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Episode #53: Why End User Experience May Be A Better Measure Of Automation Success Than ROI – GAVS Technologies’ Balaji Uppili

November 16, 2020    Episodes

Episode #53: Why End User Experience May Be A Better Measure Of Automation Success Than ROI

In today’s episode of Ayehu’s podcast, we interview Balaji Uppili, Chief Customer Success Officer for GAVS Technologies. 

Wikipedia defines Customer success as “…the business method ensuring customers achieve success, their desired outcomes while using your product or service.”  If automation is your product/service, what are the keys to ensuring it succeeds for your internal and/or external customers? When digital transformation projects have a documented failure rate as high as 84%, how should you approach implementation of automation to be part of the 16% that succeed? 

For answers we turn to Balaji Uppili, Chief Customer Success Officer at GAVS Technologies, one of the leading global IT service providers for midsize enterprises.  As the man tasked with assuring GAVS clients get the desired outcomes they demand, he’s developed critical insights on how to increase their odds of success.  He shares some of his accumulated wisdom with us, including why cost reduction shouldn’t be your most important measurement of success. 



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 Balaji Uppili, Chief Customer Success Officer for GAVS Technologies. For those of you unfamiliar, GAVS is a global IT services provider focused on AI-led managed services and digital transformation. AI and digital transformation of course are two of the primary topics we focus on at this podcast. And as Chief Customer Success officer for a global MSP providing those two services, we’re very interested in hearing from Balaji what the keys are to successful implementations in those disciplines. Balaji, welcome to Intelligent Automation radio.

Balaji Uppili: Hi Guy. Very good morning. Hi. Good to talk to you.

Guy Nadivi: Balaji, please tell us how you ended up in your current role as Chief Customer Success Officer for GAVS Technologies.

Balaji Uppili: Very well. Thank you. This is my 10th year at GAVS Technologies. We started off as a global delivery officer, and then when we realized that delivery is to deliver towards an SOW or a contract or a predefined SLA, we felt we need to go way beyond that and deliver to the outcomes which the customer wants, deliver to the roadmap and aspirations which they want. And hence, we felt delivering to the success of the customer is far more important to deliver this project successfully. And that’s why we represented as customer success. And we think we made the right choice because we are now able to measure and live up to a larger set of expectations and value to the customer than just delivering to a project. And that’s how I ended up as a Chief Customer Success Officer at GAVS.

Guy Nadivi: As Chief Customer Success Officer, I’m curious, how does GAVS Technologies define success when it comes to automation projects.

Balaji Uppili: Success actually respective of automation or otherwise, is extremely critical for the long-term relationship and connect, and more so in current days when automation has become the central need for all of our customers. We define success by understanding that individual who’s playing the key role with us from the customer organization and customer itself. When we get to interact with them, we identify and understand their larger business goals, capture those. We identify, understand what are the larger aspirations and expectations way beyond what the initiative we are working on and capturing those, measuring those, and being transparent with them as to how we are embarking on that journey is what defines success. And that’s what we actually take pride in doing at GAVS.

Guy Nadivi: I imagine that one aspect of customer success for automation projects involves user adoption, especially when it might lead to a change and a given user’s role, and this can lead to a type of anxiety we refer to as robophobia. Balaji, how does GAVS help organizations overcome their staff’s robophobia?

Balaji Uppili: Very good question, Guy. Customers actually want automation in normal circumstances, but when it comes to individuals, they feel that automation could actually replace them. They feel that their jobs may be going away. The entire change management, which is very critical, is to actually emphasize that automation actually augments human beings. Automation is not a replacement. So when we actually bring in an element of process change or technology change, we are very fast to adopt because that actually helps us. That actually makes us better. But when it comes to automation, it is felt as a replacement. The best way to address this is to actually let automation solve their mundane regular problems first, before embarking on complex ones. So involving the customer, having them participate in the journey of automation, and having them in the central part of defining what the automation can derive is actually key to success. And when they participate, their anxiety levels actually go away and probably they’re actually faster in embracing it. So the change management is extremely critical when it comes to automation.

Guy Nadivi: Balaji, can you talk about some of the more interesting and successful automation projects that you and GAVS have delivered for customers?

Balaji Uppili: Sure. For one of the largest public relations firm in the US, when we embarked on automation, it was felt that it is actually an exercise of reducing manpower, and to reduce costs for gas so that we can actually execute it using automation. But later we realized when we actually started introducing automation step by step, and we actually achieved about 40% and above automation over a period of 18 months, we then realized that the customer is actually delivering higher user experience. When the whole conversation turned from a labor arbitrage or a peek in production conversation to higher user experience, then their ability to embrace it actually improved to the extent that the CSAT went from anywhere from two to 2.5, to close it to 4.5, one a five scale, and they were able to bring a lot more value to the end user in defining what is user experience, in defining what can be the way the end users can actually have a very frictionless environment. So that’s one big customer engagement I would talk about. And quickly, the second customer engagement we’ve talked about is a very large aircraft manufacturer company in the world. They wanted to carry out a large digital transformation and towards that, they were doing a lot of manual activity in orchestration in trying to lift and move the data from as is environment to a target environment. When we brought in automation, they felt that they could focus on much larger business process re-engineering, rather than just focus on the day-to-day or heavy lifting of the applications and services from on-prem to the cloud. These are two examples, which I can relate to in the last 12 to 18 months, which has significantly brought value to our customers, Guy.

Guy Nadivi: Hearing you talk about these projects, Balaji, I’m curious when GAVS evaluates a customer process for automation, is there a minimum ROI you need to see in order to recommend automating that process to your customer?

Balaji Uppili: It does. I mean, at the end of the day, automation is felt as an overhead initially to automate the manual steps before starting to use it. But in most cases, we have found that the business case is written not for an initial investment, but over a period of time, how the benefit actually accrues. So if you plan out the automation initiative with regards to cost, with regards to availability of intellectual capital, and more importantly, with regards to enhancing end-user experience upfront these, three automatically fund the automation initiative. These are critical because cost alone is not a measure of automation. Frictionless execution, end user experience are also equally or better in terms of how automation can define the way enterprise operates. So driving these upfront, and capturing these, automatically provides a very useful ROI case for any automation.

Guy Nadivi: So speaking of ROI, is there a single metric other than ROI that best captures the effectiveness of the kind of automation you deploy for organizations?

Balaji Uppili: Very well. Good question, Guy. The biggest beneficiary beyond even costs is actually end-user experience. I take a small example, in one of the largest consumer goods customers, the CIO actually wanted us to bring in as much automation so that he’s proactive to his business. He is proactive to how his end users are able to react. The more we provided him information and were proactive, his ability to be accepted as a technology leader within his business organization was far higher than before. So the end user experience is probably a lot more valuable parameter than even costs as part of an ROI. Every time end-user is able to get that particular activity out without difficulty, absolutely frictionless, and offer very higher quality, automation speaks for itself. I think end user experience and frictionless operations are probably much higher in the scale when compared to ROI in such automation, Guy.

Guy Nadivi: Interesting. Balaji, GAVS touts itself as a global IT services provider focused on AI-led managed services and digital transformation. Now digital transformation projects have a notoriously high failure rate, as high as 84%, according to some. Balaji, for the remaining 16% of digital transformation projects that succeed, what factors did they share in common?

Balaji Uppili: It’s very simple. Automation or digital transformation cannot be done in isolation. It has to be done in an extremely collaborative manner. I mean, to date GAVS has been successful in every digital transformation initiative. When we involve the key stakeholders at the customer, including the people who are going to be using the target state platform early on, it actually makes a big difference. Digital transformation is not about moving everything to the cloud. Digital transformation is not about automating everything. Digital transformation is changing the way of life. Digital transformation is making our customers to do the same thing in a much better fashion. It could be automation, it could be cloud, it could be analytics, it could be mobility. So having the key stakeholders of the customer participate early on in the planning process and in the transformation process is key. And second one is showing quick wins and demonstrating through pilots and proof of concepts earlier on, makes the customer to participate even better. Those are, I feel, are genuine key considerations for success of the digital transformation. And that’s what actually GAVS has taken pride in because we have created something called as a migration office template for digital transformation. And that is actually helping customers to participate in a very collaborative manner and see the benefits of it from day one.

Guy Nadivi: At the time of this podcast’s recording we’re in the middle of a worldwide healthcare crisis due to COVID-19. Balaji, how is COVID-19 affecting global IT services providers like GAVS Technologies when it comes to providing IT services to enterprise clients?

Balaji Uppili: I wouldn’t be doing justice if I say it did not. So it definitely has impacted the way people relate to others. The way people are collaborating. It’s all being done remotely. So more and more, when we start looking at this new normal of working remotely, collaborating remotely, ability to deliver value remotely, I think automation plays even more, a larger role. The reason why I say that is automation doesn’t necessarily mean it’s a script running to be able to drive some work done. Automation is identifying certain elements or certain processes within an enterprise, which could have either been avoided manually, or could be done much differently, and then figuring out how to do it differently. So we have a principle. We say that if in case you want to do something second time, why do it through a human? Why not automate it through a bot? Now, if I use that, it actually applies even better for remote collaboration and remote working. Wherever we feel that we can actually use the remote workforce to do much more higher value, pass it on to them to do it, and bring in these automation scripts and bots, to be able to do the day-to-day stuff in a human and remote manner. So the actual remote working, while it has changed the culture while it has changed the working patterns, has actually lent itself very well for automation. And we are seeing a lot more customers wanting to automate, wanting to look at digital transformation during these times.

Guy Nadivi: So with that in mind, what has been the pandemic’s effect on enterprise decisions about implementing automation, AI, and other digital transformation initiatives?

Balaji Uppili: It’s actually gone up. While they were cautious in the early days, what has happened is now the customers want to do remote collaboration. They want even more better frictionless applications or business process, because they now don’t have the touch and feel of the customer themselves in helping them out. That is one aspect. The second aspect is cost. The cost optimization has become significant. People are wanting to reduce costs because of the uncertain times. That automatically means that automation is an answer to some of that. And lastly, because it is remote collaboration, newer techniques of Agile, newer techniques of execution are happening, which automatically lends itself to automation. I think the new norm is actually going to drive digital transformation and automation in a much better way without impacting lives of people, because I think cost and user experience are automatic increased expectations in this new norm

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

Balaji Uppili: Don’t just work towards an ROI which has costs as the primary driver. Bring in end user experience, involve the various stakeholders who are going to contribute to the end user experience. Bring in a cultural change in the way you would like to execute, bring in elements of digital transformation, elements of things which can be eliminated like a waste elimination, or use the Lean Six Sigma principles to arrive at some of that. Don’t just consider this as a cost optimization exercise, but consider it more as a transformative exercise, consider it as an enhancing end-user experience and making your enterprise frictionless, which means you have to have a larger participation of your enterprise stakeholders in this initiative. Doing it in isolation, and only as cost will probably not help you achieve some of those good results which come out when you think of automation and digital transformation

Guy Nadivi: Making your enterprise frictionless, I think is some great insight. All right, looks like that’s all the time we have for on this episode of Intelligent Automation Radio. Balaji, you are the first Chief Customer Success officer we’ve ever had on the podcast, and I think you’ve really enlightened our audience about the importance of customer success for the advanced technologies many enterprises are adopting as part of their digital transformation initiatives. Thank you very much for coming on the show.

Balaji Uppili: Thank you Guy. It was my pleasure and we look forward to more and more of such conversations, but please keep in mind, automation is the new norm, digital transformation is the new norm. Don’t get scared, embrace it. It will give you lots and lots of benefits and make people’s lives better. Thank you very much Guy for the opportunity. Good luck. Have a great day.

Guy Nadivi: Balaji Uppili, Chief Customer Success Officer for GAVS Technologies. Thank you for listening everyone. And remember don’t hesitate, automate.



Balaji Uppili

Chief Customer Success Officer for GAVS Technologies.

Balaji has over 28 years of experience in the IT industry, across application value management, digital transformation, managed services in infrastructure and applications, BPO and Strategic Operations handling P&L, global Delivery, Operations and customer success management across multiple verticals which includes Healthcare, Information Services, Retail & Hospitality, Media & Publishing, Manufacturing & Logistics, and Consumer goods. Balaji has played various roles across geographies viz., USA, Continental Europe, and Asia Pacific, through his previous stints at Patni Computers, L&T Infotech, Cognizant Technology Solutions, and Virtusa Software Solution Limited. 

Balaji’s enthusiasm, energy and customer focus are a rare gift, and he plays a key role in bringing new clients into GAVS.  Balaji heads the Delivery and passionately works on Customer success delight.  He says work is worship for him, and enjoys watching cricket, listening to classical music and visiting temples. 

Balaji can be reached at: 

Website:        https://www.gavstech.com/service/sqa/  

Email:             inquiry@gavstech.com  

Quotes

“Customers actually want automation in normal circumstances, but when it comes to individuals, they feel that automation could actually replace them. They feel that their jobs may be going away. The entire change management, which is very critical, is to actually emphasize that automation actually augments human beings. Automation is not a replacement.” 

“Digital transformation is not about moving everything to the cloud. Digital transformation is not about automating everything. Digital transformation is changing the way of life.” 

"People are wanting to reduce costs because of the uncertain times. That automatically means that automation is an answer to some of that." 

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

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Disclaimer Note

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

5 Highly Impactful, Real-World Applications for Intelligent Automation

On paper, intelligent automation seems amazing. And in practice, when implemented properly and applied to the appropriate processes and workflows, it absolutely can be. Many business leaders, however, struggle to make the transition from theory to action because it can be easy to become overwhelmed and overloaded. To help with this, we’ve identified five use cases where intelligent automation can be applied to deliver long-term, sustainable value.

Processing, interpreting and leveraging unstructured data.

There is a virtually endless amount of data being generated on a minute-by-minute basis. All of that information is useless if it is not properly vetted, and humans are simply incapable of keeping up with the sheer volume. Intelligent automation, on the other hand, is capable of sifting through mountains of data, instantaneously analyzing it and extracting relevant insights that can be used to improve business operations.

Proactive remediation.

Wouldn’t it be nice if, instead of worrying about responding to an incident as quickly and effectively as possible, you could remediate the problem before it even occurs? This is entirely possible with intelligent automation. Not only can you use this technology to predict potential issues, but you can leverage it to autonomously resolve said issues without the need for any human intervention.

Trend detection and escalation.

Not all anomalies in data are detrimental, but many are, and the outcome can be downright devastating to an organization. By introducing intelligent automation into the mix, you can apply the power of AI to continually scan and pinpoint trends that may be worthy of attention. You can even set parameters for which types of anomalies should be resolved automatically and which should be escalated for review.

Real-time, strategic optimization.

Processes that aren’t working to the full benefit of the organization can be worse than processes that are broken altogether. Annual, quarterly or even monthly audits to identify and correct inefficient workflows are simply not sufficient in today’s competitive landscape. The right intelligent automation platform will provide real-time updates to ensure ongoing strategic optimization on the fly.

Forecasting and decision-support.

Planning for the future is an essential component of executive leadership. Intelligent automation can become an invaluable tool for this, turning data into insights that can help management more accurately forecast and make more effective decisions.

These are just a handful of the many applications for intelligent automation that IT and other business leaders should consider when preparing for the months to come. These technologies will continue to push boundaries, overcome limitations and become fundamental to successful digital transformation.

But you don’t have to take our word for it. You can begin experiencing the power of intelligent automation for the five use cases above (and much more) by launching your free trial of Ayehu today.

When it Comes to AI, Slow and Steady Wins the Race

When it Comes to AI, Slow and Steady Wins the Race

Adopting artificial intelligence for your organization can be an intriguing prospect – especially when you start forecasting all the many benefits doing so will afford. But as exciting as it may be, it’s imperative that you not get ahead of yourself. By moving too quickly – that is, before you fully understand the implications of AI – you could easily end up with a costly mess on your hands. Instead, you should focus on gaining knowledge of and becoming familiar with AI. This will ultimately enable you to develop and implement a thoughtful strategy that will deliver consistent, sustainable value.

So, where is a good place to begin? Which projects should you apply your attention, efforts and investments toward? This initial phase will take some time and careful planning, but the payoff for being prudent will be well worth it in the long run. Start by brainstorming a handful of projects. Then, for each of these projects, spend some time doing your due diligence, both business and technical, to map out the potential impacts. This practice will help you identify the most lucrative areas to which you should commit your resources.

If you are feeling pressure from the “powers that be” to generate proof of concept more quickly than taking the above approach will allow, specify a few workflows and projects into which AI and intelligent automation can be rapidly integrated. Specifically, dedicate a portion of your resources to smaller initiatives that have the potential to make a substantial impact. By automating this “low hanging fruit,” you can generate some quick wins and provide quantifiable evidence of value. Once you’ve satiated the interested stakeholders, you can then turn your attention back to the larger-scale, longer-term projects.

The goal with AI should be to develop and establish a solid foundation upon which to build and grow over time. By starting smaller and working steadily toward the bigger picture down the road, you’ll lay the groundwork and gain the momentum you need to harness the power artificial intelligence and use it to propel your organization ahead of the competition.

Ready to get started with AI but not sure where to begin. Download your free 30-day trial of Ayehu NG and access our library of 200+ ready-to-use, codeless workflow templates. Be up and running with intelligent automation in literally minutes!

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
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Ayehu Automation Academy is Now Available

Links

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

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Disclaimer Note

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

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.