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Episode #51: Why Cognitive Architecture Might Be An Early Glimpse Of A Future With Artificial General Intelligence – Aigo.ai’s Peter Voss

October 15, 2020    Episodes

Episode #51: Why Cognitive Architecture Might Be An Early Glimpse Of A Future With Artificial General Intelligence

In today’s episode of Ayehu’s podcast, we interview Aigo.ai’s Peter Voss

The Defense Advanced Research Projects Agency (DARPA) has divided AI’s evolution into 3 distinct waves. Currently, we find ourselves in the 2nd wave, dominated by machine learning and big data. The 3rd wave however, is nearly upon us, and will allow AI to go from learning and perceiving to reasoning and possibly even generalizing. The ability to generalize is AI’s holy grail. Though rarely mentioned by its name – Artificial General Intelligence (AGI) – it’s often depicted in SciFi movies and books. DARPA predicts that next-generation methods will be required in order to achieve AGI.

Peter Voss is the man who coined the term AGI and is one of the field’s foremost thought leaders. Peter joins us on this episode to discuss cognitive architecture, a theory of computational structure he advocates for, and which he believes is our best path to an AGI future. We chat about a number of interesting subjects, and along the way learn why the very nature of cognitive architecture may eliminate the problem of bias in AI, why conversational AI is the killer app for cognitive architecture, and why the Turing Test isn’t very useful for appraising a machine’s intelligence.



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 Peter Voss, founder, CEO, and chief scientist Aigo.ai. Peter is one of the world’s luminaries in the field of AI, whose stated mission in life is, “studying and understanding all aspects of intelligence, and actually creating an AI system with general intelligence that can learn, think, understand, and reason more like the way we do.” Peter believes that something called cognitive architectures is the true path to artificial general intelligence. And we’re going to be speaking with him about that today. Peter, welcome to Intelligent Automation Radio.

Peter Voss: Thanks for having me, Guy. .

Guy Nadivi: Peter, you started your professional career as an electrician, a number of years ago in South Africa. Please tell us a bit about how you ended up in the field of artificial intelligence. .

Peter Voss: Yeah, certainly. One of my first jobs was as an auto electrician, then I progressed to electronics engineer. I started my own electronics company. Then I fell in love with software and my company turned into a software company. I’ve developed several technology platforms, including programming language and a database system, and also an ERP software system. That became quite successful, company grew very rapidly. We actually did an IPO, so that was super exciting. But that also allowed me, when I exited the company, to take off five years to study intelligence because what struck me, as proud as I was off the software that I developed, software today doesn’t really have any intelligence. If the programmer didn’t think of some particular scenario, it will just give you an error message or do something silly. So I really wanted to figure out how we can make software more intelligent. .

Peter Voss: I had the opportunity to actually take off five years to study intelligence from many different angles, from starting with philosophy, epistemology, how do we know anything? What is reality? How do we know it? What is certainty? Then from cognitive psychology, what are IQ tests? What do they measure? How do children learn? How does our intelligence differ from animal intelligence? And those kinds of questions. And then of course also finding out what had already been done in the field of artificial intelligence. .

Peter Voss: Over that five-year period, I came up with a design for a cognitive engine, a sort of a thinking machine. And then in 2001, I launched my first AI company, hired about 12 people, and for several years we were just in R&D mode, basically turning my ideas into actual prototypes and code. So that’s sort of my journey. By 2008, then actually had our first commercial product. And since then, I’ve basically been switching my time between getting commercial systems off the ground as a CEO, and also continuing to increase the intelligence of our system. .

Guy Nadivi: So let’s talk about Artificial General Intelligence, or AGI for listeners unfamiliar, which is for simplicity sake, the ability for a machine to understand, learn, and basically mimic human intelligence. Now we’re nowhere near achieving AGI, but there’s great expectations that we’ll get there before the end of the century, perhaps even by its midpoint or possibly sooner. Peter, you’re on record is advocating for cognitive architectures as the path to AGI. Can you please help our audience understand what differentiates cognitive architectures from the other approaches used up until now, and why you’re advocating for it? .

Peter Voss: Yeah, certainly. Another useful way of looking at the sort of artificial intelligence area and the approaches is DARPA talk about the three waves of AI. And what they mean by that is the first wave of AI is sort of logics, formal logic systems, expert systems and that. And that really dominated artificial intelligence for several decades and Deep Blue, the chess champion that IBM built, is an example of that. But then about eight, nine years ago, the second wave hit like a tsunami. And that is basically all to do with deep learning, machine learning, statistical systems, big data. So it’s when the big companies that had a lot of data and had a lot of computing power figured out how they could build neural networks or connections systems that could do really useful things. .

Peter Voss: So that’s currently dominating the AI space, machine learning, deep learning. But when DARPA talk about the third wave, they indicate that something more is needed, that you basically need interactive learning, one-shot learning. You need to get away from just statistical systems. You need adaptive systems, systems that can basically learn immediately, adapt their behavior, and also to be able to reason. And they call that the third wave of AI. And I identify that really as what is needed, the architecture needed to implement that as a cognitive architecture, something that is inherently designed to have all of the capabilities that intelligence require, such as immediate learning and reasoning and deep understanding and so on. .

Guy Nadivi: What are some of the more interesting use cases you’ve applied cognitive architecture based AI to, and what kind of results did you get? .

Peter Voss: Right. There’s a relationship between cognitive architectures and artificial general intelligence. So just to briefly talk about that, in 2001 I actually coined the term AGI, Artificial General Intelligence, together with two other people when we wrote a book on the topic. And the idea behind that was really to get back to the original dream of AI, the term that was coined some 60 years ago. That was to build thinking machines. So we felt in 2001 that the time was ripe to refocus on that original objective, where up to that point AI really had focused on narrow AI, solving one particular problem at a time. And so cognitive architecture to tie it back to artificial general intelligence, is in a much better position to cover a very wide range of different applications and to be able to adapt and learn to changing circumstances. And that’s really what AGI requires. .

Peter Voss: So my focus over the last 15 plus years has really been on natural language conversation, rather than robotics or vision or any of those other fields of AI. And the applications in this area for conversational AI are tremendous, are really large. On the one hand, obvious use cases are to have it as an assistant that can help you with customer service, whether that is for a retailer, or for a bank, or financial institution, or for a phone company, a cable company, that kind of thing. Or internally in the company as well. There are also other applications, many medical applications, as a medical coach for example, that can help you manage diabetes or some other condition. As an elder companion. In the car, you want to be able to talk to your car and you want it to understand you and to remember you and to learn your preferences. .

Peter Voss: In robotics as well, if you have a robot in a hospital or a hotel, you want to be able to talk to the robot and tell it what you want to achieve. In hospital, go to the dispensary, pick up this order and deliver it to that room. Or in a hotel, bring me a shower cap, and tomorrow morning I want two eggs over easy. That kind of thing. So you need conversational AI that is adaptive to those areas. Gaming is another application. So really anywhere where you want an intelligent conversation that is personalized to the individual user, that can learn your individual requirements. .

Peter Voss: We’ve done work with, for example, a sales assistant as a front end to Salesforce, because salespeople are notoriously bad at using Salesforce. So if they have a conversational AI that they can just talk to, they’re much more likely to actually be able to use it, where they can just say, “Tell me about my next appointment. What are their hobbies? What product are they interested in? Do they have any kids?” And can tell you that. And then when you’re done with your appointment, you can say, “Remind me next Tuesday to follow up. Set this to high priority. Send them brochure X, and let my boss know what’s going on.” So just myriad of applications. .

Peter Voss: We’ve having some great success as a hyper-personalized concierge for a gifting company, where our agent can basically learn your individual preferences, who you buy gifts for, who are the important people in your life, what kind of gifts they prefer, when you want them, and so on. So just many, many applications are possible with this general conversational AI. .

Guy Nadivi: Now, speaking of conversational AI, there are some concerns about biases creeping into the AI that powers things like chatbots. How do cognitive architectures address the issue of biases differently than current machine learning methodologies? .

Peter Voss: Yeah, it’s a good question. So a big source of the biases that you get in machine learning applications is basically that you just feed massive amounts of data, and it’s not really curated very much. So whatever bias is in your data is going to be reflected in the outcome of the AI. Whereas with a cognitive architecture, you typically have an ontology that is specifically trained where you have a human in the loop. So it’s not the quantity of data that matters, but rather the quality of data. And so that allows you to look for potential biases and eliminate them as you build your ontology, the knowledge base and the business rules that you have in there. .

Peter Voss: But there’s actually another angle to this that helps. With the second wave of machine learning, deep learning, the system is inherently a black box. If it gives a certain response, you can’t really pinpoint why it gives that response. If you see bias in it, the only remedy is really to retrain the system, train it with a different data set and hope that that fixes it and doesn’t break something else, or doesn’t create some other bias. Whereas with the cognitive architecture, at least in principle, it’s not opaque, you can actually figure out exactly why it’s giving you a certain response and you can then remedy it, and you can give it the extra knowledge that it may be missing or the extra business rules. So it’s much more manageable to be able to eliminate undesirable biases. .

Guy Nadivi: Harvard Business School published an article not long ago, calling for the auditing of algorithms, the same way companies are required to issue audited financial statements. Peter, what do you think about AI algorithms being audited for bias in the same way? .

Peter Voss: I think it sounds pretty impossible actually in most cases. I think it’s a really, really hard problem, because almost every situation is unique. There’s even a more fundamental thing of, what is a good bias and what is a bad bias? I mean, the word bias has a negative connotation attached to it, but there is sort of experience that you have, there are statistical facts that you have. So it really I think comes down to more having the business itself audit itself, and having the right moral structure in place in the company itself. I think external auditing is very, very difficult except maybe for very certain narrow industries. So one would hope that the leadership in companies care enough about the issue to basically eliminate the bad kind of biases. But that’s hard, as with any kind of business ethics, because on the one hand you have huge push towards maximizing profitability, and any sort of moral imperatives that would undermine that just requires really strong leadership. .

Guy Nadivi: Historically in the software business, a killer application was needed to help a hardware platform achieve commercial success. Is there a killer app, or short list of killer apps, for cognitive architecture based AI that will help it achieve breakthrough commercial success? .

Peter Voss: Yes. I think it’s a very obvious that conversational AI is sort of the killer app for cognitive architectures. Cognitive architectures themselves don’t limit themselves to conversational AI, because they also need it for robotics and vision, so basically sense acuity and dexterity aspects of AI. But conversational AI just has such a huge potential market. There’s such a demand for having hyper-personalized conversational assistance. Whether this is an elder companion, whether it’s a personal assistant that helps you, or whether it’s something for large companies where they’re trying to provide better, more personalized hyper-personalized service, consistent service to their customers at a much lower cost. I mean, the promise there is essentially as if you had a dedicated service representative or sales representative, whatever the case may be, allocated to you, remembers you, remembers your previous conversations, remembers what you said, what your preferences are. So I think that’s a killer app. In this case, hardware doesn’t really come into it that much. But for cognitive architectures, yes, I think conversational AI seems to be the obvious choice. .

Guy Nadivi: Most people have heard of the Turing Test, which basically states that if a human can’t tell if they’re communicating with another human or a machine, then that machine or computer has passed the Turing Test. I recently learned that Steve Wozniak of Apple fame proposed an alternative called the coffee test, and this tests some machine’s intelligence by seeing if it can enter an average American home and figure out how to make coffee, which if you think about it is not entirely straightforward. It has to find the coffee machine, find the coffee, find a coffee cup, add water into the coffee machine, and then brew some coffee by pushing the correct buttons. I know some humans who would have trouble passing that test, myself included since I don’t drink coffee. Peter, what’s your personal favorite test when it comes to appraising machine intelligence?

Peter Voss: Yes, I think the coffee test is certainly a good one, if you ultimately want to confirm that you have reached human level AI. But as you said, I would also personally fail the test because I’m a tea drinker. So the Turing Test itself is not actually very useful. In some ways it asks too much, in other ways it asks too little. In the way it asks too much, it basically expects you to be able to fool people that you’re a human when you really aren’t. So if, for example, you want to divide a nine digit number by another nine digit number and come up with a result to 10 decimal places, it would have to say, “Oh, I don’t know how to do that” when it really could. But it also asks too little, in that you just have to be good enough to fool the human judges. So then it depends on the rules of the game, you can kind of game the system.

Peter Voss: As far as the work we’re doing in conversational AI, it’s actually very clear when you give customer service in any different domain, or whether you have an AI in a car or in a VR experience, or wherever the conversation is, and you want to hold an ongoing conversation. It very, very difficult to actually do that well. So it’s easy to see improvements in that area. Basically, how many conversations can the system hold in the real world, and for how long can it maintain? How well is the quality of the conversation? And I think these are not really generalized benchmarks. In fact, I am not a fan of academic benchmarks at all, because you’re then optimizing to the benchmark rather than optimizing to intelligence. So I think in conversational AI, it becomes pretty obvious how well it handles a wide variety of real conversations with real humans.

Guy Nadivi: Whenever we have an AI expert like yourself on the show, I always like to ask them the following question. Over the long-term, do you think that AI machine learning or cognitive architectures will ultimately augment more people or replace more people?

Peter Voss: So I think it will free us up in many ways and improve our lives. Ultimately AI will be able to do pretty much everything that humans are paid for doing right now. That’s in the longer run, and that will free humans up to do the things that they want to do, whether it’s creativity or learning or human relationships, raising a family, whatever it might be, without having to work. That I think is a long-term view. But it will also augment us, in the way that we will essentially have, I like to call it an XL cortex, an extension to our brain, an extension to our mind, that will allow us to make better decisions in life. It will be almost like an angel on your shoulder that can help you make better decisions in life, provide you with more information, better information to make a decision. It will also maybe prevent you or slow you down from reacting at the spur of the moment emotionally about something that you might regret later on.

Peter Voss: Having that personal assistant that becomes part of you, part of your life, I think will just enhance our lives and make us better humans. So I think both are true. It will replace human labor in many ways, but will also enhance our lives.

Guy Nadivi: Peter, 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 cognitive architecture based AI?

Peter Voss: I think there are quite a few different things but first of all, cognitive architectures are still very new. We’ve been working on it for more than 15 years, but deep learning machine learning is really dominating the field right now. So I think it’s important for management to really understand the limitations of machine learning approaches. They are inherently static. You basically collect a whole lot of data, you train a model, and that model is then deployed in the field. And it cannot learn interactively, it cannot adapt to each individual. You basically have a one size fits all. So if they really want to have a better conversational AI, and again that’s my area of expertise, is to say, well, will the technology that you’re buying, will it actually be able to do that? Will it be able to learn interactively? Will it be able to do one shot learning? Somebody tells you a fact, I’m going to Oregon next week. Will it be able to learn that and use that information without having to be specifically programmed for it? Does it have deep understanding? Can it reason? To really ask those hard questions.

Peter Voss: Now to implement cognitive architecture well, you need to really deeply integrate it into the enterprise or into the application that you want, for it to be effective. It needs to have access to the backend information, to your business rules and so on. And it’s non-trivial, it needs to have commitment. It’s not something you just have a fancy looking tool that you can simply quickly put together some flow chart of a conversation and expect that to work. And therefore it requires really a commitment from top levels of management to have a successful implementation of a cognitive architecture that you understand what you’re trying to achieve, the steps you need to go through to implement it and integrate it. But the rewards of course to doing it well, are just very, very significant. Most large companies, or most companies implementing chatbots right now, are deeply disappointed in the performance that they get, for the reasons I mentioned. They actually don’t incorporate a cognitive architecture and they’re not deeply integrated into the company’s ontology and business rules and so on.

Guy Nadivi: Interesting food for thought for the many executives now budgeting for future investments in this field. All right. It looks like that’s all the time we have for, on this episode of Intelligent Automation Radio. Peter it is a real honor to have someone so highly regarded in the field as our show’s first expert guest on artificial general intelligence. You’ve certainly enlightened me on the topic and I suspect our listeners got great insights from you as well, and it’s something they’ll be thinking about more than they previously did. Thank you so much for coming onto the podcast.

Peter Voss: Yeah. Thank you again for having me, Guy.

Guy Nadivi: Peter Voss, founder, CEO, and chief scientist at aigo.ai. Thank you for listening everyone. And remember, don’t hesitate, automate.



Peter Voss

Founder, CEO, and Chief Scientist at Aigo.ai

Peter Voss is the world's foremost authority in Artificial General Intelligence. He coined the term 'AGI' in 2001 and published a book on Artificial General Intelligence in 2002. He is a Serial AI entrepreneur and technology innovator who for the past 20 years has been dedicated to advancing Artificial General Intelligence. Peter Voss' careers include being an entrepreneur, engineer, and scientist. His experience includes founding and growing a technology company from zero to a 400-person IPO.

For the past 20 years his focus has been on developing AGI (artificial general intelligence). In 2008 Peter founded Smart Action Company, which offers the only call automation solution powered by an AGI engine. He is now CEO & Chief Scientist at Aigo.ai Inc., which is developing and selling increasingly advanced AGI systems for large enterprise customers. Peter also has a keen interest in the inter-relationship between philosophy, psychology, ethics, futurism, and computer science.

Peter can be reached at:

Website: https://www.aigo.ai/resources

Email: srini@Aigo.ai

Quotes

“…software today doesn't really have any intelligence. If the programmer didn't think of some particular scenario, it will just give you an error message or do something silly. So I really wanted to figure out how we can make software more intelligent.

“…so cognitive architecture to tie it back to artificial general intelligence, is in a much better position to cover a very wide range of different applications and to be able to adapt and learn to changing circumstances. And that's really what AGI requires."

"With the second wave of machine learning, deep learning, the system is inherently a black box. If it gives a certain response, you can't really pinpoint why it gives that response. If you see bias in it, the only remedy is really to retrain the system, train it with a different data set and hope that that fixes it and doesn't break something else, or doesn't create some other bias. "

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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

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Episode #50: How Automation Helped LPL Financial Grow Into The Largest Independent Broker Dealer In The US – LPL Financial’s Dwayne White

October 2, 2020    Episodes

Episode #50:  How Automation Helped LPL Financial Grow Into The Largest Independent Broker Dealer In The US

In today’s episode of Ayehu’s podcast, we interview Dwayne White, Vice President of Technology Automation at LPL Financial Services

Some of you were introduced to automation in school, or at work, or perhaps even by a family member or friend.  Our guest on this episode began his automation journey in the US Marine Corps.  Corporal Dwayne White’s first automation project involved “digitally transforming” a manual process done on a Xerox typewriter into a far more efficient solution done on a PC. From there, he progressed through successive levels of technical & executive responsibility, until reaching his current position as VP of Technology Automation at LPL Financial Services. 

As the largest independent broker dealer in the country supporting nearly 17,000 financial advisors, technology plays an outsized role in LPL Financial’s success.  Dwayne was instrumental in leveraging automation to power LPL’s growth, both as a hands-on techie and as a manager.  He shares some key insights with us in this episode, and we learn how automation mitigates risk for LPL Financial, the specific kinds of processes LPL automates, and why you shouldn’t look at automation as a replacement for your staff. 



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 Dwayne White, Vice President of Technology Automation at LPL Financial Services. For those not familiar, LPL Financial is considered to be the largest independent broker dealer in the US, with nearly 17,000 financial advisors and $762 billion in advisory and brokerage assets under management, as of June 2020. Dwayne is a hands-on IT operations executive in charge of technology infrastructure automation. He’s also worked a lot with L1 and L2 NOC engineers, overseeing their efforts in a never ending pursuit of reducing incident MTTR. Automation has played a big role in Dwayne’s success at LPL Financial, and since he brings a unique dual perspective, with both first-hand and managerial experience in automating various IT processes, we wanted to bring him onto the podcast and see what we could learn. Disclosure: LPL Financial is a customer of Ayehu, the company sponsoring this podcast.

Dwayne, welcome to Intelligent Automation Radio.

Dwayne White: Thank you, Guy. It’s a pleasure to be here today. I always enjoy listening to the Automation Radio podcast.

Guy Nadivi: We appreciate that, thank you. So Dwayne, you started out at LPL Financial as a developer, and 20 years later, you’re Vice President. What were some of the bigger IT challenges you overcame during those two decades while the company was growing as rapidly as it did?

Dwayne White: Well, for me, it was both a personal, and I guess you can say, IT challenge. On a personal nature, for me, early on it was deciding in my career if I wanted to be a techie, or did I want to be a developer, did I want to be a manager? And for me, I wanted to be a manager. And what that meant to me is it meant I can go ahead and mentor and train new staff, and have a voice with my managers, my leadership. Outside of that, early on still in the back at LPL. We were a small company, and as a small company, we had our issues, our challenges, and some of those having to deal with how we managed incidents. Back in that time, our pagers would go off, we would jump on a phone call, everyone would talk over themselves. It was just organized chaos. Now, that seemed to work for us at the time, but like everything else, we knew we can do better. And what better meant for us is we started looking at ITIL. ITIL basically is a framework that people used for instituting IT business processes. And once we started moving down the ITIL model and we started maturing our processes, things got a lot better for us. It was a good thing to do. Outside of that, was learning how to adopt to our change and transformation. Most people aren’t comfortable with it. I’m not comfortable with it sometimes. And usually, when change is implemented and transformation is implemented, sometimes it’s done well, sometimes it’s done poorly, and when it’s done poorly it’s mainly due to lack of communication out to the teams. What we found is that our teams, they want to be involved. They want to know what change is coming down the pipe. They want to have a voice in that change. As an example, if you were to come in on a Monday morning and tell your team that instead of working Monday through Friday, they’re now going to work Tuesday through Saturday, they wouldn’t be too keen on that change. But if you involve them in the process, communicate to them the need, not only for business, but for themselves, we can start getting their buy-in on it. We found that actually works a lot better for us.

Guy Nadivi: So what were some of the bigger reasons you decided to look into automation at LPL Financial?

Dwayne White: Well, for us, my boss actually got me down the path of looking at it, and he had concerns about how we were delivering our services. And services can be defined as how we processed our service request tickets, how we responded to incidents, how we communicated. And bringing automation into it just seemed like a natural fit for us. So, back in 2015, that’s when we started looking at the various tools, the different processes, we dealt with Gartner, started looking at some of their Magic Quadrant work, and we started moving forward. So, for example, one of the inconsistencies that brought us forward would be if you had a ticket assigned to somebody, and let’s say it had to do with the Windows server, a disk problem. You can assign that to three different admins and you’ll get three different results. One person has one way of cleaning up disk space, the other person has another. So we needed consistency in that. Automation? That was the clear path. It does one thing. You tell it what to do, it does it, and it repeats. The nice thing with automation, just like a disk space problem, is you learn as your applications grow and capacity changes. You change the script. You change the automation to adapt to it. The other part of it was risk. No one’s perfect, but with automation you can reduce the even element of risk. Even elements could be missed alerts due to outages. Staff’s not always at their desk watching their ticket queues 24×7. Or a slow response time to an incident. Again, you’re counting on your staff to be sitting at their desk, glued to their ticket queues, or waiting for their phone to ring. With automation, it’s there, it’s live. It’s 24×7 and it’s waiting to be called upon. It was a natural fit for us. I’m glad we did it.

Guy Nadivi: Okay. Now, you mentioned the need for uniformity and consistency, and it’s well known that financial services firms have very rigorous regulatory requirements to comply with also. Given that reality, how challenging was it to persuade management that automation was worth a try?

Dwayne White: It’s true. I mean, we’ve got a whole bunch of regulations. It’s interesting, we learn more and more each day as our government changes them, or FINRA or the SEC. There’s always something new to adapt and change to. But at a high level, when you look at the financial industry, its regulations are there to protect our consumers, prevent fraud, and limit risk. And the risk limiting is mainly what can that financial institution do to investor’s money? So, looking at risk, that was probably one of the big selling points. So, as we approached our architectural review board, I basically highlighted basically how automation can help lower the risk, as previously mentioned, as well as the rewards, such as consistent delivery of service, improving mean time to restore service improved SLAs, satisfactions. I mean, the list can go on if you can put the case properly. And basically, with the help and guidance of the review board, we were able to adopt it and we’re now moving in the right direction.

Guy Nadivi: Dwayne, I think it would be very interesting for the listeners if you could talk a bit about the platforms that you’re performing automation upon in your environment.

Dwayne White: So, platforms, integrations, an example would be Active Directory. So we currently have automations that deal with self-services to modify Active Directory to find user accounts. We revoke user accounts. We add users to various security groups. And we can schedule the automations. We have hooks that are tied into Microsoft Exchange, we’re hooked into the file shares, we’re doing work with ServiceNow. With ServiceNow, basically, we use that as the catalyst to monitor the ITSM queues, to pull back service requests and incidents that we can respond to with automation, as well as you’ve also got to look at your automations you’re creating as not being dealt with. So, for example, if you have an automation that is going to reach out and do something with Active Directory, what happens if your Active Directory controller is not working? You need to put in some error checking and then detect the error and then go ahead and create an incident for someone else to look at it. So that’s how we go ahead and create incidents with ServiceNow. We also hooked into Microsoft SQL servers, we’re doing access provisioning, space consumption and alerting. We do work with Windows servers, we take incidents, we call them “level zero triaging”, as well as we’ve got scheduled service restarts. We’ve also got automations that are tied into third party web services. With that we do REST APIs, data extraction and reporting, as well as we create incidents off of those. What we’re working on now, and hopefully over the next few months is the integration packs for AWS. So we’re starting to hook into the Cloud and see what we can automate out there. We’re going to start working on SharePoint here soon. Also, let’s see, Oracle database access provisioning. And it’s interesting, in talking about the regulatory stuff above, when we first got the platform, our regulatory team came to us with some automation suggestions, and we got those implemented for them. So it was nice to have that as one of our first automations. We’re happy.

Guy Nadivi: Okay. Clearly you’re automating and attempting to automate lots of different processes. So how do you define success once you’ve automated a process?

Dwayne White: Success, we actually try and get the success defined before we automate. And what we do is we basically engage with the teams. So an example, if someone comes to our team and says, “Hey, I got this widget and I need this widget and process automated.” So what we do is we look at the number of requests that would come in, that’s being done manually. We’d take a look at the triggers that would trigger the workload, and the time that it would take to complete the request manually. And what is the current SLA for the request and/or work incident, what’s the mean time to resolve it? Outside of that, we also look at the effort we put into the automation versus a manual task. So, for example, if the manual task, right now it takes an hour, but you’re only doing it three times a month, maybe that’s something we’ll keep in place. That’d be a manual process because it’s the development effort to design the automation might be longer than that. So, for example, recently we did a thing with Active Directory, a simple automation to add somebody to a security group. Our current statistics have basically shown us that the manual process had averaged about 1.5 days for the team to actually do the work. And that’s with the ticket coming in, somebody approving the ticket, and then, over the next one and a half days, somebody actually doing the work outside of their normal daily routine. We found out we were averaging about 265 of those requests a month, but once the team actually got the ticket and did the work on it, it actually only took about five minutes. So, doing the math, and this goes back to the success part, the manual process was 265 tickets, five minutes each; basically we were spending about 22 hours a month on that one ticket item in a manual process. Also, you’ve got to remember, it took one and a half days for them to finally get this ticket done. Now, you throw in some automation with it, you get to take the same 265 tickets, instead of being five minutes, it’s now done in 30 seconds, and we’re only spending 2.2 hours of automation time on it. So we just freed up 22 man hours. And the other nice cherry on the top is, if you remember, it took one and a half days for the manual process to finally open and close. With automation, we’re talking 30 seconds, because we’re constantly polling our ITSM tools for the next ticket. So now you have a clear, consistent, repeatable process that your customers will see, and it’s a win/win.

Guy Nadivi: Okay. So now I’m a bit curious; do you ever get more formal by calculating the ROI of automating a particular process? And if so, other than time-savings, what kind of metrics are most relevant for you?

Dwayne White: So for us, I present my management, I try to at least monthly, to know how our team is performing. And part of that is showing the automations that we took over and what the manual process was keyed up at as far as SLAs and time to resolve for incidents … I’m sorry, mean time to resolve for incidents. And I’ll put together a nice little dashboard showing them the savings done with it, as well as any failures within our automation, which is very, very limited. But it’s a nice dashboard that shows them the KPIs that they want to look at.

Guy Nadivi: Dwayne, with all the processes you’ve automated, I’m curious if there’s any particular one that stands out as the most successful IT business process you’ve automated?

Dwayne White: So I would say the most successful one that we’ve automated … oh, let’s see. So we have about 43 automations right now. Of those, most successful, I would probably have to say it’s probably the employee offboarding automation. What makes it successful is that one’s actually looked at from a internal audit perspective. So for an example, when an employee decides to move on or is terminated, whatever the case may be, once the effective date comes in, we have two business days to get them terminated and off-board properly out of our systems. So what was different about this is they can actually put the ticket in a couple of weeks in advance. So, for example, if a guy wanted to move on from Ayehu, he would tender his resignation and they would go ahead and submit the termination paperwork, but it wouldn’t be effective until let’s say two weeks down the road. So for us, it was trying to pull in and design the automation to where when the ticket was put into the system and it had all the approvals, how not to process it until the actual effective date. So that one did stump us for a while, but we did work through that one finally, and we’ve got it in place and it has been running successful right now for probably about four or five months. And the nice thing is we’re able to use that as a framework for other automations that had similar characteristics with it. If you look at what we had to do with our employee offboarding, it would take a system admin 30 minutes to go through and touch upon the different areas that they had to remove accounts from, or pull them out of different security groups or distribution lists. Now, automation does it in two minutes, and it does it within the 2-day SLA, the second the effective date happens at, let’s say this Friday at 5:00 PM, automation is kicking off. So we don’t have to worry about, “Uh oh, you were working over the weekend,” or, “You didn’t have to work over the weekend to make sure you kept your SLA.” And it’s kept our internal audit team happy, Infosec’s been pleased, and haven’t had any issues. So.

Guy Nadivi: Dwayne, you were in the Marines for nearly six years, but not in an IT capacity. First off, I’m sure I speak on behalf of all of our listeners when I say thank you for your service. Secondly, I’m curious to hear how did being a Marine prepare you for a career in IT operations?

Dwayne White: Yeah, let’s see. And thank you, it was my pleasure. I actually enjoyed the service. Actually I joined when I was in high school. So they had a program back then called a delayed entry program; you could join in your senior year, and upon graduation, you can go ahead and jump into the Marine Corps. So that’s what I did. I had a nice two week summer vacation. And this was … oh, let’s see, back in 1984. So I served in the Marines from ’84 to 1990. And Marines, there’s different jobs in the Marine Corps. So I was assigned to a helicopter squadron and my role was an aviation ordinance technician. Normally you’d think that sounds like fun, and it was, but as an aviation ordinance technician, a lot of people think, “Oh, cool, you get to play with bombs and missiles and things like that.” And there’s not many things to automate, it’s a lot of manual work, it’s heavy lifting. But what people don’t think about, and sometimes they forget, is our government and the military, they love their paperwork. And the paperwork has to deal with maintenance on the aircraft, personnel changes, scheduling, you name it. There was a ton of paperwork that was being done. And we didn’t really have a computer back then, but we had a nice typewriter, a beautiful … probably, if I think about it, maybe a Xerox typewriter. And that’s how I learned how to type. That plus high school. But from there, I went ahead and took a course at a local community college. I was stationed out in Jacksonville, North Carolina. And the class I took was around data processing because I thought that would be a good fit because I got tired of trying to type on the typewriter and make everyone happy, because my penmanship’s not the best in the world. And after graduating from the community college, I was able to come back to my shop, convince our Master Sergeant that, “Hey, we really need to get a computer because I can help us with all this typewriter stuff by taking it from a typewriter and putting it into a computer and then producing the same paperwork, the same forms, changing what we needed to change.” And I got that implemented. It took a little while, a lot of hit and misses, but it soon started working for us. And that’s how I got going with the automation. Outside of that, I’m in the Marine Corps, it’s great. I mean, how they prepared me, like everything else, it’s how they train you. It’s the discipline, it’s the operational efficiency of the Marine Corps. If we wouldn’t be efficient, we wouldn’t be disciplined. That’s it, pretty much, in a nutshell.

Guy Nadivi: Dwayne, 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 automation?

Dwayne White: I would probably tell them that they need to invest into a dedicated automation team. What I mean by that, it’s not going to be someone’s part time job, it’s not going to be an afterthought, it’s truly a team dedicated to automation. And once you have that in place, you get them involved with your projects. You get them involved early on, because they’re going to bring a skillset that they haven’t seen before, because there’s still a lot of IT companies who are used to manual processes. If you look at a lot of the data center operations or batch operations, you’ve got these operators glued to your computer monitors, and all they’re doing is staring at it and waiting for something to happen. Or maybe they have a task list that they have to go through at nighttime to make sure the batch processes are running properly. Those are opportunities for automations, and you’re not going to see those unless you have a dedicated team who’s out looking for them. The other thing, and I tell this to my team and the teams that I work with when I’m trying to get them to bring me more ideas for automation, is do not look at automation as a means to replace your staff. That shouldn’t be your first thoughts, thinking that you could save a few dollars. You need to look at it as an opportunity to give back to your staff time. As we mentioned earlier, that one automation, it went from 22 hours down to two hours. There’s 20 hours of time back to your staff for them to focus now on your manager’s next big project, or give them time to invest back into their training through their career development. It’s a win. You can’t go wrong.

Guy Nadivi: Great advice from a hands on practitioner. All right, looks like that’s all the time we have for on this episode of Intelligent Automation Radio. Dwayne, our listeners have told us they want to hear more firsthand insights from automation practitioners like yourself, and you’ve definitely delivered that to our audience today. Thank you very much for coming onto the show. Dwayne White: Oh, thank you. Have a good day.

Guy Nadivi: Dwayne White, Vice President of Technology Automation at LPL Financial Services.

Thank you for listening everyone. And remember, don’t hesitate, automate.



Dwayne White

Vice President of Technology Automation at LPL Financial Services

Dwayne White has been with LPL Financial almost 20 years and a Developer for almost 30 years.  LPL Financial is the largest independent broker/dealer in the country supporting nearly 17,000 financial advisors.  While at LPL Financial, Dwayne has held various roles:  Software Developer, Incident Management, Problem Management, Data Center Operations, Service Delivery Management, NOC Operations and he has managed diverse teams to include Database Admin’s & Developers, Network Engineers, Developers, Windows Operations & Engineering, Unix Operations & Engineering, Active Directory, Exchange Engineers & NOC Engineers. 

Dwayne’s current role with LPL Financial is Vice-President Technology Automation.  In his new role, he and his team focus on delivering Infrastructure as Code & IT Process Automations focused on Service Delivery and Incident triage.  Dwayne is a strong believer that Technology Teams need to embrace Automation as a tool for their arsenal as part of their Digital Transformation and let them know Automation is not a threat to their job.  Let the automation handle the day-to-day grunt work and allow your teams to focus on the company’s larger efforts & priorities.  The end result will be a happy customer & client. 

Dwayne can be reached at: 

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

Email:              Dwayne.White@lpl.com 

Quotes

“ITIL basically is a framework that people used for instituting IT business processes. And once we started moving down the ITIL model and we started maturing our processes, things got a lot better for us. It was a good thing to do.” 

“…usually, when change is implemented and transformation is implemented, sometimes it's done well, sometimes it's done poorly, and when it's done poorly it's mainly due to lack of communication out to the teams. What we found is that our teams, they want to be involved. They want to know what change is coming down the pipe. They want to have a voice in that change.” 

"…I tell this to my team and the teams that I work with when I'm trying to get them to bring me more ideas for automation, is do not look at automation as a means to replace your staff." 

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

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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

5 Ways to Quantify Intelligent Automation ROI

5 Ways to Quantify Intelligent Automation ROI

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

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

Drastically Saving Time and Money

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

Distinct Edge Over the Competition

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

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

Agility and Scalability

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

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

Maximizing Uptime

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

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

Data-Driven Decision Support

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

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

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

How to get a higher return on your Intelligent Automation investment

maximizing your intelligent automation ROI

Despite the fact that intelligent automation offers a multitude of benefits to organizations of all sizes and industries, there are still many key decision makers that fail to recognize the value this technology can have for their own enterprises. In order to demonstrate how important automation is for the future of business, IT managers must find a way to maximize ROI and demonstrate the quantifiable benefits to the powers that be. Here are five simple strategies for getting those numbers headed in a positive direction.

Define Needs, Benefits and Expectations

You can’t focus on improving anything – whether it’s efficiency through intelligent automation or the actual ROI it delivers – unless everyone understands what to expect. Time should be taken to identify and define the specific needs of the organization, and then specify how automation can solve those problems and meet those needs. Once this information is gathered, you can then more accurately measure all of the specific areas where automation is producing a solid return and how. A few key places to start include effort reduction, mean-time-to-resolution (MTTR), lowered rate of error, compliance and system up-time. Improving each of these areas will directly boost your return on investment.

Understand the Process and Where Automation Fits

The driving purpose behind intelligent automation is to use technology to replicate repetitive, manual tasks. To improve automation ROI, one must dig much deeper than this basic concept to understand the entire process at hand and identify exactly how automation can be integrated for optimal results. Important questions to ask in this analysis include:

  • What factors should trigger an automated process?
  • What must occur before and after the automated process?
  • What variables and inputs will be necessary to achieve the best outcome?

Most importantly, how does automation fit with the big picture – the larger business process as a whole? While individual tasks could certainly be automated, automating the entire process or workflow may actually produce a greater value for the business.

Recognize the Context and Customize Accordingly

Calculating accurate ROI involves understanding the specific context in which the automated process in question is running and customizing that process for optimal results. For instance, the automated response to a critical incident, such a systems outage, during peak business hours should be markedly different than the response to a similar outage that occurs in the middle of the night. These contextual considerations should be built into the automation process and they should also be considered whenever measuring results. By customizing the process, the intelligent automation platform can execute different actions based on each scenario, thereby producing a greater return overall.

Test Thoroughly Prior to Release

Testing an automated process manually or in a development system can certainly be time consuming, but it’s absolutely critical to achieving maximum ROI. Before an automated process is deployed in a live environment, it must be adequately measured to ensure that it is not only producing the desired results, but is doing so consistently. Once the automated process is released, ongoing testing is still strongly recommended, as this helps to ensure that the triggers, inputs, actions and outputs are all running as smoothly and efficiently as possible. Routine audits can also help to identify areas that could be improved upon for even greater benefit.

Ongoing Evaluation and Improvement

Intelligent automation may feel like a “set it and forget it” solution, and theoretically it is to a certain degree, but the organizations that reap the greatest rewards from this technology do so by taking a continuous process improvement approach. Regular evaluation of how automated process are working and analysis of where they may be expanded to produce even better results is a must if you are looking to maximize ROI. IT professionals should be asking whether additional tasks could be automated, or whether existing automated processes could be integrated with one another or built upon for greater efficiency.

Individually, each of these five tips can have an impact on your overall return. When combined, however, they can help to both improve short-term goals as well as drive long-term strategies to produce the desired results of reducing human effort, improving operational efficiency, boosting service levels, reducing errors and downtime, remaining compliant and much more. The end product is a consistently favorable return on investment, which can help to win over those who are not yet on the intelligent automation bandwagon.

Want to see some real-world numbers that can be generated by intelligent automation? Check out our latest case study below.

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How to Achieve Quick ROI for your Intelligent Automation Project

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

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

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

Clearly define your outcomes.

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

Choose the right tool.

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

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

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

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

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

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

Five Steps to Getting a Quick, Positive ROI on Your IT Process Automation Project

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

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

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

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

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

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

For more detailed information on calculating ROI for ITPA, check out the below free resource.



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