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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 shopping.bot or travel.bot 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: 

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

Beerud’s blog:       https://blog.gupshup.io/ 

Quotes

“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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Ayehu NG Trial is Now Available
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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

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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

Essential Use Cases to Jump Start Your IT Process Automation

At any given organization, there are always many, many manual IT processes that make great candidates for automation. From time to time though, we run across some process automation candidates that deliver noticeably higher ROI. As people started working from home due to the Coronavirus pandemic, and more staff needed to start using ZOOM, we stumbled upon a manual process that really stood out as an excellent use case to help jump start automation at organizations.

Ayehu keeps track of the highest value automation use cases with the broadest applicability to our customers. We display those on our website where you can drill down and get more information on each one. That list is updated from time to time when we come across great new uses of Ayehu to automate toil out of a process. The use case we’ll be talking about today is one of those examples, and we think you’ll be intrigued by how it involves Ayehu NG tying together ServiceNow, ZOOM, Active Directory, and a chatbot in a very timely way.

We’re just about 6 months or so into the pandemic, so scenes like this ought to be pretty familiar to most everyone by now.

A lot of you, maybe even all of you, are working from home. Being remote and away from the office necessitated a big shift in how employees, contractors, and staff interacted with each other.

That left the door wide open for a company called ZOOM to step in and fill that interaction gap previously provided by the in-office experience. So suddenly, it seems the entire world is using ZOOM.

BTW – One way you can tell a product has really entrenched itself in the minds of consumers is when its name becomes a verb. Right?

You don’t just hail a ride-share to the coffee shop, you Uber to Starbucks.

You don’t just edit that image, you Photoshop it.

And now, we don’t just put together a web conference, we setup a Zoom call.

Now in case you’re unaware just how much Zoom usage has increased; I’d like to share a few metrics with you that might leave you stunned.

In the past, ZOOM was criticized for being a platform only small organizations used. In their financials, they report on how many customers with more than 10 employees are using their service.

A little over a year ago at the end of Q1 2019, they had 59,400 customers with more than 10 employees

One year later at the end of Q1 2020, they had 265,400. That’s a growth rate of 347%!

It’s not just smaller firms using ZOOM though. There’s a banking firm that deployed around 175,000 new ZOOM seats in Q1 and a global law firm called Baker McKenzie with over 6,000 attorneys worldwide adopted ZOOM as well.

Here’s another great visualization of ZOOM’s growth.

Back in 2013, ZOOM had just 3 million daily meeting participants.

That’s grown dramatically, and as of the end of March 2020, they now have 300 million daily meeting participants. I’m betting that number will go up when their Q2 financials are released.

Here’s the metric that made my jaw drop to the floor.

In January 2020, the number of meeting minutes ZOOM’s customers were consuming on an annualized basis was 100 Billion. That’s right 100 Billion meeting minutes.

Can you see the “bar” representing that number? No? Let’s zoom in, no pun intended.

100 Billion meeting minutes is that razor thin sliver of a yellowish vertical line that’s thinner than the grey border representing the y-axis of this bar graph. Why does 100 Billion meeting minutes look almost invisible on this bar graph?

Because just 3 months later in April 2020, ZOOM was on a run rate to consume 2 Trillion annualized meeting minutes (see previous graph). So while 100 Billion may sound like a lot, it’s a drop of water compared to the ocean that is 2 Trillion. This is a growth rate that must’ve left their DevOps team gasping for air the entire first quarter of 2020.

And what has all that growth in customers, meeting participants, and meeting minutes done? It’s led to a lot more of this.

It seems like ZOOM is everywhere and everyone is using it all the time.

That in turn has led to a problem for IT Operations in provisioning ZOOM accounts efficiently, while also documenting their distribution and assignment.

So I’d like to give you an overview of the workflow powering the use case we think is a great way to jump start your IT process automation efforts. It highlights Ayehu NG’s ability to be that single pane of glass tying together so many different pieces in your environment.

It’s going to start with an end user sending a request through Slack that they would like a ZOOM account.

The request goes directly to Ayehu, which looks up the manager that user directly reports to on Active Directory.

When the manager is identified, Ayehu passes along the user’s request to the manager, and awaits an approval or a denial.

In our use case, the manager approves the request, which BTW – is all done through email.

Ayehu then does three things:

  • It provisions an account on ZOOM
  • Sends the user an update via Slack that their request for a ZOOM account was approved
  • It also emails the user their new ZOOM credentials

Finally, Ayehu opens a ticket in ServiceNow, and documents every aspect of this request, automatically creating a complete record of everything that transpired.

That’s it. If you wanted the whole thing to run completely on auto-pilot, without requiring manager approval, you could easily configure it to do that too.

If you’re interested in test driving Ayehu NG to easily provision ZOOM accounts for your end users, download your very own free 30-day trial version today by clicking here.

5 Tips for Maximizing Efficiency in ITOps

What started out as a promising year for CIOs has quickly and drastically gone off the rails. Today, most IT executives find themselves attempting to guide the train back toward the tracks as the focus has dramatically shifted from thriving to merely surviving.

With 61% of IT budgets being negatively impacted by the COVID-19 pandemic, one of the 2020’s highest priorities has become saving as much as money as possible. And while “doing more with less” is something most CIOs are adept at, finding ways to drive revenue and secure their organization’s position in the marketplace in the midst of a world health crisis is quite the unique challenge.

The key to overcoming this hurdle will ultimately lie in maximizing efficiency levels, and not just in terms of cutting costs, either. Today’s CIOs must focus strategically on changes and initiatives that will enable them to achieve the leanest operation possible while also adding value to the organization through improved services. 

That being said, here are five ways to improve ITOps efficiency without compromising on quality.

Audit existing operations.

When it comes to maximizing efficiency in IT operations, doing so will lie heavily in existing policies, practices and platforms. Now’s as good a time as any to conduct an audit to identify areas of waste to eliminate and other potential opportunities for improvement. The end-goal should be having an infrastructure in place that optimizes the use of seamless, cohesive technologies so that the IT team can focus their talents and efforts on revenue-generating activities and other ways to support the business.

Be willing to trim the fat.

After several years of enjoying annual budget increases and the freedom to explore and innovate freely, many CIOs now find themselves facing budgetary cuts and a bleak spending forecast. As such, it will almost certainly be necessary to trim back some of the spending and say goodbye to poor performers, both in terms of personnel as well as technology. Where before, experimenting with the unknown was feasible, in the current climate, IT leaders must focus on projects, platforms and people that they know will deliver value. Remember – occasional pruning is necessary for growth.

Establish a dedicated vendor-management team.

Keeping on top of vendor contracts is a full-time job in and of itself. For a busy CIO with dozens of tasks on their plate, this one often drops down the to-do list. But with so much opportunity for cost-cutting and service improvements, it should be a top priority. Rather than trying to juggle this on top of the multitude of other duties, it may make more sense to delegate this function to another individual or team, either in-house or third-party. The money that can be saved through continuous monitoring and negotiations should far outweigh the expense, and the potential for improved service will be well worth the investment.

Bring automation into the fold.

Most CIOs have already embraced automation to some degree, and for good reason. The technology is capable of speeding up operations, eliminating human error and freeing up staff from routine, repetitive and time-consuming tasks. It can also prevent outages, which helps to maximize profitability. All that aside, there have been many advances in automation technology of which many IT leaders are still not taking advantage. Intelligent chatbots and virtual support agents, for example, can help alleviate the burden on the IT team while providing faster end-user service. This creates a much more efficient, productive environment.

Incorporate efficiency into the long-term scope.

While maximizing efficiency becomes a natural reactionary goal during economic downturns, the most successful CIOs view lean operations as an ongoing objective. They consistently evaluate whether the people, processes, policies and technologies they currently have in place are serving their optimal purpose and they make proactive changes whenever and wherever necessary. Simply put, achieving operational efficiency isn’t just a one-and-done task. It’s something that should become an integral and permanent component of an organization’s very culture.

Like it or not, the world around us is evolving rapidly. Without the right attention, foresight and action, many CIOs risk being left behind. The five tips above should help IT leaders simultaneously streamline ITOps and minimize costs while still driving business growth.

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How to Deploy Virtual Support Agents in 5 Steps

How to Deploy Virtual Support Agents in 5 Steps

Intelligent bot technology is disrupting almost every industry, with everyone from Verizon and Capital One to NASA jumping onboard. But while artificial intelligent is certainly not a new concept, developing and implementing virtual support agents in a practical and profitable way is still in its relative infancy. Unlike other, more established technologies, there aren’t necessarily any real standards for using bots. Thankfully, there are things we can learn from those already paving the way. Here are five real-world tips to help your company bring a VSA initiative to fruition.

Identify audience and need.

For VSAs to produce ROI, they must solve a specific problem (or set of problems) and/or deliver real, measurable improvement (such as with staff efficiency or productivity). As such, the initial phase of your virtual support agent strategy should involve identifying who you are trying to help and exactly why. The narrower you can get with this step, the better the outcome. Keep in mind you may have multiple iterations of the same engine, based on the user you are targeting.

Select a platform.

Once you have a clearer picture of your target user and target problem, the next step should involve choosing a platform through which the bots will be built and managed. This is the phase of the project that can overwhelm some decision makers. The good news is, there are platforms (like Ayehu) that are so easy to use and quick to implement that you can be up and running in mere minutes – no coding or scripting required. Even if you have a highly talented IT team, this would be the best case scenario.

Define your measure(s) of success.

One of the biggest challenges of virtual support agent (and artificial intelligence in general) is proving financial value. The easiest and most straightforward way to approach this is to determine as early as possible which metrics matter the most. What type of ROI do those in the C-suite and/or other stakeholders expect out of this initiative? Bear in mind, also, that some measures of success aren’t as easy to quantify, but are just as – if not more – important, such as end-user engagement levels.

Start fast – don’t wait for perfection.

Many people make the mistake of trying to make things perfect before rolling out their project. Instead, the focus should be on building fast and executing fast, even if that involves some degree of failure in the process. Take, for instance, NASA, which approaches each VSA initiative as a small startup with the goal of launching as quickly as possible. If you cannot iterate that fast, optimize the process as much as possible. For example, while Verizon was developing their Mix and Match bot, the consumer plan was being developed simultaneously. This made the actual rollout more seamless and successful.

Adjust and learn continuously.

A virtual support agent strategy isn’t something you set and forget. There is also the need for continuous adaptations and ongoing training to consider. Artificial intelligence is a fluid technology, which means your bots should continue to learn and improve over time. There will almost always be something to add, whether it’s a new term or a tweak in “personality” to better serve end-users. The main thing to remember is that VSA development is an ongoing process and must be treated as such if it is to be successful.

Want to give our intelligent automation a test drive and put the power of virtual support agents to work for you? Try it free for 30 days. Click here to launch your trial today.

Hired! How to Put Digital Labor to Work for Your Service Desk

How to Put Digital Labor to Work for Your Service Desk

Author: Guy Nadivi

With the proliferation of all kinds of bots the last few years, “digital labor” is a term you’re going to be hearing more and more about going forward.

Lee Coulter, who chairs the IEEE Working Group on Standards in Intelligent Process Automation says that “digital labor” is really just another term for “intelligent automation”. However, digital labor represents a paradigm shift that’s disruptive to the status quo. From what we’ve seen so far, you can expect that it will change how we work. It will change the kinds of work we do, and it will also create enormous new opportunities for cost cutting, as well as career opportunities for those who will be working with and managing digital labor.

A few years ago, an analyst at HfS Research coined the phrase “Welcome to Robotistan”, which referred to a corporate world where humans intermingled with virtual FTE’s, primarily in the form of bots that could take on the boring, repetitive tasks so many humans despise doing. With the proliferation of automation the last few years, that vision has turned into a reality, perhaps quicker than many thought it would.

Now with the COVID-19 pandemic, we’re seeing interest really skyrocket from organizations wanting to deploy chatbots to relieve humans of robotic-type tasks, and free them up for more important things, like for example, reshaping the digital workplace to accommodate all the people now working from home.

The worldwide chatbot market continues to experience extraordinary growth. According to Business Insider, in 2019 the market was worth a bit more than $2 ½ Billion, but they’re forecasting that by 2024 it will approach $10 Billion! That’s a compound annual growth rate of over 29% a year, which by any measure is very impressive.

Last year Salesforce.com released a major report entitled the “State of Service”. In this study they found that nearly a quarter of their respondents (23%) currently use AI chatbots and nearly another third (31%) said they plan to use them within 18 months.

That represents a projected growth rate of 136% in the use of AI chatbots from Spring of 2019 to early Autumn of 2020. Another clear indication of serious growth!

As we’ve often said at Ayehu, the biggest factor driving enterprise adoption of AI chatbots is probably service desk Cost Per Ticket.

The generally-accepted industry figure for the average cost of an L1 service desk ticket is $20. Enterprises deploying AI chatbots to enable self-help or self-service capabilities for their end users are finding that they can drive down the cost of those L1 tickets to just $4. Tell a CIO, CTO, or any senior IT Executive that there’s a way to reduce their single biggest expenditure on IT Support by 80%, and they’re likely going to be very interested in hearing more.

However, AI chatbots with automation that shift ticket requests to end-users for self-service can do much more for the Service Desk than just lower ticket volume and costs.

When AI chatbots are deployed as digital labor, service desks can also:

  • Slash MTTR by accelerating resolutions of incidents and requests
  • Liberate IT staff from doing tedious work and free them up for more important tasks
  • Raise customer satisfaction ratings, an increasingly critical KPI for IT Operations

Last year, Ayehu conducted an inquiry with a Gartner VP focused on the AI chatbot market, and he shared with us what they believe the biggest value propositions of digital labor are, based on an organization’s AI chatbot maturity level.

If your organization is interested in the technology but hasn’t deployed anything yet, in other words you’re in pre-production or your plans are still on the drawing board, then your biggest value proposition from digital labor is going to be cost reduction and deflection rates.

If your organization already has AI chatbot solutions in place, then your #1 benefit from adding automation and turning that AI chatbot into true digital labor will be increased customer satisfaction.

Regardless of whether you’re in pre-production and have yet to deploy digital labor, or have rolled out chatbots and are looking to add automation, here are some questions you should ask yourself and have answers to in order to guide your enterprise to the best possible outcomes.

What would AI chatbots mean inside of my enterprise?

How would they change business processes? How would they impact our cost structure? How would they increase our capacity?

How do I want our people to be able to work with digital labor?

This is another important question to ask in order to clearly demarcate where digital labor ends, and escalation to humans begins.

How do I want our people and digital labor to engage with customers?

This is actually important to answer whether the customers are internal or external.

There are a lot of important questions to ask your digital labor vendor as well before deploying, but here are 3 that really stand out.

What kind of scalability would an AI chatbot be capable of inside of my enterprise?

How many users can it handle? How many inquiries can it handle simultaneously? This is very important to know beforehand.

How easy is it to use?

How hard is it going to be to configure the AI chatbot for my enterprise? Do I need expensive highly-skilled programmers, or will one of my junior-level sys admins be enough?

Finally, how many other systems can I integrate my digital labor with?

The number of platforms your digital labor can connect to will dictate how much of your workload it can automate for you.

If you’re interested in test driving Ayehu NG as the automation platform that powers your digital labor efforts, download your very own free 30-day trial version today from the link below:

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How Slackbots and Ayehu Add Automation to BMC Helix Remedyforce

Author: Guy Nadivi

BMC Helix Remedyforce is a version of the BMC Remedy platform popular among organizations already using Salesforce.com, making it easy to deploy rapidly for IT organizations who value being nimble.

Since that’s a growing segment of the market, and given the surge of interest in chatbots, BMC and Ayehu have partnered to showcase how to add Slackbots and automation to the Helix Remedyforce platform.

BMC Helix Remedyforce provides a robust IT Service Management platform for running an IT organization and supporting the business. It takes a modern customer-focused perspective, and adds in very intuitive self-service capabilities that empowers non-IT staff to request services and solve problems on their own. BMC Helix Remedyforce is comprised of numerous modules, including:

  • Self-Service
  • Service Catalog
  • Knowledge Management
  • Service Level Management
  • Dashboards
  • Reporting and Analytics
  • Incident and Problem Management
  • Configuration Management
  • Asset Management
  • Agentless Discovery
  • Client Management
  • Multi-Cloud Data Center Discovery
  • Change and Release Management
  • Mobile Apps for IT and Business
  • Collaboration via Chatter and Chat
  • IT Best Practices and Smart Practices

Together, all this functionality allows BMC Helix Remedyforce to offer a unique value proposition of a short time to value, with light effort, yet still yielding a powerful delivery.

If your organization uses a cloud computing platform like BMC Helix Remedyforce, then being very lean and very responsive is most likely a priority. But there’s a way to take that leanness and responsiveness one level higher to help your organization become a self-driving enterprise through the addition of Slackbots and automation from Ayehu.

At Ayehu, we often talk about the self-driving enterprise, which is our guiding vision that influences every aspect of our automation platform.

What is a self-driving enterprise and how do we define it? Very simply – becoming a self-driving enterprise means becoming less reliant on people, and leveraging intelligent automation to handle more of the robotic kinds of tasks humans really shouldn’t be doing anyways.

Ayehu’s platform comes with numerous features an enterprise needs to become self-driving:

  • SaaS-Ready Multi-Tenancy
  • Agentless architecture
  • Codeless interface
  • And overall it’s very easy to use

It also has two features which really extend automation’s ability to help enterprises become self-driving, and thus less reliant on people:

  • AI and Machine Learning
  • Slackbots, which are an extension of AI and Machine Learning that provide end users with an almost human-like channel as an alternative to calling the help desk everytime they have an incident or a request.

Slackbots of course, are part of the overall chatbot market, which is big and getting bigger. Lest anyone think chatbots are a fad, according to Business Insider, in 2019 the market was worth a bit more than $2 ½ Billion. In 2024 they’re forecasting it will approach $10 Billion!

That’s a compound annual growth rate of over 29% a year. Very impressive growth!

I think we can safely say that chatbots are here to stay.

Gartner published a report about the chatbot market (“Market Guide for Conversational Platforms: July 30, 2019 – ID G00367775), which calculated that “31% of enterprise CIOs have already deployed conversational platforms.”

That number “represents a 48% year-over-year growth in interest.”

This is a strong leading indicator that the market is ready, if not eager, for conversational AI in the form of things like Slackbots.

One big reason enterprises are so eager for conversational AI and Slackbots is the impact they’re having on one of IT’s biggest KPI’s – Cost Per Ticket.

There’s a general industry figure published by Jeff Rumburg of MetricNet, an IT research and advisory practice, that a service desk’s average cost per L1 ticket is $20.

However, if you turn any given service request into a self-help or self-service function with chatbots, you can drive that cost down by 80% to just $4 per L1 ticket. 80%!

If you’re a CIO, CTO, or any senior IT Executive, and someone tells you that there’s a way to reduce your single biggest expenditure on IT Support by 80%, without reducing service effectiveness (in fact, possibly speeding it up), you’re probably going to want to hear more.

Enterprises are looking at chatbots as a way to divert calls or tickets or work away from the Service Desk, meaning people, and re-routing that load to chatbots, meaning software.

BTW – It’s not just because of bottom line costs and reducing calls and/or ticket volume to the service desk.

There are other value propositions for enterprise IT executives deploying chatbots:

  • Slashing MTTR by accelerating resolutions of incidents and requests
  • Liberating staff from doing tedious work so they’re freed up for more important tasks
  • And last but not least, raising customer satisfaction ratings, an increasingly critical KPI for IT

Today, there’s another big reason to start using chatbots – the Coronavirus COVID-19.

The Coronavirus pandemic is creating a new reality for everyone, and that’s led to widespread adoption of numerous precautions:

  • Washing one’s hands more frequently
  • Not shaking each other’s hands
  • Wearing protective facemasks

Perhaps the most relevant precaution being adopted, from an IT perspective, is the sudden surge in employees and contractors working remotely.

Numerous governments and health officials are imploring organizations to let their employees work from home, wherever possible, as a way of minimizing community transmission of the Coronavirus.

This has created a new reality for those workers, because now that they’re working from home, they can’t just walk over to the help desk cubicle to make a casual request. They might not be able to do it by phone either because the help desk staff is also working from home, and they’re pretty busy right now at most organizations just keeping the lights on.

Wherever remote workers may be though, they can always submit their service requests through a chatbot, and they can do it from both a web or mobile interface 24×7.

The great news about that is that there’s really no training required for someone to start using a chatbot or Slackbot, especially if it’s on their smartphone, an interface they’re already familiar with.

Slackbots can play an increasingly important role in a self-driving enterprise, allowing users to converse with the bot naturally (so to speak), and in their own language. The bot can understand the request, or if not, request clarification. Once it has the information it needs, the bot simply goes out and executes the request. It’s just that straightforward.

In addition to BMC Helix Remedyforce, there are many other systems you can quickly plug into Ayehu, which then acts as an integration hub across just about every platform in your environment.  This allows your users to initiate automated tasks via chatbot for every system you integrate with Ayehu. Best of all, almost every system Ayehu connects to can be seamlessly integrated without writing a single line of code.

If your organization aspires to be a self-driving enterprise, Ayehu automation + BMC Helix Remedyforce + Slack chatbots can provide a powerful combination which add value to such IT functions as:

  • Incident resolution
  • Alert-driven notification
  • Cross-IT change management
  • Service request management
  • Configuration management and infrastructure provisioning

If you’re interested in test driving Ayehu NG v1.6 with all its cool new features, download your very own free 30-day trial.

https://info.ayehu.com/download-free-30-day-trial-ng