s a lot of talk these days about low-code/no-code automation and the rise of citizen developers as a result. When will orchestration tools be simple enough to use, that they’ve democratized automation for the masses?

Robert Hutter: Yeah. The markets, as you said, there’s like heavy code, low-code/no-code, as a way how to approach our technology problems or application engineering problems. Of course, heavy code is not the way to go because this is where we come from. It’s complex. It causes a lot of maintenance and a lot of costs on top. On the other hand, the other extreme, no-code is very much hitting the roof too quickly, when tackle more complex process problems. So ideally the way we want to tackle problems is a low-code approach, which applies to the 80/20 rule. So let’s say 80% of the engineering you can do with involving business users in certain parts of the process and just doing like the last mile, the last 20% for specific more complex parts of the process, where you actually need to go to a coding level. But in that combination, you have both scopes on how to tackle a problem in a meaningful resource usage, to engineer the solutions that you want to have. And I would definitely see low-code as the future because like the citizen developer approach, with the end goal, every citizen developer can do automations and application engineering on his own. I wouldn’t see that this should be the goal to reach because we already came from excellent macros and you know where we ended up with that one, and you see like the similar risk here, that it’s at the end of the day about engagement, that different stakeholders can do different parts of the engineering process. But at the end of the day, it’s for a citizen user, I would say. And the more understanding a citizen user has from the engineering process, the better he can be involved in the whole requirements, engineering, and testing, and roll out of the solutions. But it’s not necessarily that I see as the big goal that these users should be the one engineering the process. Maybe as an example, if you pick a manufacturing customer. So there are professionals coming into assembling a production line from electricity, from manufacture, putting the right pieces of the machinery together, with the goal that the workers then working in the process are perfectly aligned with every step to do in the process. And the same applies to process engineering. You need to have engineers, you need to have architectures, that know their profession in order to make the whole design right and that everything fits perfect. But it’s a profession and you need to bring professionals into the process because you don’t want to have the workers, every worker building his own assembly line, so that he needs to have all the knowledge because he will not be able to have all the knowledge to do that. And this is the way how I would see it.

Guy Nadivi: Getting back to enterprise workflow automation. Can you share results from one or two of the more interesting enterprise workflow automation projects you’ve worked on?

Robert Hutter: Yeah, sure. So we had really a lot of different customers and errors in the last two years, ranging from big corporates to really small businesses, applying BPM and workflow automation. Maybe one from the larger enterprise cooperates. We had a really successful project at a very large retail company with about 30,000 people worldwide, where they really had the need of applying a unified workflow strategy along the company, so that people are really harmonized in the way they are engaging in processes and just be more efficient in the way they work it. And it was actually a pretty cool project. We, from zero to production, had about two weeks to really roll out the first workflows for about 500 users involved. And there was literally no announcement, no training involved. And once we kicked off the project, the first two projects, which were about, I think one of the cases was HR onboarding, they had around 200 onboardings a month that they needed to schedule. And on the other hand, the second use case was the contract management with their suppliers because they have about 4,000 suppliers, where they need to do regular updates on the contracts they have. And these were the two use cases we rolled out. And the funny thing was like, after the rollout, the customer turned completely radio silent for about two months. So we had no feedback about how it’s working, what is the feedback? And then after two months, I got the call from the project manager that was leading the project. And I just approached him, saying, “Hey, how’s it going? What is the feedback?” And then he paused. And the first thing he told me was like, “Oh, it’s so cool. It’s so cool. I have to tell you.” And this was really just a very good memory because it really, the whole emotion spread out of him and you really could see the emotional attachment to the solutions we have built. And it really rolled out like clockwork. And they already had, after the first two months about another 20 projects in the pipeline that came from the business users with a lot of problems. They haven’t really figured out how to tackle them. But with that approach they really had a complete different way of thinking and how they tackle now, other problems. And we doubled the speeds of the processes within the first two months. The engineering effort was a fraction of multiple of 10, versus the heavy code approach they used to have. So they used to code everything on top of SAP, which was the core ERP backbone system. And now with the more agile process driven approach and FireStart, they virtually brought down the efforts and speed with a multiple of 10, which is pretty huge. And over time they rolled out, I think now, they are at 100 plus projects they executed on our platform. And they are a super happy customer for us for many years. And this was really very cool to see how our core technology and principles could be applied so successful on such a huge organization. On the other hand, we had a small customer in Switzerland, which is the Funk Insurance Group. They are managing a lot of processes regarding insurance management for the clients. And their biggest pain point was always that they were super dependent on the vendors with every little process change. They always had to go to the vendor and kind of begging for a change. And it was super expensive for every little process step that they turned. They would get an invoice with a couple of thousand euros for the adaption of the process. And that was kind of what was really frustrating for them. And then they got in touch with FireStart, installed the platform. Then they didn’t really know for one or two months how to tackle it because they had no experience. And then they called us. “Okay, we will lock ourselves up for a week. We will even change the building and we will do nothing else, then work our knowledge in FireStart and align our internal users on this newer way, to approach.” And after that one week, they were 100% up to speed, to do every process development and change completely on their own. Not with us involved, not with a partner involved. They kind of really got the freedom back to be 100% in the driver’s seat of their own process engineering. And this is also like a feeling, if you’re always dependent on whatever, it kind of frustrates you because you always have to, the feeling that you need to go back for something. And that’s especially the frustration that you mostly see in the business side, that IT resources are limited and there’s a lot of lost in translation effect and that causes a lot of frustration on both sides. And it’s really cool to see this freeing up the process and getting my freedom back and my process design was implemented on a small customer that literally had no IT experts, or software engineering experts in the team. It was mostly driven by the organization development department. And that was another thing, like really cool project, at the other end, on typical customer sizes that we address.

Guy Nadivi: Cool stuff. Given your experience with enterprise workflow automation, what do you think are going to be some of the biggest disruptions we’ll see in the next one to three years, with respect to automation, AI, and other digitally transforming technologies?

Robert Hutter: Yeah, of course, AI is currently the big thing in the market, since the technology is evolving that much and really at a incredible speed, which is amazing. And it will kind of freely enable a lot of predictability in the engineering. So if you think about predictable outcomes, predictable resource allocations for a specific process. So the better you can predict a certain future state, the better you can start now aligning towards it with your process adaptions. And I think in that regard, AI will play a massive role in how much predictability you will get into your process design. And on the other hand, data, the amount of data sets available is also massively exploding. You see a lot of data platforms popping up that really can provide your data as a service. And that again, in combination with AI, super powerful. And as we already discussed process mining. I would see process mining as a key technology in that space, helping in the translation from the raw data to the more intelligent process insights, that you can then again take as a starting point for initiating enterprise workflows, initiating robots to do the right job in the right timing and getting the state of predictability, is key in that regard. And I think a third thing that I would see in the future is in general, the way how you approach process design. Because from the history, it was mostly somebody who knows the process, designed the process, in order that other people can use it and run the process. And I think it’s always like the adaption always comes from the design part. So always the process manager has to be involved to adapt the process. And with that technologies, I think that will also be more self-aligning and kind of self-adapting, so that the process designer will start building the process design on top of just watching people doing the work. And with people adapting the work, it will have an implication on the process design and the change that will help in the background. So I think it will really reward the whole way, like learning by doing and by people changing their behavior, it will also change the process design in the backbone and therefore be a lot more self-adapting to changing to new requirements in the market.

Guy Nadivi: Robert 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 enterprise workflow automation at their organization?

Robert Hutter: The first piece of advice would be that whatever you do, start with the people in process first approach, because this is like the foundation to bring out successful outcomes of whatever you do. Then second advice would be don’t shoot for this. Like everybody should be able to fully do automations because it’s like there are professionals that should build the architecture, bring in automations and there are business users or citizen users that should consume the solutions with the least complexity. And this is the way to go. Remember that enterprise workflow automation and robotic process automation are at the end engineering disciplines. So you should also tackle the problem like you would tackle any other engineering problem, thinking about architecture, thinking about abstraction, thinking about reusability and thinking about the overall strategy behind it. And really value the process. It’s the most relevant asset in your company. It what separates you from other competitors and peers in the market and it’s the most relevant asset that will define the success or failure of your organization. And coming to a famous quote from Charles Darwin, “It’s not the strongest, the most intelligent ones that will survive. It’s the ones that are most adaptable to change.”

Guy Nadivi: Wise words. Always good to end on a note about evolution, which is very applicable to our field. Looks like that’s all the time we have for on this episode of Intelligent Automation Radio. Robert, it’s great hearing about an automation company focused on the mid-size market because it not only reflects the growing reach of automation, but also the growing opportunity for companies such as yours, with a unique solution. I think that bodes well for everyone and I thank you very much for sharing your insights with us on the podcast today.

Robert Hutter: Thanks a lot for the invite. It was a pleasure.

Guy Nadivi: Robert Hutter, Founder and CEO of FireStart. Thank you for listening everyone and remember, don’t hesitate, automate.



Robert Hutter

Founder and CEO of FireStart 

Robert Hutter is founder and CEO of FireStart, one of the leading software vendors for Business Process Management and Enterprise Workflow Automation. After graduation with a Masters Degree in Software Engineering, he worked in the banking industry as an application development and enterprise software architect before founding FireStart. Today, Robert is supporting process digitalization and transformation programs with the FireStart iBPMS platform at customers like Swarovski, KTM, and LENZE, helping to connect the dots between people, process and technology.   

Robert can be reached at: 

LinkedIn: https://www.linkedin.com/in/robert-hutter-78413b19/ 

Email: r.hutter@firestart.com 

Website: www.firestart.com 

About FireStart: https://youtu.be/jcyG9c5dMos 

Quotes

“…when you don't have a proper process governance and process strategy in place, you very often end up in dead ends and you are kind of reinventing the wheel each time and you have nothing you can build upon.” 

“So process architecture is also about having segregation of duties, but that at the end of the day, everybody can collaborate on working together as a team, creating the results. And this is also what process management and workflow automation, it's about getting people together so that they actively can work as a team in the whole engineering process." 

“…ideally the way we want to tackle problems is a low-code approach, which applies to the 80/20 rule. So let's say 80% of the engineering you can do with involving business users in certain parts of the process and just doing like the last mile, the last 20% for specific more complex parts of the process, where you actually need to go to a coding level.” 

“Remember that enterprise workflow automation and robotic process automation are at the end engineering disciplines. So you should also tackle the problem like you would tackle any other engineering problem, thinking about architecture, thinking about abstraction, thinking about reusability and thinking about the overall strategy behind it.” 

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

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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

Episode #52: Chatbots Aren’t Human, So Don’t Expect People To Pretend They Are – Charli.ai’s Kevin Collins

November 2, 2020    Episodes

Episode #52:  Chatbots Aren’t Human, So Don’t Expect People To Pretend They Are

In today’s episode of Ayehu’s podcast, we interview Kevin Collins, CEO & Founder of Charli.ai. 

Is conversational AI all it’s cracked up to be, or is hype eclipsing hope when it comes to deliverables?  Has the gap between expectations and reality grown so wide that disappointment is inevitable, both for end-users and enterprise decision-makers?  Or have the majority of chatbot vendors simply been targeting the wrong use cases, inadvertently leading their customers to insurmountable dead ends? 

One man with a clear-eyed vision of the market opportunity uncluttered by misconceptions about the technology’s potential is Kevin Collins, Founder & CEO of Charli.ai.  Following GE Digital’s acquisition of his IoT company Bit Stew, Kevin set out to build a personal AI Chief-of-Staff front-ended by a chatbot.  With Charli.ai recently emerging from stealth mode, Kevin joins us on the podcast to explain why despite expert predictions falling short about conversational AI’s advances he’s still enthusiastic about the technology; why front-end conversational interactions must never exceed back-end automation capabilities; and how CIO’s should approach conversational AI implementations. 



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 Kevin Collins, CEO of Charli.ai, which recently emerged from stealth mode to provide intelligent automation for everyday tasks in the digital workplace. Kevin and his partner, Alex Clark, previously co-founded and built a software company that was eventually acquired by GE Digital. Charli.ai then became their next project, which they began for the very personal reason that they were tired of doing their own administrative tasks, and what they plan to deliver to the marketplace is a tool that leverages automation, AI, machine learning, and chat to handle that administrivia, and as a bonus, requires zero coding or any technical skills to use or configure. That’s intriguing. So, we’ve asked Kevin Collins to come out of stealth mode himself and join us on the podcast to discuss Charli.ai and the overall state of the conversational AI market. Kevin, welcome to intelligent automation radio.

Kevin Collins: Oh, hi, Guy. Thank you for having me on the radio.

Guy Nadivi: Kevin, please tell us a bit about your background and what led you to launch Charli.ai, which I’ve read has been touted as a conversational AI chief of staff.

Kevin Collins: Yes. That’s a good description of where we want Charli to go. My background has been 30 years in the high-tech sector, and I think as you mentioned, Alex and I had started a company called Bit Stew that eventually got sold to GE Digital back in 2016. Bit Stew had a lot of AI on how we were doing integration. That AI capability is what I fell in love with and it’s something I wanted to bring into the world of Charli to really get rid of a lot of the pain that I go through on a day-to-day basis of just doing the administration work.

Guy Nadivi: Kevin, in 2019, Business Insider estimated the worldwide chatbot market is worth a bit more than $2.5 billion, but they forecasted that, by 2024, it will approach $10 billion. That’s a compound annual growth rate of over 29% a year. How do you think the COVID-19 crisis will affect this growth rate?

Kevin Collins: That’s a good question, and COVID-19 has impacted quite a bit from what we have seen just in the past few months alone. It’s created a lot of uncertainty in the market. It’s an area where we believe that uncertainty will continue at least for the near-term future and something that we at Charli are trying to navigate. But I do believe that COVID-19 will have a positive impact on that growth rate. If you’re looking at people’s attitudes to working from home, the remote work, that’s putting more pressure on software companies to automate. Automation is going to come with heavy AI involvement. It’s also going to come with an easier interface that people are looking forward to work with their software. Instead of being in the office where they’ve got more tolerance for the enterprise way of doing work, they’re going to want simpler ways of working with their software, and this is where I believe that chatbot innovation is going to come from and where that investment into chatbot technologies, especially when you’re looking at the enterprise and the corporate world.

Guy Nadivi: Okay. Now, speaking of chatbots, according to Gartner, there are 1,500 chatbot providers currently. Inevitably, there is going to be a shakeout that will cause that population to decrease precipitously, I believe. At the TechExit.io Conference earlier this year, you and Hans Knapp of Yaletown Partners had a discussion about creating barriers of entry for competitors as one strategy to preserve your market position. What barriers to entry can Charli.ai erect to defend itself against 1,500 others competing in the same market?

Kevin Collins: I completely agree. I believe that entire market for chatbots is ripe for consolidation. There’s far too many of the technologies that are out there. There’s also a massive amount of hype that’s gone into the chatbot area and people are now realizing the reality of chatbots will only take you so far. They’re really, for us, a channel into the intelligence within the systems we have. If I look at Charli’s approach to chatbots, we want the chatbot to be an input into Charli, but we need Charli to automate all of those administration functions that need to happen, and the automation needs far more intelligence than just a chatbot. We use the chatbot for that natural language processing, but we don’t use the chatbot for natural language understanding and how to translate that understanding into action, and that’s where the real intelligence on Charli is. So, we’re building defensible technology into how we automate those tasks for that chief of staff function that you mentioned earlier, and that requires heavy lifting from an AI perspective. Far more than what we would see just on the chatbot, which needs to be natural language processing. We really have to do natural language understanding and translation of that. So, two questions there. Will the chatbots consolidate? Definitely. I believe there’s far too much out there and it’s a natural consolidation. For us on the defensible side of it, it’s more than just a chatbot. It’s now understanding and translating that into action.

Guy Nadivi: Sticking with Gartner, in 2017 they predicted that “By 2020, 40% of all mobile interactions will be via virtual assistance.” Virtual assistance, of course, being a type of smart chatbot. Clearly, we’ve fallen short of that forecast. So, let me ask you, when do you predict we’ll reach peak app as it were, and start transitioning to conversational AI as the predominant user interface going forward?

Kevin Collins: Definitely have fallen short. I think the reason for that is a reality check. This is hard. It’s really hard. There’s a combination of not just that conversational ability. There’s also translating that into the action as I mentioned previously. And then you also have to have a conversation which is a behavior change for a lot of people. I think the virtual assistants that we see today are simple one task. You ask it to do something. It’s very simple. You spoon-feed it. You get the task done. But for a human to have a full conversation with their computer is a lot different and a lot harder. It’s easy to tackle low-lying fruit opportunities around customer support. Walking a user through a particular scenario and then manually following up with it at the end. It’s a completely different challenge to have a human converse with a computer and that computer to completely automate what the human wants to do and have a full-on interaction. I believe that we’re going to take some pretty key baby steps on really having the human converse with the computer in order to get an action done rather than get onto full conversational, which is going to take many years for that to happen.

Guy Nadivi: You mentioned low-lying fruit. So, let me ask you in broad terms, what are some of the lowest hanging fruit best suited for conversational AI applications within an organization?

Kevin Collins: Some of the ones that we’ve seen today that I think are perfect for it are the customer service, the customer support. Even if I look at it for me personally, I much prefer to chat through my support issue, really to understand the frequently asked questions and answers or to walk me through getting a refund on a purchase I had made. The flow for that is fairly predictable, and I would prefer to chat with a computer over chatting with a person to get that done. That is low lying opportunity for people to address, and it does take a big burden off of corporates and enterprises that have to invest heavily into their customer support.

Other areas that we see are prime opportunities for this are areas that Charli is targeting as well, and that’s the administration side. We’re finding people spending 20% to 50% of their day just on the minutiae of administration, and that’s distracting them from a lot of the work that they have to get done that’s a real value. We can spend a lot of time on automating that and having a chatbot in front of that. So, the conversational ability to really instruct the computer to get administration done is another low-lying fruit opportunity. One that we certainly want to jump on.

Guy Nadivi: Now, there are some concerns about biases, intentional or otherwise, creeping into the AI that powers things like chatbots. In order to root out bias, Harvard Business School published an article not too long ago, calling for the auditing of algorithms, the same way companies are required to issue audited financial statements. Kevin, do you think AI algorithms should be audited in the same way financial statements are for publicly traded firms?

Kevin Collins: Very interesting question. When I hear audit and I hear regulations, the hair stands up on the back of my neck right away. I think that’s just a natural reaction. As CEO of companies, I understand the need for regulation. I definitely understand the need for audit. It’s a bit of a balance between the innovation you want to see and then getting into that regulatory red tape. Biases are very real, and biases get introduced by the data scientists that put the models together. It gets introduced by the training data that’s going into these models, and those biases are very real, similar to the biases you might have in an organization just dealing with people, and you have to ensure that diversity gets introduced into your data science and into your AI. It’s one of the key areas that we love about the AI that we’re innovating at Charli because we have to test, and we have to test out the biases, and testing becomes an automated routine for how the AI needs to get deployed because we want to always act on the best interest of our users and our audience. That means that that needs diversity in the training sets. It needs diversity in the models. When you’re getting into auditing of the algorithms, I feel it’s far more important for the auditors to look at how these models are tested and continuously tested and implemented in order to avoid the introduction of bias, rather than just auditing of the algorithms. I don’t think that’s a fair approach. I believe it’s far better to continuously test these models as they’re operating and they’re being trained.

Guy Nadivi: Given the current state-of-the-art, what do you think are some of the most unrealistic expectations currently plaguing the field of conversational AI?

Kevin Collins: I believe the biggest AI missed expectation that we’re seeing is that the users, from a behavior perspective, don’t want to converse with their computer. That’s a big one, and that was a big highlight for me recently is watching how the users want to interact with their computer, either on their mobile device or through their laptop and desktop. Conversational interaction with a computer or a piece of software just became unnatural. Humans are expecting the computer to behave like a human, and we’re nowhere close to that today. There’s a lot of nuances on how human beings interact and how they ask for items. There’s a lot of expectation that the computer is completely automated behind the scenes. So, natural language processing that conversational is really just the front end. Then, there’s a missed expectation of this translating it into intents and having it fully automated by the computer. It’s just not there. That automation can’t match what the user expectation is, so when you get into conversing, there’s a lot of edge cases. There’s a lot of failure scenarios, and we end up spending a lot of time addressing the failure scenarios, the error conditions. We also try to spend a lot of time in putting up guard rails to guide the user conversation. What we’ve had to do is take a step back from just understanding conversational AI to maybe the user just wants to make a request and then wants some clarification on that, rather than having a conversation, and it’s avoiding the unrealistic expectations that we’re seeing. We don’t have the cognitive ability in AI to match what the consumers and the users are looking for today.

Guy Nadivi: Speaking of expectations, in technology, to paraphrase the economist, Rüdiger Dornbusch, things take longer to happen than you think they will, but then they happen faster than you thought they could. Kevin, what are some of your predictions for conversational AI over the next three to five years?

Kevin Collins: Things do certainly take longer to happen than you think it will. So, looking forward, I get impatient and it certainly can take a longer time than what I’m anticipating. But hindsight is, “Oh, that actually went pretty quick.” And that’s just, I believe, human nature. It is going to take longer to get conversational AI where we need it to be. If I look at what we have to invest time on is I think stepping back from just a full-on conversation with an AI to this request-response to clarification elements that can happen just with language understanding.

I believe the other area that we have to get into with the innovation that needs to happen over conversational AI is the automation to support the conversations or the interaction, and that automation is where I believe in the next three to five years the innovation is going to be. It’s going to be on no-code solutions. It’s going to be on the ability to have these models trained such that the user gets more out of the conversation through the automation, rather than having to get frustrated. We need to get the automation matching what the natural language processing can do, and that requires more than just the scripting and the coding that happens today. So, a lot more around this no-code capability, a lot more around the continuous training of the models and tweaking of the models to match the expectation.

Guy Nadivi: Interesting. Last year, there was an article in MIT Technology Review about Artificial General Intelligence or AGI. In that piece, the author, Karen Hao, who’s been on our podcast, wrote, “There are two prevailing technical theories about what it will take to reach AGI. In one, all the necessary techniques already exist. It’s just a matter of figuring out how to scale and assemble them. In the other, there needs to be an entirely new paradigm. Deep learning, the current dominant technique in AI, won’t be enough.” Kevin, what do you and your team at Charli.ai think it will take to achieve AGI?

Kevin Collins: Really pertinent question today. This one has come up a number of times, especially now that Charli is very focused on the AI technology that we’re doing. Our belief is that AGI is a long ways off. We’ll see in the various studies, and it can be anywhere from 10 to 40 to 50 years, depending on who you ask. I do believe it’s a 20-, 30-year journey before we see the massive innovation that’s needed for an AGI. But the other side of us goes, “Who cares?” There’s a lot of brilliant technology in the AI world today, and that’s what needs to be leveraged. If we’re looking at it from a corporate and an enterprise perspective, AGI is coming at some point with new algorithms but the reality of what we have today is brilliant. We’ve got deep learning, we’ve got machine learning, and I believe that the paradigm shift that we do need is more around the scale and the assembly. We’re completely missing that in the world of AI. You have to be able to scale your AI, not just to perform, but it has to scale from the perspective of models have to work for the individual, as well as the corporation, as well as an industry. You have to scale that because you have to apply context, and context awareness is one of the keys that we needed within Charli. How do we achieve context awareness at scale? The other part of it becomes assembly, and assembly of the models is a critical challenge. This is why I believe it’s the paradigm shift of scale and assembly because you need to bring context and you need to bring continuous learning and continuous testing of those models. You also have to assemble those models because the decision-making isn’t just a machine learning algorithm. The decision-making becomes a collaboration of various models to take in various inputs and to resolve conflicts in order to take action. This is why I believe that paradigm shift is all about scale and assembly. That’s what we need. Regardless of new models and methods that may come, scale and assembly is still a massive problem that needs to be solved today.

Guy Nadivi: Kevin, 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 introducing new technology that leverages conversational AI and, in particular, what change management considerations do you think they should keep in mind?

Kevin Collins: Fantastic question. We’ve actually had the benefit of comparing and contrasting various enterprise organizations that we work with to see how to introduce this into it and what the success or failure rate has been. I would say there’s a high failure rate if you’re expecting too much around the conversational UX and that you can go and converse on any number of things simply because your automation on the backend cannot match the user expectations on the conversation. I think the biggest advice that CIOs need to take away from this is that you need to go in this eyes wide open and tailor the chatbot experience or that conversational input to what you can automate on the back, and make sure that you’re keeping the user interaction with your software guided. But the other big thing around that is that I do believe that this conversational AI element is where the future is going. We want software to work with the user far better than what it is today. We don’t want the user to have to learn the software. We want the software to learn about the user. So, there has to be a big innovation and a big investment into the conversational AI, but take these baby steps. Make sure that you are allowing the user to interact with the computer and interact in a way that you can automate, and you’re not frustrated. You don’t want to go down to say, “I’m just going to do conversational AI and put in a chatbot.” There’s a significant investment into scripting how the user flow needs to go. Similar to how you had to build up the UX or the user experience with your web-based interface, you’re going to have to invest into how you guide the user on their conversational flows, and that is an area of innovation that CIOs really need to look at.

Guy Nadivi: All right. Well, it looks like that’s all the time we have for on this episode of Intelligent Automation Radio. Kevin, thank you very much for joining us today and sharing your thoughts about the current state of conversational AI. We’ve really enjoyed having you as a guest.

Kevin Collins: Well, thanks, Guy. I really appreciate it. These have been fantastic questions. Obviously, one where we’re pretty excited about, but I’d love to follow up if there’s any follow-on questions from folks.

Guy Nadivi: All right. Kevin Collins, CEO of Charli.ai. Be on the lookout for them as they get closer to general release. Thank you for listening, everyone. And remember, don’t hesitate, automate.



Kevin Collins

CEO & Founder of Charli.ai.

Kevin is founder and CEO of Charli AI, a startup focused on helping busy workers get more life back in their work-life balance. As a serial entrepreneur and technology company founder, Kevin has experienced the explosion of productivity software and the tools we use to work, yet productivity is declining and people are more stressed than ever. Enter Charli, a novel conversational AI that eliminates productivity killers from your workday. Part workflow automation, organization wizard, and search engine - Charli simplifies some of your most time-consuming tasks. 

Kevin brings more than 30 years of experience in architecting and designing software, both as a start-up entrepreneur and as a corporate executive. Kevin has extensive knowledge about artificial intelligence and machine learning. Before founding Charli, Kevin was CEO and Co-Founder at Bit Stew Systems, a data intelligence platform, which was acquired by GE Digital for its AI and ML capabilities in 2016. Prior to his time in Silicon Valley, Kevin worked in the high-tech networking and security field, and led technology firms specializing in cryptography, public key infrastructures and high-performance and scalable networks. As a second time founder, Kevin is passionate about sharing his expertise in building successful startups. 

Kevin can be reached at: 

Website:          www.charli.ai 

Twitter:           @charliai 

Newsletter:    https://charliai.substack.com/p/hey-were-new-here 

LinkedIn:        https://www.linkedin.com/company/charliai/ 

Facebook:      https://www.facebook.com/charliai 

Quotes

“I believe that entire market for chatbots is ripe for consolidation. There's far too many of the technologies that are out there. There's also a massive amount of hype that's gone into the chatbot area and people are now realizing the reality of chatbots will only take you so far.”

“It's a completely different challenge to have a human converse with a computer and that computer to completely automate what the human wants to do and have a full-on interaction.”

"We're finding people spending 20% to 50% of their day just on the minutiae of administration, and that's distracting them from a lot of the work that they have to get done that's a real value. We can spend a lot of time on automating that and having a chatbot in front of that. So, the conversational ability to really instruct the computer to get administration done is another low-lying fruit opportunity. One that we certainly want to jump on." 

“When you're getting into auditing of the algorithms, I feel it's far more important for the auditors to look at how these models are tested and continuously tested and implemented in order to avoid the introduction of bias, rather than just auditing of the algorithms.”

“I believe the biggest AI missed expectation that we're seeing is that the users, from a behavior perspective, don't want to converse with their computer. That's a big one, and that was a big highlight for me recently is watching how the users want to interact with their computer, either on their mobile device or through their laptop and desktop. Conversational interaction with a computer or a piece of software just became unnatural. Humans are expecting the computer to behave like a human, and we're nowhere close to that today.”

About Ayehu

Ayehu’s IT automation and orchestration platform powered by AI is a force multiplier for IT and security operations, helping enterprises save time on manual and repetitive tasks, accelerate mean time to resolution, and maintain greater control over IT infrastructure. Trusted by hundreds of major enterprises and leading technology solution and service partners, Ayehu supports thousands of automated processes across the globe.

GET STARTED WITH AYEHU INTELLIGENT AUTOMATION & ORCHESTRATION  PLATFORM:

News

Ayehu NG Trial is Now Available
SRI International and Ayehu Team Up on Artificial Intelligence Innovation to Deliver Enterprise Intelligent Process Automation
Ayehu Launches Global Partner Program to Support Increasing Demand for Intelligent Automation
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Ayehu Automation Academy is Now Available

Links

Episode #1: Automation and the Future of Work
Episode #2: Applying Agility to an Entire Enterprise
Episode #3: Enabling Positive Disruption with AI, Automation and the Future of Work
Episode #4: How to Manage the Increasingly Complicated Nature of IT Operations
Episode #5: Why your organization should aim to become a Digital Master (DTI) report
Episode #6: Insights from IBM: Digital Workforce and a Software-Based Labor Model
Episode #7: Developments Influencing the Automation Standards of the Future
Episode #8: A Critical Analysis of AI’s Future Potential & Current Breakthroughs
Episode #9: How Automation and AI are Disrupting Healthcare Information Technology
Episode #10: Key Findings From Researching the AI Market & How They Impact IT
Episode #11: Key Metrics that Justify Automation Projects & Win Budget Approvals
Episode #12: How Cognitive Digital Twins May Soon Impact Everything
Episode #13: The Gold Rush Being Created By Conversational AI
Episode #14: How Automation Can Reduce the Risks of Cyber Security Threats
Episode #15: Leveraging Predictive Analytics to Transform IT from Reactive to Proactive
Episode #16: How the Coming Tsunami of AI & Automation Will Impact Every Aspect of Enterprise Operations
Episode #17: Back to the Future of AI & Machine Learning
Episode #18: Implementing Automation From A Small Company Perspective
Episode #19: Why Embracing Consumerization is Key To Delivering Enterprise-Scale Automation
Episode #20: Applying Ancient Greek Wisdom to 21st Century Emerging Technologies
Episode #21: Powering Up Energy & Utilities Providers’ Digital Transformation with Intelligent Automation & Ai
Episode #22: A Prominent VC’s Advice for AI & Automation Entrepreneurs
Episode #23: How Automation Digitally Transformed British Law Enforcement
Episode #24: Should Enterprises Use AI & Machine Learning Just Because They Can?
Episode #25: Why Being A Better Human Is The Best Skill to Have in the Age of AI & Automation
Episode #26: How To Run A Successful Digital Transformation
Episode #27: Why Enterprises Should Have A Chief Automation Officer
Episode #28: How AIOps Tames Systems Complexity & Overcomes Talent Shortages
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

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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

Episode #48: How Microsoft Will Change The World (Again) Via Automation – Microsoft’s Charles Lamanna

September 16, 2020    Episodes

Episode #48:  How Microsoft Will Change The World (Again) Via Automation

In today’s episode of Ayehu’s podcast, we interview Charles Lamanna – Corporate Vice President, Low Code Application Platform at Microsoft

“To me, Microsoft is about empowerment… we are the original democratizing force, putting a PC in every home and every desk.” That quote by CEO Satya Nadella, of course, reflects the ubiquity achieved by the Windows operating system. His company’s feat of democratization however, is just prologue to the coming revolution Microsoft foresees in automation. An upheaval expected to be so disruptive to the status quo, it will empower the information worker masses to finally overthrow the oppressive yoke of robotic tasks smothering their productivity.  With newfound freedom to unleash their ingenuity, they’ll not only enrich their own lives, but add greater value to the organizations employing them. 

The Microsoft executive charged with redressing the imbalance between toil & talent plaguing white collar wage earners is Charles Lamanna.  As Corporate Vice President, Low Code Application Platform, his portfolio of responsibility encompasses all the critical assets needed to bring Microsoft’s lofty vision to life.  In this wide-ranging discussion, we get first-hand insight from a senior executive on the vital role automation plays in the software giant’s Cloud-First, Mobile-First strategy.  Along the way we’ll also learn why the shifting ratio of repetition to creativity within a given task will determine which automation type it’s best suited for; the automation skills one should master to position themselves for success in the future; and what the single biggest disruptor for automation will be over the next few years. 



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 Charles Lamanna, Corporate Vice President, Low Code Application Platform at Microsoft. Now, there are many products at Microsoft that Charles is responsible for which I’m sure our audience members are very familiar with, including the Dynamics 365 platform, Power Apps, Power Automate, Power Virtual Agent, AI Builder, and the Common Data Service products. And as everybody in IT knows, Microsoft almost always becomes a major player in any market they enter. The automation and AI markets which we focus on, are unlikely to be exceptions to that. So given the strategic automation and AI assets Microsoft has entrusted Charles with overseeing, he’s someone we absolutely had to have on this podcast. And we’re thrilled, he’s taken time out of his very busy schedule to join us today. Charles, welcome to Intelligent Automation Radio.

Charles Lamanna: Thank you for having me on the show today, Guy. I’m super excited to get into the thick of automation with you. I know I’ve been a listener recently, so really excited to get into the dialogue.

Guy Nadivi: So let’s start with that, Charles, please tell us a bit about your role at Microsoft and the types of automation you’re focused on.

Charles Lamanna: Sure thing. So as you mentioned, I’m the Corporate Vice President of Low Code Platforms at Microsoft. So I’m on the R&D side, engineering and product management, lead those teams related to the Power Platform and Dynamics 365. The most relevant products that I oversee when it comes to automation and AI is Power Automate, which is our robotic process automation offering at Microsoft. Power Virtual Agent, which is our low code chatbot experience, and AI Builder, which is our low code AI and machine learning tool for business users and business developers. And those three things really together combine to create what we call our automation platform.

Guy Nadivi: So earlier this year, Microsoft made a big splash in the automation market by acquiring Softomotive, an automation vendor with 9,000 global customers. And that capped off a sequence of high-profile transactions in the automation space, which made it clear this is going to be a market of intense focus for some very significant players. Charles, how does Microsoft’s automation fit into its Cloud-First, Mobile-First strategy?

Charles Lamanna: That’s a great question. The first thing I’d say is that we’re super and incredibly excited about the Softomotive acquisition within Microsoft. We think it really is a great compliment to Power Automate, Power Virtual Agent, AI Builder. To really kind of map back what we were thinking around Softomotive in our general automation strategy, I do want to spend a second just talking about the overall automation vision at Microsoft, because that I think helps paint a really good picture for it. When we talk about that vision, there’s really four main pillars to it. The first one is that automation is more than just UI Automation. One of the big trends recently in automation technology has been robotic process automation or RPA. But RPA historically is incredibly centered and focused on UI Automation and more specifically Windows-based automation. And it’s our view that that UI Automation is necessary, but insufficient to really enable interesting automation scenarios and really transform every aspect of every company in every country around the world. So things like API connectivity or API-based automation or AI capabilities like natural language understanding or NLU or a whole bunch of other exciting AI technologies, those are also key ingredients. So just being more than UI Automation is really important. The second is being cloud first. We think there’s I’d say a legacy of automation being very PC-centric, very on-premise centric, and there’s some real potential to reimagine what automation looks like in a cloud native, cloud first way. So that’s really important to our vision. Number three is that automation has to be generated and supported by AI. Has to be enabled by AI capabilities in terms of identifying what can be automated and the best way to automate it. As well as when those bots run, use AI to go make them better over time. And last but not least, by far my favorite aspect of our vision is low code. And this permeates really a lot of work we’re doing at Microsoft these days. But low code is a whole idea that you want to make it so anybody and everybody can be a developer, can build bots, can contribute to the automation revolution. That’s not just constrained to a small group of experts or developers. So those are the four aspects more than UI Automation, cloud first, generated & supported by AI, and low code. But getting back to the Softomotive acquisition, the WinAutomation desktop app was really in our view the leading low code RPA tool out there, has a huge fan base, very frequently adopted virally and by business users. So people just going to a .com, downloading it, and starting to automate. Additionally, WinAutomation was built in such a way as well as with the new open source Robin programming language for easy accessibility into the cloud. So it can very easily integrate with a cloud provider. So that combination of that low code desktop application, as well as the cloud integration and cloud readiness made Softomotive a great fit for the Power Automate vision and the Power Automate offering, and therefore a really great fit for the broader and mobile centric cloud first approach that Microsoft is taking these days.

Guy Nadivi: Interesting strategy. So there are many professionals today Charles, that can be categorized as information workers and maybe eventually some of them will also become app developers in the sense that you were talking about. How do you envision automation changing the jobs of information workers, and which roles or positions do you think will be the most difficult to automate?

Charles Lamanna: Yeah, I would say, we think that automation will be an important part of really any modern information worker job in the future. But the importance and the criticality will be a spectrum from say, somewhat impactful to very impactful, but really all information workers will feel that impact, will feel the responsibilities change and evolve over the next five years or so. And a major reason for that is the advancements in artificial intelligence to understand unstructured content like voice, video, text, as well as the fact that basically all the information worker’s job is captured digitally, is running on a PC or on a mobile device or mirrored in the cloud. So you can really start to enhance and automate almost any profession. However, I’d say that this transformation isn’t, I’d say all or nothing. There’s actually two really different ways that we look at what automation will do. The first is what I would call human assisted automation. And the second is autonomous automation. When we talk about human assisted automation, the idea is it’s all about helping a human do their job better, be faster, reclaim time, spend more energy on creativity and high value, high cognitive tasks. Less on doing data movement or simple rote activities. And autonomous automation is automation happens in the background. Does things without interacting with the human, pulling work off a queue or responding to events. And there are two fairly different approaches in terms of how automation can impact an information worker. And we think that some jobs will be heavily impacted by autonomous automation while others will be impacted primarily by the human assisted automation. And the difference between the two is really going to depend on how much repetition and how much creativity happens as part of the job. There’s more repetition and less creativity, there’s going to be more autonomous automation. If there’s less repetition and more creation, there’s going to be more human assisted automation. And that human assistant automation can sometimes be really subtle. If you imagine, in Outlook these days there’s suggested responses to emails, right? That’s automation. That helps me respond to emails more quickly, things like that. So we’re really going to see this permeation of applications, whether they’re integrated with the standard SaaS apps, standard OS’s or custom bespoke automation built for a company, we’re really going to see an emergence across both those front, the human assisted and autonomous-based automation.

Guy Nadivi: Okay. Let’s expand a bit on what you said and assume that automation will be part of the role for all or most information workers in the future. If I’m a college graduate entering the IT job market, or a seasoned IT professional looking to change specialties and interested in automation either way, what skills should I focus on acquiring to accelerate my career?

Charles Lamanna: Yeah, the first thing I’d say is automation is a fairly broad ecosystem. So if you want to be part of the automation revolution in terms of contribute to building out these different automations within the enterprise, you have to I think go to a few different places. The first one I would say is UI Automation, RPA is a really key aspect of it. And that’s because there’s a whole lot of systems which can only be reached via UI Automation. Like I said earlier, it’s necessary but insufficient, but you definitely want to go out and get familiar with RPA tools and UI-based automation. The second thing is get a good view of APIs, and what enterprise application integration looks like, whether that’s APIs or event-driven architecture, because that’s the more future-looking way that automation will be built and created in the enterprise. And having a good view of service-oriented architecture, microservice, API catalog, event catalog, things like that will be very useful. The third is interesting because I’d say it’s not really related to technology at all. And that’s understanding business processes and designing business processes. Because automation is all about improving a business process. So knowing the language, the terminology, and the aspects you want to optimize when it comes to business process engineering is a really great tool. The fourth thing would be just a standard software development life cycle. If we go look at what it looks like to build out these automations, there is a design phase, there’s a planning phase, there’s development, there’s deployment. And the automation tools out there are compressing these cycles to be just hours or days long as opposed to weeks or months. But you still should know that cycle because that’s important to be successful in the enterprise. And the fifth, which I would say is the extra credit, but it’s going to really separate the good from great is going to be AI and ML understanding. And you don’t have to be a deep learning expert, doing super 40 megawatt AI model training like GPT-3 or something. We should feel comfortable around the … At least the high level concepts of machine learning. So things like reinforcement learning or transfer learning, which are going to be really important in the future when it comes to building durable, reusable, high value automation, at least from Microsoft’s perspective. So those are kind of the five things. Four, I’d say standard, and one, a little bit extra credit that would be important to really be successful in the IT space when it comes to building automation and the enterprise.

Guy Nadivi: Let’s talk about one form of durable automation that you’re referencing. In the form of AI-driven assistance or bots, which are growing in prevalence for a lot of customer-facing applications. Gartner believes there are as many as 1,500 chatbot vendors worldwide. And I quote, “The majority of these conversational platform vendors offer very simple platforms using modified open source components to deliver simple question and answer chatbots.” Now I think it’s safe to assume the market doesn’t need 1,500 vendors in this space and an inevitable shakeout is coming. Charles, what kinds of things do you think will differentiate the survivors in this market space from the vendors who will fall by the wayside?

Charles Lamanna: Yeah, I think like any kind of technology market, as it becomes more mainstream, you start to see more consolidation and all-in-one offerings start to emerge as opposed to lots of little point solutions. And folks that know me well, I love my numbered lists. So I’d say there’s probably four different aspects that will really define what chatbots in the future will look like, that at least map into our strategy at Microsoft. The first is omni-channel, and what we mean by that is a chatbot that only works in a web-based chat experience is going to be insufficient in the future. You’re going to need to support I’d say WhatsApp, SMS, Facebook, voice, things like Alexa. There’s really going to be a need to support all kinds of different form factors to be successful. And you need to abstract away those channels from the bot authors and bot developers. So the ability to go reach into tons of different channels will be absolutely essential. The second is really being deeply, deeply AI-enabled. Chatbots are probably one of the best places where we see amazing return on advanced AI right now. Things like reinforcement learning, where you actually improve the control flow in your chatbots based on experiments and the results, and improve your models basically for matching entities are intense. For the bots that have used that for our technology at Microsoft, that significantly improves the success rate of sessions. And that’s pretty sophisticated AI capabilities. The third is really integrating with broader business processes, because what we see is no bot is an island, right? No man is an island, no bot’s an Island. No bot just exists on its own. It has to integrate with other systems in the enterprise to take actions, to fulfill returns, to help a customer purchase something. That requires integrating with other workflow systems, other databases, and really kind of running the end to end workflow. So really that integration to a broader ecosystem is going to be key. And the fourth one, the last one I think is not always super obvious, but it’s the ability that go hand off to a human, to work with a human, to have a chatbot and humans work side by side. And this, the reality is that there is no general artificial intelligence. There is no chatbot that you can run that can answer any question of a customer with anything. So any chatbot will always run against its limit – the limit of its abilities. And in those cases, you can’t churn the customer out or push them to a dead end, or say “We’ll call you back.” Instead, you need a seamless amnesia-free handoff to a human agent that can continue the conversation and grind it out. And what we’re finding is that this is what allows the usage of chatbots as a main line dependency in mission critical enterprise defining business processes, because you can cover the 75% with the chatbot and then you can cover the 25% with the human. And this is a great example of that human assisted bot versus human assisted automation I was talking about earlier. And one of the great examples of this at Microsoft internally, we have a chatbot which we use for our support experiences at support.microsoft.com. There’s over a million support cases that run through it every month. And the chatbots and human agents work hand in hand to address the needs of our customers. This produced, when we rolled it out, demonstrably, better customer satisfaction. We improve the customer experience and at a much lower cost. So that last one we think is going to be key. And if you do just a chatbot and don’t have a story for human agents behind the scenes, you’re really going to be leaving a bunch of use cases on the table. Because when a customer runs up against those dead ends, they’re going to go bail on the chatbot entirely. So I think those are the four main things that we really think are going to be defining for the space over the next few years and therefore be defining for the vendors in the space over the next few years.

Guy Nadivi: Speaking of humans, when you talk with your customers, what are they telling you are some of the biggest challenges they’re experiencing in deploying automation within their organization?

Charles Lamanna: Yeah, I’d say the biggest thing we hear really with everybody is finding the right skills, finding employees and experts with the right skills. And when I talk about skills, it’s not just related to the technology. It’s also about how do you understand the problem, how do you understand business process engineering. How do you understand the business needs, where automation will provide value. And that combination of being able to understand the business process, understand the business needs, in addition to understanding the automation technology, whether it’s RPA, AI, or DPA – that combination is really what we see a lot of organizations struggle with, and things like creating automation centers of excellence or doing internal skilling and training programs. The combination of those things is really what customers are trying to do today to go respond to this challenge. I mean, there’s probably a tale that’s been around for quite some time for disruptive or interesting technologies, which is just that change management skilling and mapping into the business need is what’s really slowing down adoption in most cases. So I’d say having a good strategy for skilling and training is a really important aspect for a lot of the customers that we work with to go make them be successful with the technology that they have.

Guy Nadivi: We’re hearing more and more about process mining and other AI-based discovery platforms being deployed as part of digital transformations. Charles, how do you think these tools are impacting adoption rates for automation?

Charles Lamanna: They’re accelerating them. Massively accelerating them. And I think process mining and the AI discovery capabilities is probably the most interesting thing that we’re watching at Microsoft when it comes to automation over the next couple of years. And the reason is because it’s still early days today, of course, but these tools help you very quickly identify processes that are ripe for automation. And even more interestingly is they actually help with the calculation of the ROI for an automation project. And this really is the dream of most IT transformation projects. Before you invest in building out the bots, before you invest in building out the AI, you can quantitatively analyze your workforce, understand where there’s inefficiencies, and then project the actual efficiency gains and customer experience gains once you roll out automation. And once you have this kind of closed loop of mine the processes that are happening, identify the upside, address the automation need, then confirm that you saw the results you expected. This is going to create a very virtuous cycle of building bot after bot, after bot, after bot, that will really start to change the landscape in IT and for information workers. So we think this is really going to help just accelerate it, but it all goes back to just helping understand the economics, understanding the ROI, and understanding whether or not you were successful with a IT or automation project. It all goes back to those basic principles which every IT manager is always worried about. It makes that be much less of a guessing game. And if I were to make a comparison, it reminds me of the shift in marketing, from broadcast marketing, where it’s very hard to understand the impact of a marketing campaign, because you didn’t know how many people actually went to go purchase something as a result of a commercial or something in a newspaper. When you went to digital marketing, for the first time you could know for every ad, how many people actually purchased the good that you were advertising, because you have tracking and things like that. That is – and that actually drove a ton more adoption, a ton more digitalization of advertising. We think a similar phenomenon is going to occur because you are going to be able to go track from ideation of an automation project, to the ROI of the automation project end to end. So that is what I would say is the acceleration we’re seeing and the why, and how we really imagine it shifting over the next couple of years, going forward.

Guy Nadivi: Some organizations have really embraced automation, at least partially because they understand the ROI potential you just spoke about, and they’ve made it a core part of their IT operations. Other organizations have more of a wait and see attitude. Regardless of where an enterprise falls on the automation maturity spectrum, what do you think are the key factors they should consider when formulating their automation strategy?

Charles Lamanna: I’d say the most important thing to consider is that any automation project or automation transformation, is recognizing that it’s going to help your business processes go faster and be more efficient where it doesn’t inherently reimagine or transform your business process. And the reality is that automation is just like any other tool in the tool chain in IT. Whether it’s like cloud or mobile machine learning, event driven architecture, things like that. It’s going to help accelerate and reimagine your business process, but it’s not something you can really adopt in a vacuum and it’s not going to solve all of your problems. You frequently need to go look at the business process that you actually want to automate to make sure that you’re reimagining it, updating it, making it more modern in the process, because if all you do is automate every process exactly as it is, you may get some gains, but you’re really not going to be transformational. And that’s kind of a … Just one of the most important things that we really work through in terms of being successful with your automation strategy. And whenever I talk about this with customers, it always reminds me of a great quote from a football coach, Lou Holtz. He had a quote which was something like, “You’d rather have a slow guy moving in the right direction than a fast guy moving in the wrong direction.” Automation is kind of like that in that if you roll out automation for the wrong project, you’re just going to have the wrong business process go a heck of a lot faster in the wrong direction. So you really want to make sure you’re building your automation, you’re reimagining the business processes along the overall strategy and direction of the company, which maximizes for your business, as opposed to just taking what’s already there and making it go faster.

Guy Nadivi: There’s a variation on that Lou Holtz quote that I personally love, which is, “To err is human, but to truly screw up requires a computer.”

Charles Lamanna: Yes, too true.

Guy Nadivi: Charles, you’re certainly in a position to know. So I’d love to hear what you think are going to be some of the biggest disruptions we’ll see in the next one to three years, with respect to automation, AI, and other digitally transforming technologies.

Charles Lamanna: So I can only answer this in one way and with one thing. I’m a little bit biased about what I work on, but I have to say the single biggest disruptor for automation is going to be the low code development of automation. And the main reason we see this being the case is because of, I’d say four big shifts, four big changes that are happening in IT development today. The first is a workforce shift. 35% of the workforce today are millennials or 75% of the workforce will be millennials by 2025. This audience has incredibly high expectations for modern digital experiences at work. And they aren’t the folks that like to copy-paste data between 17 different systems or in green screen terminal applications and things like that. They really want modern, not rote, creative and innovative experiences at work. So the workforce is shifting. The second thing is because of the shift in workforce and because of these expectations and in general push. And that every company everywhere is that there’s a huge surge in demand and the need to digitally support and enable your employees. We can see this in really unprecedented demand in terms of the acceleration of digital projects. We project over the next five years, there’ll be as many digital solutions built in the enterprise as were built over the last 40 years. Just huge, huge demand. So a new workforce with high expectations and huge demand. But there’s a third problem, which is an incredible, a staggering shortage of professional developers and coders. In the United States alone, we’re going to be short a million developers over the next decade. There just aren’t enough developers out there to go solve and address this growing demand. And then to kind of top it all off is a fourth thing, with COVID-19 the associated recession, which we’re calling the great lockdown, we’re going to be in a period of time where IT is going to need to do more with less, or do more with your existing resources and your existing technology. So all of these things are kind of mixing together right now. These four things. And what we’re seeing is that low code is really going to be, I think, emergent as a result of these changes. Because low code automation technology, which make it possible for everyone to be an automation specialist, a business user, an IT professional, or a coder can all start to automate tasks is what low code is all about. This completely changes the calculus of automation projects, completely solves the problem that I’ve talked about throughout the discussion of, do you understand the business need as well as the automation need? It actually makes, so the people who understand the business need in the business can solve the automation problems with these low code automation tools. And this starts to change just what the IT landscape looks like, drives more collaboration between business users and IT, and the end result is a heck of a lot more automation being built in a fairly short period of time. So really over the next one to three years, we see that low code is going to be one of the most disruptive forces and in particular, low code automation tools, which have visual authoring environments, visual debugging environments, as well as easy to understand concepts and management configuration is going to be one of the most disruptive aspects. And that really is the heart of our thesis at Microsoft of where automation is heading over the next three years.

Guy Nadivi: I think you’re absolutely right about low code because the history of technology is that it gets easier to use over time, which democratizes its abilities for the masses. So history would seem to agree with you. Charles, 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 moving forward with automation.

Charles Lamanna: This may be obvious to some of your listeners, but I’ll repeat it because it bears repeating. Which would be the best time to start building out your automation strategy and getting serious about automation for all functions of your company was probably three years ago. The second best time is now as the saying goes. And just the reason is that the return on the ROI on projects in the automation space really is phenomenal. There’s a huge amount of digital processes that have been accumulated over the last two decades that are just waiting to be enhanced and improved through automation. You can improve the employee experience, make your employees happier, keep them working at high cognitive high value tasks. You can improve customer experience, solve the customer’s problems faster and more efficiently than ever before. And you can do all of this with relatively minimal budget, but large outsize return. These things all just make it easy and straightforward to go build the case, to go build … To create an automation strategy, and to go chart what automation is going to need over the next few years. So if anything, I would just say now is the time to really make sure that you’re getting serious, that you’re looking at how automation can reach all parts of your company. And I think just speed and moving quickly is going to be key because as we go look out over the next couple of years, which are going to be a little bit challenging, macro economically speaking, automation is going to be key to being efficient, being lean, and building a great customer experience during those times. So I will say, move, move fast, move now, if you haven’t already, when it comes to automation, that would be the biggest takeaway.

Guy Nadivi: I think that’s very prudent advice because if you don’t, your competitors certainly will.

Charles Lamanna: Absolutely.

Guy Nadivi: All right. Looks like that’s all the time we have for on this episode of Intelligent Automation Radio. Charles, it’s been fantastic having you here today and learning about your perspective on the automation and AI space. I think everyone is going to keep a keen eye on what you and your team at Microsoft will be doing to shape the future of this market and continue moving it forward. Thank you very much for joining us.

Charles Lamanna: And thank you for having me, Guy.

Guy Nadivi: Charles Lamanna, Corporate Vice President, Low Code Application Platform at Microsoft. Thank you for listening everyone. And remember don’t hesitate, automate.



Charles Lamanna

Corporate Vice President, Low Code Application Platform at Microsoft.

Charles leads the Engineering teams for the Low Code Application Platform (LCAP) in the Business Applications Group at Microsoft. The LCAP team includes the Dynamics 365 platform, Power Apps, Power Automate, Power Virtual Agent, AI Builder and the Common Data Service products.  

Under his leadership, the Dynamics 365 service moved to Azure and evolved into a fully managed SaaS–on a single version, with regular updates. The Dynamics 365 platform is now one of the largest fully Azure hosted SaaS products in the world, deployed to over 30 datacenters and supporting the entire Dynamics 365 business. 

Before that, Charles worked in Azure for 4 years, leading the engineering teams that created Azure Resource Manager, Azure Autoscale, Azure Logic Apps, Azure Activity Logs and several other management related capabilities. Before Azure, Charles founded MetricsHub, one of the first offerings for public cloud cost management and service health monitoring. MetricsHub was acquired by Microsoft in 2013. 

Charles can be reached at: 

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

Twitter:                  @clamanna  

Try it out:               Power Automate 

Quotes

“…RPA historically is incredibly centered and focused on UI Automation and more specifically Windows-based automation. And it's our view that that UI Automation is necessary, but insufficient to really enable interesting automation scenarios and really transform every aspect of every company in every country around the world.” 

“We think there's I'd say a legacy of automation being very PC-centric, very on-premise centric, and there's some real potential to reimagine what automation looks like in a cloud native, cloud first way. So that's really important to our vision.” 

"…low code is a whole idea that you want to make it so anybody and everybody can be a developer, can build bots, can contribute to the automation revolution." 

“…we think that automation will be an important part of really any modern information worker job in the future. But the importance and the criticality will be a spectrum from say, somewhat impactful to very impactful, but really all information workers will feel that impact, will feel the responsibilities change and evolve over the next five years or so.” 

“I think process mining and the AI discovery capabilities is probably the most interesting thing that we're watching at Microsoft when it comes to automation over the next couple of years. And the reason is because it's still early days today, of course, but these tools help you very quickly identify processes that are ripe for automation. And even more interestingly is they actually help with the calculation of the ROI for an automation project. And this really is the dream of most IT transformation projects.” 

“35% of the workforce today are millennials or 75% of the workforce will be millennials by 2025. This audience has incredibly high expectations for modern digital experiences at work. And they aren't the folks that like to copy-paste data between 17 different systems or in green screen terminal applications and things like that. They really want modern, not rote, creative and innovative experiences at work. So the workforce is shifting.” 

“…there's a huge surge in demand and the need to digitally support and enable your employees. We can see this in really unprecedented demand in terms of the acceleration of digital projects. We project over the next five years, there'll be as many digital solutions built in the enterprise as were built over the last 40 years. Just huge, huge demand.” 

“…the best time to start building out your automation strategy and getting serious about automation for all functions of your company was probably three years ago. The second best time is now as the saying goes. And just the reason is that the return on the ROI on projects in the automation space really is phenomenal. There's a huge amount of digital processes that have been accumulated over the last two decades that are just waiting to be enhanced and improved through automation. You can improve the employee experience, make your employees happier, keep them working at high cognitive high value tasks. You can improve customer experience, solve the customer's problems faster and more efficiently than ever before. And you can do all of this with relatively minimal budget, but large outsize return.” 

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

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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

Ayehu Launches its Next Generation IT Automation and Orchestration Platform Powered by Artificial Intelligence

SaaS-Ready platform is the force multiplier for overwhelmed and understaffed IT and Security operations

Ayehu today launched its next generation automation and orchestration platform for IT and Security operations. The new platform is Software-as-a-Service (SaaS)-ready for hybrid deployments and is powered by artificial intelligence (AI) and machine learning driven decision support, for fully enhanced and optimized automated workflows.

Today’s IT and Security operations teams are overwhelmed by the increasing influx of alerts, incidents, and requests. This state of affairs combined with a growing shortage of skilled, talented IT and security professionals, has created the need for intelligence-backed, automated solutions.

“We’ve received overwhelmingly positive initial feedback from our partners and customers who have previewed our new platform and are excited to now make it generally available,” said Gabby Nizri, Co-founder & CEO of Ayehu. “We developed it because we wanted to make it even easier for our customers to incorporate and use automation as a game changer in their business.  The SaaS-ready, multi-tenant platform is now able to deliver efficiencies across hybrid environments. This sets the stage for CIOs around the world to start the journey to enable the Self Driving Enterprise.”

The platform includes an architecture redesign to support managed service providers (MSP) and businesses with hybrid deployments across on-premise, private and public cloud environments such as AWS and Azure. It also enriches product security in areas such as message encryption across internal and external networks and presents a brand new user interface. Key features include:

  • AI Powered – Machine learning delivers decision support via prompts to optimize workflows and dynamically creates rule-based recommendations, insights, and correlations
  • SaaS Ready – Ideal for hybrid deployments, supports multi-tenant, communication encryption, OAuth2 authentication, and internal security improvements
  • High Availability and Scalability – Ayehu easily scales to support organizations with a high volume of incidents and safe guards against a single-point-of-failure
  • Workflow Version Control – Ayehu is the first IT automation and orchestration platform to provide version control on workflows, allowing users to rollback changes and review, compare or revert workflows
  • Tagging and Labeling – Ayehu users can associate workflows with keywords through tags to quickly search and return commonly used workflows
  • User Interface Enhancements – The new angular 2.0 web-based interface, offering easy and user-friendly workflow designer and template navigation, as well as white labeling options for OEM partnerships

Ayehu acts as a force multiplier, driving efficiency through a simple and powerful IT automation and orchestration platform powered by AI. The next generation automation platform helps enterprises save time on manual and repetitive tasks, accelerate mean time to resolution (MTTR), and maintain greater control over IT infrastructure. IT and security operations teams can fully- or semi-automate the manual response of an experienced IT or security operator/analyst, including complex tasks across multiple, disparate systems. Ayehu’s response time is instant and automatic, executing pre-configured instructions without any programming required, helping to resolve virtually any alert, incident or crisis.

For more information and to request a live, personalized demonstration of the next generation platform, visit https://ayehu.com/nextgen

About Ayehu

Named by Gartner as a Cool Vendor, Ayehu’s Intelligent IT automation and orchestration platform 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 major enterprises and leading technology solution and service partners, Ayehu supports thousands of automated processes across the globe. For more information, please visit www.ayehu.com and the company blog.  Follow Ayehu on Twitter and LinkedIn.

PR Contact:

Christy Kemp

Dahlia Public Relations

303-898-3390

ckemp@dahliapr.com

eyeShare 4.8.3 is Here! With Improved Features & Integration with HyperV

We are excited to announce the rollout of an updated version of our flagship product, eyeShare. Along with the correction of several bugs, this latest version (eyeShare 4.8.3) also contains a number of valuable new features and integrations.

 So, What’s New?

Integrations:

  • ServiceNow integration now supports Security Protocol TLS 1.2 and duplicate labels in forms.
  • BMC Remedyforce integration now supports integrating with a Sandbox instance.
  • Salesforce integration also now supports integrating with a Sandbox instance.
  • New integration with HyperV, including 18 activities.

Activities now support:

  • Reading a specific Excel sheet
  • Single and multi SSH commands (setting a certificate within activity settings and selecting an authentication method)
  • Start SSH session
  • Using headers, certificates and redirecting to another page using credentials (URL check)

Users can also now hide any activity’s result from the Active Logs and database by adding [hide] to any of the fields in the activity.

For more information on the eyeShare product or to download your free 30-day trial and start using it today, please click here.

Ayehu Introduces Next Generation IT Automation and Orchestration Platform Integrated with Machine Learning Intelligence

Ayehu’s next generation platform, driven by machine learning intelligence, is a force multiplier for overwhelmed and understaffed IT and security operations teams.

Ayehu today introduced its next generation IT automation and orchestration platform for IT and security operations. With intelligent machine learning driven decision support, the platform dynamically creates rule-based recommendations, insights and correlations that provide the operator/analyst with suggestions for how to optimize fully- or semi-automated workflows.

Today’s IT and security operations teams are plagued by a seemingly continuous flood of alerts, incidents and requests. This is compounded by the fact that as businesses scale their systems complexity grows, placing an increased workload on an already inundated workforce. This trend combined with a highly-publicized shortage of skilled, talented workers across both IT and security, has driven the need for intelligence-backed, automated solutions.

“Our next generation platform is the evolution of our successful IT automation solution, designed around our customers’ direct feedback regarding their additional, specific needs,” said Gabby Nizri, CEO of Ayehu. “We believe automation should be simple to implement, manage and maintain, from one, unified platform. Now a SaaS ready platform, Ayehu allows customers and partners to gain efficiencies across their hybrid environments and provide their overworked operators and analysts with intelligent machine learning driven decision support, further increasing productivity. This is a game changer, and we can’t wait for our customers to experience the next generation of IT automation.”

The platform includes significant enhancements, including an architecture redesign to support hybrid deployments across on-premise, private and public cloud environments. It also enriches product security in areas such as message encryption across internal and external networks, and presents a refreshed user interface.

The next generation Ayehu IT automation and orchestration platform features:

  • SaaS Ready – Ideal for hybrid deployments, Ayehu supports multi-tenant, network encryption, OAuth2 authentication, and internal security improvementsHigh Availability and Scalability – Ayehu easily scales to support organizations with a high volume of incidents, and safe guards against a single-point-of-failure
  • Machine Learning Driven Support Decisions — Ayehu provides decision support via prompts to optimize workflows and dynamically creates rule-based recommendations, insights and correlations
  • Workflow Version Control – Ayehu is the first IT automation and orchestration platform to provide version control on workflows, allowing users to rollback changes and review, compare or revert workflows
  • Tagging and Labeling – Ayehu users can associate workflows with keywords through tags to quickly search and return commonly used workflows

Ayehu acts as a force multiplier, driving efficiency through a simple and powerful IT automation and orchestration platform. Ayehu helps enterprises save time on manual and repetitive tasks, accelerate mean time to resolution (MTTR), and maintain greater control over IT infrastructure. With Ayehu, IT and security operations teams can fully- or semi-automate the manual response of an experienced IT or security operator/analyst, including complex tasks across multiple, disparate systems. Ayehu’s response time is instant and automatic, executing pre-configured instructions without any programming required, helping to resolve virtually any alert, incident or crisis.

Ayehu will provide live demonstrations of its next generation platform at RSA Conference 2017 (San Francisco, Moscone Center, February 13 -17) in its booth # 4914 (North Expo Hall). The platform is currently in beta and will be generally available later this year.

For more information and to request a live, personalized demonstration of the next generation platform, visit https://ayehu.com/ayehu-it-automation-orchestration-platform-preview/ 

About Ayehu

Named by Gartner as a Cool Vendor, Ayehu’s IT automation and orchestration platform 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 major enterprises and leading technology solution and service partners, Ayehu supports thousands of automated processes across the globe. For more information, please visit www.ayehu.com and the company blog. Follow Ayehu on Twitter and LinkedIn.

PR Contact
Christy Kemp
Dahlia Public Relations
303-898-3390
ckemp@dahliapr.com