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The Impact of AIOps on the Future of Work

If there’s anything we’ve learned from the past several months, it’s that flexibility and the ability to adapt are the key to success. With the sudden and rather unexpected shift to remote work, many organizations have quickly discovered the need for a new approach to IT management. AI for IT operations (AIOps) has the potential to become the golden ticket for improving efficiency and creating a collaborative, supportive and secure environment for distributed workforces.

Bigger companies who have opted to spread their workforces across multiple satellite locations stand to benefit greatly from AIOps. In fact, with intelligent tooling, organizations with 50, 75 or even 100 remote offices are capable of operating cohesively. As the number of offices scales up, AIOps becomes even more critical. One area where it is of particular value is in automated remediation. Ideally, the goal is to have technology do the heavy lifting, with the ability to pinpoint when and where something has gone awry and preemptively correct it.

From a productivity standpoint, AIOps helps, both in terms of IT management, as well as helping remote employees stay on top of monitoring activity and environmental changes. With machine learning and artificial intelligence at the helm, human effort is reduced tremendously. Given the recent – and likely permanent – shift to satellite and remote operations, it’s becoming abundantly clear that AIOps is the approach of the future.

This isn’t to say AIOps is infallible. There is still a margin of error to account for. This is good news for humans, as this is where creating a hybrid approach that has people and robots working together comes into play. Where AIOps can really standout is in its capability of identifying subtle transient issues that might not otherwise trigger a ticket or catch the attention of the support desk.

A good example of this would be changes in latency that only occur for mere seconds. Independently, these subtle problems may otherwise go undetected, or may not seem significant enough to warrant attention. But, when viewed collectively as a trend, AIOps could potentially identify the changes as something that could eventually cause more significant and widespread issues.

Another area where AIOps can help is by prequalifying remote employees for the applications they run and the quality of their network connection. Workloads can then be automatically shifted and optimally distributed based on these pre-qualifiers. Furthermore, AIOps technology can limit event volumes, predict future outages, and leverage intelligent automation to reduce downtime and alleviate staff workload.

The most exciting part of all of this is that, for all intents and purposes, AIOps is still really only in its infancy. For those wishing to jump on the AIOps bandwagon, there’s still plenty of room. And we’ve got a quick and easy way for you to get started. Simply click here to launch a free, 30-day trial of Ayehu NG and start putting the power of AIOps to work for your organization.

Separating Fact from Fiction: 5 Biggest AI Myths Debunked

There’s a ton of hype surrounding the topic of artificial intelligence, and unfortunately, where there’s hype, there’s also usually a good amount of misinformation. Sadly, many of the mistruths being perpetuated are causing undue fear and trepidation. The good news is, these myths and misconceptions about AI can easily be debunked. Let’s tackle a few of the more common ones below.

Myth #1 – AI is going to eliminate the need for humans in the workplace.

This is, by far, the biggest fear around artificial intelligence, and thankfully, it’s mostly false. Yes, AI is going to automate mundane, boring and repetitive tasks. Yes, intelligent automation is even capable of taking on complex and multifaceted processes and workflows. But the reality is, for every job AI replaces, several more will be created in its place. After all, someone’s got to manage and oversee all that advanced technology, right?

(Note: if you’re a worker who is concerned about how AI will impact you, our free Automation Academy is a great way to shore up your skills and future-proof your career.)

Myth #2 – AI is smarter than people.

Another frightening idea that’s being perpetuated is that artificial intelligence is somehow capable of outsmarting its human counterparts. This is simply not true. In fact, AI is really only as smart as you program it to be. You see, intelligent automation requires data. And not just any data, either. It requires a steady stream of high-quality, relevant information. As long as you provide this, the outcome will be successful. But don’t worry. Robots are not about to go rogue and take over the workplace autonomously. That’s the stuff of science fiction.

Myth #3 – AI is nice to have, but not really a necessity.

Perhaps this was true a few years ago, but today, organizations that are not prioritizing a plan for artificial intelligence will undoubtedly find themselves behind the curve before they even realize it. In fact, experts predict that over the next decade, there will be no company or industry that isn’t touched by AI in some way. The fact is, AI and intelligent automation make it much easier to innovate, scale and quickly pivot based on market changes. Failing to have a strategy in place is a risky proposition, especially since your competition likely does.

Myth #4 – There’s no way to know what AI is up to, and therefore, it’s impossible to trust.

When the concept of AIOps was first introduced, admittedly there was a sense of ambiguity surrounding it. For early adopters, it was this mysterious system that somehow produced results without providing any real insight as to what its underlying algorithms were doing and why. As time passed, however, these solutions have matured and become much more transparent. In fact, AIOps platforms like Ayehu place a significant emphasis on providing insight and maximum visibility. The result is a solution users can easily understand and – more importantly – trust.  

Myth #5 – As long as I test well, my AI project will be successful.

All AI initiatives should start with test projects. But it’s important to recognize that just because the results are great during the testing phase, doesn’t mean they’ll stay that way once you deploy AI into production. Remember that point we made above about the importance of quality data. The truth is, real world data changes often, and sometimes at a breakneck speed. If your AI and machine learning models aren’t being continuously fed up-to-date and accurate information, your accuracy level will begin to decline. The key to consistent, sustainable success with AI is ensuring that your training data is the same as your production data.

Have you fallen victim to one or more of the above AI myths or misconceptions? It’s never too late to learn the truth and course-correct. Get started with intelligent automation, powered by AI and machine learning, by downloading your free 30-day trial of Ayehu today.

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. "

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

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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

3 Ways to Fine-Tune Your AI for Continuous Process Improvement

3 Ways to Fine-Tune Your AI for Continuous Process Improvement

Getting up and running with artificial intelligence in your organization can be an exciting and even liberating experience. Suddenly, your infrastructure has more unity. Productivity and efficiency are through the roof. Errors have all but been eliminated. Profitability is skyrocketing. But then, over time, things start to level out. Suddenly, the numbers aren’t as impressive. Problems have been slowly but surely ticking back upward.

What gives? You and your team worked so hard to adopt and deploy AI across the organization. You were hailed heroes by your fellow business leaders. Yet, now, it seems complacency has set in. What’s more, as is the case for many organizations, a lack of ownership and collaboration between teams has begun to erode the progress that was previously being made.

The truth is, AI, just like any other major technology initiatives, is something that is fluid. Digital transformation doesn’t just occur one day and stop. It requires ongoing evolution, which means that, in order to continue to get the most out of artificial intelligence and automation capabilities, you must routinely prune and fine-tune your efforts.

Not sure where to begin? Here are three ways to ensure the time, talent and other resources you’ve invested into AI will remain relevant and profitable.

Develop a collaborative team.

In order for AI to be universally beneficial, different teams and departments must work together toward shared goals. But that doesn’t mean people will naturally step up to the plate. In many organizations, it’s necessary to establish a designated team with the purpose of collaborating on and contributing to the development of policies and procedures that will deliver continuous improvement of AI initiatives. In particular, there should be representatives from key groups, including IT, data science and the end users.

Keep your cycle active.

One area where many organizations fall short when it comes to successful implementation of artificial intelligence is their machine learning cycle. We’ve said it time and time again, but it bears repeating that AI is only as good as the data that’s driving it. The fact is, the logic and data you used to set up your initial AI project may no longer be relevant. The best way to ensure consistent accuracy between your algorithms and the areas in which they are applied is to keep your cycle active and pivot whenever and wherever it’s deemed necessary.

Employ retirement policies.

We’ve all heard the expression, “If it isn’t broke, don’t fix it.” The opposite could be said for AI initiatives. In order to remain agile and competitive, you must be willing to scrap the things that are no longer delivering value. Otherwise, you will be wasting resources that could be better used elsewhere. Develop and implement policies that include routine audits and strategies for next-steps, whether it be modifying to improve or retiring wasteful workflows altogether.

In a successful AI deployment, there are a lot of moving parts. The last thing you want is for any of those parts to become stagnant. To avoid this, you must continuously work to not only keep things running smoothly, but also optimize your strategy over time. Doing so will enable you to maximize the benefits of artificial intelligence and keep you a step ahead, both in terms of internal operations as well as with your competition.

cio guide to operational efficiency

Episode #49: How One Man’s Automation Journey Took Him From Accidental CIO to Unconventional VC – Ridge Ventures’ Yousuf Khan

September 24, 2020    Episodes

Episode #49: How One Man’s Automation Journey Took Him From Accidental CIO to Unconventional VC

In today’s episode of Ayehu’s podcast, we interview Yousuf Khan – Partner at Ridge Ventures

Perhaps you’ve heard the famous African Proverb “If you want to go fast, go alone, but if you want to go far, go with others”. Our guest on this episode has gone far, but he’s also gone pretty fast. Yousuf Khan has been CIO for a number of high-profile startups, a couple of them quite notable in the automation space.  His talent and vision led him to those roles, but his networking and outreach allowed him to excel.  Now as Partner with an early stage venture capital fund, he advises both CIOs and startups on how they can work together to bring next generation innovations to market. 

We learn quite a few insights from Yousuf in this discussion, including when it’s better to use artificial intelligence versus automation, how IT executives can prepare themselves to become CIOs, and why the CIO Group Therapy Dinners he started have not only led to better CIO decision-making, but better features in technology products. 



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 Yousuf Khan, Partner at Ridge Ventures an early stage venture capital fund. Yousuf is also a former five-time CIO, most recently at Automation Anywhere and Moveworks, two well-known and high-profile automation providers. Yousuf’s unique perspective as both investor and practitioner in the automation space, makes him a highly coveted thought leader. And given the stepped-up adoption of automation due to the COVID-19 pandemic, we wanted to tap into his considerable insights and gain a better understanding of what senior IT executives should be aware of as digital transformations are accelerated for business resiliency, as well as competitive advantage. Yousuf welcome to Intelligent Automation Radio.

Yousuf Khan: I thank you so much for having me here. I’m really excited to talk to you. I know you had 17 other people that you wanted to have on this slot, but when you call me up at 2am in the morning and say you’re exceptionally desperate and you need that extra guest, I’m your man. So I appreciate being over here and thank you for having me.

Guy Nadivi: You were the first one to say yes, and we’re happy to have you.

Yousuf Khan: I don’t want to brag, but that’s how I roll. But thank you.

Guy Nadivi: So Yousuf tell us about your background and how you became a CIO in the field of automation?

Yousuf Khan: So first of all, you’ve made a very generous introduction. I think the long short summary is I’ve been a CIO and the first CIO of five companies. And that gave me the exposure to have a very broad mandate, especially if you’re the first technology leader for a company. When you’re looking at internal functions, you can look at it very broadly. And that allowed me to look at everything for business applications and cyber security, of course build out an automation strategy, look at data management and then also advise on go to market. And so my CIO experiences have really looked at those sort of functions and a little bit more. And after two decades of plans and operating, I decided that it was time for me to be able to aid the next generation of startups in enterprise software in my little way that I can having advised companies in the past. And so I decided to join a great firm in Ridge Ventures and looking to invest between three and 7 million into late seed and series A enterprise companies. And hopefully that will aid the next generation of companies, and hopefully I can be helpful in their journey.

Guy Nadivi: Something very interesting is that you founded a group called the CIO Group Therapy Dinner. I know that therapy sessions are always supposed to remain confidential, but can you share some details about any breakthroughs made by participating CIOs at these dinners?

Yousuf Khan: Very interesting question. And yeah, it was kind of a little bit of a joke, but it, as I like to say, I was the CIO of several companies and the chief dinner organizer for a number of CIOs in what I termed as group therapy. And that’s been going on for several years. It started on the back of me joining a company where I was having a real tough time with a specific initiative, which was pretty highly visible and highly critical. And candidly speaking, I felt a little bit out of my depth and wanted to connect with other peers. And I felt that going to large events was probably not the most useful way, at least for me at the time. And when I set up a dinner between two CIOs, myself and one other, 12 people showed up and discovered that a lot of us were having the same issues and we wanted to have those discussions. So over the years I organized those dinners now ranging in the several hundred, which were either sponsored by a VC or a startup. And the objective of those dinners was really to give candid feedback and have a candid discussion. And a lot of really interesting insights. I think first and foremost, I got to understand personally how to make better decisions as a CIO when it comes to specific initiatives, because I was able to learn the lessons from my peers. Second, I think a lot of the startups which wanted feedback. It wasn’t a pitching session. There was no PowerPoint. There was no presentations or even demos. It was a candid conversation between the founding team and the group of CIOs. Product feedback and I think some of the output and some of the features you’ve seen in some of the products that go out there was directly driven from some of those dinners. And I think probably the most important piece was there was a sense of pretty great camaraderie. I’ve been a big advocate for the CIO role, advising CEOs on their first CIO hire as well as being able to, and so part of that is also about sharing the lessons and being able to create a community. And I think those were some of the benefits that sort of came out of that. It largely was just a lot of fun. I continue to organize dinners even virtually now. And I think one of the things that I’ve learned is that you learn a lot from your community around you, and I think it’s something that we can all benefit from.

Guy Nadivi: Since you’re now on the investment side, let’s talk a bit about startups. The vast majority of innovation in automation, AI, and other digitally transformative technologies comes from startups, which can be challenging for enterprises accustomed to working with larger players. Yousuf, how should CIOs work with startups?

Yousuf Khan: One of the things that I’ve found in talking with CIOs has been that they are risk averse in some cases about talking to startups. They’ll happily take a meeting. In some cases, some will not take a meeting, but very few really want to engage at the early stage. I think I’ve definitely been on the opposite side of that having been a design partner of a number of startups. I’ve been early advisor, I’ve been an informal advisor to a number of companies. And so what the way I think CIOs should think about this is as follows. I think if you want to innovate at scale fundamentally and you’re trying to solve a problem in a very creative way, the high chance is that there’s a startup that is focusing on the exact same problem to a certain degree. And so therefore tapping into that with a very focused effort and collaborating and partnering on that is really beneficial for you, number one. Number two is by engaging with startups, it’s a very good development opportunity and professional opportunity for each of your teams and your direct reports. IT teams don’t get enough credit, and that’s why I’ve been such an advocate for the CIO role and mentor IT directors and IT managers. Being able to help even in the infancy of building out a product at an early stage and being able to say that, it’s a great exercise for professional growth. It’s a great opportunity for that. And it allows people to figure out and discover different skills. I think the third piece is that, I would go on to say make a resolution that you want to engage with one startup or two startups a year that you can choose. And that basically means that you need to basically spend a little bit of time connecting with VCs. And sure, I can say that from a vested interest standpoint and hopefully I can be helpful to my CIO peers, but I think being able to say, “Here are the one or two problems that I care about most, and I want to be able to talk to companies in this space,” and you’d be very surprised that you’d actually be able to see some very creative approaches. You haven’t got much to lose by being able to engage. And I think it’s a very, very good use of time. So that’s my recommendation typically.

Guy Nadivi: Interesting. Given your extensive experience Yousuf, where can automation be most useful to an organization and how can a CIO best lead that automation journey?

Yousuf Khan: So the most key thing that you’ve pointed out in your question is that it’s a journey. Automation is a journey in a company and people need to start that journey for a very good reason. Number one is the compute power is now available for us to be able to really be able to drive automation into our businesses. Number two, the data sets are now there. And number three, the innovation and the thinking is there, both internally and externally with companies like Moveworks and Automation Anywhere, which I’ve had the honor to work with in the past. And so understanding that is a journey is the first step. The second part is to be able to think about where you can get a very clear visible win, and that requires you to be able to spend time being able to think about your processes just a little bit more, and then being able to drive that further forward. Third, and probably most important piece is, when you think about RPA, you think about repetitive work and you’ve seen companies, and that’s why companies like Automation Anywhere have been successful, because they’ve been able to drive and deliver customer value consistently by being able to take repetitive processes and being able to completely reduce the human effort of them and automate them entirely. Moveworks has fundamentally been able to understand that you can apply powerful techniques, like natural language understanding and machine learning to be able to resolve IT support issues and IT operational issues in a very, very great user experience using messaging systems like Slack and Teams. And they’ve done that super well. And so, all of those started off in a journey by being able to understand a problem and framing that. Number two, being able to understand that you can do it differently. And number three, understanding that this is the start of the journey that it could basically lead on to. And that’s been pretty consistent with a lot of the CIOs I’ve spoken to as well as being able to look at the number of different solutions.

Guy Nadivi: Artificial intelligence and machine learning are becoming more common in enterprises, especially with all the digital transformation initiatives out there. Yousuf, where is it best to use artificial intelligence and machine learning and where is it best to use automation?

Yousuf Khan: Well, that’s an excellent question. I think it’s really important that we take a little bit of a step back, because the buzzwords have been out there for a little while and I think a lot of people get enamored by them. And so, my biggest advice is when you think about use cases, I would break it down into a couple of things. Number one is, are you solving the business problem first and foremost? And what that business problem is will help you try to figure out whether it’s a problem of manual repetitive work for which automation is a very good use case, versus one which requires more automation using more creative techniques, such as NLU and machine learning or otherwise. And so I think framing that problem is probably the most important first step. Second is thinking about use cases from an industry standpoint, which could be beneficial. So if you think about things like fraud prevention, fraud prevention that’s been very common for people to look at statistical processes and techniques and pattern matching to combat fraud prevention for large say telcos or retailers, because that’s a use case that has been sort of beneficial, versus something which is overly creative where the data sets are too varied, machine learning becomes a huge problem. If you think about companies like Moveworks, Moveworks was successful because it understood the business outcome which was being able to resolve IT support tickets. It understood the goal of being able to do that across some very specific areas and then grow it its impact. Automation Anywhere looked at repetitive processes and then built onto intelligent automation. And I think that was important to be able to think about from a use case standpoint. The use cases for each varied, right? If you think about automation, well there’s a lot of repetitive process. So use cases that are typical for automation can range from data entry where you are, I was experienced, for example, migrating one system to another. And the data structures for those were completely varied. And so rather than having to convert them through human effort, being able to do that through a bot designed by Automation Anywhere was great. If you think about a contract order process, which runs in the back end between connecting two systems, you could use automation for things like that. Whereas if you think about chat bots, and if you think about being able to have user experience, then solutions like Moveworks are a great outcome and they’re focused on an end goal, which applies across the entire company. Everyone needs IT support, and so that’s where ML and techniques like NLU become really beneficial. I think the important thing to think about is, one, think about the clear outcome that you’re trying to solve. Number two, the method that you’re basically using, and probably number three is, what is the incremental journey that you’re on being able to continue to add value by using either one of machine learning or automation techniques?

Guy Nadivi: Specifically with regards to automating processes like some of the ones you just listed, how do you think CIOs can best manage teams for automation?

Yousuf Khan: I think probably the most important thing is to actually have that as a focus area. And I do think that there’s a function within companies that should be focused on automation and automation teams should be created. Number one, I think it’s a huge opportunity for professional growth and development. Number two, I think it’s a huge business need, and number three I think it requires dedicated focus. And I think one thing people have not really appreciated, the benefit of automation is it empowers people. It’s much better for morale in a lot of cases where you’re able to take away manual repetitive tasks. And it’s a great sign of innovation for IT teams. And so definitively they should be thinking about building an entire function for that. And that’s one aspect. The second is you’ve got to re-think differently about your processes. I said that earlier, I think it’s important to not be able to migrate. That requires a fresh set of thinking. Companies expand, they grow. In some cases they contract, they take on more technology than they’ve ever done before. Companies are now becoming more technology-driven companies. Software is not just driving companies. It is being embedded in companies. And so therefore you can’t just simply take your old process and put them into this new way of thinking. And probably the third thing is they’ve got to be very focused on goals and put kind of measurements around success. And I think that’s important to do. I think you can be in a journey and it could be a very long journey unfortunately, and you’ve got to be able to iterate along the way. And I think that’s very, very important as well.

Guy Nadivi: Having worked for some automation vendors Yousuf, you’ve seen a lot of automation deployments. I’m curious, which industries did you see that had particularly successful results from deploying automation?

Yousuf Khan: It’s important to basically point out that the opportunity for automation exists across multiple verticals, I think number one. Number two is in terms of the functions, there are some that do really, really well. And some that require a little bit more effort and a little bit more thinking. Let me give you some examples. If I think about customer service, well the opportunity for automation there has really been when you have a high level of demand and you’re not able to, for example, scale-up your customer service center like an airline, for example, may have done over the last several months or a hotel chain. You can’t hire people fast enough, but their processes are very much specifically the same. Refunds, cancellations, otherwise. Could that process be automated? And so definitively the answer to that is yes. And that’s been proven, that’s been done. In some cases in financial services I’ve seen those verticals work well. We’ve experienced that myself. If you want to basically, you’ve gone away from being having to call and actually speak to someone about a lost or stolen credit card. You’re able to fundamentally run through that function through a combination of voice commands, but also candidly speaking through text and be able to basically get a new card ordered without any friction in the process whatsoever. And so I think those things, again, fundamentally if it’s repetitive that’s going to be easy to do, and it’s going to apply across a number of different verticals. I think it’s important to think about the fact that there’s a lot of opportunities which automation is not just about a customer experience. It’s really just about being able to get the work done. That’s a simple way of basically saying is, if you think about a data entry work across different systems, being able to just eradicate that because you understand the type of data going from one system to the other and being able to convert that could be easily done simply because you’re able to train a bot to do it. That’s a definitive value. And that applies across any company because systems are being changed and upgraded all the time. If you are looking at supply chain and inventory management, being able to track those and being able to not just automate the reordering of something, but also to be able to send out warnings and notifications is definitively of value. All of these fundamentally say that automation is a very, very big opportunity for enterprises and companies, and they should be thinking about very deeply about how to be able to benefit from that.

Guy Nadivi: Let’s talk about cybersecurity, which is always a topic of paramount importance to CIOs. And you’ve been CIO of some cybersecurity companies as well as automation vendors. Yousuf, what should CIOs be aware of and concerned about when it comes to the security of their automation initiatives?

Yousuf Khan: I think there’s a couple of things here. First of all, I think, cybersecurity is gone from being kind of something to be discussed, which is something that people will get round to talking about to now being very much in the mainstream and being discussed at boardrooms on a regular basis as an agenda item. Right, so it’s evolved over time. And so I don’t think it’s as specific as saying, “Well, should we think about security in the automation space?” Fundamentally you are buying software or you’re building software from leading vendors. I think that the vantage point that you would apply is really about what are the key areas that that automation is touching. Number one, it’s having access to your systems and being able to touch a number of different systems. So being able to make sure that the APIs are secure and robust. Second, if it’s touching sensitive data, then you’ve got to be very diligent about figuring out how that actually is happening and how you’re able to test that out. Number three, probably most importantly is to really think about the testing that goes into it. Automation use cases can vary from as simple as an email notification to data migration, to financial transactions happening. And it’s really, really important to be able to make sure that you understand that there’s going to be regulations around that, and you have to provide that vantage point as well. So I don’t think it’s massively different to what a CIO would look at when they look to buy a software solution. I think the difference is because this is something that you’re more deeply involved in from a creative standpoint, it’s important to take that extra step to be able to understand what parts of your infrastructure the automation is actually touching and how is that basically protected. And of course, how you’re avoiding making misfires by basically being able to send data out accidentally because you’re in control of that.

Guy Nadivi: Yousuf, for the IT executives and others listening in, what advice would you give them if they’re looking to move into a CIO role?

Yousuf Khan: I’m very grateful to have learned from a number of CIOs. I’ve also taken the objective to learn from different members of the C-suite, of what they expect from CIOs. And so with that in mind, I think I would probably give some key piece of advice. Advice number one is fundamentally the CIO role is a leadership role. And so you have to ask yourself a very honest question, which is, what type of leader do you actually want to be? And I think that’s important because people in IT teams are looking for more leadership, for guidance, both in their careers, but also in terms of direction. I think the second is, how do you navigate complex decision-making and prioritization of work? There’s no shortage of work for the IT organizations, the very hardworking IT organizations that I’ve worked with and I’ve met. And I think it’s really important that CIOs understand how they’re able to, if you want to be a CIO, how you are able to prioritize and how you’re able to basically make decisions. I think the third thing is probably, figure out how you’re able to communicate more effectively across, not just your team, but also across the C-suite and other executive leaders and across the company. I think the CIOs who are most effective in their roles are ones who are able to communicate with customers and partners as well as internal to their teams, as well as the company. Because change management enablement is very, very hard. And I think technological change is something which is really happening more and more in companies. And that’s being driven by CIOs and being able to explain that change, being able to drive that change, being able to see the success of it is a CIO’s responsibility. And so being able to do that, communication is one of the key things that they need to be able to focus on. So that would be my other piece of advice.

Guy Nadivi: Great words of advice from a former five time CIO.

Yousuf Khan: Well, yeah, I would say former is good and a want-to-be VC. I’m the accidental CIO and definitely the unconventional VC as I’ve said a couple of times, but I hope it’s been a benefit to many of my CIO peers.

Guy Nadivi: All right.

Yousuf Khan: Yeah.

Guy Nadivi: Well, it looks like that’s all the time we have for on this episode of Intelligent Automation Radio. Yousuf, It’s always great hearing from an investor in the automation space, but especially someone like yourself, who’s got hands on background that affords him a bit of an advantaged viewpoint. I’m sure our listeners found great value in hearing your perspective today. Thank you very much for coming on the show.

Yousuf Khan: Thank you. Thank you very much Guy. And one thing I do want to say is I do want to thank you for all that you’re doing for the community. I think these conversations are important, not just because I’m having them, but I think you’ve done a great portfolio of work, and I think we could all benefit from that. So thank you for all your work in this community.

Guy Nadivi:I appreciate that. Thank you very much. Yousuf Khan, partner at Ridge Ventures, an early stage venture capital fund. Thank you for listening everyone. And remember, don’t hesitate, automate.



Yousuf Khan

Partner at Ridge Ventures

Yousuf Khan is a Partner at Ridge Ventures and technology leader. Prior to Ridge Ventures he spent several years in executive leadership roles as the CIO at Automation Anywhere, Moveworks, Pure Storage and Qualys. He has been the first CIO at these companies where he led teams covering intelligent automation, IT operations, business applications,  cloud operations to information security. In each of these roles, he has held a broad executive ownership of functions ranging from driving cyber security to executive go to market programs as well as customer success.  

He has been an active member of the CIO community as someone who drives collaboration in the community as well as advising CEO’s of fast growth companies about their technology operations strategy. Yousuf has also been involved in advising early stage founding teams on product and go to market strategy.   

Yousuf can be reached at: 

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

Ridge Ventures:        https://ridge.vc/ 

Quotes

“I think if you want to innovate at scale fundamentally and you're trying to solve a problem in a very creative way, the high chance is that there's a startup that is focusing on the exact same problem to a certain degree. And so therefore tapping into that with a very focused effort and collaborating and partnering on that is really beneficial for you…” 

“…I think one thing people have not really appreciated, the benefit of automation is it empowers people. It's much better for morale in a lot of cases where you're able to take away manual repetitive tasks. And it's a great sign of innovation for IT teams.” 

"Companies are now becoming more technology-driven companies. Software is not just driving companies. It is being embedded in companies. And so therefore you can't just simply take your old process and put them into this new way of thinking." 

“I think it's important to think about the fact that there's a lot of opportunities which automation is not just about a customer experience. It's really just about being able to get the work done.” 

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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News

Ayehu NG Trial is Now Available
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Ayehu Launches Global Partner Program to Support Increasing Demand for Intelligent Automation
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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

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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 Things the Aviation Industry Can Teach Business Leaders about Being Successful with Automation

It’s not uncommon for business leaders to borrow phrases from the aviation industry when discussing their job duties. “Take charge of the cockpit,” “navigate through turbulence,” and “ensure a safe landing,” are just a few that come to mind. All of these things can also be applied within the context of digital transformation. Specifically, if you’re planning an automation initiative, here are five key things you can learn from a pilot’s point of view.

Be Prepared

In aviation, proper preparation is critical. Prior to each flight, the pilot must meticulously work through a pre-flight checklist. Is it tedious? Certainly. But it’s absolutely necessary to ensure a safe and successful journey. Similarly, business leaders must prepare well in order for their automation project to be “ready for takeoff.” In particular, they must do the legwork to develop a deep understanding of the processes and workflows they wish to improve before attempting to automate them.

Map the Journey

If a pilot were to take flight without knowing where they were heading and how best to get there, the results could be catastrophic. Likewise, it’s essential to adequately map out the specific path you intend to take for automation within your organization. This involves not only determining the processes you want to automate, but also prioritizing them to achieve quick, measurable wins. This will allow you to lay the foundation for more widespread adoption of automation in the future.

Become an Expert

A person cannot simply hop into the cockpit of a plane and fly. Being a successful pilot requires learning, practice and continuous improvement. From a business perspective, automation should never be a temporary solution. It should be fluid and evolutionary. It should focus not only on achieving the desired results as they exist currently, but also with an eye toward achieving ongoing growth and improvement. It should never just be a “set it and forget it” type of project.

Use Your Tools

The beauty of modern aircrafts is that they are loaded with advanced technology designed to assist pilots and make their jobs infinitely easier. The same can be said for the right automation platform. IT teams can and should leverage technology to help guide and support their efforts. For instance, artificial intelligence can be deployed to identify potential problems so they can be remediated right away, before they have the chance to wreak havoc. Meanwhile, machine learning and predictive analytics can be used to help business leaders make better, more data-driven decisions.

Learn from Your Mistakes

Just as every pilot will face turbulence at some point, even the most successful automation initiatives will experience a few bumps in the road. How you navigate those turbulent times will make or break the outcome. Like pilots – wise business leaders know that drawing from their past experiences will help them better handle future uncertainties. The pressure to get everything perfect the first time will be great, but acknowledging, preparing for and learning from the snags that will inevitably occur along the way.

While experience in the cockpit can teach business leaders a lot about success, winning the race for automation doesn’t require a pilot’s license. By following the five key lessons above, you should be able to coast your way to a perfect landing.

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