How to Create an Outstanding Experience for Your Cherwell ITSM Users [Webinar Recap]

on-demand-webinar-cherwell-ayehu-present-how-to-create-an-outstanding-experience-for-your-itsm-users

Author: Guy Nadivi and Ayla Anderson, Technology Alliances Manager, Cherwell

The discipline of ITSM has undergone significant evolution since its earliest incarnations. Today with the drive towards automation, increasing use of artificial intelligence, and the push for digital transformation, ITSM occupies an increasingly high-profile position for many organizations. This is especially true as many enterprises are seeking competitive advantages in their customer experience and service quality offerings.

With that in mind, we’ve partnered with Cherwell, an increasingly common ITSM platform choice for many of our customers, to demonstrate how to create an outstanding experience for Cherwell ITSM users.

According to Gartner, it isn’t too surprising why Cherwell’s popularity is on the rise. In a recent report they wrote that “Cherwell continues to enjoy mind share among Gartner clients looking at intermediate ITSM tools. Cherwell was the second most frequently shortlisted vendor by Gartner clients in 2018.” BTW – Cherwell held that same distinction in 2017 as well. So Gartner is seeing the same increase in demand for Cherwell that we’ve been seeing.

Gartner has also identified Cherwell as a “Challenger” in its most recent Magic Quadrant for ITSM tools which was just published in August of 2019. So there seems to be a lot of momentum building in the ITSM market for Cherwell.

Since Ayehu is very customer-driven, we give priority to developing new features and new integrations based on what our customers are asking for the most. As a result, we’ve added some Cherwell-specific functionality lately, and we think that many Cherwell customers will be intrigued to see how much more they can do with the platform, once it’s integrated with Ayehu.

For those not familiar with their ITSM solution, Cherwell transforms the way businesses deliver service. Its technology provides a centralized system through which all services can be managed and monitored. This gives unprecedented visibility to all processes, helping teams measure and manage services more effectively and efficiently.

Along with nearly 100 technology alliance partners (like Ayehu), Cherwell aims to help customers modernize their IT service management. Today, Modern Service Management is foundational to transforming the experience of employees (ITSM users).

Before explaining why though, let’s define Modern Service Management (MSM).

MSM is the evolution from legacy ITSM practices with minimal impacts on the business and its employees, to a philosophy in practice that leverages self-service, automation, visibility and agility to generate business outcomes and improve employee experiences.

The visual below from Forrester Research shows that in the past, automation associated with digital initiatives focused on cost reduction. More recently however, the focus has been around customer experience (CX), as more companies take a customer-centric approach.

Forrester Research

By 2020 the focus will shift to accelerating transformation with both Employee Experience (EX) and CX automation initiatives — because employee experience has a direct correlation to employee happiness and efficiency, which in turn impacts customer experience. As businesses continue moving up the ladder of ITSM maturity, speed and efficiency won’t just be critical for customer facing apps, but will also be expected across the entire organization.

So what’s stopping businesses from transforming their service experiences? In general, they lack a centralized way to architect and automate end-to-end processes across multiple services, systems, and teams. The four primary barriers to achieving this transformation include:

  1. Disparate Systems

Individual services and departments within a business often have their own systems and tools. This not only impacts employee experience, but it impedes businesses from monitoring the performance of services, and cross-functional processes, due to lack of centralized visibility across all systems.

  1. Fragmented Data

Since many services run on legacy databases, integrating data sets across services can be difficult and time consuming.

  1. Manual Service Steps

Most businesses struggle to integrate data, systems, and processes, leaving many teams stuck in an endless cycle of using antiquated systems to get their work done. Whether they’re responding to service requests, onboarding a new employee, or managing the maintenance logs of a fleet of vehicles — this creates inefficiencies and challenges in keeping up with service requests.

  1. Resource Intensiveness Required to Transform Digital Operations

Often times architecting new services, and evolving existing processes, requires teams of developers to write code. This is both time consuming and expensive.

This is where Cherwell integrated with Ayehu automation can help businesses.

If you’re currently a Cherwell customer or have it as one of your shortlisted vendors, then you may already be asking yourself whether you should add automation to Cherwell. And if you do add automation, what kind of boost would it give to your investment in Cherwell?

To determine that, it helps to look at some costs associated with helpdesk operations.

Based on Ayehu’s research conducting standard helpdesk data assessments for organizations, we’ve discovered that the 5 largest categories of incidents represent as much as 98% of their total tickets!

When those incident numbers get sliced and diced to see how many get handled by Tier 1 vs Tier 2 support, they often reveal a surprise.

As much as 70% of Tier 1 incidents get escalated to Tier 2!

That means that if you can somehow focus your automation efforts on just the 5 largest categories of incidents while they’re still in tier 1, automation is going to provide a very big payoff, not just at your service desk but in your customer satisfaction metrics as well.

Now, let’s take a closer look at what kind of a return we’re looking at from automation.

If we go real conservative by estimating that it costs $20 to remediate a ticket, then multiply that by the number of tickets your helpdesk handles, it’s likely going to add up to some serious money your organization is spending on manually resolving these incidents. (BTW – $20 per ticket is a rough number calculated by Jeff Rumburg of MetricNet for 2017)

Now I’m going to shock some of you. If you automate incident resolution, your cost per ticket drops down to $4, and that’s also playing it conservative.  Applying automation to incident resolution has a dramatic effect on your costs, so if you’re looking for a high-impact way to bring savings to your organization’s ITSM costs, automation is a pretty good way to go.

In case you’re wondering what kinds of specific incidents you would likely be automating with Ayehu, here are some of the most common processes we see:

  • Application/Service/Process/Server Restarts
  • Monitoring Application Log Files, looking for specific keywords, and taking some action based on what’s found
  • Low disk space remediation (always a popular thing to automate)
  • Running SQL Queries, perhaps at 3am then compiling the results into a report which gets emailed to appropriate personnel
  • Onboarding and offboarding employees (another popular one)

And there are many, many more tasks the service desk will want to automate for itself. Now how about the kinds of processes we can push out to end users to remediate in a self-help paradigm?

  • Password Resets or Account Unlocks are an obvious one
  • How about letting users provision their own VM’s whether it’s VMware, AWS, Azure, or Hyper-V?
  • And how about letting them modify or resize a VM’s memory or disk space without any help from the service desk?

When you think about it, there’s really no limit to the kinds of things you can automate, once you’ve integrated Ayehu with Cherwell.

Cherwell & Ayehu Present: How to Create an Outstanding Experience for Your ITSM Users

Helping the IT Help Desk – What you Need to Know about Virtual Support Agents

What you Need to Know about Virtual Support Agents

This post was originally published as a guest article on InsideBIGDATA.

IT help desks everywhere are handling a growing number of requests from multiple channels every day. And the more time the service desk spends putting out fires by phone, through email, or in person, the less time they have to focus on resolving the bigger issues and applying their cognitive skills to more meaningful projects.

Are chatbots or virtual support agents the answer? The success of virtual support depends on several key factors. Here’s how to identify those factors and evaluate whether or not VSAs are right for your organization.

Chatbot vs. VSA

The first important piece of the puzzle is understanding the difference between chatbot and virtual support agent technology. While the concept is similar, there is a distinct and critical difference, particularly as it relates to use in the help desk arena. This difference can be summed up in one word: context.

If you’ve ever visited a website and used the “live chat” feature to ask a question, chances are the party you interacted with was a chatbot. And chances are even greater that the responses you received were basic and scripted based on a set of common inquiries. Simply put, chatbots are one-dimensional. They cannot engage beyond the basic communication that they’ve been programmed for.

Virtual support agents, on the other hand, when set up properly, have far greater functionality and flexibility than chatbots. Thanks to underlying technologies like artificial intelligence, machine learning and natural language processing, VSAs are capable of understanding the meaning and intent behind human communication, even if it’s vague or ambiguous.

In other words, VSAs can understand context. As such, they are able to hold realistic conversations, generate authentic dialogue and provide intelligent responses based not only on the data they’ve received (like chatbots), but also on the context of that data.

VSAs and the Help Desk

As mentioned, help desk agents field a mind-boggling volume of incoming requests, the majority of which are routine and repetitive in nature, but important nonetheless. For instance, password resets are a necessary evil in the IT support realm as they are required in order to keep others in the organization productive.

Yet, the process of manually resetting user passwords is not only a tremendous waste of human resources, but it’s also a massive waste of money. In fact, Forrester Research estimates that the average cost of a single password reset is $70. Multiply that cost by the number of times your support team executes this task and it really adds up.

That’s where virtual support technology comes in. VSAs enable the help desk to automate almost all routine, repetitive and manual tasks. Beyond this, however, is where the true value of virtual support becomes evident. In addition to automating the basics, the technology behind VSAs enables them to work alongside human agents, providing the same level of support and assistance.

How it works is remarkably simple. The virtual agent pulls data from various knowledge management resources to respond intelligently to incoming requests. Virtual agents are also capable of taking action on behalf of the end-user without the need for human intervention. This means fewer escalations and a more manageable workload so human support agents can focus their skills on more meaningful business initiatives.

The Key to Success

Of course, as with any technology, virtual support agents do require work in order to set them up properly. For instance, AI and NLP technologies are essential components to VSA functionality. The most fundamental key to success, however, is the establishment and maintenance of a comprehensive, dynamic knowledge-base. After all, this is the resource from which the VSA will draw its responses. Without in-depth and accurate data, virtual agents will not be capable of operating to their fullest potential.

Gartner predicts that by 2023, 40% of I&O teams will be using AI-augmented automation, resulting in higher productivity with greater agility and scalability. Given the current benefits, coupled with the promise of improving technology, it’s not a stretch to see that VSAs will continue to play an increasing role in making the help desk experience better for everyone.

Click here to view the original post on InsideBIGDATA.

Still holding out on IT automation? Here are 4 signs the time has come.

stop resisting IT automation

IT automation is certainly not a new concept. In fact, it’s been in use to some degree for over a century. Yet, there are still a great number of enterprise-level organizations that are on the fence about whether this advanced technology is really worth investing in. If you are one of these late bloomers and are still unsure of whether or not you should take the plunge and employ intelligent IT automation in your company, here are four signs that will let you know it’s time.

Your IT department is struggling to deliver services in a timely, efficient manner.

When a ticket gets opened to IT, how long does it take to achieve satisfactory resolution? In today’s fast-paced business environment, regardless of what industry you are in, agility and efficiency are absolutely critical to ongoing success and future growth. If the demands of your workforce are becoming too much for your skilled IT personnel to handle, the time to leverage technology has come. Not only will IT automation alleviate the burden of many of the day-to-day repetitive tasks, but it will also free up your talented technicians to apply their valuable skills in a more resourceful and profitable manner.

You have way too many staff members on hand just to handle those peak cycles.

Optimized resource allocation is the key to running a lean, profitable operation. If you have far too many IT employees on the payroll just so you can ensure smooth workflow during peak cycles, you are undoubtedly wasting money the rest of the year. Conversely, if your current IT department becomes completely overwhelmed during those peak cycles, your capacity is too low and you’re likely to see higher employee turnover rates. IT automation provides the ability to scale up or down as needed without having to make any changes to your human workforce.

Your employees are wasting an incredible amount of time and effort on repetitive tasks.

Even if you feel that your operation is being managed at the appropriate capacity and the turnaround time of your IT department is acceptable, if your IT team is spending the majority of their day completing manual tasks and processes, you’re wasting money and missing out on opportunity. You’re also facing a much higher risk of costly human error. Why not let artificial intelligence handle these simple, routine tasks? That way you’ll be paying an appropriate salary to workers who are able to better utilize their valuable skillset and the work will be completed faster and more accurately.

Your legacy systems and applications are operating independently.

Of course it doesn’t make sense to invest in an entire system overhaul, but what kind of operation are you running if every application you’ve got in place is functioning in its own silo. The problem many organizations face is the fact that legacy systems which offer useful benefits individually don’t have the capability of working together. This leads to tremendous inefficiency. The beauty of most modern IT automation and orchestration platforms is that they are designed to integrate existing systems, platforms and applications to create a more cohesive and streamlined infrastructure. This allows the organization to avail itself of all the benefits of each legacy system as they work in tandem, complementing and enhancing each other’s capabilities.

If you can relate to any of the four challenges listed above, the time to consider adopting intelligent IT automation is now. Get started today with your free 30 day trial and see for yourself what you’ve been missing out on.

eBook: 10 time consuming tasks you should automate

Episode #25: Why Being A Better Human Is The Best Skill to Have in the Age of AI & Automation – Center for the Future of Work’s Robert H. Brown

September 15 2019    Episodes

Episode #25:  Why Being A Better Human Is The Best Skill to Have in the Age of AI & Automation

In today’s episode of Ayehu’s podcast we interview Robert H. Brown – Vice President of the Center for the Future of Work.

How do you envision the future of work?  More utopian or more dystopian?  Are we destined for a scenario where robots do all the work, leaving our biggest challenge to be deciding what to do with our abundance of leisure time?  Or are we doomed to experience massive social unrest owing to the multitudes of unemployed displaced by those same robots?  As any good lawyer will tell you, the answer depends. 

While we don’t retain attorneys to help us divine what lies ahead, we do rely on futurists like Robert H. Brown to produce educated predictions.  As Vice-President of Cognizant’s Center for the Future of Work, Robert leads a team that’s articulated a fascinating picture of that future based on extensive research and global insights.  We delve into some of his team’s findings, which turn out to be neither utopian nor dystopian.  Along the way we’ll discover why despite rapid advances in AI, Automation, and other technologies, the key to being gainfully employed in the future of work, might just come down to being a better human.



Guy Nadivi: Welcome everyone. My name is Guy Nadivi and I’m the host of Intelligent Automation Radio. Our guest on today’s episode is Robert H. Brown, Vice President of The Center for the Future of Work, a Cognizant Technology Solutions think tank.

Guy Nadivi: Regular listeners of our show will recognize Robert as the inaugural guest from our first podcast episode, and still one of the most compelling people we’ve had on the show, as is the topic of the future of work.

Guy Nadivi: Given the pace at which several rapidly advancing technologies are changing the nature of work today and driving us towards that future, in some cases much faster than people are comfortable with, we felt it was imperative to bring Robert back and query him on his think tank’s latest findings about this immensely absorbing topic. Robert, welcome back to Intelligent Automation Radio.

Robert H. Brown: Great to be with you, Guy. Thanks for having me back.

Guy Nadivi: Robert, I want to start out today with a quote from Peter Drucker, the famous management consultant who once said, “Trying to predict the future is like trying to drive down a country road at night with no lights, while looking out the back window.”

Guy Nadivi: Now, you make a living predicting the future of work, but I’m pretty sure you’re not doing it by driving down a dark country road at night staring out the back window. Something I’m sure our audience is eager to hear about is how does The Center for the Future of Work go about the fascinating and important task of predicting how we will work in the years ahead?

Robert H. Brown: It is a great question and I love the Drucker quote. We have our own spin on this, Guy. It’s that the future of work funnily enough is it’s always in the future, but your future starts tomorrow. That may sound glib, but the idea behind that is you and I have been working for long enough to know that days turn into weeks, weeks turn into months, months turn into quarters, quarters turn into years, and years turn into decades.

Robert H. Brown: The fact that the future of work starts tomorrow in some respects gives I think people some comfort in knowing that they have agency and there are steps that they can take in their present job, maybe in future jobs that they don’t even know exists yet that are waiting out there for them.

Robert H. Brown: Part of our job is to shine as bright a light as we can, not only into some of the future long range scenarios that we predict, but also what are the step stone paths right now that people just might be unaware of.

Robert H. Brown: My own background looking through the rear view mirror of going back to Drucker for a minute, I studied history in school and I think as a futurist, that is a really good setup for the type of work that we do because frankly, The Center for the Future of Work, a lot of what we do is thinking about corporate strategy.

Robert H. Brown: Not just only for Cognizant, but for our clients and for the industries in which our clients are. History, if nothing else, it’s a branch logic of if, this, then, that, all the way back through history and you can look at the Napoleonic Wars, you could look at- … To Normandy.

Robert H. Brown: Everything is if this have happened. If you flip the gatefold of that forward into the future and you see some of the embryonic technologies that are coming into the frame now and what the time cycle might be for some of those to come to pass that if this comes to pass and we cross this wire with X platform or capability or Y internet of things capability, this will be a disruptive game changer that will strategically change the trajectory for an industry and therefore, it will change the nature of work in that industry.

Robert H. Brown: There’s a couple of factors in there to think about the future of work and how you do it. The last thing I would say is just we try to read as much as we can. There’s a lot of thinkers that are writing about this subject.

Robert H. Brown: We try to consume as much of that as we can. We attend conferences all the time, we engage with Cognizant’s customers. Typically, that’s people that have a direct line of sight to strategy for their firm, and I think that you take all of that on board and it gives you a pretty rooted grounding in some of the factors that help you make good predictions.

Robert H. Brown: In my case, before I came to Cognizant five years ago, I was Managing Vice President of Research for a team of researchers at Gartner. For those of you listeners that know Gartner, there’s a lot of predictions in terms of being a market analyst.

Robert H. Brown: Taking all of that on background, The Center for the Future of Work, most of us typically hail from that analyst background. Taking all of that on balance, it’s a bit of art, it’s a bit of science, a lot of observation for sure and, again, if nothing else, the future of work starts tomorrow. It’s right in front of our face.

Guy Nadivi: Robert, with all that reading and research that your think tank does, you’re well aware that there’s some anxiety out there about how the advances in AI, automation, and other technologies are going to impact people’s jobs.

Guy Nadivi: In your recent report entitled “From/To: Everything You Wanted To Know About The Future of Work But Were Afraid To Ask”, your think tank advocates a profound yet simple countermeasure to the apprehension that some people are beginning to feel.

Guy Nadivi: Specifically, you recommend that people, especially middle-aged professionals, stop allowing so much of their identity to be based on their job. In fact, you implore them to stop holding their identities captive to their job and to start shifting how they think about what they do to be more about the tasks they perform. Why do you think this syntactical strategy is so necessary?

Robert H. Brown: I think it’s really important because there is this pervasive belief out there that my job is going to go up in a puff of smoke as a result of algorithms, automation, or AI. There’s nothing that I can do about it, I have no agency in it and it’s a foregone conclusion, and we don’t believe that for a moment.

Robert H. Brown: I think for us, the main question to ask is not will my job be automated? The question to ask is what task of my job will be automated? Guy, I can’t remember if we talked about this in the podcast we did a year ago or not, but a couple of years ago, we did – we interviewed 2,000 senior executives worldwide who had line of sight to strategy for their firms.

Robert H. Brown: We asked them, “What are the skills that you’re going to need to run your business today and by 2020?” Without fail, every single one of those executives said not only were they going to need skills that were innately human, things like communication skills, analytical thinking, global operating skills, teamwork, on and on.

Robert H. Brown: Those skills were not eroding, in fact, they were growing in the average differential, but across the time period that we asked them was they needed 15% more of those skills on average.

Robert H. Brown: I don’t know about you, but the last time I checked, the average length of a day was 24 hours. Theoretically, we’re supposed to be working five days a week. We all work hard so we work a little bit more than that. But the bottom line is where are you personally going to get your 15%? Where do you get that margin?

Robert H. Brown: Well, the answer is figuring out at the task level which tasks go to the bot and which ones can you then therefore double down on and be a better human in terms of your ability to bring those skills like analytical thinking, reasoning, being able to answer the question, “What’s the right thing to do with judgment and ethics?”

Robert H. Brown: I think a lot of work processes and work tasks that people are engaged in today is very rote and repetitive work. I mean, even doctors and lawyers, if you think about the most white-collar of white-collar employees, there’s a lot that doctors and lawyers do that are very rote and repetitive.

Robert H. Brown: I would argue if you’re a doctor and you have to tell somebody who is really sick, “Hey, you’ve got six months to live.” You must have an extraordinary amount of humanistic empathic behavior to do that well.

Robert H. Brown: I would say that’s where the rubber hits the road for really good doctors. I think we’ve all had loved ones that may have, maybe not in life-threatening situations where you have a doctor that can look you in the eye, be empathic and maybe not be a robot themselves.

Robert H. Brown: That’s really important. If paperwork burnout is at the root of some good doctors leaving a profession, that’s going to be really important that we look at those tasks that are leading to as you said, you started off with the question, the identification of oneself with work being bound up in that, and if you hate what you do and there’s a way to be able to use the machines such as they are going forward to take away some of that drudgery, let’s make it happen.

Guy Nadivi: With regards to some of your future tasks being done by bots, let’s talk about professionals using AI to augment the tasks they perform. What are some of the more interesting ways you’ve seen people using machine intelligence to, & I’m quoting you here, “….augment and expand human capability, creativity, empathy and constructive problem solving” in a quest to augment their work.

Robert H. Brown: I think you can look around and you see examples of that augmentation happening in lots of different domains. We talked about medicine for a moment and I’ve just finished doing a lot of research on augmented reality.

Robert H. Brown: One of the companies that I love talking about as a startup here in San Francisco called Augmedix, and Augmedix uses Google glass as the medium for allowing a doctor to be in the doctor’s office with a patient, talk about what’s going on, and natural language processing will actually incept that conversation and pre-populate the electronic medical record.

Robert H. Brown: The doctor just has to affirm that yes, indeed that information is correct without having to run a secondary work process to effectively fill out a piece of paperwork even though it might be on a tablet.

Robert H. Brown: I think that one’s pretty cool. That’s really augmentative. Again, in the realm of augmented reality, you could think about technicians that are going to let’s say it’s your cable guy coming to your house to fix your modem or your router and they can’t quite troubleshoot it.

Robert H. Brown: They can basically have a manager back at head office, see what they … See what I see capabilities in the moment and guide them to the right fix. Customer satisfaction goes up, they’re not left scratching their head with a customer that’s barking at them, and gives them more time in the field fixing problems.

Robert H. Brown: In fact, in a similar vein, there is a … If you think about the way that Boeing does wiring harnesses on large jumbo jets, they are now using skylight which is an augmented reality platform to allow their wiring technicians to not have to look at paper manuals, interrupt driven processes where you’re looking away and making mistakes.

Robert H. Brown: They’re actually able to do it faster, better, fewer errors, and leading to happier employees as a result. Then just think about people that are doing things like logistics that are doing delivery. Folks in supply chain, your UPS driver, being able to use some really simple augmentative tools like Google Maps, way-finding that saves a lot of time, speeds up the supply chain, customers are happier.

Robert H. Brown: I think what we’re finding is that from where we might have been, three, four or five years ago where we’re going to see a robot apocalypse, in fact, we’re seeing the assistive robots that are allowing people to do better jobs with greater job satisfaction, take away some of the pain, take away some of the drudgery.

Robert H. Brown: That’s been really exciting to see. I think if you … As they say, you squint and you look into some of the jobs of the future that we’re predicting even within the last one to two years since we started describing some of those jobs, you’re seeing examples all over the place.

Guy Nadivi: Let me divert a little bit and ask you this question. In 2017, Bill Gates proposed taxing robots at a rate commensurate with what was being paid by the worker they replaced. If a company implemented a robot that supplants a factory worker making $70K a year, the company would be taxed at the factory worker’s income level to offset losses and things like social security taxes.

Guy Nadivi: My question for you then Robert is should robots be taxed including software robots or AI? If so, should any of the tax revenue raised be used to retrain and re-skill displaced workers?

Robert H. Brown: Yeah, it’s a great question. I actually wrote a blog post about this at the time that Bill Gates made that proposal and in a word – I was against it. I mean, well, it’s not a word, it’s a phrase, but if I say I was against the idea of doing a per robot tax and there was a couple of reasons why I think that is.

Robert H. Brown: On the one hand, you could recoup the same amount of tax revenue on the basis of corporate gains. It’s a taxation by another way, but I think the problem is on a per robot basis, it will basically impede progress.

Robert H. Brown: Again, thinking about the emergency room or in getting insurance claims filed or some of the other customer service improvements that we want to see happen. If you throw that taxation on a per bot basis, it will necessarily – not plunge us into a dark age as relative to innovation, but it will hamper much needed innovation that we need.

Robert H. Brown: There’s a lot of problems that we need to fix and technology is a part of the solution. It’s one part, it’s an important part, but I think taxation of that magnitude would unnecessarily impede the progress.

Robert H. Brown: Again, I’m certainly not a poster child for the Howard Jarvis Society here, but I think you can get the tax revenue that you need to offset some of that by other means. I’m not a proponent of that.

Guy Nadivi: Another one of the really great white papers available from your think tank at FutureOfWork.com is called “21 More Jobs of the Future: A Guide to Getting and Staying Employed Through 2029”. This is a sequel of course to your original “21 Jobs of the Future”.

Guy Nadivi: Robert, can you tell us what you’re predicting, three of the more interesting jobs of the future will be over the next decade?

Robert H. Brown: We now have a basket as you said, 21 plus 21 equals 42. The Douglas Adams fans that are listening will appreciate that and Jackie Robinson fans too. But in all seriousness, we have a basket of 42 jobs now.

Robert H. Brown: Picking three is hard, but one of the things that folks will find if they go and check out those reports is that some jobs will be very highly tech-centric and may indeed require an electrical engineering, computer science degree, but a lot of them will not.

Robert H. Brown: They’ll be low in tech-centricity. They will – how an Uber driver uses a very sophisticated and technologically rich platform to do his or her job, but they don’t have to have a computer science degree to use that technology.

Robert H. Brown: We see many jobs that will be infused with technology, but are jobs that lots and lots of people can do. One fun one since that was your question, what are some of the fun jobs?

Robert H. Brown: If anybody out there is a fan of Marie Kondo, she came out with her book a couple of years ago, has the super successful Netflix show about helping people find joy through the Japanese art of decluttering.

Robert H. Brown: One of the jobs that we’ve identified is what we’re calling the “Joy Adjutant” is a condo consultant if you like, but the idea is that for people that are decluttering, probably most of that stuff is going to find its way to Goodwill and it will get thrown away into a landfill.

Robert H. Brown: What if somebody were to invent a platform that would allow you to categorize that stuff in the moment, get a price on it and move it through an online auction platform like an eBay or something and provide instant liquidity for the household.

Robert H. Brown: But the real catalyst to sparking that joy, is like Marie Kondo does, is you need the adjutant. You need the person there with you in the moment to help you make meaningful decisions about your stuff.

Robert H. Brown: That’s a fun one and the real skill set in there is how you can engage with people, but we envision there being a really slick platform on the back end of that to help people dispose of their stuff and actually, provide some liquidity for their households. That’s one.

Robert H. Brown: A little bit of a serious note, we’re looking at climate change all over the place and one of the roles that we identified was the “Tidewater Architect”. Any at sea level city whether it’s Osaka, Japan, Shanghai, China, Miami, Florida, New Orleans is going to have to deal with the consequences of sea level rise.

Robert H. Brown: The Tidewater Architect role is a civil engineering role that we’ve identified that will probably have a very large radius of adjacent jobs around this architect role to help deal with that, and the way that we conceived of this job is that unlike the Dutch model or what the Italians have done in Venice or even The Thames Barrier in London where you’re literally walling off the sea.

Robert H. Brown: The Tidewater Architect is how can you actually work with nature and with ecology as this inevitable sea rise happens and work within the inner city in an ecologically friendly way as that occurs.

Robert H. Brown: By the way, all of these jobs are written up as JDs (Job Descriptions). It’s like when you have to hire for a job today like a social media manager, you have a job description with skills and quals and educational background.

Robert H. Brown: All of these are written up in that way. And then just to close out, maybe one that’s a little bit further out on the time horizon, talked about a lot of the research I’ve been doing on augmented reality and we have a role called the “Augmented Reality Journey Builder”.

Robert H. Brown: The idea about that job, and this is maybe for your listeners that are very, very knowledgeable around process. Theoretically, augmented reality for any process involving people and time and space, we talked about wiring harnesses a second ago.

Robert H. Brown: Any process people in time and space will theoretically melt in a good way using augmented reality as a catalyst for rethinking that work process. For the journey builder, it could be something that is a work process. It could be an educational process. It could be a media and entertainment process.

Robert H. Brown: I would say if you can imagine if instead of Niantic inventing Pokemon Go, what if Tesla had invented Pokemon Go? And with all due consideration for safety while you’re in your morning commute, how could they use an Augmented Reality Journey Builder to gamify your route and, “Oh, by the way, help unpick the Gordian Knot of commute traffic in the morning as a part of that.”

Robert H. Brown: That’s a really fun one. I think you’re going to see a lot of interdisciplinary work to make that reality come to pass. In fact, with any luck, fingers crossed, I’m going to be hosting a panel at South By Southwest in the Spring of 2020 where we’ve got a participant from one of the big gaming engines out there if you guys are familiar with the Unreal Engine from Epic.

Robert H. Brown: I’ve got the head of the immersive, then The Immersion Lab at the USC School of Cinematic Arts, doing a lot of cutting edge stuff in the media and entertainment space on augmented reality, journey building, and then an actual practitioner, a very young, a person who’s coding these experiences on the Facebook AR Spark platform.

Robert H. Brown: With any luck, we’ll get picked to do that and it will be fun to talk about the real reality of the Augmented Reality Journey Builder.

Guy Nadivi: It’s really fascinating to hear you describe this glimpse of the future of some of these jobs. It could be a little bit difficult to envision, but then I think back to 15, 18 years ago, if somebody like yourself had started talking about the future job of Social Media Manager, people may have had difficulty understanding.

Guy Nadivi: Now every single company, even small ones have a Social Media Manager or somebody that they outsource to manage their social media.

Robert H. Brown: Yeah, that’s exactly right. It’s such a good example. We talk about that a lot. The notion of the help wanted ad for the Twitter Data Wrangler, apply within. You would’ve thought it was nuts 15 years ago because Twitter hadn’t been invented yet, but yet, here we are, welcome to 2019.

Robert H. Brown: That’s that idea that the future of work starts tomorrow, but days turn into weeks to months to years and then to decades. Suddenly, you’ve got to think about, “Well, what might the possibilities be here?”

Robert H. Brown: On some of these rather goofy-sounding jobs of the future, and our 21 Jobs of the Future, we’ve got one in there called the Digital Tailor and that the idea being that there’s some massive percentage of online things that are actually returned because they don’t fit right.

Robert H. Brown: You order the thing expecting a Savile Row suit and it’s like two sizes too small so you send it back. What I do with Digital Tailor is somebody that when I was a kid, my mom used to have … The Avon lady would come to the house.

Robert H. Brown: With Digital Tailors, somebody comes to the house and makes sure that there’s a perfect fit every time using digital tools and hey, lo and behold within the last I think 9 to 12 months, the Zozosuit has come on the market and is… we call them the “spotted in the wild” moments where it’s like, “Hey, we had this wild sounding idea, but it’s actually coming to pas.”

Robert H. Brown: You squint, you see these examples of the future of work popping up all over the place.

Guy Nadivi: There’s so many new types of jobs that are going to exist in the very near future that will entail so many different skillsets. You spoke about some of them, but I still think it’s worth asking you Robert, what do you feel are the top, let’s say 3 to 5 skills that people in today’s workforce can or should acquire to remain relevant in the workplace of the future?

Robert H. Brown: Well, we talked a little bit about that survey that we ran a couple of years ago where we asked about what are the most important skills that businesses are going to need in the future.

Robert H. Brown: They’re all eminently human skills. There is a lot of them. I don’t think right at the top of the list of our research, the primary research, but I also definitely agree with this. Analytical thinking is going to be really important.

Robert H. Brown: The more technology we put into business, into business processes, ways of working and interacting, the more data is part and parcel of that. The ability to get to grips with that and be analytical in our thinking in a world that’s awash with data is going to be very important.

Robert H. Brown: It doesn’t mean that everybody needs to be a math major or a data scientist. In fact, there’s precious few data scientists really out there today, but one of the jobs of the future we talked about was the “Data Detective”, and part of that skill set was not necessarily knowing deep, deep math, but being able to ask really good questions.

Robert H. Brown: You think about like a police detective today, that’s what they do. They ask really good questions, but in the context of looking at data, humans, the computers are really good at answering many of these questions, but if you frame the right hypothesis and frame the right question, that’s a really innately human skill.

Robert H. Brown: Analytical thinking is definitely a part of that. Again, going to our data set, and I’m just looking at it now, the number two after analytical thinking was global operating skills. The world is getting smaller. Although there’s some debate about that.

Robert H. Brown: We have in the “From/To” report, we talk about where we’re going from the internet which was the global village to the splinternet. You can look at what China is doing with their frame for their citizens and how there’s things like facial recognition everywhere, citizen’s scores.

Robert H. Brown: You can look at what happened in Europe with GDPR. The consensus is fracturing a bit, but being, if you’ll forgive the word, cognizant of the global interplay that we have within the current global posture if you like.

Robert H. Brown: I think that’s going to be very, very important. Strategic thinking was #3, and this is something that we talked about at the outset is like even in a mid-level role or a junior role, if you have a direct line of sight to the strategy of your company or the reasons why your company is making decisions, given the strategic posture they have to take in their competitive set or within their industry that might be going through lightning fast change, that’s going to serve you well because you get the context of how your role is contributing to that.

Robert H. Brown: Leadership, innovation, decision-making, selling, all of those things are going to be very important in the future and as I said before, they’re all innately human skills that bots can’t do. They can help, but they can’t do them.

Guy Nadivi: Nevertheless, it would seem that whether you need technical skills or human skills, all roads to the future of work are paved with a layer of education. What I’d like to ask you Robert is what one thing do you think our education system could do today to better prepare young people for the radical changes coming to the workplace of tomorrow?

Robert H. Brown: It’s a huge question, but I think if I had to boil it down to one thing, it’s just to be aware of the fact that the future is going to look very different from the past. Being able to practice the future. I think for schools, from elementary school all the way through to higher education, it’s going to be critical to use the notion of the future of work as a galvanizing prism through which to look through horizontally ossified, stovepipe discipline.

Robert H. Brown: The future of work is definitely something that galvanizes like I said history, economics, computer science, ethics, philosophy, biotech, the list goes on and for a lot of big universities are stove-piped. You have your College of Letters & Science, College of Engineering and they don’t really talk to each other and the future is coming at us pretty fast.

Robert H. Brown: I think that’s really, really important. I also think just with respect to education, the notion that even if you go to college, you’re going to go to college for four years and then go run off a 40 year career and never learn again is over.

Robert H. Brown: We are going to be lifelong learners. We need to be prepared for lifelong learning. My colleague Caroline Styr has just done some really good research on what that looks like and in fact, back to “21 Jobs of the Future” for a moment, one of the roles that we identified is the “Uni for Life Coordinator” and that bespeaks somebody that can help let’s say mid-level or late career individuals think about what types of learning do I need to engage in to make sure that my skills are optimized and ones that are maybe eroding, which ones do I need to refresh?

Guy Nadivi: I think everybody needs to revise their mindset about that – that lifelong learning is a part of your work life going forward.

Robert H. Brown: I think a lot of … Guy, just on that, sometimes people may roll their eyes to that notion, but I think in a lot of at least American businesses, even if your company is talking about education and training, maybe it’s this thing called the IDP, right? We all kind of have that in one form or another.

Robert H. Brown: The Individual Development Plan, maybe 5% of your overall performance rating. It’s an afterthought. It’s like one other thing that I gotta do, but how much emphasis is the company really putting on it?

Robert H. Brown: Companies need to change their mindset as well and lead by example. I use the phrase a moment ago, have a strategy for being strategic for your employees and then give people the latitude to practice the future.

Robert H. Brown: That might be using an AR/VR platform for a different type of learning. It might be giving them the latitude to go to specific conferences that they don’t feel empowered to go and take.

Robert H. Brown: It might be allowing them to set up … We’re getting better on this, but set up things like diversity and inclusion councils within businesses. Diversity and inclusion, you hear it talked about a lot today, but I can guarantee you, it’s going to be critical in the future of work.

Robert H. Brown: Not only because it’s the right thing to do and America is made up of many, many different types of people, but if we’re not including people in the development of the new machines, the algorithms, the automation, the AI, really bad things are going to happen.

Robert H. Brown: Again, back to 21 jobs of the future for a moment, one of the jobs we’ve identified is the Algorithm Bias Auditor, but this comes down to people and giving people the latitude to do things like skills refresh, giving them some guidance, giving them some support as opposed to hanging a millstone around their neck and calling it the IDP and saying, “Yeah, get to that when you can. We don’t really, we don’t really care about it anymore than you do.”

Guy Nadivi: Now, I opened today’s episode with a quote about the future and I’d like to close out with one. Here it is. “Never let the future disturb you. You will meet it if you have to with the same weapons of reason which today arm you against the present.” Robert, care to guess who that quote is from?

Robert H. Brown: Go for it. No, I have no idea. Tell me who that quote’s from.

Guy Nadivi: That quote is from Marcus Aurelius, a Roman emperor from nearly two millennia ago which as a History major you’ll appreciate, and I think his ancient words are just as applicable in the 21st century as they were in his own day.

Robert H. Brown: Yes, absolutely. Absolutely. I couldn’t agree more. In fact, Socrates going way back in the midst of time. Socrates had a quote. I think I’m going to mangle it, but it was something along the order of writing things down will create forgetfulness in the learner’s mind. There you have it. From Socrates’ mouth to your ears.

Guy Nadivi: All right, I’d like to mention again the report that The Center for the Future of Work produced entitled “From/To: Everything You Wanted To Know About The Future of Work But Were Afraid To Ask”.

Guy Nadivi: It not only focuses on the future of work, but it’s a tour de force covering many of the technologies and trends that are shaping that future. It’s a highly entertaining read and quite insightful to. Be sure to check it out.

Guy Nadivi: All right, it looks like that’s all the time we have for on this episode of Intelligent Automation Radio. Robert, once again, you’ve accomplished what I think is the greatest thing a guest can do on our show which is make us think and leave us curious for more.

Guy Nadivi: Thank you so much for coming back and updating us about the future of work. It’s been fantastic having you on the show again.

Robert H. Brown: Thanks a lot, Guy. Great to be with you. I really appreciate it.

Guy Nadivi: Robert H. Brown, Vice President of The Center for the Future of Work, a Cognizant Technology Solutions think tank. Thank you for listening everyone and remember – don’t hesitate, automate.



Robert H. Brown

Vice President of the Center for the Future of Work.

Robert Hoyle Brown is a leader in the Center for the Future of Work, a global think tank with a charter from Cognizant Technology Solutions to examine how work is changing, and will change, in response to the emergence of the Age of Algorithms, Automation and AI.

As a futurist, he has focused extensively on the topics of robotics, automation, privacy and augmented reality and their impact on business processes.

Since joining Cognizant in 2014, he has served as head of strategy for Cognizant's Digital Operations practice, and worked intensively with Cognizant’s Business Accelerator leadership to drive the development of its intelligent automation strategy, messaging and go-to-market outreach.

Robert can be found at:

Email:                                    robert.h.brown415@gmail.com

Twitter:                                https://twitter.com/robthbrown

Website:                              https://www.cognizant.com/futureofwork/

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

Quotes

“Part of our job is to shine as bright a light as we can, not only into some of the future long range scenarios that we predict, but also what are the step stone paths right now that people just might be unaware of.”

" …there is this pervasive belief out there that my job is going to go up in a puff of smoke as a result of algorithms, automation, or AI. There's nothing that I can do about it, I have no agency in it and it's a foregone conclusion, and we don't believe that for a moment. I think for us, the main question to ask is not will my job be automated? The question to ask is what task of my job will be automated?”

“The more technology we put into business, into business processes, ways of working and interacting, the more data is part and parcel of that. The ability to get to grips with that and be analytical in our thinking in a world that's awash with data is going to be very important.”

“Leadership, innovation, decision-making, selling, all of those things are going to be very important in the future and as I said before, they're all innately human skills that bots can't do. They can help, but they can't do them.”

“We are going to be lifelong learners. We need to be prepared for lifelong learning.”

About Ayehu

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

GET STARTED WITH AYEHU INTELLIGENT AUTOMATION & ORCHESTRATION  PLATFORM:

News

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

Links

Episode #1: Automation and the Future of Work
Episode #2: Applying Agility to an Entire Enterprise
Episode #3: Enabling Positive Disruption with AI, Automation and the Future of Work
Episode #4: How to Manage the Increasingly Complicated Nature of IT Operations
Episode #5: Why your organization should aim to become a Digital Master (DTI) report
Episode #6: Insights from IBM: Digital Workforce and a Software-Based Labor Model
Episode #7: Developments Influencing the Automation Standards of the Future
Episode #8: A Critical Analysis of AI’s Future Potential & Current Breakthroughs
Episode #9: How Automation and AI are Disrupting Healthcare Information Technology
Episode #10: Key Findings From Researching the AI Market & How They Impact IT
Episode #11: Key Metrics that Justify Automation Projects & Win Budget Approvals
Episode #12: How Cognitive Digital Twins May Soon Impact Everything
Episode #13: The Gold Rush Being Created By Conversational AI
Episode #14: How Automation Can Reduce the Risks of Cyber Security Threats
Episode #15: Leveraging Predictive Analytics to Transform IT from Reactive to Proactive
Episode #16: How the Coming Tsunami of AI & Automation Will Impact Every Aspect of Enterprise Operations
Episode #17: Back to the Future of AI & Machine Learning
Episode #18: Implementing Automation From A Small Company Perspective
Episode #19: Why Embracing Consumerization is Key To Delivering Enterprise-Scale Automation
Episode #20: Applying Ancient Greek Wisdom to 21st Century Emerging Technologies
Episode #21: Powering Up Energy & Utilities Providers’ Digital Transformation with Intelligent Automation & Ai
Episode #22: A Prominent VC’s Advice for AI & Automation Entrepreneurs
Episode #23: How Automation Digitally Transformed British Law Enforcement
Episode #24: Should Enterprises Use AI & Machine Learning Just Because They Can?

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YouTube: https://www.youtube.com/user/ayehusoftware

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

4 Tech Trends to Watch for in 2020

4 Tech Trends to Watch for in 2020Technology has been evolving since the dawn of time. As we prepare to enter another new decade, we can expect to see even more accelerated change on the tech front. With so much happening so remarkably quickly, it can be difficult to know which trends to track. To narrow things down, we’ve rounded up the top four adaptations that we believe will bring the greatest innovation and growth in 2020 and beyond. Take a look below.

Intelligent Automation

Not surprisingly, intelligent automation topped our list of technologies that will drive progress and success over the next several years. Thanks to the growing proliferation of cloud computing, big data and increasingly “smart” robotics, the future is a place where automation will no longer be an option, but rather a necessity. Leveraging these highly advanced technologies will enable organizations in every industry to streamline operations, maximize efficiency and uptime, dramatically lower costs and remain competitive.

Intuitive AI

While artificial intelligence plays a role in the big-picture automation trend, its capabilities and ongoing advancements warrant a separate mention on this list. The computers of tomorrow will be able to learn and evolve much the same way we do, which means that in addition to increased computing power, AI will be able to carry out tasks that were once reserved for humans and at a lightning speed. Underlying technologies, like machine learning, facial recognition and natural language processing will enable AI to continue to learn and grow smarter without the need for human intervention.

Voice Command

We’ve already begun seeing rapid and advancing developments in voice technology, thanks to the increasing adoption of voice assistants, like Siri and Alexa. Over the coming months and years, expect to see voice technology continue to develop and improve, particularly in the way of its ability to interpret and understand the context of the spoken word. This is where NLP will really begin to have a significant impact on our day to day lives.

Analytics

Enterprises across the globe are already leveraging analytics as a key driver of growth and innovation. Not only can analytics confirm whether you are successful in your industry, but they can help predict which direction the market will likely head in over the coming months and years. Data processing, facilitated by AI and machine learning, will continue to be used to turn massive amounts of information into actionable insights, as well as identifying issues and recommending next steps.

Without question, we are entering an exciting era in technological advancement. The most exciting part is that you don’t have to wait until next year to experience the power of these amazing tech trends. Download your free 30 day trial of Ayehu today and put the power of intelligent automation, powered by AI and machine learning, to work for you! Click here to get started.

How is AIOps Really Used in IT?

How is AIOps Really Used in IT?

Digital transformation has simultaneously simplified and added a layer of complexity to the modern world of IT operations. Managing multiple environments across a number of locations invoked the need to introduce several disparate tools and platforms, leaving IT siloed and, oftentimes, overwhelmed. This has perpetuated the need for artificial intelligence for IT operations, or AIOps for short. For those not yet leveraging AIOps, or who are still in the beginning stages, here are three real-world, value-added use cases to consider.

Threat Detection – AIOps is the perfect complement to a security management strategy because its machine learning algorithms are capable of mining massive amounts of data for scripts, botnets and other threats or anomalies that could potentially harm a network. This is especially true for threats that are complex and sophisticated, which is why it’s such a valuable addition.

Intelligent Alerting – Today’s ITOps teams are being inundated with alerts of which only a small portion are actually critical. AIOps can manage these alerts autonomously, evaluating, identifying core issues, prioritizing and either escalating or remediating them without the need for human intervention. Imagine trimming that overflowing inbox of alerts down to just one or two that truly matter.

Capacity Optimization – Through the use of AI-based statistical analysis, IT operations teams can optimize application workloads and availability across the entire infrastructure. This technology is capable of proactively monitoring bandwidth, utilization, CPU, memory and much more, with the goal of maximizing application uptime. AIOps can also be used for predictive capacity planning.

Of course, this is really just the beginning. As environments become increasingly complex and technology options continue to grow, IT operations teams will find themselves under even more pressure to deliver maximum business value with minimal downtime. AIOps emerges as the ideal solution, facilitating infrastructure monitoring and management that is much faster and far more efficient. It’s no surprise, that IT leaders and other key decision-makers are starting to take notice.

Today, AIOps is all about threat management, streamlined alerting and maximizing uptime. Tomorrow, IT automation powered by artificial intelligence, machine learning and natural language processing technology is positioned to forge entirely new pathways for innovation and growth. In other words, the journey has just begun and the future is beaming with possibility.

Want to get in on the ground floor? Grab your free 30-day trial of Ayehu NG and put the power of AIOps to work for your organization.

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Bridging the NOC and SOC for an Integrated IT Powerhouse

The similarities between the role of the Network Operation Center (NOC) and Security Operation Center (SOC) often lead to the mistaken idea that one can easily handle the other’s duties. Furthermore, once a company’s security information and event management system is in place, it can seem pointless to spend money on a SOC. So why can’t the NOC just handle both functions? Why should each work separately but in conjunction with one another? Let’s take a look a few reasons below.

First, their roles are subtly but fundamentally different. While it’s certainly true that both groups are responsible for identifying, investigating, prioritizing and escalating/resolving issues, the types of issues and the impact they have are considerably different. Specifically, the NOC is responsible for handling incidents that affect performance or availability while the SOC handles those incidents that affect the security of information assets. The goal of each is to manage risk, however, the way they accomplish this goal is markedly different.

The NOC’s job is to meet service level agreements (SLAs) and manage incidents in a way that reduces downtime – in other words, a focus on availability and performance. The SOC is measured on their ability to protect intellectual property and sensitive customer data – a focus on security. While both of these things are critically important to the success of an organization, having one handle the other’s duties can spell disaster, mainly because their approaches are so different.

Another reason the NOC and SOC should not be combined is because the skillset required for members of each group is vastly different. A NOC analyst must be proficient in network, application and systems engineering, while SOC analysts require security engineering skills. Furthermore, the very nature of the adversaries that each group battles differs, with the SOC focusing on “intelligent adversaries” and the NOC dealing with naturally occurring system events. These completely different directions result in contrasting solutions which can be extremely difficult for each group to adapt to.

A new set of problems arise, however, when the two teams become siloed, with each group focused on only half of the equation. The resulting gap, particularly in terms of data that is not being shared, perpetuates an even broader gap in the necessary knowledge to maximize the effectiveness of each team. Efforts by the SOC that fail to take into account operational requirements or efficiencies cause bottlenecks that can result in a disruption in network performance. Likewise, fingers can be pointed at the NOC for implementing network designs that leave critical resources exposed and vulnerable.

The best solution is to respect the subtle yet fundamental differences between these two groups and leverage a quality automation product to link the two, allowing them to collaborate for optimum results. The ideal system is one where the NOC has access to the SIEM, so they can work in close collaboration with the SOC and each can complement – rather than impede – the other’s duties. The SOC identifies and analyzes issues, then recommends fixes to the NOC, who analyzes the impact those fixes will have on the organization and then modifies and implements accordingly.

So, what’s the best way to achieve this cross-functional collaboration and optimization? The most important goal is to eliminate operational and/or technical silos. By leveraging a cross-silo intelligent automation platform, security incidents can be detected and resolved while events simultaneously trigger automatic changes both to security as well as network device configurations. This essentially closes the loop on cyberattack mitigation while effectively bridging the distance between security and ops teams.

As the IT environment introduces increasingly complex applications and workflows across a spectrum of systems and devices, and oftentimes in a variety of different locations, the demand for a more streamlined, holistic approach also continues to grow. The time has come to rethink the way the NOC and SOC work together. With an orchestrated approach, powered by intelligent automation, organizations will be able to close the gap between the two departments to more effectively address today’s multifaceted threats, regardless of where they happen to occur within the network.

Ayehu NG is an intelligent IT Automation and Orchestration platform built for the digital era. As an agentless platform, Ayehu is easily deployed, allowing organizations to rapidly automate tasks and processes, including interoperability across disparate solutions and systems, all in one, unified platform.

If you’re ready to bridge the gap between your NOC and SOC to create an integrated IT powerhouse, click here to start your free trial.

Solving your “what if” scenarios with intelligent automation

When it comes to convincing businesses that intelligent automation is the way of the future, the biggest objection to overcome is the age-old question, “what if….” Many IT professionals and other key decision makers within an organization carry the fear that automated tasks which are put in place to solve a problem may actually end up causing more harm than good.

What could go wrong? What if the whole thing blows up in our faces and we end up with an even bigger mess on our hands? The answer is simple: when automation is designed and tested properly, everything should work out just as it is planned, and the results will be well worth the effort.

Creating and Designing Your Workflow

The first step in setting up intelligent automation so that it works properly is creating and designing your workflows. You have to have your end result in mind, and then figure out the steps necessary to achieve that end result. Various criteria will need to be identified, so that you know whenever a certain function or task occurs, the next step in the workflow will automatically be triggered. So, to summarize, establish your desired end result, and then develop a list of steps to help you achieve that result. List each criterion in the process and determine what next step each criterion would trigger.

Testing 123…

The next important step, once you’ve created your workflow, is to try it out in a controlled environment. Test each step in the process to verify that the desired result for each is achieved. If something isn’t working properly, re-evaluate to determine why and then work to fix that piece until the entire process is functioning correctly. We recommend starting small and testing a variety of situations and scenarios to really be sure everything is working properly. Continue this process until you are confident that your automated workflow is working precisely as it should.

Implement

Once you’ve tested your automated workflow enough to be confident that it’s functioning as it is meant to function, it’s time to put it into action. It can be a bit nerve wracking to implement a workflow for the first time live, but once you see it in action, you’ll become that much more confident that it will be there to meet your needs whenever necessary.

Call on the Experts

If, at any time during the above outlined process, you feel as though you’re not getting the results you’re looking for, or you need some guidance and support, don’t be afraid to reach out to the experts. Remember, part of choosing the right intelligent automation product is choosing a company that offers plenty of training and support to its customers. Any company will be there for you when you’re in the process of making a purchasing decision, but you want to make sure that you choose someone that will also be there for you after the fact. If you’re feeling overwhelmed or just have a few questions, don’t be afraid to reach out to your software partner for assistance.

The hands-off nature of intelligent automation can make some professionals feel uneasy. They may wonder if the very system that’s being put in place to solve a particular problem within the organization will actually end up causing more harm than good. The truth is when you know the steps to take, and you’re careful to work through each step just as you should, the result will be exactly what you’re hoping for. When automation is created, developed, tested and supported properly, there is no longer the need to ask “what if”, but rather “why did I wait so long to do this?”

What are YOU waiting for? Contact us or better yet – download your free trial today to start leveraging intelligent automation for your organization.

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Transform Your Organization with AI in 5 Steps

According to IDG’s 2018 State of the CIO report, 73% of IT executives struggle with striking a balance between the need to innovate and the demand to achieve operational excellence. One of the main reasons for this is the fact that IT frequently gets bogged down with a growing list of tools and competing priorities, all of which chip away at precious time and available resources. As a result, more organizations are turning to artificial intelligence as a way to bring technology, data and people together to drive digital transformation. Here’s how you can use AI to do the same in five easy steps.

Step 1: Understand what you can and cannot solve.

While AI has the potential to transform an entire organization, machine learning technology is not yet capable of fully replacing the experience of skilled professionals. Instead, IT teams can leverage automation powered by artificial intelligence to free up skilled workers to do what they do best: apply their expertise to develop solutions for highly prioritized issues.

Machine learning algorithms can sift through mountains of data to spot trends, deliver insights and identify potential solutions. Automation can assist in resolving certain issues. But it’s up to the IT department to apply the deep analysis necessary to achieve business goals.

Step 2: Identify and prioritize problems to address.

Artificial intelligence can help address the two biggest IT challenges: maximizing operational efficiency and improving the customer experience. The role of CIO has taken on much greater importance, with 80% of businesses viewing IT managers as strategic advisors for the business. As such, these individuals, along with others in IT, are responsible for defining key areas of focus for new technology, such as AI solutions. In order to achieve buy-in, new solutions should be presented in a way that closely aligns with broader organization-wide goals.

Step 3: Pinpoint gaps in technology and skills.

The IT skills gap is an ever-present problem, and it doesn’t appear to be going away any time in the near future. In addition to the talent shortage, IT budgets are stagnating. AI solutions can help to mitigate both of these issues by empowering IT teams to do more with less, and at a much faster rate than they could on their own.

Keep in mind, of course, that key skills are still necessary in order to drive these solutions. To address this, many organizations are looking to reskill existing staff. Thankfully, today’s automation tools do not require a PhD to operate them. Regardless, decision-makers should look for a data-based platform that features AI-powered technology.

Step 4: Develop your strategy.

Once you’ve identified which problems AI is capable of solving for your organization, defined the specific challenges you’d like to overcome, achieved buy-in for adoption and assessed what resources you have to work with, the four step is to develop your strategy for deployment. This strategy should include the following main segments:

  • Roadmap – from proof of concept to continuous process improvement
  • Testing Plan – defining what you want to accomplish and what metrics will indicate progress
  • Team – investing in and arranging training for IT staff

Step 5: Prepare for scale.

Any broader AI strategy should involve mapping out data across all systems, services, apps and infrastructure. This includes both structured and unstructured data as well as data in a variety of different formats. It’s essential to select a solution that is capable of ingesting, normalizing and formatting all data sources for analysis.

Further, it’s critical to choose a platform that offers room to mature and scale. And keep in mind, also, that while the “land and expand” concept may work for some companies, others – particularly those with a higher risk tolerance – may be better off to push transformation across the entire organization at once. Generally speaking, however, stable and sustainable change begins by starting small and building on early successes. The key is leaving enough room to grow.

Want to experience some of those early successes now? Launch your free 30-day trial of Ayehu NG and put the power of AI and intelligent automation to work for your organization today!

Episode #24: Should Enterprises Use AI & Machine Learning Just Because They Can? – MIT Technology Review’s Karen Hao

September 1 2019    Episodes

Episode #24: Should Enterprises Use AI & Machine Learning Just Because They Can?

In today’s episode of Ayehu’s podcast we interview Karen Hao – Artificial Intelligence Reporter for MIT Technology Review.

The legendary Yogi Berra once said “The future ain’t what it used to be.” We’re living in that future Yogi alluded to, & his quote accurately reflects the surprising ways advances in AI & Machine Learning are unfolding.  For instance, nobody envisioned that one day Pavlovian techniques used to condition animal behavior might be the model for training a computer algorithm.  Yet that approach powered the breakthrough victory by AI firm DeepMind’s AlphaGo program in 2016 when it defeated world champion Lee Sedol in the ancient game of Go.

What specific impact will these technological advances have on us as individuals, and on the enterprises seeking competitive advantages in the marketplace?  To explore this and other questions we speak to Karen Hao, AI Reporter for MIT Technology Review, a publication claiming to be “the oldest technology magazine in the world.”  Karen’s data-driven reporting focuses on demystifying the recondite world of AI & Machine Learning.  We tap into her extensive insights to learn why despite proliferation of “deep fakes” & AI bias she’s so optimistic about the field, and what commonly overlooked aspect of AI & Machine Learning IT management should focus on before deploying it.



Guy Nadivi: Welcome, everyone. My name is Guy Nadivi and I’m the host of Intelligent Automation Radio. Our guest on today’s episode is Karen Hao, Artificial Intelligence Reporter for MIT Technology Review. Karen’s focus is on the ethics and social impact of AI, as well as its applications for social good, and she also writes an AI newsletter called The Algorithm, which was a 2019 Webby Award nominee. Previously, Karen was a reporter and data scientist at Quartz, a digital news site, and before that she worked as an application engineer at the first startup to spin out of Google X. Karen is a real expert on the current state of affairs in artificial intelligence, something that we’re very interested in, and we’re thrilled to have her on our show. Karen, welcome to Intelligent Automation Radio.

Karen Hao: Thank you so much for having me, Guy.

Guy Nadivi: Karen, earlier this year you published a piece about a study you conducted which covered 25 years of AI research, encompassing nearly 17,000 articles to determine where AI is going next, and you predicted that deep learning, the predominant class of machine learning over the last decade, may actually soon be on the way out, while something called reinforcement learning was gaining momentum in its stead. Can you please tell us a bit about reinforcement learning and its applications in the enterprise?

Karen Hao: Yeah, so I wanted to clarify your point, which is that reinforcement learning is actually a sub-category of deep learning. When I sort of broadly claimed that deep learning is on its way out, I was referring very much to the research world, and that in the research world when I conducted the analysis, you can see that they’re sort of in waves of interest in different types of techniques. Deep learning is just one category of AI research, but there are other categories that have sort of risen and fallen in interest over time.

Karen Hao: The prediction comes from the idea that deep learning is really good at finding. It’s a class or a category of statistical methods for finding patterns in data, specifically correlations in data. There’s a rumbling in the AI research community that this can only get us so far because correlations aren’t really, ultimately, the only element of human intelligence that we need to be able to replicate. So the claim that I made was sort of that deep learning is probably going to not be the star of the show in a decade, and there will be other techniques and methods that we’ll be starting to look at replicating other aspects of human intelligence and take it from there.

Karen Hao: But reinforcement learning is this interesting thing in that it is still a sub-category of deep learning, but it started getting popularity more recently, when Deep Mind’s program AlphaGo successfully defeated the greatest human Go player, and that was sort of a really big milestone achievement because up until then the idea of reinforcement learning was kind of cast aside as this silly thought experiment. But the theory behind it is that when you train animals, like in Pavlov’s dog scenario for example, you will incentivize it to do behaviors that you want it to by giving it rewards, you will de-incentivize behaviors that you don’t want it to do by giving it punishments. Reinforcement learning is like the software equivalent of that where you give points to an algorithm when it starts moving towards a goal that you want it to achieve and you take away points went moves away from that goal.

Karen Hao: Before AlphaGo had really had this milestone achievement of beating the human Go player, people didn’t really understand how to make reinforcement learning work. But since that milestone, now there has been a rush of interest in trying to use reinforcement learning for various applications, including self-driving cars. In the self-driving car scenario, you would simulate a car essentially through trial and error, figuring out how to avoid crashes, and then ideally whatever algorithm you have from the end of that trial and error process, you can then deploy to a real car on the road, and that car would be able to successfully navigate the road safely. Those are the two overarching themes of that question.

Guy Nadivi: Now, you mentioned a rush of interest lately in reinforcement learning and there’s been a rush of interest in AI in general. You’ve proposed a checklist of five questions one can use to cut through AI hype, which I think is a very valuable reference tool for evaluators at corporate enterprises and other organizations. I won’t recite all five, but I am particularly curious about your fifth checklist question, which is, “Should the company be using machine learning to solve this problem?” The very first thing you wrote about answering this question is that it’s “more of a judgment call. Even if a problem can be solved with machine learning, it’s important to question whether it should be.” So Karen, in your opinion, what are the criteria that a corporate enterprise should use to make its own judgment call on whether or not to use machine learning to solve a particular problem?

Karen Hao: Yeah, that’s a good question. I guess I’ll start with an example of where this has become particularly relevant. Face recognition is obviously becoming quite controversial, and initially when face recognition became deployed as a technology, I think a lot of companies that were developing face recognition platforms simply asked the question, how can we build the best face recognition platform ever? How do we improve accuracy? A lot of human rights activists and civil rights activists have pointed out that even if you make a highly, highly accurate face recognition platform, that doesn’t necessarily mean that it won’t infringe on people’s civil liberties. Actually, in fact, having a highly accurate face recognition platform can do a lot to constrain people’s civil liberties in ways that are being done both in the US and particularly in China.

Karen Hao: So I think when I wrote that fifth tip or fifth question in my Cutting Through AI Hype article, I was really thinking about sometimes if you have a technology hammer and you’re just looking for nails to hit, you don’t take the step back to say but wait, is this actually even a challenge that we should be tackling with technology or should we consider another approach that is perhaps more ethical?

Guy Nadivi: So speaking of the social impact, given your level of immersion in the fields of AI and machine learning and your focus on their social impact, I’m very curious if you think that these technologies will ultimately augment more people or replace more people? Karen Hao: This question is interesting, because when talking about the future of work and the impact that AI will have on it, I think we often reduce the complexity a bit into saying like how many jobs will be replaced versus how will people just be better at their jobs because they have like an AI assistant or something like that. But recently at Tech Review, we actually hosted a conference dedicated to the future of work, and a lot of the researchers that were speaking at the conference said that AI is fundamentally just going to change the nature of work. So it’s not necessarily that it’s replacing jobs per se. There will be jobs that will go away, but there will also be many, many jobs that will be created because of AI and it will just look different.

Karen Hao: For example, manufacturing jobs might go away, but data labeling jobs have become a huge thing because AI algorithms need lots and lots of data and you often need to label that data or clean that data or do some kind of pre-processing before you feed it into the algorithm. Now there are whole industries that have bloomed because of that.

Karen Hao: I think in thinking about the social impact, it’s kind of tough to answer whether it will augment or replace. But I will say that I think it is within the technologists’ hands and within the consumer’s hands to make sure that AI will be more beneficial to people. I think people should feel that they have agency to partake in this revolution and make sure that AI will be augmenting people and not replacing them.

Guy Nadivi: Let’s switch gears a little bit, Karen. You’ve written recently about the use of AI to manipulate media and create what are called deep fakes. And that brought to mind something Vladimir Putin said in 2017, “Artificial intelligence is the future not only for Russia, but for all humankind. It comes with colossal opportunities, but also threats that are difficult to predict, and whoever becomes the leader in this sphere will become the ruler of the world.” Given the advances in AI you’ve been reporting on, do you agree with his statement?

Karen Hao: I hesitate to, because it kind of creates this narrative that is pretty prevalent right now of this idea of an AI arms race, that all these countries are engaging in and whoever reaches better AI capabilities faster, they will be capable of obliterating essentially everyone else or perhaps just ensuring superior dominance or had global hegemony or whatever the term you want to use. I don’t think that that’s a very productive narrative. I think it’s a self-fulfilling prophecy. So if countries do believe that is happening, then of course the arms race will start, will accelerate, but it doesn’t have to be that way.

Karen Hao: The research community in AI is extremely global and extremely open. The community has always been founded on the idea of open collaboration, open sourcing, and a lot of the advancements that are currently happening right now or that we already see the products of from advancements prior came from significant collaboration across borders, particularly among American scientists and other scientists who immigrated to the US and work at American universities or Chinese scientists and American scientists at some of the biggest tech giants in the world. So I hesitate to agree with that in the sense that it is a self-fulfilling prophecy, but it would be quite detrimental to I think the research and to our world if that prophecy were bought into.

Guy Nadivi: So with some of these concerns that are cropping up about the misuse of AI and machine learning, do you see any economic, legal, or political headwinds that could slow adoption of these advanced technologies? Or is the genie out of the bottle at this point to an extent that they just can’t be stopped and perhaps not even effectively regulated?

Karen Hao: I think that there are pathways forward for effective regulation, at many different levels of government, so the local, the national, the international. In the US, particularly, there is a small contingent of Congress members that are currently looking very carefully at this issue and trying to understand the best way that they can regulate the technology that makes sure that it is a force for good, beneficial for everyone, an equalizer for everyone, but also not going to hinder innovation. It’s a particularly tricky problem because AI is what they call a migratory technology, so it is a tool that can be used in so many different ways across so many different industries. You have to slice the regulatory knife very finely to make sure you’re cutting it in the right direction, if that makes sense.

Karen Hao: One of the recent bills that came out of this, called the Algorithmic Accountability Act, I think is a really great example of an initial effort to start regulating this effectively. Essentially what they proposed is that all companies that are engaged in creating automated decision making systems should be evaluating them to make sure that they don’t have bias or other negative unintended consequences. I think that’s an interesting and nuanced approach to this, in that they aren’t necessarily saying in this particular industry for this particular type of algorithm, it should be used in XYZ way, but more like across all of these industries, there are many, many different types of automated decision making systems. You need to make sure to audit them and make sure that they are not unintentionally harming people, particularly at vulnerable populations.

Karen Hao: So yes, I do think that there are regulatory pathways forward and it’s an ongoing discussion. I think that there are a lot of policymakers that are thinking about it in the right way.

Guy Nadivi: You’ve argued in one of your articles earlier this year that, “There is no such thing as a tech person in the age of AI.” You also revealed that part of your driving mission with MIT Technology Review’s AI newsletter, The Algorithm, was to dismantle our outdated notions that technology is for the tech people and social problems are for the humanities people. In other words, engineers could use a good dose of liberal arts education and the non-techies need to brush up on their technical skills.

Guy Nadivi: Now, not to perpetuate any stereotypes, but most if not all, most, not all, but most of the talented technology people I know are only interested in technology, and most of the non-techies I know are too intimidated or uninterested in the nuts and bolts of technology to dive into it and get their hands dirty. How do you bridge this divide? Karen Hao: I actually had a really great conversation with a researcher today, actually just like 30 minutes ago, about the work that they’re currently doing to educate kids, specifically middle school students about AI ethics. So they are having hands-on workshops with 10-year-olds about algorithmic bias and about the fact that algorithms are opinions that you can actually change. I think that that’s one of the ways that we can really bridge that divide is there’s this false dichotomy in not just our society but in many societies that from a very early age that we’re ingrained with, which is you can either be a tech person or you can be a humanities person. Like I’m Chinese American and I also speak Mandarin. In Mandarin, there’s a phrase like you are either a tech person or you’re a humanities person, and people will ask you when you start demonstrating particular interests in certain subjects, “Oh, are you going to take the humanities track or the tech track in the future?”

Karen Hao: I think early stage education, such as what that researcher is doing with middle school students to dispel that notion, is a great way to really make sure that we are holistically educating kids to have interests in both. But I know at an older level, so I think that there’s a lot of opportunities for universities to be integrating ethics curriculum and humanities curriculum directly into their engineering requirements and vice versa, to integrate intro to coding courses in humanities curriculum.

Karen Hao: I think there are two main efforts right now that I see that are pretty exciting in that regard, which is the new College of Computing at MIT and the Human AI Institute at Stanford that are both trying to be as interdisciplinary as possible in their approach to teaching the engineering/AI curriculum, where they’re being really intentional about developing the curriculum so that every class would have ethics imbued into it. It’s not just like a tacked on module at the end. I don’t know the exact details of how that would actually work, but just in general, I think educating people, breaking down the boundaries between STEM and humanities is step one, and then really educating people to understand the interplay between the two is the next step.

Guy Nadivi: Overall, Karen, given everything you see and report on, what makes you most optimistic about AI & machine learning?

Karen Hao: I think the thing that I’m optimistic about is the ambition is pure and the people that are working towards it are genuinely good and they really care. What I mean by that is that the ambition for AI really is to recreate intelligence that can help us solve problems that we aren’t able to solve by ourselves. That’s the grand ambition of the field. Part of that is about tackling problems like climate change, hunger, poverty, things that if we could alleviate those issues then it would really bring so much good to the world and improve the quality of lives for so many people.

Karen Hao: So I’m optimistic about the fact that when I talk to researchers in this space, they do have that in mind. That is the driving motivation for many of them. Now that we’ve seen the fact that there are challenges currently in the way that AI is being developed, there’s been a pretty strong reaction to making sure that they get it right, to correcting the path and making sure that they get it right. I’m optimistic about the fact that the spirit of the field is in the right place and people are actively working to get us back in alignment with that.

Guy Nadivi: Of course, I’m obligated to ask you as a correlation to my last question, what makes you most concerned about AI and machine learning?

Karen Hao: Just all of the things that we currently see in the news constantly about AI bias, about abuses of deep fakes, about other things that we just have not… I think in the early stages of the fields, people were just so excited about the innovation and the pace of innovation that it was hard to really step back and understand the gravity of some of the technology that was being developed. I hope that we slowed down a bit, and as I said, I am optimistic that that will happen.

Guy Nadivi: Karen, 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 deploying AI and machine learning at their organizations?

Karen Hao: That’s a great question. This is the most obvious answer, but I have to say it anyway. It’s just to really, really think about the ethics. Make sure whatever you’re doing, ethics should be right along, in the very first meeting, you should be talking about, okay, this is a problem we want to tackle through machine learning. What are some of the potential consequences that could arise if we didn’t do it right, and how do we make sure therefore to avoid that and do it right. I think a lot of organizations are starting to do this, and I hope that it will just become second nature and it will no longer even be a question in the future that that’s how you approach the conversation of machine learning.

Guy Nadivi: I think that would be a very positive trend. All right, it looks like that’s all the time we have for on this episode of Intelligent Automation Radio. Karen, you’re the first AI reporter we’ve ever had on the podcast, and I really appreciate you coming onto the show to share your insights with us on some of the more intriguing issues the fields of AI and machine learning are currently facing. It’s been great having you on the show.

Karen Hao: Thank you so much, Guy.

Guy Nadivi: Karen Hao, Artificial Intelligence Reporter for MIT Technology Review. Thank you for listening, everyone, and remember, don’t hesitate, automate.



Karen Hao

Artificial Intelligence Reporter for MIT Technology Review.

Karen Hao is the Artificial Intelligence Reporter for MIT Technology Review, where she covers the latest research developments, ethics, and social impact of the technology. She also writes the AI newsletter, The Algorithm, which was nominated for a Webby in 2019. Prior to joining the publication, she was a reporter and data scientist at Quartz and an application engineer at the first startup to spin out of Google X.

Karen can be found at:

Email:                                    Karen.Hao@TechnologyReview.com

Twitter:                                https://twitter.com/_KarenHao

Website:                              https://www.karendhao.com/

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

Quotes

“There's a rumbling in the AI research community that this can only get us so far because correlations aren't really, ultimately, the only element of human intelligence that we need to be able to replicate.”

"A lot of human rights activists and civil rights activists have pointed out that even if you make a highly, highly accurate face recognition platform, that doesn't necessarily mean that it won't infringe on people's civil liberties. Actually, in fact, having a highly accurate face recognition platform can do a lot to constrain people's civil liberties in ways that are being done both in the US and particularly in China.”

“…I think it is within the technologists' hands and within the consumer's hands to make sure that AI will be more beneficial to people. I think people should feel that they have agency to partake in this revolution and make sure that AI will be augmenting people and not replacing them.”

“The research community in AI is extremely global and extremely open. The community has always been founded on the idea of open collaboration, open sourcing, and a lot of the advancements that are currently happening right now or that we already see the products of from advancements prior came from significant collaboration across borders, particularly among American scientists and other scientists who immigrated to the US and work at American universities or Chinese scientists and American scientists at some of the biggest tech giants in the world.”

“Now that we've seen the fact that there are challenges currently in the way that AI is being developed, there's been a pretty strong reaction to making sure that they get it right, to correcting the path and making sure that they get it right.”

About Ayehu

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

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Links

Episode #1: Automation and the Future of Work
Episode #2: Applying Agility to an Entire Enterprise
Episode #3: Enabling Positive Disruption with AI, Automation and the Future of Work
Episode #4: How to Manage the Increasingly Complicated Nature of IT Operations
Episode #5: Why your organization should aim to become a Digital Master (DTI) report
Episode #6: Insights from IBM: Digital Workforce and a Software-Based Labor Model
Episode #7: Developments Influencing the Automation Standards of the Future
Episode #8: A Critical Analysis of AI’s Future Potential & Current Breakthroughs
Episode #9: How Automation and AI are Disrupting Healthcare Information Technology
Episode #10: Key Findings From Researching the AI Market & How They Impact IT
Episode #11: Key Metrics that Justify Automation Projects & Win Budget Approvals
Episode #12: How Cognitive Digital Twins May Soon Impact Everything
Episode #13: The Gold Rush Being Created By Conversational AI
Episode #14: How Automation Can Reduce the Risks of Cyber Security Threats
Episode #15: Leveraging Predictive Analytics to Transform IT from Reactive to Proactive
Episode #16: How the Coming Tsunami of AI & Automation Will Impact Every Aspect of Enterprise Operations
Episode #17: Back to the Future of AI & Machine Learning – SRI International’s Manish Kothari
Episode #18: Implementing Automation From A Small Company Perspective – IVM’s Andy Dalton
Episode #19: Why Embracing Consumerization is Key To Delivering Enterprise-Scale Automation – Broadcom’s Andy Nallappan
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 – Crown Prosecution Service’s Mark Gray

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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