Episode #46: Why Chatbots Are Critical For Tapping Into The Most Lucrative Demographics – Gupshup’s Beerud Sheth

September 2, 2020    Episodes

Episode #46: Why Chatbots Are Critical For Tapping Into The Most Lucrative Demographics

In today’s episode of Ayehu’s podcast, we interview Beerud Sheth – Co-Founder and CEO of Gupshup

The Millennial generation is still retail’s most coveted demographic, but Generation Z isn’t far behind.  Both cohorts grew up as “digital natives” with computers, game consoles, and mobile phones.  It’s no surprise then that being online is second nature to them, and increasingly, chatbots is where Millennials & Generation Z can be found interacting online with retailers & other businesses.  Conventional marketing wisdom has long dictated that you have to meet your customers where they are.  Our guest on this episode argues that means chatbots are where marketers should go. 

As Co-Founder and CEO of Gupshup, Beerud Sheth knows quite a bit about chatbots.  His bot-building platform is already being used by over 30,000 businesses worldwide, and was selected by Amazon as one of two preferred bot builders for their .bot registry services. With bots increasingly automating many tasks & activities currently done manually, Beerud also has interesting insights on how they may disrupt the labor market, another topic he’s well-versed in having previously co-founded eLance which kick-started the gig economy. Beerud shares his vision with us about the opportunities that lie ahead, and the steps businesses must take to maximize their return from automating this growing customer touchpoint. 

Guy Nadivi: Welcome, everyone. My name is Guy Nadivi. I’m the host of Intelligent Automation Radio.

Our guest on today’s episode is Beerud Sheth, Co-Founder and CEO of Gupshup, an advanced bot-building platform used by over 30,000 businesses worldwide, and which handles over 4 billion messages per month. Prior to Gupshup, Beerud was one of the co-founders of Elance, which many listeners will remember was a pioneering online platform for freelance workers that really helped to kickstart the global gig economy. Those accomplishments alone give Beerud a unique perspective on how digital transformation will impact the future of work, two topics of immense importance on this podcast. We’ve asked Beerud to join us today and help us peek over the horizon a bit to better understand what that future might look like. Beerud, welcome to Intelligent Automation Radio.

Beerud Sheth: Thanks, Guy. Thanks for having me here.

Guy Nadivi: In 2019, Business Insider estimated the worldwide chatbot market as worth a bit more than two and a half billion dollars, but they forecasted that by 2024, it will approach $10 billion. That’s a compound annual growth rate of over 29% a year. We are, of course, now in the middle of a global pandemic, and so, Beerud, I’m curious, how do you think the COVID-19 crisis will affect this growth rate? Beerud Sheth:Oh, I think it’s certainly going to accelerate it. In many at least technical areas, we’ve seen decades of progress compressed into these three months, with a rapid adoption of, let’s say, work from home and telecommuting and remote meetings and so on. I think the same will apply to the chatbot market as well. It is accelerating the interest in developing bots and implementing them and so on for a variety of reasons. One is people are more conscious of social distancing and not getting into physical contact and so on. Once you get into remote customer support situations, whether it’s a human agent or an automated chatbot providing the responses, it matters less. It’s different when you’re face to face at a desk, but once it’s remote, it’s a lot easier to automate. So customer support is certainly one of those big areas. Also, there’s this massive shift to online commerce from offline. Suddenly retailers and shopping assistants and transactions, ordering pain, a lot of that is moving online, which again is rife for automation through chatbots. That’s happening across many, many different aspects, but as we move more and more into a tech & virtual economy, most of those interactions can be automated, can be enabled to a conversational chatbot. So I see it increasing dramatically.

Guy Nadivi: You mentioned customer support. It turns out that customer service and the IT help desk seem to be the two most popular use cases for chatbots right now. Beerud, what other functional areas of the enterprise do you envision will experience growth in chatbot usage?

Beerud Sheth: The way I think about it is, for a business, literally every customer touchpoint is going to get transformed with conversational experiences through chatbots. If you think about it, the entire customer life cycle, businesses, it starts with marketing on the front end, and then sales and transactions in the middle, and then support at the end of that customer life cycle. Across all of those touchpoints, you will see a greater adoption of chatbots. Let’s take the example where a customer is looking for products. If you had a chatbot that can educate, that can explain … I think Sephora had a chatbot where you can look for different colors and different shades of cosmetics long before the buying decision is made. Or you have complex products. If you’re buying insurance, if you’re buying health care services or large complex products, you want to look at review, want to look at products, look at options and so on. It makes it … So that’s the whole front end of the buying cycle, the marketing part of it, or even sending deals and offers and coupons and engaging with that. That’s on the marketing side. Then when you come to sales, the actual transaction, the merchandising, the purchasing, the shopping cart, the checkout and so on. Then even the support long after that, in case there are problems, returns and so on. I think every customer touchpoint really is going to get transformed. Today, if you think about it, most businesses have websites and apps. You really have to think of chatbots as the latest reincarnation of websites and apps. Because consumers, especially millennials, the younger ones, they prefer the messaging … They’re using mobile devices. On the small screen, messaging is used far more heavily than either browsers or apps. So businesses will have to meet consumers on their turf, on their terms. You have to meet customers where they are. If they prefer the chat and conversational experiences, then clearly businesses will have to interact in the same way. Everything from browsing to window shopping to ordering food and items and so on. You can see a version of this future if you look at WeChat in China. Everything there, a lot of it happens through the messaging app where you can interact with, not just businesses, but even every vending machine or every billboard or a restaurant table. You just arrived at a restaurant. You want to order food at that table. You can just do that through conversational experiences. In their case, it’s mini programs. But that’s the idea. Just through the messaging app, it becomes a super app that allows you to do virtually everything. I think businesses will have to transform their digital touchpoints into chatbot.

Guy Nadivi: Gartner believes there are as many as 1,500 chatbot vendors worldwide, and that “The majority of these conversational platform vendors offer very simple platforms using modified open source components to deliver simple question and answer chatbots.” I think it’s safe to assume the market doesn’t need 1,500 chatbot vendors, & there’s an inevitable shakeout coming. Beerud, even though we’re still fairly early in the conversational AI era, what kinds of things do you think will differentiate the survivors from the chatbot vendors who will fall by the wayside?

Beerud Sheth: I’m a little more optimistic about the future for chatbot vendors. There are different elements. Of course, some vendors provide platforms, and the number of platforms will be few. But a lot of other vendors provide services, and there there’s limitless potential. The analogy I like is think of it as website development or app development. There are numerous, perhaps thousands or more, dev development agencies, app development agencies, and so on, or even, let’s say, social media marketing agency and such. Those provide very custom solutions to brands. So if we say that virtually every business and brand will need to build conversational chatbots, and not just one, but multiple ones across different use cases, then there is a need for lots and lots of agencies and chatbot vendors to provide these services. To survive, what the vendor should do is get deeper into the business workflow. If somebody specializes in banking chatbots, and even within that it could be retail banking, or banking in the US versus in Europe versus in Brazil. There may be differences in each of these cases. The workflows can get fairly deep, fairly complex. There will be a need, because all these organizations are going to need to build these chatbots. So I think there is room. The key is, like in any business, you have to figure it out your core competency, your unique differentiation, something that you can offer that somebody else can’t replicate. If you do that, then I think there’s enormous room for growth, but I think we’ll need lots of … If you’re going to have millions of bots, you are going to need thousands of vendors providing some of these services. The caveat is, I think, some of the underlying platforms will be fewer and will get standardized on a smaller set. There, again, it depends on the comprehensive nature of the capabilities, the breadth and depth of the platform, how many things it can provide, what it integrates into and so on. That’s what the platforms have to watch out for.

Guy Nadivi: Interesting point. Beerud, there are some concerns about biases, intentional or otherwise, creeping into the AI that powers things like chatbots. In order to root out bias, do you think that AI algorithms should be audited in the same way financial statements are for publicly traded firms?

Beerud Sheth: I think it’s a little early to institute audits. It’s important to think about it certainly. There’s no doubt. Well, let’s start at the beginning. Yes. The AI is trained based on existing documents, which are created by humans. Therefore human biases may be inherited by these AI models which just learned from these documents. The academic community is working on fixing these biases, on quantifying and measuring these biases. There’s been a lot of progress on that already. The awareness keeps increasing as well, given the social injustice movements going on. I think there’s certainly a greater awareness in the community around that. So firstly, the community is self-motivated to fix these long before the software gets into production. Nonetheless, there may be biases, ones that we may not be aware of or conscious of today. I think that’s been one of the human failings, that a lot of unconscious bias creeps in, things that we think okay today may not be okay years from now and so on. Certainly AI software is improving. The community is working on fixing these biases. Then as we go further, we need to make sure that businesses deploying AI use it in ethical ways. We need to hold them accountable if they don’t. So certainly there is room for some oversight, some audit, some policing of AI, not just of the AI models as well as the applications. Basically what’s inside the model and what they are applied for, both of those need to be … There needs to be some governance mechanisms around this. There’s no question. It’s just that it’s still a little early in the development of these things. I think we should let innovation flourish, but in parallel, there are a lot of conversations going on about how to use it, what to use it for. There are valid concerns around the surveillance state, around privacy, in addition to the biases and equal treatment and so on. So yeah, I think both those will have to progress in parallel, even as innovation gallops on. We need to move quickly to figure out how to monitor it. I think it will. I’m optimistic. I think the AI community is already familiar, both with its capabilities, as well as shortcomings, are already working on fixing many of the known issues. I think it’ll evolve over time, but yeah, I think we should be thinking about governance mechanisms.

Guy Nadivi: Your company, Gupshup, is one of two preferred bot builders that Amazon chose for their .bot registry services. Can you please tell us about that relationship and how you’re enabling the bot building market?

Beerud Sheth: Sure. Amazon happens to own the .bot top level domain. It’s like .com but .bot. I think it’s a wonderful domain name for a whole variety of bots that want to create a unified identity, that can use it for marketing purposes and so on. Those of us that were around in the late 90s remember how critical the .com domain names were in the development of the early Internet, and being able to create a memorable marketing identity to drive the adoption of websites at that time. In a similar way, as more and more bots are proliferating, each bot has a unique or a different identity on the appropriate channel. On Facebook messenger, it could be some name. On WhatsApp, it’s a phone number. On SMS or RCS, it could be different kinds of identities. It makes it very challenging. If a business wants to tell a user to come find its bot, there’s no single central location where it can direct it, because there are different … the identity’s fragmented. That’s where the .bot domain name comes in. It could provide that single central identity, which could then redirect the user to the appropriate bot on the appropriate channel. It can make it easier to identify bots, to market your own bots, to drive traffic and so on. So I think that’s the … In addition to identity, there could be additional services that could be provided right there around perhaps authentication, verification, and other services as well. That’s a shared vision that Amazon has and we have too. We want to make it easier for bot developers to get discovered, to drive traffic, market themselves, because I think it’s good for the overall chatbot ecosystem. We’ve been engaged in conversations with Amazon for a long time. I think ultimately when they went live with it, they selected Gupshup as one of the key platforms. What it means is really any bot developer on Gupshup can very quickly and easily, I mean, literally check the box and get a .bot domain name. It could be or and so on. They can easily get those domain names. It’s seamlessly integrated. That makes it easier. So we’re excited about the partnership. I think it’s good for the ecosystem, and it will drive greater adoption and standardization in the bot ecosystem.

Guy Nadivi: Beerud, before your foray into conversational AI and chatbots, you helped launch the gig economy by founding Elance, which eventually merged with oDesk to become Upwork, which today is the largest freelancer marketplace in the world. Do you think that one day we’ll see a similar marketplace for task-specific bots that organizations and even individuals can hire on a per hour or per project basis?

Beerud Sheth: That’s certainly conceivable isn’t it? In fact, for all you know it may already be happening and it’s just not visible. It is entirely possible that some freelancers might already be deploying bots to automate their tasks and activities. That may very well be happening. At that point, what does it even mean to have a per hour billing, because bots operate on a different timescale? Per project, of course, makes sense. So yeah, I think that’s exactly what’s happening. Once you automate tasks that were previously done solely by humans, it blurs this boundary between a human delivered task versus a chatbot delivered task. Or it could be a hybrid task, part human, part bot. Over time, that distinction may disappear altogether, as in the customer may not know, or may not even care, so long as the deliverable is done. Upwork often describes itself as a talent cloud, which is you get access to, just like you have the computing cloud and the storage cloud, I think of it as a talent cloud, where with one click, you can get access to loads of talent. It’s just that talent gets redefined as not just humans, but also automated AI programs delivering these things. So I think we’re in for some very interesting times as this evolves. I think it just opens up a whole lot of possibilities. It’ll be very interesting to see how it develops.

Guy Nadivi: Beerud, 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 conversational AI at their enterprise?

Beerud Sheth: Well, the one piece of advice would be to get started on this journey. It’s a long journey for multiple reasons. AI itself is developing. AI is not always accurate, but the accuracy rates are improving very dramatically. It behaves differently in different domains. There’s a lot of trial and error and testing that has to be done. But it is critical that executives get started on this journey, and take small incremental steps towards creating conversational experiences, enabling chatbots, leveraging AI to enable this. Because the cost of not doing something will far outstrip the cost of doing something. If you do something, and if it fails or if there’s some mishaps, I think the learning experiences, that will help you get there faster. We’ve seen these sort of transitions before as well. In the mid 90s, there was a time where a lot of marketing executives would say, “Well, why do I need a website? I already have a phone number. I have this offline retail presence. Nobody’s visiting us on our website.” Very quickly, it switched over to you were fired if you didn’t have a website, if you didn’t have a digital strategy and so on. Then it was the same thing with mobile experiences about a decade ago, with people needing to create apps and such. I think we are, yet again, at a similar point, where we are going to go from, “Why do I need a chatbot or conversational interfaces?” to it’s going to become the critical, the primary way in which consumers interact with brands. Anyway, I think the opportunity is huge. Yes, there are certain challenges in getting it right, but it’s critical to get started on the journey and get started now.

Guy Nadivi: Great perspective. All right. Looks like that’s all the time we have for on this episode of Intelligent Automation Radio. Beerud, it is a real honor to speak with someone whose previous work with Elance positively impacted so many people around the world, and whose current project with Gupshup has the potential for an even greater impact. Thank you for joining us today. It’s been great having you on the show.

Beerud Sheth: Guy, thanks for having me.

Guy Nadivi: Beerud Sheth, co-founder and CEO of Gupshup, an advanced bot-building platform, and previously one of the co-founders of Elance, known better today as Upwork. Thank you for listening, everyone and remember, don’t hesitate, automate.

Beerud Sheth

Co-Founder and CEO of Gupshup

Beerud Sheth is the cofounder and CEO of Gupshup, the world's leading platform for cloud messaging and conversational experiences. It is used by over 30K+ developers and handles over 4.5 billion messages per month. He previously founded and led Elance (now Upwork, a publicly listed company), the pioneer of online freelancing and the gig economy. Prior to founding Elance, he worked in the financial services industry – modeling, structuring, and trading fixed income securities and derivatives at Merrill Lynch and Citicorp Securities. His graduate research, at the MIT Media Lab, involved developing autonomous learning agents for personalized news filtering. Beerud earned an M.S. in Computer Science from MIT & a B.Tech. in Computer Science from IIT Bombay, where he was awarded the Institute Silver Medal. 

Beerud can be reached at: 


Beerud’s blog: 


“Suddenly retailers and shopping assistants and transactions, ordering pain, a lot of that is moving online, which again is rife for automation through chatbots.” 

“The way I think about it is, for a business, literally every customer touchpoint is going to get transformed with conversational experiences through chatbots.” 

"Today, if you think about it, most businesses have websites and apps. You really have to think of chatbots as the latest reincarnation of websites and apps." 

“So if we say that virtually every business and brand will need to build conversational chatbots, and not just one, but multiple ones across different use cases, then there is a need for lots and lots of agencies and chatbot vendors to provide these services.” 

“…as more and more bots are proliferating, each bot has a unique or a different identity on the appropriate channel. On Facebook messenger, it could be some name. On WhatsApp, it's a phone number. On SMS or RCS, it could be different kinds of identities. It makes it very challenging.” 

“It is entirely possible that some freelancers might already be deploying bots to automate their tasks and activities. That may very well be happening. At that point, what does it even mean to have a per hour billing, because bots operate on a different timescale? Per project, of course, makes sense.” 

“Once you automate tasks that were previously done solely by humans, it blurs this boundary between a human delivered task versus a chatbot delivered task. Or it could be a hybrid task, part human, part bot. Over time, that distinction may disappear altogether, as in the customer may not know, or may not even care, so long as the deliverable is done.” 

About Ayehu

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



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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

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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

Imagine a World With No IT Outages. Is It Possible? Yes! Here’s How.

Imagine a World With No IT Outages. Is It Possible? Yes! Here’s How.

Over the past few decades, the IT world has undergone what can only be described as a revolution. The recent COVID-19 pandemic has brought even greater awareness of these advances in technology, particularly as it relates to the ability for organizations to operate semi or fully virtually. IT teams across the globe have worked tirelessly behind the scenes, leveraging every tool and strategy at their disposal to ensure that critical support functions remain intact and service carries on uninterrupted.

Today, more than ever before, ITOps teams are focusing on ways to seamlessly identify and address incidents, not as they arise, but before end users are even aware there is a problem. Are we nearing a world in which IT outages are a thing of the past? It’s quite possible. Here’s the scoop.

Greater complexity demands more intelligent technology.

AIOps has become a widely accepted and generally celebrated approach to help organizations adapt and scale to modern complexity using the advanced capabilities of AI and machine learning. The goal is to transition IT monitoring and analysis from human agents to intelligent machines through automated detection and remediation.

Ticket overload and manual workflows have long burdened IT teams – that’s nothing new. Over the years, however, the rapidly evolving IT ecosystem has multiplied the challenges and increased the demands exponentially. Simply put, the traditional human-centric way of operations management is no longer sufficient.

Leveraging the innovative, intelligent technologies that are currently available to handle the workload is effectively equivalent to “fighting fire with fire,” if you will. Advanced AI/ML is capable of sifting through mountains of data in seconds, pinpointing anomalies and either alerting the appropriate human agent, or carrying out the necessary remediation steps entirely autonomously. This allows IT teams to stay ahead of the curve, actively preventing incidents and outages rather than scrambling to mitigate the aftermath.

Navigating the new “reality” using tech for offense vs. defense.

As the dust continues to settle and organizations across all industries begin to settle into their “new normal” of remote work, business leaders are beginning to shift their focus to ensuring operational continuity and establishing the necessary infrastructure that’s needed to sustain this new way of work indefinitely.

Companies already harnessing intelligent IT technologies will enjoy improved visibility, enhanced efficiency and greater competitive advantage. By using AI, ML and intelligent automation, these forward-thinking firms will achieve faster and more effective root-cause analysis and resolution, enabling them to maximize uptime by staying out in front of potential IT outages. In other words, they will take a proactive approach to ITOps rather than a reactive one.

When intelligent process automation is used to do the heavy-lifting, IT teams will be able to focus on other innovative and revenue-generating activities. In a world where IT outages are no longer an issue, everybody wins – the customer, the end-user, the IT worker, and ultimately the organization as a whole.

Would you like your organization to be a front-runner in this outage-less world? It’s as easy as adopting the right technology. Click here to start your free trial of Ayehu NG and put the power of AI, ML and intelligent automation to work for your business!

3 Reasons AIOps is a Must for Your Network

As organizations’ reliance on enterprise networks continues to grow at a rapid pace, so do the pressures on network professionals. These individuals are expected to swiftly, accurately and effectively carry out essential tasks, such as determining a problem’s root cause and whether it’s related to a device, application, server, service or the network itself, as well as formulate a way to resolve the issue. Amidst increasingly complex networks, maintaining the visibility to accomplish this at a granular level is not only difficult, but oftentimes unachievable. This is where AIOps can be an absolute game-changer.

What AIOps Can Do For Your Network Team

AI for IT Operations – a.k.a. AIOps – refers to the various technologies that, when integrated together, enable IT to automatically monitor, collect and analyze device and network health information. More importantly, it provides much more in-depth visibility, facilitates intelligent problem identification and offers much more precise root-cause analysis of performance-related problems.

Let’s explore specifically how AIOps can empower network teams by addressing each of these three necessary tasks.

Pinpoint the Source – Anyone in IT knows you cannot adequately address a performance problem unless and until you identify precisely what the issue at hand is. Unfortunately, the more complex the network, the more challenging this becomes. An AIOps platform is capable of simultaneously monitoring data from all sources to quickly and accurately locate a problem’s source. This saves the network team a tremendous amount of time and eliminates the risk of false positives and potential misdirection.

Identify the Cause – AIOps platforms operate in the background, round-the-clock, using artificial intelligence to measure network activities from end-to-end. Whenever something veers outside of statistical norms, the AIOps platform will quickly identify it and take appropriate next steps to address the issue. In-depth analytics can sift through relevant data to determine the problem’s cause as well as which networks and devices have been impacted.

Develop a Resolution – Once the AIOps platform has pinpointed the problem and identified its cause, it is then capable of presenting information to the network administrator in a contextual manner, ultimately suggesting the best way to resolve the issue. In many instances, problems can be completely remediated via automation, negating the need for human intervention. Again, this saves the IT team time and allows human resources to be used more strategically.

Thinking of implementing AIOps in your organization? It starts with the right technology platform. Click here to try Ayehu NG free for 30 full days and experience the power of AIOps for yourself!

Leveraging AI to Level-Up Your CIO Career

Leveraging AI to Level-Up Your CIO Career

If you look up “stressful career choice,” the role of CIO would probably be at the top of the list. Not only is there the task of overseeing current day-to-day IT activities, but today’s IT executives are also facing mounting pressures to consistently identify and deploy cutting-edge, game-changing innovations. It’s enough to drive even the strongest of individuals over the brink. Thankfully, there’s one technology that could easily become the secret weapon for sustainable career success. That technology is AI. Here’s how to get started.

Recognize the key difference that AI brings to the table.

The first thing that needs to happen in order for a CIO to take full advantage of what AI can do for them is to take on the right perspective. Specifically, unlike a survey or file-sharing tool, AI isn’t simply a tactical technology. Instead, it’s strategic – particularly in that it’s capable of not only enhancing the current way a business operates, but it’s also capable of paving the way for new revenue streams.

Provided it’s fed an adequate amount of data that is both relevant and of high quality, AI can provide targeted predictions and support complex business decisions that can shape the direction a company moves in.

It’s also important not to fall into the trap of viewing AI as solely a solution for everything that’s going wrong in an organization. Rather, this technology can and should also be used to identify vulnerabilities that may have otherwise gone undetected. AI is capable of sifting through massive amounts of data, prioritizing it and even exposing unknown biases that might have caused issues down the road.

These important disparities are not mere nuances. If leveraged properly, they can become key differentiators, both for the company as well as the CIO’s career.

Start small, win quickly.

With so much pressure weighing on their shoulders, it can be incredibly tempting for an ambitious CIO to attempt AI adoption on a grand scale. Go big or go home, right? Not necessarily – at least not as it relates to being successful with artificial intelligence.

Because the technologies that drive AI are constantly evolving, it’s best to focus primarily on experimenting and learning. This enables a more close alignment with the underlying business needs that are being addressed. It also allows greater control over the scope and results of the AI initiative.

The most effective way to get started with AI is to target opportunities that are the most ripe for optimization. Because these are likely to be smaller initiatives, they are also more likely to quickly produce measurable results.

These initial projects will have the best chance of succeeding if you:

  • Experiment on smaller issues with the potential to produce higher ROI
  • Start with existing workflow templates rather than building your own model (i.e. resist the urge to “reinvent the wheel”)
  • Use data that is of the highest quality and relevant
  • Incorporate AI into existing business processes where disruption will be minimal
  • Maximize productivity by automating highly repetitive and well-defined decisions

Bottom line is this: quick wins will help build confidence and gain traction for larger initiatives that have greater impact and visibility.

Think like a CEO.

As mentioned, artificial intelligence is a strategic technology. Therefore, to get the most out of it, there must be strategic thinking. The path to widespread permeation of AI is paved with many challenges. Taking more of a CEO-like approach to these initiatives can help CIOs become more proactive in recognizing and addressing evolving priorities, misaligned strategies, and the obstacles that stand in the way of true and effective collaboration.

Furthermore, because the goal will inevitably be to deploy AI across the entire organization, developing a deeper understanding of all business departments (like a CEO would have) is critical. Working with each business segment to identify those smaller issues that represent the highest and fastest return will make enterprise-wide adoption of AI much easier to achieve. 

A great way to start is to approach each LOB and pose a simple question, like “If we can do X, then we can accomplish Y.” In other words, for the sales department, this question might look something like, “If we can score leads better, we can make our sales team more efficient.” In customer service, it might be, “If we can automate commonly asked question, we can improve customer response rate and optimize our support staff.”

With this information in hand, the CIO can then determine how AI can be applied as a solution to as many of these scenarios as possible and then begin tackling them one by one, ultimately delivering value across the entire organization.

Accept that the time is now.

The tech industry is notorious for tossing out buzzwords and fly-by-night concepts that gain instant notoriety but then never really seem to go anywhere. AI is not one of these terms or concepts. In fact, it’s become abundantly clear that artificial intelligence will soon become an integral part of global business operations in every field and industry.

Don’t be the last one to jump on the bandwagon. Find out what AI is capable of doing for your organization and your career by starting your free 30-day trial of Ayehu today.

5 Mid-Year Artificial Intelligence Trends to Watch For

We’ve officially reached the mid-way point of 2020, and what a year it’s been so far! Between political turmoil and the worldwide health pandemic, the economy has seen its share of ups and (way) downs. One thing that has remained constant through all the uncertainty is technology – in particular, artificial intelligence. In fact, AI has quickly emerged as a versatile and viable solution to almost all of the problems businesses are facing currently. Let’s take a look at what our experts believe will take place in the AI sector over the next six months.

An increase in availability and accuracy of data will make AI even more useful.

We don’t mean to beat a dead horse, but we simply cannot state it enough: artificial intelligence is only as good as the data it is fed. Because of this, many organizations that have tried to adopt AI in the past have failed, primarily due to a lack of relevant and accurate data. As technology continues to improve at a lightning pace, however, more and more quality data is becoming available. In particular, today’s technology is now capable of simulating real-world scenarios, which reduces risk and cuts costs, resulting in AI that is even more powerful, accurate and ultimately more valuable.

Collaboration between human and digital workers will continue to increase.

Let’s face it. Artificial intelligence is here to stay. And as we’ve learned over the past decade or so, it’s not here to displace human workers, but rather make their jobs and their lives easier. This trend will continue as we wrap up 2020 and move into 2021. As people get comfortable with the idea of working alongside intelligent bots, more processes and workflows will be transitioned from human to machine, skyrocketing productivity and enabling organizations to maximize the human skills that AI isn’t quite capable of just yet. For many, this will require learning new skills, so prepare accordingly.

AI will play a more prominent role in cybersecurity.

They say the best defense is a good offense, and this is certainly true in the area of cybersecurity. The fact is, cybercriminals are leveraging the most up-to-date technology to carry out their nefarious plots. The most effective way to combat these criminals and ward off their attacks is to utilize the same advanced technology against them – essentially, fight fire with fire. AI-powered intelligent automation will increasingly be used to autonomously and continuously monitor systems and infrastructures, identifying areas of concern and raising alarm before breaches occur and preventing sensitive data from becoming compromised.  

Interactions with AI will become more mainstream and far less detectible.

In recent years, despite tremendous advancements in technologies like natural language processing (NLP), interactions with robots vs. human agents were relatively easy to spot. With NLP algorithms becoming increasingly capable of understanding context, distinguishing between humans and machines will become much more challenging. And even though recent numbers indicate that the majority of people prefer to receive support from humans than robots, as technology continues to advance at a rapid pace, there’s a very good chance that will change. In fact, there’s a good chance that over the next several months, we’ll begin engaging with intelligent bots without even realizing it.  

Remote work and augmented workforces will become part of the norm.

Over the past several months, thanks to a global health crisis, many organizations around the world were forced to make sudden changes to their workforce. One of the biggest shifts was toward a remote work environment. Technologies like AI and intelligent automation have made this transition much more feasible for many. However, as the dust begins to settle, business leaders are discovering that, with the right technology and approach, these types of arrangements are not only possible, but are actually more favorable for the long term. As such, many will continue operating either partially or entirely remotely, leveraging artificial intelligence to augment and balance their workforce.

Are you behind the curve when it comes to AI, machine learning and intelligent automation? Get up-to-speed and in the game quickly by downloading your free 30-day trial of Ayehu NG today. Click here to get started!

5 Common AI Pitfalls that Trick Unsuspecting CIOs

If there’s one thing CIOs are good at, it’s ignoring the hype and extracting the value out of technology. Except, however, when it comes to artificial intelligence. Whether it’s all the pomp and circumstance that clouds their reason or the lofty promises that tend to distract them, for some reason, a surprising number of IT leaders seem to fall into similar traps. It can be a costly lesson filled with delays and missteps. To avoid heading down this path in your organization, here are five risks to watch for.

Forgetting the importance of data.

We’ve said it time and time again, but an artificial intelligence solution is only as good as the data it has to work with. It’s sort of like having a Lamborghini and using subpar fuel. If you haven’t given adequate attention to ensuring that your AI platform is being fed data that is both accurate as well as relevant, then you will not realize much of any return on your investment (and you’ll probably end up holding the bag for it at the end of the day). A good strategy is to first identify which datasets are needed for each desired outcome and then building from there.

Being too quick to choose a solution.

A lot of CIOs get so amped up over the promises of what AI can deliver to their organization that they fail to perform due diligence when selecting a platform. Not only are solutions different in quality and potential benefits, but vendors themselves can vary widely on the spectrum of trustworthy vs. fly-by-night operations. Don’t fall for lofty promises. They may be legit, but it’s up to you to verify that by asking to see tangible results before signing on the dotted line.

Solving for the wrong problems.

Just because an AI solution works, doesn’t mean it’s delivering value. Ultimately, the goal isn’t to simply deploy artificial intelligence technology for the sake of doing so, but to generate actual return on your investment. To do this, you must define realistic, achievable objectives and outcomes that can be effectively tracked and measured. Otherwise, you’ll just end up with a fancy automation tool that does nothing more than cost you money.

Being lured by bells and whistles.

There are some pretty amazing artificial intelligence solutions out there. Unfortunately, many of them are so complicated, they’re actually not worth the investment to begin with. In fact, when a system is too complex, not only does it become detrimental, but it can lead to ancillary problems, like a lack of experienced personnel to manage the technology. Again, the goal is to deliver value, not get the fanciest tool on the market. Instead, you want a solution that’s advanced but also intuitive and as easy to use as possible.

Equating price with worth.

The fanciest tool on the market may have far more features than your organization even needs. And, as mentioned, the more bells and whistles, the more complicated and challenging it will be to deploy. Don’t be fooled into thinking that a hefty price tag means you’re getting the best possible solution. To the contrary, the platform that most closely matches your business needs may not only save you money upfront, but may also be more valuable in the long run as well.

Artificial intelligence has the potential to bring your organization to the next level. But only if it’s implemented the right way. If you’re just starting out on your AI journey, be cognizant of the above pitfalls so you can avoid them for yourself. If you’ve already fallen into one or more of these traps, the good news is, it’s never too late to right the ship.

Ayehu NG lets you harness the power of AI in minutes with a code-less, drag and drop visual designer and over 200 ready-to-use workflow templates. Schedule a live demo or get started today with our free 30-day trial.