How to Build an AI Team

Once viewed as a technology of the distant future, AI is quickly becoming an integral component of many an IT/business strategy. The rapid advancement of data science and machine learning technology, combined with the accessibility and affordability of artificial intelligence platforms in the cloud, are enabling companies in every industry to uncover new ways to extract business value from data. But in order to fully capitalize on AI, an organization must first assemble a strong team. Let’s take a look at three steps for creating such a team in your business.

Learn what successful AI looks like.

When establishing a department dedicated to AI, it’s important to recognize that successful artificial intelligence initiatives require a variety of different roles and skillsets. If you are focused solely on one role – data scientist for example – you will almost assuredly come up short. Instead, take a more well-rounded approach paying particular attention to three distinct areas: a person (or people) who can generate data, a person (or people) who can interpret that data, and a person (or people) who can make judgments about that data.

Recruit/train (and retain) top talent.

It’s no secret that skilled AI professionals are in high demand. In order to develop a good AI team, recruitment and retention are key. The good news is, you don’t necessarily have to look outside of your company to do so. In fact, developing AI talent from internal staff can be just as, if not more effective – particularly given the talent shortage. Investing in training and upskilling can produce a higher return on your investment than external recruiting.

And remember, it’s not just about assembling a team. You also need to focus on keeping turnover at bay. Offering things like professional development and autonomy can make long-term employment with you more attractive.

Tap freelancers.

What if your company simply isn’t prepared or doesn’t have the budget to hire a Ph.D. in computer science? What if your existing staff is too small, doesn’t have the potential or lacks the bandwidth to recruit internally? There are still other ways to get started with AI. Some organizations have had tremendous success hiring artificial intelligence specialists via online talent marketplaces, like Upwork. By eliminating the need to hire in-house, and all the ancillary expenses that come with such an arrangement, you can tap into global AI talent at an affordable price.

With Gartner forecasting that 85% of CIOs will be piloting AI projects by the year 2020, it’s abundantly clear that artificial intelligence is the way of the future. Having a team of skilled individuals dedicated to your AI initiatives can help you maximize the long-term benefits and give your organization the competitive advantage it needs to thrive in the digital era.

Ready to get started with AI but not sure how? For a limited time, we’re offering a free trial of Ayehu’s Next Generation Intelligent Automation platform. Use the full product for 30 days and don’t pay a penny. Get yours before it’s too late!

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Overcoming the AI Talent Shortage

Artificial intelligence has become a huge contributor in the battle for digital transformation. In fact, according to a recent PwC report, global GDP could reach up to $15.7 trillion as a result of AI. But no organization can fully realize the value of this technology without adequate talent at the helm.

The PwC report goes on to point out: “If your business is operating in one of the sectors or economies that is gearing up for fast adoption of AI, you’ll have to move quickly if you want to capitalize on the openings, and ensure your business doesn’t lose out to faster-moving and more cost-efficient competitors.’’

Thanks to this rapid development and adoption, however, many companies are now dealing with an AI talent shortage. This problem exists even in sectors where adoption is slower or the potential for disruption is lower. In fact, staffing skills are the number one challenge for the majority of CIOs looking to adopt AI. The firm also predicts that by the year 2020, 85% of CIOs will be piloting AI projects. In order for that to happen, something has to give.

Roles will evolve, but the need for people will remain constant.

There has long been whisperings that artificial intelligence will eliminate jobs and replace human workers. But while some tasks will certainly be shifted to machine, the need for humans will still exist. They will simply need to master new skills that will enable them to work alongside AI. Those skills, which cannot be replicated by machines, include creativity, communication, leadership and emotional intelligence.

Meanwhile, the demand for data scientists, AI and robotics engineers and other experienced tech specialists continues to grow, and at a fast pace. Unfortunately, given the rapid rate of change and low barrier to entry, these talented individuals are becoming harder and harder to come by. As a result, forward-thinking organizations are focusing their efforts to helping existing employees develop the skills they need to navigate the changing workplace landscape.

Building a Pipeline

One way that organizations are addressing the shortage of AI talent is to establish relationships with resources such as universities and trade schools. This enables them to engage in learning projects, become involved through speaking and mentorship and – most importantly – tap into emerging talent at an early stage, before students enter the workforce.

Internships provide valuable real-world experience and the opportunity to hone their skills and network with other like-minded professionals through meaningful, hands-on projects. The company benefits through the development and ongoing growth of a talent pool from which to draw. It’s a win-win.

Development from Within

The beauty of AI is that it’s a technology that can draw interest from individuals with many different backgrounds. For instance, people with strengths in math, statistics and engineering make excellent candidates for working with machine learning. As such, many companies are discovering that they are already sitting on a gold mine in terms of sourcing talent for their AI initiatives. Internal training and development can be an incredibly effective alternative to external staffing efforts.

For those organizations that lack existing talent or simply don’t have the capacity to transition current employees into new AI-related roles, there is also the option to hire for soft skills and train for the rest. For instance, many futuristic leaders are seeking out candidates that are highly collaborative, possess aptitude and are open to learning new things. Once hired, they can then work on growing AI experts from within.

Closing Thoughts

Whichever way you look at it, the AI talent shortage is very real and it’s not something that can easily be solved, at least not for the foreseeable future. Organizations looking to adopt AI and work toward digital transformation must begin thinking outside the box to solve their staffing needs. In many cases, that means making connections, nurturing relationships and building talent in-house.

If you are interested in adopting artificial intelligence for your business, a good place to start is with a great tool. Take our Next Generation AI platform for a test-drive, free for 30 days. Click here to get started.

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Intelligent Automation – Which Processes Should You Start With?

When discussing intelligent automation, one of the first questions (even before platforms) is – which processes should you automate?

Operational or Business Processes?

There’s a popular distinction between business processes and operational, or data center processes. A business process may be, for example, change management, escalation, notification, etc.

Operational/data center processes have stronger focus around IT operational systems and procedures – for example, backup and recovery, access management, etc.

The line between these two types of processes is not clear cut, and in many cases a process may be both operational and business oriented. Understanding exactly what you’re working with and what your goals are is key.

Top Areas for Intelligent Automation

It’s always interesting to see what others are automating – even though you have your own priorities and unique needs. A recent Information Week survey asked how critical was it to automate specific process areas? According to the survey, leading the pack were mostly hardcore operational processes, such as the following:

  • Backup and restoration
  • Disaster recovery
  • Service fulfillment
  • Incident management
  • Data movement

This seems natural, as most of these (with the exception of disaster recovery) are repetitive tasks that consume many hours from IT teams. In fact, some of these tasks are relatively easy to automate. We’ve rounded up 10 of the most popular processes that are ideal for intelligent automation.

Researchers have also identified the following 5 “key win areas” for automation, which provide fast and measurable value.

Service Desk
  • Change management
  • Configuration management
  • Provisioning
  • Routine maintenance
  • Identity and access management

So which processes should you automate?

Now that you know a bit about what others think should be automated, we’re back to our initial question – which processes should you automate?

As a first step, start by mapping your current processes and their key operational metrics for each of the service and business applications.

Once you create such a list, try to prioritize them based on the following two questions:

  • Time. What are the quantifiable benefits from automating each process? Consider how many hours are currently spent on the manual process – both by employees, as well by management.
  • Effort to Automate. What would be the required effort to implement automation, as well as maintain the automated process? Obviously, this question is more challenging to answer, since it requires a familiarity with one or more tools, and depends on the skills of your team.

Once you’ve answered these two key questions, it’s time to jump in and get started. The best way to do that is to leverage a platform that is designed to make implementing intelligent automation fast, easy and seamless. The good news is, you can now try Ayehu absolutely free for a full 30 days. Click here to download your trial today.

The 4 Secrets to Effective Digital Transformation

Often times, when it comes to solving a business problem or achieving a certain organizational goal, it makes more sense to begin with the end in mind. That is to say, you need to know what you’re trying to accomplish and what the journey to that objective is all about before you begin investing time, money and resources into new strategies and technologies. This is a particularly effective way to approach digital transformation.

Despite the overwhelming trend pushing businesses toward digital change, many have tried and ultimately failed, simply because they didn’t start the process with a clear understanding of what digital transformation truly is. When decision makers start with a tech-first mentality, they end up missing the point of what this transformation is actually about. To help you avoid going down the wrong path in your own digital journey, consider the following best practices.

Don’t lose the human element.

So many business leaders focus so much on putting all the latest and greatest tools, technologies and applications in place that they forget about the most important element – humans. Simply put, a digital strategy cannot be successful without a people strategy to support and carry it out. Look closely at the organizations that have made great strides in achieving digital transformation – like Pitney Bose and GE – and you’ll find that what they have in common is a staff of people who are curious, passionate and motivated to affect positive change.

Be customer-centric.

Being people-minded doesn’t only apply to employees, either. It’s equally important that you take into consideration the evolving behaviors and preferences of your customers if your goal is to drive digital transformation. The companies that have gotten it right have done so with an outside-in mentality, focusing first on the customer journey to identify things that are broken or missing, and then taking the necessary steps to fix those things from a digital standpoint. After all, if you’re not keeping your customers satisfied, what’s the point?

Make collaboration a priority.

While digital transformation may start with IT, it’s not a singular effort. To the contrary, for transformation to truly occur, it has to permeate the entire organization. Still, you will most likely begin smaller to start the process going. Undoubtedly, mistakes will be made, and from those mistakes, best practices will be discovered and defined. For optimal results, these trail blazing teams should play a pivotal role in helping to roll out change to the rest of the company through documenting, sharing and ongoing collaboration.  

Be unapologetic.

Let’s be honest. Change is hard. It’s scary. It can be uncomfortable. And as a result, it will almost always be met with some degree of resistance. If you want to forge ahead with digital transformation, you have to accept this fact, and then ignore the naysayers. Focus instead on the small group of innovators and early adopters. They will become the champions, influencers and drivers of change that you need to break through, overcome objections and achieve your end goals. Above all, be unapologetic. When you are doing what’s right for your employees, your customers and ultimately your business, everything will eventually fall into place.

Once you’ve got all these things in place – the people, the approach, the perspective and the attitude – you’ll be ready to take the next step toward successful digital transformation. And we’ll be ready to help you with that by placing the power of AI and machine learning in the palm of your hand. Check it out with an interactive demo or experience it firsthand with a free, 30-day trial of Ayehu.

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Episode #9: How Automation and AI are Disrupting Healthcare Information Technology – Change Health Care’s Andrew Brill

Jan 15, 2018    Episodes

Episode #9: How Automation and AI are Disrupting Healthcare Information Technology

In today’s episode of Ayehu’s podcast we interview Andrew Brill – Vice President of Engineering, Cloud Services, and RPA/IA for Change Healthcare.

How can automation in the highly regulated, fiercely competitive field of healthcare give an organization a leg up on its rivals?  What are the biggest quantifiable benefits a healthcare organization can expect to enjoy by deploying automation?  And what are some of the most effective tactics to employ in getting your organization to embrace automation, despite trepidations some may have about the possible repercussions to their jobs?

Andrew Brill is one of the most experienced and knowledgeable practitioners of leveraging technologies like automation, artificial intelligence, and machine learning in the healthcare field.  Having overseen the implementation of automation workflows in a very complex environment that have yielded hundreds of hours of savings, he’s learned a lot about what works, and what doesn’t.  Andrew shares his valuable insights with us, as well as why the proliferation of API’s combined with automation, could lead to some of the biggest opportunities yet for optimizing processes in any type of enterprise.

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 Andrew Brill, Vice President of Engineering, Cloud Services, and RPA/Intelligent Automation for Change Healthcare. Now for those of you not familiar, Change Healthcare, often abbreviated as CHC, occupies a unique niche in the healthcare market, offering lots of very different products and services for hospitals and health systems, physicians, pharmacies, and others that are really too numerous to list out here. But on their company overview page, they state that they “partner with our customers to reduce costs, create efficiencies, and effectively manage complex workflows.” Workflows, of course, are core to automation and given Andrew’s extensive experience overseeing the automation of workflows in CHC’s complex environment, we felt we could really learn a lot from him about how best to leverage automation in a competitive field like healthcare. Andrew, welcome to Intelligent Automation Radio.

Andrew Brill: Thanks for having me. It’s great to be able to discuss this with you and your audience.

Guy Nadivi: Andrew, let’s start off by learning a little bit about what you’ve done with automation. What kinds of processes has CHC automated so far?

Andrew Brill: Sure. Like most companies where the focus started in information technology teams, the first areas of automation were highly focused on the internal elements of how we run the IT shop. Whether those things are related to alerts and alarms from core systems or processing requests that come through from individuals, we’ve worked on a variety of those elements that were the highest volume and created the most value.

Andrew Brill: Many of those things in particular are things like disk space too full in a virtual system and resolving that, providing access to folders by requests from users from our ITSM tools, and placing those users directly into the folders that are requested, assuming that they have the right level of permissions and have been approved. And several other remediation actions by moving a ticket from a certain stage into another stage, based on rules, without too much human intervention.

Andrew Brill: Then there’s a whole number of regular run books that humans used to run, where we put that into code and, assuming that they meet all the criteria, more or less a gatekeeper will queue it into automation for resolution by assessing that all the information is in there. So we’ve gone after more than 100 of those different run books and put those into production, over the past few years.

Guy Nadivi: When you’re evaluating what to automate, what kinds of criteria does CHC apply to that process?

Andrew Brill: Everyone has different principles that they’re looking at, in terms of delivering their automation solutions to their company. Ours certainly was largely financial based. How do we take the cost out of running that particular area out of the business by reducing headcount and/or creating the alternate side, improving some revenue opportunity by, for example, getting someone onboarded quicker who’s working on development activities.

Andrew Brill: Then of course frequency is really critical. We want a high enough frequency so that we can essentially tick that box that we get the advantage each time. Capacity creation is a really fundamental part, also, of doing this work. It doesn’t always free up an entire resource, but if a resource is freed up some certain percentage of the time, then of course they get to do higher value work and that higher value work can directly relate to either revenue or, again, other opportunities, where they get to use the human brain more for their focus.

Andrew Brill: The other one is highly regulated areas. If we have an area where traditionally we’d have to have a human interact with a system but we prefer them not to have to see patient health information or the payment credit card information or other personal identifiable information, we can allow automation to interact with the details of the data and just really provide outputs, in terms of success or failure, alleviating the number of individuals who can see that information.

Andrew Brill: Those are all part of what goes into the criteria. Of course, things like variability and exceptions are a big part of it too, making sure that the variability in the particular automation isn’t too high and that the number of exceptions we have to account for aren’t too varied. So there’s some rules of thumb that we use. Some of those involved number of total steps, amount of time to actually deliver value, meaning how long it takes to code the automation, and then of course, lastly, the ability to support any ongoing changes that could occur as part of our established process.

Guy Nadivi: Of the manual processes you’ve automated according to this criteria, is there any one that sticks out as the most successful you’ve deployed so far? If so, why?

Andrew Brill: Sure. There’s a few that have really delivered incredible value. One of the things that we’ve seen at aggregate, instead of just a single one, is just time to deliver. Most organizations who don’t have automation measure their deliverables in a matter of days from the request or from the incident, not in terms of minutes and hours. That’s mostly due to things like staffing, or seasonality, or time of day. In our case, we had certain requests, I think one good example is a voicemail password that might have gotten away from a user, and they don’t realize what it is or it expired or too many failed attempts. They go in, and they request their voicemail password be reset, say on a Friday.

Andrew Brill: Well, that’s not something we necessarily staff for over the weekend. In fact, when you use offshore resources, you can have a time off shift a little bit impactful, if it’s sort of the start of the day, say on a Friday. On the West Coast, the time that it’s picked up by an offshore resource could be essentially Sunday night. So you’ve got this 48 hours or longer to wait to have that person attended to. Through automation, we’re actually able to handle a request like that within five minutes of the requester’s submitting a ticket, because we’re constantly pulling for those queues and looking for that interaction. So what really took days in now measured in minutes.

Andrew Brill: In general, we’ve actually been able to improve across about 100 different workflows. We’ve taken something to the neighborhood of 850 hours out of the time to wait in the average amount of monthly work that we’ve done through automation. That translates into all kinds of opportunities, depending on the situation, and certainly as it relates to things where we actually have to pay an SLA penalty if a particular situation occurs. We really allow for containing costs that really hurt the bottom line by keeping us in compliance and responding to a system incident or item much quicker.

Guy Nadivi: What do you think have been some of the biggest challenges you had to overcome in implementing automation at CHC?

Andrew Brill: Probably it was people and culture, more than technology. Really what had happened prior to my arrival is I think there were a lot of early starts in various automation, but people were nervous about what it meant to their job and their function. The idea of turning labor into code is not something that everyone’s comfortable with. Anyone who’s involved in automation needs to think carefully and understand the change management side of things. We’re not talking ITSM. We’re talking about people change management. Especially if you’re actually directly affective jobs.

Andrew Brill: One of the things that people have gotten used to over time is the labor arbitrage story, where individuals who do high cost labor in a first world market end up having to do offshoring to a lower cost provider. That’s understood, and it’s been going on for many years. The concept of resourcing changes that go to robots is a whole other level of human change that you have to account for. It brings up the whole concept of Skynet and other people, robot overlords that people get nervous about. When you think about if your role essentially is outsourced to a robot, what does that mean for you?

Andrew Brill: So you have to be prepared to understand what that means for you. Does that mean retraining? Does that mean capacity creation, for the individual to do other work? Does that mean you train them to handle exceptions for the robots themselves and the processes? If you’re not prepared for having those conversations, the change in the organization can be significant.

Andrew Brill: Information security concerns. Once you put the rules and passwords and other components directly into say a single system that was maybe spread out amongst multiple people, that is a good target for hackers. So info sec definitely wants you to take a strong and defensive approach against what could occur if that particular system performing by automation is compromised. So getting the right handles around your controls, password protection, and system hardening is really important.

Andrew Brill: That took us a while, because I wouldn’t say that that’s a well-established practice amongst all the automation options. Maybe the individual software has protections, but when you think of it as a total system, you really have to approach it with that security in-depth method. So it took us a while to hone in on that. Then of course affecting all the different things they needed through logging and audit and verification.

Andrew Brill: Then of course the last part was the individuals who would be experiencing the change as the requester, making sure they’re notified that their process is no longer Jimmy over there, who they would maybe tap on the shoulder to ask them to do a thing. Really Jimmy is not doing that work anymore. John is not doing that work anymore. There’s the human organization communications you have to undertake, to make sure that everyone is familiar with what’s happening.

Andrew Brill: All those were things that went into the work, long after we had finished the code. We developed the code in a matter of six weeks, but that change management took us maybe six months to go through and communications and all the verification.

Guy Nadivi: You talk about that change management in a transition to a self-help or a self-service kind of a paradigm. One of automation’s touted benefits is the ability to relieve the burden of incoming requests on the service desk and shift them to a self-service function that end users can execute themselves. Sometimes though, the adoption of self-service functions by end users can go slower than expected, as you suggested there, when there’s nobody left to tap on the shoulder to ask a question anymore. What strategies have you seen that can help accelerate adoption of self-service automation by an end user community?

Andrew Brill: One of the things we noticed is, actually remember I mentioned this voicemail password reset. We introduced a self-service voicemail password reset tool two years prior. The actual uptake of that was less than 15%. It’s been available, and those people know how to use it. They get their service at their convenience. There was training. There were lunch and learns. There is information on our main portal, as to what you get when you click the little button. The tool itself is fairly easy to use. Yet the adoption is quite low.

Andrew Brill: That creates a situation to decide how do you want to reduce that burden on the service desk. One of the things that we’ve actually done is to essentially remove the option. You cannot actually use the service desk to perform your password reset. The only option is for them to call in to report that the self-service tool is down. Essentially the link saying your IT service management platform to do a password reset sends you to the self-service portal. A menu option in your IVR, your voice response system that goes to the help desk when you choose to do password reset, it informs you that you need to go to this website. Lastly, you even take the responsibility away from help desk and service desk members. They can’t actually access the tool to do this, because you have to be an authenticated user, as that user, to do the reset. Sometimes you have to do that forcing function, where you just change the options for how they access the systems.

Andrew Brill: Secondarily, we have used the option where they can log a ticket, but essentially that action of doing the work is just queued up to an automation queue to take it, so that we are doing some triage with just making sure that that really is the problem for the user and it isn’t something else, maybe user error, and you need user education. We don’t want to eliminate the experience entirely, to talk to help desk about situations, since essentially sometimes they’re just not entering things correctly into a system.

Andrew Brill: Lastly, it is still just constant communication. You look at the metrics every month, and you think, “Where’s the best opportunity to get people to use the tools you have available?” And you work on a campaign. That campaign can happen one at a time, by informing the help desk to say, “Hey, have you used our self-help tools lately?” You can add it to your IVR system at the beginning of prompts, to remind them. Although it’s a bit nagging, these reinforcement activities work. Also we introduce new features, things we were talking about, like chat bots, if the interaction is smoother, we offer the sort of “as-you-work” options, have it your way option, to interact with it. It isn’t just a singular method, go to this website. There may be an introduction of an IVR system, or there may be an introduction of a chat system.

Andrew Brill: So those are things we’re exploring. We’ve tested a few options and really, at this point, it’s just about how well do these tools handle the various exceptions. We know they’re good when the user enters precise information, but as you get trickier with the natural language stuff, you’ve got to make sure the system is robust enough. Luckily in our company, we’re dealing with really just one language, which is English. When you deal with a multinational company with a lot of languages, there are other things that come about that I haven’t had to face too much here.

Guy Nadivi: You mentioned chat bots, which are part of a larger buzz we hear these days, about artificial intelligence and machine learning. I’m curious, what are your thoughts about how technologies like artificial intelligence and machine learning are going to impact automation?

Andrew Brill: Well I think behind the scenes they’re doing incredible work already, in making sure that entries are correct and that systems responses aren’t duplicated, and all the things that used to come up in a traditional event management view, where you’d have 150 of the same alerts, and then correlated alerts for the network switch, and then correlated alerts for the database server. Just reducing that noise, to recognizing the relationships of the systems and really trying to say, “Well, there’s an outage over here.” It’s power, and I see that over there. It’s because I know that power essentially controls all, or it’s network and I know that controls all. Those are some things that are happening behind the scenes, in some of the modern tools that are leveraging some of the machine learning and AI basic algorithms.

Andrew Brill: I think the opportunities in AI are really exciting for information security, because of patterns that are too low level for us to normally pay attention to and detect. So that’s a really exciting area that I think we’re going to see more and more from. At the same time, we have to be aware that the hackers are using some of the same technology, and they’re familiar with the technologies to try to thwart our attempts. It is a little bit of an arms race in most setups.

Andrew Brill: In IT operations and systems, I think there is going to be new opportunities that relate to understanding better about causality, the triggers that come from service views of things. As an example, what if you use a tool like Datadog and you’re looking at application performance management and you have some indicators around slow transaction performance, say for your shopping cart. You have other indicators of record locks on say your SQL database. Well that, there may be causality there. If you build enough of a database of let’s say information about what causes what, you can present better options to the operator or to the let’s call it automated operator to go after testing a few of those things out, by trying to perform an outcome.

Andrew Brill: The second part is, with the ML part, you can go ahead and do run books, and if the outcome doesn’t deliver the result as expected, let’s say it restores service, that data helps inform the system what other actions might have been taken to resolve that, by looking at the actions. Then hopefully the next many times later around, you end up with a better set of outcomes or option trees that it could go through. It could be a try this, that doesn’t work, then try this, that doesn’t work, then try this. As opposed to trying to run a specific thing and then have it fail and then just kick it back to the human operator to go ahead and do lots of troubleshooting. You might even end up with just a bunch of ticket enrichment data. I think sometimes just getting enough good data to the human can be incredibly helpful, in isolating and restoring service, if you’re dealing with an incident or if they’re trying to fulfill a request, essentially gathering enough information about the individual.

Andrew Brill: AI and ML do a little bit to help you understand things, without getting explicit always on all programmatic elements. You don’t have to build every element of the code because there is a model that represents the kinds of things you might need. I’m definitely excited about it. As a company, we have a dedicated organization that’s really focusing on it in our healthcare market and trying to deliver significant value, really high dollar improvements, in the way we do things, and adding those outcomes and making it better for our payers and our providers. So we want to do the same thing.

Andrew Brill: Obviously I’m not going to directly impact the top line by improving health outcomes with the IT automation, but if the service restoration is better or the service delivery is better, that reduces our operating costs and just improves the way we can deliver value to our customers, through operating efficiently.

Guy Nadivi: Given that CHC is in healthcare, a highly regulated field, I think the people listening who are also in that field would be very interested to hear how you feel automation has enabled CHC to better comply with regulatory standards like the Health Information Trust Alliance, or HITRUST.

Andrew Brill: Yeah, so one thing that’s really come about is, instead of these explicit organizational objectives like ISO 27001 or SOC 2, that dictate a whole bunch of very explicit things with a set of criteria that are success or failure, some of the HITRUST opportunity is a large gradient of improvements, where you get to score it against how compliant you are towards it and then the weighted average of all those things, that push you to get better in lots of areas. You end up with this really broad set of areas to improve on. If there’s an area you’re particularly excellent at, that helps bring you over the line for how we’re seen as compliant with HITRUST. There’s a minimum in all the areas, and most of them involve things like the opportunity to insure you can prove you’ve done whatever it is you say you’re doing.

Andrew Brill: One of the challenges with not automating something is you end up in a situation where essentially an operator has to pull screen shots of what it is they do. The documentation that goes along with their audit that’s done manually has to be done in sort of log books. With automation, you just end up with all those things just out of the box, time stamps, service accounts that can be accounted for centrally, the scheduling and proof of scheduling of the activity. You can show programmatically what you’re actually doing to check, and so you can submit code. It also makes it easier if you choose to prove some of this stuff through some level of de-identification, there’s an opportunity to submit some data to an auditor that has less sensitive information into it because you can do that programmatically.

Andrew Brill: A lot of areas, like quarterly access reviews, how we handle certificates, how we de-provision systems, how we provision systems, when we have the automation in place, it’s significantly easier to document that. On some level, it’s self-documenting, if you use a proper code pipeline and you use traditional quality analysis methods and you store things in repos. You end up having this nice archive of what it is you do, how you got it onto the system, and people can observe those through logging behavior of your system. It’s been a huge thing.

Andrew Brill: The other thing is, when you talk about the sensitivity to accessing systems directly through human eyes, when you remove those humans from seeing it, these job role matrix things, you have to worry about who has what level of access, it kind of goes away. You evaluate that at the service account level or the system account level. System accounts, obviously you want to protect those, but you don’t have to worry as much about that person is in payroll and they can do this, versus that person is in accounting and they can do that, or lastly, that person is in HR and they can do this because you have really that sort of “lights out experience”. You’re really only, again, dealing with sort of exceptions and logging of the outputs.

Andrew Brill: It doesn’t take all humans out of the equation, but it definitely reduces their exposure to some of that sensitive data. Your vector of and the blast radius of people who can see all this kind of sensitive information is now reduced to a very clearly identifiable set of trusted system administrators, whose activities are often very well logged.

Andrew Brill: At the same time, an organization has to think about other insider threat behavior. Those are things we all need to take very seriously. But with programs that are becoming well established in lots of companies, there are new methods to kind of track the behaviors of those who are handling that sensitive information through automation, too. We can handle it through lots of new means, versus the say 500 people who logged onto the website and did it. Now we’re talking about one system account performing those functions.

Guy Nadivi: Andrew, knowing what you know now, what advice would you give to IT executives considering taking the plunge into automation?

Andrew Brill: Well, I think I mentioned earlier this change management thing. It is not to be underestimated. I certainly over the years, if somebody had come to me and said, “The best way for us to run say capacity management is for us to essentially build a set of principles and a set of guidelines for how we’re going to do capacity analysis and then put that all into code and automate it, so the only thing you have to do is make some of those important human decisions.” I know as an analyst I liked to look at the data. I liked to touch the data. It made sense to me. It was what I enjoyed doing. So it fit really well for me that probably, as the guy involved in the organization, it was most likely that I was part of this classic Shirky Principle, that an institution is going to try to preserve the problem to which they are the solution. You have to be aware of that when you approach a group of people.

Andrew Brill: Sometimes the concept of automation by participation is going to be problematic. Maybe automation by proclamation might be a better approach towards some critical elements. Or rallying your organization around some key areas around either cost savings or regulatory or service improvement or capacity creation. Those are all things that we can focus on and get our heads around, at least at the leadership level, and then try to get everyone on the bus. So it’s really don’t underestimate the human change side of things.

Andrew Brill: Additionally, I would say the technologies that you already have in place change at a rapid enough pace that whatever automation you deployed some years ago does usually deserve to be revisited. Sometimes, also, you have probably crossed things off the list. You said, “Well, that would be very challenging to automate.” I can give you an example. I think about three years ago, Checkpoint, one of the largest firewall providers in the market, they had a firewall version that was really only available to be automated through an SDK. Through many, many years of work they’ve introduced software now that can be addressed through an API. So what that means is now pretty much all the major firewall vendors on the market now have APIs. You might have used to buy custom software to do that work, and there are companies out there who make their money just doing homogenous or heterogeneous firewall change management. That’s because not everything had APIs. Now we can revisit all that work and workflows and possibly readdress that. So that’s a very exciting time.

Andrew Brill: Then in general, table stakes, it’s important to start talking to all your suppliers and ensure that table stakes are that they have an API that is as robust as their graphical user interface. You can expect that from them. I haven’t had too many people come to me lately and say, “This is the best tool ever, and you should use our web interface. By the way, that’s the only way you can interact with our tool.” More and more they are saying, “If you don’t like our web interface, we have a whole web services framework that’s identical, and you can perform those same functions.” When that happens, we have a whole new set of options that can come through, and we can start thinking end to end.

Andrew Brill: Last, don’t just think about automation as tasks. They’re not just about tasks. Revisit the end to end process. See where the opportunities live, well into the entire lens of the customer making the request, or in the eye of the total outcome. Tasks are good. You can get some great chances, but the really big “moving the needle” opportunities are when you look at the entire process and look to see what can be done. Sometimes it’s not just about automating the technology. Sometimes it’s a combination of leading a process change AND introducing technology, so introducing concepts around lean or Kaizen that get you to the right set of behaviors that then you can touch and improve through an automation framework.

Guy Nadivi: Well, sounds like outstanding advice. All right, looks like that’s all the time we have for on this episode of Intelligent Automation Radio. Andrew, thank you so much for joining us today and providing some really great insights about automation in the healthcare field. We’ve truly enjoyed having you as our guest.

Andrew Brill: Well, thanks. I think I’m really excited to see what comes next from Ayehu. As a partner, it’s been really good to talk to you. Thanks for having me on.

Guy Nadivi: Andrew Brill, Vice President of Engineering, Cloud Services, and RPA/IA for Change Healthcare. Thank you for listening, everyone. Remember, don’t hesitate, automate.

Andrew Brill

Vice President of Engineering, Cloud Services, and RPA/IA for Change Healthcare

Andrew Brill is a senior IT Executive with extensive experience overseeing complex environments with multi-site staff and large operating budgets.  His specialties include managing Software Development, Robotic Process Automation, Technology Engineering, Network and Security Architecture, Data Center and Public and Private Cloud Design, Contact Centers and Call Center Integration, and Unified Communications.

Andrew can be found at:




“If we have an area where traditionally we'd have to have a human interact with a system but we prefer them not to have to see patient health information or the payment credit card information or other personal identifiable information, we can allow automation to interact with the details of the data and just really provide outputs, in terms of success or failure, alleviating the number of individuals who can see that information.”

"We've taken something to the neighborhood of 850 hours out of the time to wait in the average amount of monthly work that we've done through automation. That translates into all kinds of opportunities, depending on the situation, and certainly as it relates to things where we actually have to pay an SLA penalty if a particular situation occurs."

“The idea of turning labor into code is not something that everyone's comfortable with.”

“The concept of resourcing changes that go to robots is a whole other level of human change that you have to account for. It brings up the whole concept of Skynet and other people, robot overlords that people get nervous about."

“I think the opportunities in AI are really exciting for information security, because of patterns that are too low level for us to normally pay attention to and detect.”

“Obviously I'm not going to directly impact the top line by improving health outcomes with the IT automation, but if the service restoration is better or the service delivery is better, that reduces our operating costs and just improves the way we can deliver value to our customers, through operating efficiently.”

“A lot of areas, like quarterly access reviews, how we handle certificates, how we de-provision systems, how we provision systems, when we have the automation in place, it's significantly easier to document that.”

“Sometimes the concept of automation by participation is going to be problematic. Maybe automation by proclamation might be a better approach towards some critical elements.”

“Last, don't just think about automation as tasks. They're not just about tasks. Revisit the end to end process. See where the opportunities live, well into the entire lens of the customer making the request, or in the eye of the total outcome.”

About Ayehu

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



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Episode #1: Automation and the Future of Work
Episode #2: Applying Agility to an Entire Enterprise
Episode #3: Enabling Positive Disruption with AI, Automation and the Future of Work
Episode #4: How to Manage the Increasingly Complicated Nature of IT Operations
Episode #5: Why your organization should aim to become a Digital Master (DTI) report
Episode #6: Insights from IBM: Digital Workforce and a Software-Based Labor Model
Episode #7: Developments Influencing the Automation Standards of the Future
Episode #8: A Critical Analysis of AI’s Future Potential & Current Breakthroughs

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Disclaimer Note

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

Want to go green? Automate.

The term green IT became popular more than a decade ago. Since that time, however, it’s been mostly just giant IT organizations and large data centers that have implemented green IT practices  – and this was due more to regulations and the wish to cut costs than any internal agenda for reducing global warming.

Yet, according to Gartner, PCs, monitors, laptop computers and networked devices represent close to 60% of the total ICT consumption.

Considering the great interest in going green with IT, both from a financial and energy savings perspective, why so few projects?

The answer is simple. More importantly, it’s misleading many people. The main reason is that green IT is still not a top priority task is the erroneous notion that it’s too costly to implement.

“We are not that big to make a difference,” many IT managers would say. The truth is, going green can make IT operations much more efficient, regardless of the size of the company. Not only that, but it can also reduce costs significantly. And yes, it does contribute to a decreased carbon footprint and combined, it will make a difference.

What does go green with IT mean?

Simply put, green IT means having an IT infrastructure that consumes less energy and is subsequently cheaper. The next logical question is how it can be done – in particular, with minimal impact on your day to day work?

The good news is, there are several simple steps you can implement immediately:

  • Shutdown remote computers – Power-off servers that hardly work and consume more power than CPU. You can schedule a shutdown process on a daily or weekly basis for lab, tests, etc.
  • Automated remote workstation shutdown or standby – Turn idle workstations and user PCs to standby mode every time users are not using their computers – i.e. overnight, on weekends and holidays.
  • Go virtual – Take your physical servers and virtualize them, as one strong hardware can run multiple operating systems. You can turn servers off when they are not in use or start them only upon user request (usually this works for R&D, QA and lab testing).

How much you can save with IT automation?

Average energy savings per computer for a 12 month period can reach up to $36 each. Multiple this figure by the number of workstations, and you realize the potential may reach hundreds of thousands of dollars per year.

How to go green with IT Automation without adding extra work?

Use IT automation for scheduling maintenance tasks, and applying remote workstation shutdown or standby policies.  Workstations that are turned off not only save energy consumption but also increase data security and lower the possibility of failures.

Ready to give it a try? Claim your free 30-day trial of Ayehu and get started on your path to a greener, more efficient and much more environmentally-friendly IT environment.

Is Intelligent Automation Really Replacing Human Workers?

When intelligent automation first hit the market, some thought it was too far-fetched to ever become a reality. But as more and more organizations began recognizing the many benefits – from increased productivity and efficiency to lower costs and fewer errors – people started worrying, wondering whether this technology would spell the end of the human workforce as we knew it. Would artificial intelligence really start taking over jobs? To answer that question, those asking it must look inward.

In reality, the impact automation has on the workforce will depend largely on how humans themselves respond. When faced with the rising adoption of AI, workers will likely take one of two paths. The first group will continue to focus on the type of work they’ve always done, but do so more efficiently thanks to the assistance of machine learning. The second will take this as a golden opportunity to pursue their ambitions, further their education to broaden their skill sets, put their creativity and innovation to work and move on to more value-added, meaningful work. In either case, the organization will benefit, as will most of the employees.

In particular, roles that have a primary focus on people, such as customer support and HR, have the potential to benefit greatly from intelligent automation. Instead of being bogged down by repetitive, menial tasks that can easily (and more quickly) be handled by software, agents will be freed up to tackle more complex issues requiring a human touch. Furthermore, the improved allocation of resources afforded by AI will enable agents to prevent issues from occurring in the first place. This can dramatically improve both customer and employee satisfaction rating.

This concept can also be applied to the IT help desk. Rather than waiting until system problems arise and scrambling to fix them in a timely and effective manner, help desk agents can use the extra time automation provides them with to monitor and proactively address technical issues before they occur. Imagine how impressed the CEO will be when he gets a call from IT letting him know his hard drive was about to fail, but it’s been taken care of.

In both of these scenarios, the human worker is enhancing their interactions with their colleagues and/or customers. And since intelligent automation is there to take on the routine, manual tasks, the human agents themselves are also able to improve.

The reality is, very few organizations are focusing on using AI to eliminate jobs. Instead, they are focused on automating tasks, which in turn will improve productivity, streamline how work is completed, eliminate errors and cut costs. In other words, companies implementing automation are not doing so to replace human workers, but rather to augment and make their lives easier. As a result, everyone benefits – from employees and management to clientele and ultimately the organization’s bottom line.

Still not completely sold on the idea of intelligent automation and the value this technology can bring to your business? Don’t take our word for it. Try it for yourself. Click here to download a free 30 day trial of Ayehu. You have nothing to lose!

eBook: 10 time consuming tasks you should automate

3 Business Areas that are Ideal for Machine Learning

At the current rate, AI systems worldwide are on pace to hit nearly $50 billion in revenues by the year 2020. The proof is in the pudding. And if you’re not yet leveraging the power of machine learning, you can bet your competitors are. The good news is, you don’t need a massive budget or a team of experienced data scientists to start putting machine learning to use in your business. In fact, to follow are three practical areas where almost any organization can get started with ML technologies.

Internal/External Support

If you have an IT help desk for employees or a support team dedicated to customer inquiries, you have a great opportunity to leverage machine learning technology. Chatbots can be trained to handle everything from the most basic FAQs to complex issues, working in tandem with human agents.

Not only will a chatbot strategy free up your support staff to focus on more important business initiatives, but it’ll also improve service levels, so it’s a win-win. (Not sure where to start? Here’s a step-by-step guide to implementing bots along with some tips for what not to do.)


According to research by Ponemon, the average cost of a single ransomware attack is $5 million. And that’s just one strategy hackers use. If you think cybersecurity is not a big deal, think again. The problem is, cyber criminals are becoming savvier and using more sophisticated methods by the day. Staffing enough people to handle the onslaught isn’t just challenging. It’s next to impossible.

The good news is, machine learning can be used to augment your IT security team, providing an added layer of protection against potential breaches. Intelligent automation can work around the clock, constantly monitoring and analyzing mountains of data and identifying/addressing anomalies before they have a chance to wreak havoc.

Human Resources

While there are certainly areas of the human resources function for which a human touch is still needed, such as discussing sensitive matters with employees, the reality is, the vast majority of today’s HR processes and workflows can easily be automated.

For instance, machine learning algorithms can be used to weed through job applicants, saving recruiters time and aggravation, while intelligent automation can handle new employee onboarding far faster and more efficiently than a human agent could. To get you started, check out these 5 tips for optimizing HR with automation.

Of course, none of these things will be possible without the right technology. Thankfully, you don’t have to be an AI guru to leverage machine learning, nor do you have to hire a team of experts. In fact, you don’t even have to know how to code. Experience the power of plug-and-play intelligent automation by requesting an interactive demo of Ayehu or jump right in with your free 30-day trial today!

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Automation Engineers: 5 Essential Skills for Success

Everyone talks about the changes in the IT world – the increased complexity, the pressures to improve efficiency, and the need for tighter links between IT and business.

But how does this influence your IT personnel and their skill set requirements? Obviously, there’s a need for IT professionals with backgrounds in data center operations, systems integration, virtualization etc. Yet the demand for closer links between IT technologists and business operations implies new skills. Particularly with intelligent automation becoming an essential element, IT engineers need new skills beyond familiarity with technologies.

If in the past requirements focused solely on technical expertise and you were only looking for scripting wizards and troubleshooting superheroes – then today your IT group needs a much wider set of abilities. You need Automation Engineers who are able to understand the needs and processes of the business, translate those needs into IT activities, and prioritize and implement them in the most productive way.

So what are the additional skills an automation engineer needs? Here are 5 of the top ones in our opinion.

  1. Business perspective. A business/financial state of mind that enables the consideration and application of non-technical data inputs. For instance, figuring out key KPI’s affecting the IT project,  measuring return on investment (ROI), and optimizing an IT project implementation to successfully achieve financial goals.
  2. Process analysis. The ability to define and implement processes such as incident management, change management, operations, information security, business continuity & disaster recovery, and business service management.
  3. Project Management. Individuals who can not only oversee and monitor projects, but also identify business users’ needs and translate them into IT requirements. Automation engineers that can clearly justify how a business may increase its staff productivity and efficiency using different processes and tools.
  4. Process Implementation. Modern automation engineers must be capable of grasping end-to-end processes, have a deep understanding of workflows and the ability to create them in an automated environment.
  5. Interpersonal skills. The need for stronger communication with business managers requires solid interpersonal skills – i.e. the ability to communicate effectively with a wide range of people outside of the IT domain, understanding business peoples’ needs, concerns and different points of view, and that rare ability to negotiate and make compromises on both sides.

What skills do you think are required for a successful automation engineer? Tell us in the comments section. And don’t forget to download your FREE 30-day trial of Ayehu today!

Planning a Chatbot Strategy? Here’s What NOT to Do

When it comes to utilizing chatbots, there are plenty of resources out there to tell you what you should do, our own blog included. But as with anything in business, it’s just as important to know what not to do as it is to know best practices. By learning from the many common mistakes made by others, you can hopefully avoid going down the same wrong paths with your own chatbot initiative. That said, let’s dive into a few of those common mistakes below.

Not Gauging Need

Chatbots are great, but only if you’re using them the right way and for the right purpose. Adopting this technology just for the sake of it isn’t going to produce sustainable ROI, if any at all. To be successful with chatbots, you must first identify what you are trying to accomplish and what the desired end results should be.

For instance, are you trying to automate a simple process or are you looking for something more sophisticated, interactive and that will learn and improve over time? This will help you choose the right platform and strategize a plan for implementation.

Focusing on a Single Use Case

One of the trickier things about chatbots is that they are capable of far more than many business leaders realize. Unlike other packaged software and SaaS products, which are typically designed to meet a specific business need, the more a chatbot system learns, the more use cases it can take on.

For example, as a Q & A bot answers questions from customers and/or employees, its company knowledge and language understanding grow. As a result, the same core technology can be trained and used for a variety of different instances, thereby multiplying its value. If you limit your approach to just one or two use cases, you also limit the potential return you can achieve.

Overlooking the Human Element

With so much emphasis on training chatbots, it’s easy to forget that your human users also need to be brought up to speed. According to recent data, 43% of people who haven’t used chatbots yet are merely unfamiliar with the technology. And these aren’t tech illiterates, either. 65% routinely use SMS and 61% Facebook Messenger. They simply haven’t been exposed to chatbots nor given adequate guidance for their use.

Furthermore, even users who are familiar and comfortable with chatbot technology may need a reminder that it’s available. For instance, if a user only interacts with the IT helpdesk two or three times a year, they could easily forget that self-service bots are at their disposal. This is another powerful reason for leveraging bots for multiple use cases.

And on the other side of the coin, it’s also important not to overlook the value of the human connection. A shift to 100% chatbot support, for example, could result in frustration and backlash from end-users. Ideally, a bot-human relay should be established through which escalation from machine to human occurs when necessary.

In Conclusion…

With investment into chatbot development expected to top $1.25 billion by 2025, it’s clear that this technology is here to stay. Realizing savings and other benefits from chatbots, however, requires the right training and implementation. Knowing what mistakes to avoid, such as the three key areas above, can prevent your organization from having to deal with costly consequences.

The good news? You can now implement intelligent chatbot technology without the need to code or program. Resolve common IT actions, manage HR tasks, handle incoming customer support inquiries and more – and all via the interface of your choice. Click here to try Ayehu free for 30 days.  

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