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

December 15, 2020    Episodes

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

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

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

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



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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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



Chaz Perera

Co-Founder & CEO of Roots Automation

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

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

Chaz can be reached at: 

Email: chaz@rootsautomation.com 

Phone:(973)713-3585 

Quotes

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

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

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

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

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

About Ayehu

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

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Links

Episode #1: Automation and the Future of Work
Episode #2: Applying Agility to an Entire Enterprise
Episode #3: Enabling Positive Disruption with AI, Automation and the Future of Work
Episode #4: How to Manage the Increasingly Complicated Nature of IT Operations
Episode #5: Why your organization should aim to become a Digital Master (DTI) report
Episode #6: Insights from IBM: Digital Workforce and a Software-Based Labor Model
Episode #7: Developments Influencing the Automation Standards of the Future
Episode #8: A Critical Analysis of AI’s Future Potential & Current Breakthroughs
Episode #9: How Automation and AI are Disrupting Healthcare Information Technology
Episode #10: Key Findings From Researching the AI Market & How They Impact IT
Episode #11: Key Metrics that Justify Automation Projects & Win Budget Approvals
Episode #12: How Cognitive Digital Twins May Soon Impact Everything
Episode #13: The Gold Rush Being Created By Conversational AI
Episode #14: How Automation Can Reduce the Risks of Cyber Security Threats
Episode #15: Leveraging Predictive Analytics to Transform IT from Reactive to Proactive
Episode #16: How the Coming Tsunami of AI & Automation Will Impact Every Aspect of Enterprise Operations
Episode #17: Back to the Future of AI & Machine Learning
Episode #18: Implementing Automation From A Small Company Perspective
Episode #19: Why Embracing Consumerization is Key To Delivering Enterprise-Scale Automation
Episode #20: Applying Ancient Greek Wisdom to 21st Century Emerging Technologies
Episode #21: Powering Up Energy & Utilities Providers’ Digital Transformation with Intelligent Automation & Ai
Episode #22: A Prominent VC’s Advice for AI & Automation Entrepreneurs
Episode #23: How Automation Digitally Transformed British Law Enforcement
Episode #24: Should Enterprises Use AI & Machine Learning Just Because They Can?
Episode #25: Why Being A Better Human Is The Best Skill to Have in the Age of AI & Automation
Episode #26: How To Run A Successful Digital Transformation
Episode #27: Why Enterprises Should Have A Chief Automation Officer
Episode #28: How AIOps Tames Systems Complexity & Overcomes Talent Shortages
Episode #29: How Applying Darwin’s Theories To Ai Could Give Enterprises The Ultimate Competitive Advantage
Episode #30: How AIOps Will Hasten The Digital Transformation Of Data Centers
Episode #31: Could Implementing New Learning Models Be Key To Sustaining Competitive Advantages Generated By Digital Transformation?
Episode #32: How To Upscale Automation, And Leave Your Competition Behind
Episode #33: How To Upscale Automation, And Leave Your Competition Behind
Episode #34: What Large Enterprises Can Learn From Automation In SMB’s
Episode #35: The Critical Steps You Must Take To Avoid The High Failure Rates Endemic To Digital Transformation
Episode #36: Why Baking Ethics Into An AI Project Isn't Just Good Practice, It's Good Business
Episode #37: From Witnessing Poland’s Transformation After Communism’s Collapse To Leading Digital Transformation For Global Enterprises
Episode #38: Why Mastering Automation Will Determine Which MSPs Succeed Or Disappear
Episode #39: Accelerating Enterprise Digital Transformation Could Be IT’s Best Response To The Coronavirus Pandemic
Episode #40: Key Insights Gained From Overseeing 1,200 Automation Projects That Saved Over $250 Million
Episode #41: How A Healthcare Organization Confronted COVID-19 With Automation & AI
Episode #42: Why Chatbot Conversation Architects Might Be The Unheralded Heroes Of Digital Transformation
Episode #43: How Automation, AI, & Other Technologies Are Advancing Post-Modern Enterprises In The Lands Of The Midnight Sun
Episode #44: Sifting Facts From Hype About Actual AIOps Capabilities Today & Future Potential Tomorrow
Episode #45: Why Focusing On Trust Is Key To Delivering Successful AI
Episode #46: Why Chatbots Are Critical For Tapping Into The Most Lucrative Demographics
Episode #47: Telling It Like It Is: A 7-Time Silicon Valley CIO Explains How IT’s Role Will Radically Change Over The Next Decade
Episode #48: How Microsoft Will Change The World (Again) Via Automation
Episode #49: How One Man’s Automation Journey Took Him From Accidental CIO To Unconventional VC
Episode #50: How Automation Helped LPL Financial Grow Into The Largest Independent Broker Dealer In The US
Episode #51: Why Cognitive Architecture Might Be An Early Glimpse Of A Future With Artificial General Intelligence
Episode #52: Chatbots Aren’t Human, So Don’t Expect People To Pretend They Are
Episode #53: Why End User Experience May Be A Better Measure Of Automation Success Than ROI
Episode #54: How Digital Dexterity Will Generate Competitive Advantage For Agile Enterprises

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Neither the Intelligent Automation Radio Podcast, Ayehu, nor the guest interviewed on the podcast are making any recommendations as to investing in this or any other automation technology. The information in this podcast is for informational and entertainment purposes only. Please do you own due diligence and consult with a professional adviser before making any investment

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

November 2, 2020    Episodes

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

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

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

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



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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Guy Nadivi: Kevin, for the CEOs, CTOs, and other IT executives listening in, what is the one big must-have piece of advice you’d like them to take away from our discussion with regards to introducing new technology that leverages conversational AI and, in particular, what change management considerations do you think they should keep in mind?

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

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

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

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



Kevin Collins

CEO & Founder of Charli.ai.

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

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

Kevin can be reached at: 

Website:          www.charli.ai 

Twitter:           @charliai 

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

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

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

Quotes

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

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

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

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

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

About Ayehu

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

GET STARTED WITH AYEHU INTELLIGENT AUTOMATION & ORCHESTRATION  PLATFORM:

News

Ayehu NG Trial is Now Available
SRI International and Ayehu Team Up on Artificial Intelligence Innovation to Deliver Enterprise Intelligent Process Automation
Ayehu Launches Global Partner Program to Support Increasing Demand for Intelligent Automation
Ayehu wins Stevie award in 2018 international Business Award
Ayehu Automation Academy is Now Available

Links

Episode #1: Automation and the Future of Work
Episode #2: Applying Agility to an Entire Enterprise
Episode #3: Enabling Positive Disruption with AI, Automation and the Future of Work
Episode #4: How to Manage the Increasingly Complicated Nature of IT Operations
Episode #5: Why your organization should aim to become a Digital Master (DTI) report
Episode #6: Insights from IBM: Digital Workforce and a Software-Based Labor Model
Episode #7: Developments Influencing the Automation Standards of the Future
Episode #8: A Critical Analysis of AI’s Future Potential & Current Breakthroughs
Episode #9: How Automation and AI are Disrupting Healthcare Information Technology
Episode #10: Key Findings From Researching the AI Market & How They Impact IT
Episode #11: Key Metrics that Justify Automation Projects & Win Budget Approvals
Episode #12: How Cognitive Digital Twins May Soon Impact Everything
Episode #13: The Gold Rush Being Created By Conversational AI
Episode #14: How Automation Can Reduce the Risks of Cyber Security Threats
Episode #15: Leveraging Predictive Analytics to Transform IT from Reactive to Proactive
Episode #16: How the Coming Tsunami of AI & Automation Will Impact Every Aspect of Enterprise Operations
Episode #17: Back to the Future of AI & Machine Learning
Episode #18: Implementing Automation From A Small Company Perspective
Episode #19: Why Embracing Consumerization is Key To Delivering Enterprise-Scale Automation
Episode #20: Applying Ancient Greek Wisdom to 21st Century Emerging Technologies
Episode #21: Powering Up Energy & Utilities Providers’ Digital Transformation with Intelligent Automation & Ai
Episode #22: A Prominent VC’s Advice for AI & Automation Entrepreneurs
Episode #23: How Automation Digitally Transformed British Law Enforcement
Episode #24: Should Enterprises Use AI & Machine Learning Just Because They Can?
Episode #25: Why Being A Better Human Is The Best Skill to Have in the Age of AI & Automation
Episode #26: How To Run A Successful Digital Transformation
Episode #27: Why Enterprises Should Have A Chief Automation Officer
Episode #28: How AIOps Tames Systems Complexity & Overcomes Talent Shortages
Episode #29: How Applying Darwin’s Theories To Ai Could Give Enterprises The Ultimate Competitive Advantage
Episode #30: How AIOps Will Hasten The Digital Transformation Of Data Centers
Episode #31: Could Implementing New Learning Models Be Key To Sustaining Competitive Advantages Generated By Digital Transformation?
Episode #32: How To Upscale Automation, And Leave Your Competition Behind
Episode #33: How To Upscale Automation, And Leave Your Competition Behind
Episode #34: What Large Enterprises Can Learn From Automation In SMB’s
Episode #35: The Critical Steps You Must Take To Avoid The High Failure Rates Endemic To Digital Transformation
Episode #36: Why Baking Ethics Into An AI Project Isn't Just Good Practice, It's Good Business
Episode #37: From Witnessing Poland’s Transformation After Communism’s Collapse To Leading Digital Transformation For Global Enterprises
Episode #38: Why Mastering Automation Will Determine Which MSPs Succeed Or Disappear
Episode #39: Accelerating Enterprise Digital Transformation Could Be IT’s Best Response To The Coronavirus Pandemic
Episode #40: Key Insights Gained From Overseeing 1,200 Automation Projects That Saved Over $250 Million
Episode #41: How A Healthcare Organization Confronted COVID-19 With Automation & AI
Episode #42: Why Chatbot Conversation Architects Might Be The Unheralded Heroes Of Digital Transformation
Episode #43: How Automation, AI, & Other Technologies Are Advancing Post-Modern Enterprises In The Lands Of The Midnight Sun
Episode #44: Sifting Facts From Hype About Actual AIOps Capabilities Today & Future Potential Tomorrow
Episode #45: Why Focusing On Trust Is Key To Delivering Successful AI
Episode #46: Why Chatbots Are Critical For Tapping Into The Most Lucrative Demographics
Episode #47: Telling It Like It Is: A 7-Time Silicon Valley CIO Explains How IT’s Role Will Radically Change Over The Next Decade
Episode #48: How Microsoft Will Change The World (Again) Via Automation
Episode #49: How One Man’s Automation Journey Took Him From Accidental CIO To Unconventional VC
Episode #50: How Automation Helped LPL Financial Grow Into The Largest Independent Broker Dealer In The US
Episode #51: Why Cognitive Architecture Might Be An Early Glimpse Of A Future With Artificial General Intelligence

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Disclaimer Note

Neither the Intelligent Automation Radio Podcast, Ayehu, nor the guest interviewed on the podcast are making any recommendations as to investing in this or any other automation technology. The information in this podcast is for informational and entertainment purposes only. Please do you own due diligence and consult with a professional adviser before making any investment

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
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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