Pursuing Digital Transformation in 2019? Here’s how to do so securely.

There’s a lot of talk about the topic of change management, and with so many of today’s forward-thinking companies going through digital transformation, mergers and acquisitions and any number of other updates, upgrades and changes, it’s for good reason. Keeping everything running as smoothly as possible is essential to a business’ ability to emerge on the other side stronger and even more successful. One such area of significant importance is IT security. If your organization is currently or will soon be navigating major changes, here are some specific tips to ensure that your critical data remains safe during the process.

Make it a top priority.

Regardless of what type of reorg you’re going through, the subject of cyber security incident response should be at the top of the list, and remain there throughout the entire process. Designate at least one individual (or preferably an entire team) whose sole purpose is maintaining maximum security at all times. If it’s placed on the back burner, your company will become vulnerable to impending risk and very likely to become a victim of a breach.

Plan ahead.

For situations, such as mergers and acquisition, determining whether there are any concerns with the other company’s cyber security incident response ahead of time is crucial, yet often overlooked even by top management and key decision makers. According to a 2014 survey from Freshfields Bruckhaus Deringer, an incredible 78% of respondents said cyber security was not carefully analyzed prior to an acquisition. Don’t make this same mistake.

Take advantage of technology.

Don’t leave the heavy burden of manually managing IT security on the shoulders of your technicians. Even under the best of circumstances, this task is monumental and impossible for humans to handle alone. Add in organizational change and you’ve got an entirely new and incredibly more challenging cyber security landscape to navigate. Use technology, such as automated incident response, to ease this burden and improve the chances of an uneventful transition.

Be aware of new targets.

A company going through major reorganization can be an attractive target for cyber criminals. In fact, even the very information surrounding the internal changes – such merger data and documents – may become a point of increased risk. The person or team charged with IT security should remain acutely aware of this information at all times and carefully monitor who has access and whether that access is legitimate. Otherwise, trade secrets and other confidential info could end up in the wrong hands.

Train and communicate.

It’s been said plenty of times, but it’s worth iterating again: cyber security incident response is everyone’s job – not just IT. Every employee should be trained on how to protect sensitive data and spot potential security concerns so they can be addressed immediately. Senior executives must also be involved in the cyber security discussion. When everyone takes some level of ownership, the risk to the organization as a whole can drop significantly.

Account for more exposure.

Organizational change often requires the addition of a number of external parties, such as lawyers, consultants, bankers and contractors. These additional people will ultimately mean greater exposure of sensitive data. This must be expected and adequately accounted for well in advance to ensure that all information remains as secure as possible throughout the entire transition. Again, the person or persons in charge of IT security should make managing access to information a top priority.

Is your company planning on rolling out some big changes in the near future? Is there a merger or acquisition on the horizon? Whether it’s adopting a new company-wide software product, making changes to corporate culture or partnering with another firm, the changes that will take place within can potentially leave you exposed to greater risk of a security breach. By taking the above steps and solidifying your cyber security incident response plan in advance, your company will be in a much better position to navigate the upcoming challenges and come out on the other side as a success story.

If you could use some upgrades, particularly in the technology you use for IT security and incident management, you can get started today by downloading a free 30 day trial of Ayehu.

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7 Time-Saving Tips for Busy IT Leaders

Without a doubt, the IT industry is one in which time is a precious commodity. It’s incredibly easy to become bogged down with the nitty gritty details and waste resources putting out fires to the point where other key areas of the organization begin to suffer. If optimizing your time is a priority for you (and/or your team), this article is for you. Read on to learn a few expert tips on how to find efficiencies, eliminate time-wasters and kick bad habits to the curb once and for all.

Tighten up your email practices.

Checking, sending and responding to emails is a huge time suck. But until it officially becomes a thing of the past, email is still something most IT folks will have to deal with. Optimizing your practices can make things more efficient. For instance, schedule specified time to manage email and use other communication methods, such as SMS, for urgent requests. Also, watch who you cc. If you’re including people on your messages who don’t really need to be included, you’re wasting your team’s time as well.

Ditch the waterfall.

Once a widely accepted project management methodology, waterfall has proven to be more of a hassle than what it’s worth, mainly because it can result in tremendous inefficiency. For instance, if developers discover something faulty with a previous step, the entire project must be scrapped and started afresh. And because testing doesn’t happen until later in the process, any existing bugs could have resulted in incorrect coding. If your team is still using waterfall practices, it may be time to consider making the switch to agile.

Expand your network.

It’s easy to feel as though the problems you, your team or your organization are experiencing are unique, but in reality most IT leaders are struggling with the same issues. Some of these other folks may already have figured out the best solution. Rather than wasting time, spinning your wheels and brainstorming on your own, why not tap into your network of peers. By leveraging the insight and advice of others, your decision-making will be faster and more on-point.

Automate.

To some, this one may seem like a no-brainer, but it’s surprising how many IT leaders are still dragging their feet on the automation front. Yet, when you look at the actual, quantifiable numbers, the benefits of automation and AI are staggering. According to a recent report by WorkMarket, 53% of employees say automation could save them up to 2 work hours per day (240 hours per year) and that number goes up to 3 work hours (360 hours per year) for 78% business leaders. At an average workweek of 40 hours, that equates to a time savings of 6 weeks for employees and 9 full weeks for leaders. What could you and your team do with that much time savings?

Scratch the standups.

Daily standup meetings may seem like a good idea on the surface, but when you gather your team on such a frequent basis, the results hardly make it worth the time. The real value of meetings lies in problem-solving, brainstorming and real-time collaboration. Daily scrum, on the other hand, tend to be more about status updates, which isn’t really the best use of anyone’s time. If daily huddles are currently your thing, you may want to consider spacing those meetings out and reserving them for specific needs rather than check-ins.

Fail fast and ditch what isn’t working.

Just because something’s always been done a certain way doesn’t mean it’s the best way. In fact, you or your team could very well be wasting precious time on practices and policies that are out-of-date and wildly inefficient. Agile isn’t just a methodology for project management. It’s also an important mindset – particularly for an IT leader. Make the coming year one in which you work to identify things that aren’t working and take the necessary steps to change them for the better.

Don’t be an island.

Just because you happen to be in a position of power at your organization doesn’t mean you have to solve problems entirely on your own. To the contrary, the most efficient and successful IT leaders not only value but actively seek the assistance of others. Think about it. You are already leading a team of educated problem-solvers. Your job should be to expose existing issues and then let the team determine the best resolution. Not only will this save you time and aggravation, but it’ll also enable you to develop a sense of trust and respect amongst your employees, which can go a long way toward retention.

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4 Hallmarks of a Successful AI Project

According to a recent Deloitte survey, 82% of early adopters of artificial intelligence are already realizing positive financial results from their investment, with a median ROI of 17%. What’s the biggest differentiator between a successful AI project and an unsuccessful one? The focus of the project itself. Organizations that get the most out of AI tend to focus on the specific business objectives that are to be achieved. They then use those results to prove value and scale up from there. If you’re looking for ways to translate AI into business value, here are a few things to keep in mind.

Target actual problems.

Business leaders may recognize the value of using artificial intelligence, but using AI well is where the true value lies. To do this, you must keep the focus on the concrete business problems that you are trying to solve. For instance, will implementing AI help you accomplish something in a way that is faster or cheaper? Will it help you generate more revenue? Can it be scaled? Adding business value should be at the heart of every AI project.

Understand and acknowledge limitations.

Ask an AI system that is trained on a particular set of data to make predictions based on an entirely different set of data, and chances are you’ll get a response that is completely incorrect. For someone who’s come to rely on those predictions, it can be easy to veer off in the wrong direction. To avoid misleading conclusions and misguided decision-making, make sure employees are trained enough to know which analytics model is the appropriate fit for the corresponding data set.

Listen to stakeholders.

Going back to the first point, successful AI projects keep the focus on actual business problems. Unfortunately, the individuals and teams spearheading these initiatives don’t always have the best insight into what those problems and needs happen to be. That’s why it’s so important to gather feedback and insight from key stakeholders – i.e. those who will be directly impacted by AI. Engage in detailed discussions with all interested parties right from the outset. This will save time and improve the outcome of the project in the long run.

Don’t underestimate the value of real-world testing.

The definitive proof of your AI project’s value will only become evident once it hits the real world. If you’re not prepared for this, your initiative is doomed to fail before it’s even begun. And while real-world testing is important for most new technologies, it’s especially beneficial for artificial intelligence. Why? Because early exposure allows more time for the tool to learn, adapt and improve. The sooner you can bring your project live, the sooner you can increase its ability and, ultimately, your ROI.

Artificial intelligence holds tremendous promise for businesses of every size and industry. Unfortunately, most organizations aren’t adequately positioned to take advantage. As an early adopter, the four factors above should enable you to transform your AI projects into quantifiable and sustainable business value.

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5 Surefire Ways to Derail Your Machine Learning Project

The process of implementing something new typically involves making mistakes, heading in the wrong direction and then figuring out a way to right those wrongs and avoid those risks in the future. Adopting machine learning is no exception. If you aren’t careful, the mistakes you make can become encoded, at least for the time being, into your business processes. As a result, these errors will occur at scale and will be difficult to control.

On the other hand, when you proactively detect errors and take the steps to address and correct them right away, you’ll have much more success with the technology. To follow are five common pitfalls that can wreak havoc on your machine learning project so you’ll know what to watch for.

Lack of clear understanding.

Simply put, you cannot adequately solve a problem that you don’t fully understand. The same can be said for machine learning initiatives. If you don’t completely understand what problem you are actually trying to solve, the risk of errors goes up exponentially.

To avoid this, begin with a hypothesis statement. Ask what the problem is that you are trying to resolve and which models you plan on using to address that issue. This is key, because if it’s not done correctly from the start, things can go wrong very quickly.

Poor data quality.

The old adage, “garbage in, garbage out” can easily be applied to machine learning projects. If the quality of the data you are supplying isn’t up to par, the outcome will inevitably suffer. In fact, poor data quality is one of the top concerns of data managers, as it can impact analytics and ultimately influence business decisions in the wrong direction.

The result of these poor decisions can negative affect performance and make it difficult to garner support for future initiatives. Exploratory data analysis (EDA) can help you proactively identify data quality issues so you can prevent problems before they occur.

No specific purpose.

Another common contributor to machine learning failure is implementation without a clear purpose. In order for machine learning to produce ROI, it must be applied properly – not simply because it’s the cool thing to do. In fact, using machine learning when it’s not the best solution to a problem and/or not completely understanding the use case can ultimately cause more harm than good.

In addition to addressing the wrong problem, doing so can involve wasted time and resources, both of which come at a cost. To avoid this, identify the precise problem and desired outcome to determine whether machine learning is the appropriate solution. 

Insufficient resources.

It’s easy to underestimate the amount of resources required to do machine learning right, in particular as it relates to infrastructure. Without adequate processing power, successfully implementing machine learning solutions in a timely manner can be a difficult, if not impossible feat. And without the resources in place to allow for its deployment and use, what’s the point?

To address the expense and complexity of deploying a scalable infrastructure, leveraging a cloud service that can be provisioned on-demand may be the better option. Those wishing to keep things in-house should look for a lightweight, plug-and-play solution that doesn’t require coding and can be deployed across on-premises and private cloud platforms.

Poor planning and lack of governance.

It’s not unusual for a machine learning project to start off with tremendous enthusiasm only to lose momentum and ultimately end up grinding to a halt. When this happens, poor planning and lack of governance is most often to blame. For those projects that don’t cease, a lack of guidelines and limits can result in an exorbitant expenditure of resources without the beneficial end results. 

To keep things moving in the right direction, machine learning initiatives must be continuously monitored. In the event that progress begins to wane, it can be wise to take a break and reevaluate the effort. Keeping people engaged in the process is the key.

Machine learning can be a tremendous asset to an organization, but only if it’s planned, implemented and managed properly. By avoiding the five common pitfalls listed above, you can place your company in a much better position and improve your chances of long-term, sustainable success.

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4 Cybersecurity Trends to Plan for in 2019

It’s that time of year again – a time to reflect on the past while also looking toward, and planning for, the future. As it has been in years past, the topic of cybersecurity will remain at the forefront for business and IT leaders in 2019 and beyond. As attackers continue to become savvier and their assaults more sophisticated, the methods used to defend against them must also continue to evolve. Let’s take a look at four trends that are likely to become the focus of the security industry over the new year.

AI will play a bigger role on both sides of the fence.

As the volume and range of security threats continue to increase, it’s become abundantly clear that the best and only suitable defense will be artificial intelligence. This is especially true since, historically, cyber criminals have access to the same or sometimes even better tools as the security folks. The only truly effective way to combat cyber-attacks in 2019 will be to leverage AI-based security solutions. In other words, organizations must be prepared to fight fire with fire if they are to keep sensitive data safe.

Biometrics will become more widely adopted.

The Face ID feature of Apple’s iPhone X has made facial recognition relatively mainstream. Given the fact that passwords continue to be one of the most vulnerable areas of a business, we can expect to see biometrics become more widely adopted as a safer, more secure alternative. One brand leading the pack is MasterCard who will begin requiring biometric identification of all of its users beginning in April 2019.

Spear phishing will become even more targeted.

Cyber criminals understand that the more information they have about a potential victim, the more effectively they can design spear phishing campaigns. Some attackers are already developing newer and more disturbing ways to enact their plans, including hacking into a victim’s email system, lurking and learning. They will then use what they learn to create incredibly realistic messages that appear to be from a trusted source. Security personnel must remain especially vigilant to protect against these sophisticated and costly attacks over the coming months.

Advanced cybersecurity training may become a requirement for the C-suite.

The training surrounding cybersecurity will continue to advance and mature. As such, certifications may no longer be sufficient for a security professional to progress in his or her career – at least not at the upper management or C-suite level. This is supported by the growing number of degree programs that are devoted to cybersecurity. The companies of tomorrow looking to hire CSOs and CISOs will likely require some type of higher education as it relates to infosec.

What about you? Do you have any bold predictions about what the future has in store for cybersecurity? Tell us your thoughts in the comments section below!

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Episode #7: Developments Influencing the Automation Standards of the Future – IEEE’s Lee Coulter

Dec 14, 2018    Episodes

Episode #7: Developments Influencing the Automation Standards of the Future

In today’s episode of Ayehu’s podcast we interview Lee Coulter, CEO of Ascension Shared Services, Chair of the IEEE Working Group on Standards in Intelligent Process Automation, and Chief Intelligent Automation Officer of the Shared Services and Outsourcing Network.

Should automation projects be a business activity or an IT activity? What differentiates Bot 1.0 from 2.0 & 3.0? What is task harvesting? Why do less than 5% of organizations have an automation strategy, when automation is clearly such a major component of enterprise digital transformation?

As Chair of the IEEE Working Group on Standards in Intelligent Process Automation, Lee Coulter is in a unique position to answer these and many other questions about automation.  Lee talks with us and shares his insider perspective of where automation is heading, what you’re really buying when you invest in an automation platform, and what the one essential skill is that will set apart successful individuals in a highly automated future.

Lee Coulter



Guy Nadivi: Welcome everyone! Our guest today on Intelligent Automation Radio is Lee Coulter, the CEO of Ascension Shared Services, and Chair of the IEEE Working Group on Standards in Intelligent Process Automation. Now, for those of you who may not be familiar, the IEEE stands for The Institute of Electrical and Electronics Engineers and the IEEE Working Group that Lee heads up is tasked with developing standards for, and I’m paraphrasing from their web page, the complex execution engines with fully developed management platforms, often coupled with increasingly sophisticated rules engines, analytics, machine learning, and cognitive computing, that are performing tasks previously requiring human operators. Collectively, this capability is known as Software Based Intelligent Process Automation or as abbreviated by IEEE, SBIPA.

Lee is also the Chief Intelligent Automation Officer of the Shared Services and Outsourcing Network, SSON. Recently, SSON published a report on Global Intelligent Automation with some very interesting findings. And so we’ve invited Lee to come onto our show and talk with us about the report.

Lee, welcome to Intelligent Automation Radio.

Lee Coulter: Fantastic, happy to be here.

Guy Nadivi: Your Global Intelligent Automation Market Report describes those embarking on process automation right now as “early majority adopters” which you distinguish from the “early adopters” who started three years ago. And this nomenclature sounds borrowed from the classic 1991 high tech marketing book titled, “Crossing the Chasm” by Geoffrey Moore. So if you believe we’re in the early majority phase of the market then does that mean you think automation has successfully crossed the chasm from the visionaries to the pragmatists?

Lee Coulter: Yeah, that’s a great question and I guess I’ll make a short answer and then add to it. And I would say, “Yes.” In the beginning if you look at the three unicorns of RPA, they’re all a decade or more old. So they’ve been out there for quite a while and the early adopters were 2010/2012 and in fact, we were one of the first, we were the first North American client for one of the big three RPA providers.

And at that time it was very much innovators and really learning how to do automation. It was a time when if you had a question there was really no one to ask. You got together and came up with the best possible path forward and we actually were involved in some co-IP development with some of the large consulting firms out there. And the chasm, have we completely crossed it? No. But are there lots of organizations that have? Yes.

Can I say that I feel firmly that we are standing on the leading edge of the early majority? I can. Two years ago a large consulting firm actually had published some articles about the fact that this was all a fad, and that it was going to go away, and 50% of them were failing and a lot has been done to understand why a lot of early implementations were not working out as well. And I think that there’s been enough experience over several thousand implementations worldwide that folks now know how to use this stuff. And get significant value from it.

Guy Nadivi: The report you put together is based on “real experience coming from thousands of successful, stalled, and doomed programs”. Can you please tell us about some of the more interesting automation programs you encountered that were successful?

Lee Coulter: Yeah, you know it’s always … I surely can. And what’s obviously more fun of course, Guy, is those that were doomed or explosively doomed in some cases. But those that were successful had a couple of key features that really made them different and it’s first important to understand that automation is a business activity. It’s not an IT activity. And I try to explain to folks that when purchasing an automation platform, you’re not actually purchasing a piece of software that has business logic in it.

If you go buy sales automation software or your ERP or your collection system or your payable system. These all come out of the box with business logic in them, and an automation platform is kind of like, it’s a box of potential and when that potential is guided by the business leaders, by the folks in the operation, a tremendous amount of configured business logic can now be put to work as digital labor in the enterprise. So successful programs first have business driving the program.

Second, I guess hallmark of successful programs is a mindful launch trajectory, and what do I mean by that? It means that the pilot was well conceived. A process that was chosen was not something that was regulatory or compliant influenced. It wasn’t a process that had 25 exception paths through it. It was something that was reasonably appropriate to prove that business could automate.

And the successful programs typically have a CoE (Center of Excellence) around them, which provides design authority, and change management, and interface with controls and audit, interface with IT. And all the supporting structures that need to make the program on an overall basis successful. So those are some of the successful programs. This is not … It’s not a panacea. It’s not a get-rich-quick scheme. And it’s not, as some would have us believe, something that you just open the box and automatically you’re automating stuff.

This is a serious technology that can perform serious duty in the enterprise but it does require competent management of it as a program to be successful with it.

Guy Nadivi: You alluded to some of the doomed ones and I can imagine you’ve probably come across some real humdingers and in fact, in your report you explain that the greatest cause of failures in proofs of concept or pilots, can generally be traced back to one of two major mistakes and both have the same root cause. Can you talk about those a bit and how they should be avoided?

Lee Coulter: Sure. In the study of 88 failed programs, there were really two populations of failures and in one of them in about half of that population the failure was directly because IT led the program. And that’s not to say that they were not smart people, that they weren’t a good IT function, but they weren’t the business. And automation is about digital labor. It is about executing the operating processes of the enterprise. And that is not IT’s role.

And so when IT approaches a software purchase, they develop an RFI, and they do a beauty pageant. Then they do an RFP, and then they compete everyone, and then IT procurement gets involved, and it really becomes a very expensive, very public labor-intensive effort. And then because an IT project has to have an ROI, well the most complex process was chosen that’s 160 steps long with 20 different exception paths. And so you go through six or nine months of procurement, and then another six months of building, and then it doesn’t work, not surprisingly, because a whole lot of “becauses” are in there.

And the second major population, which was about 80% of the other 50%, it was because the business treated the program like a standard software project. And here again, same series. And now in this case we had appropriate leadership and sponsorship from the business, but with a lack of recognition that this is a business activity, that the creation of configured pieces of automation and the management of them is very much a business activity and it requires your subject matter experts to be significantly available to the effort.

It requires, if you’re an agile shop, it requires regular access to the users and the processors, and the analysts, and agents of the organization doing the work. So really, as I mentioned a little bit earlier, the fact that this is a piece of enterprise software that doesn’t have any business logic per se in it is really fundamentally different, and it requires a different programmatic approach that really … this is the sort of thing the chasm of despair really was filled with, a lot of these failures that can be traced back to these kinds of same fundamental faults.

Guy Nadivi: Lee, you’ve described the evolution of intelligent automation’s history as a 3-tiered progression from Bot 1.0 to Bot 2.0 to Bot 3.0. And you say that Bot 1.0 is the early years which includes focusing on automating basic, repetitive, recurring activities which you refer to with a great phrase, “task harvesting”. Bot 2.0 is a more mature state with additional tools, increasing numbers of bots, and some experimentation with cognitive solutions. Can you please describe what you believe is Bot 3.0, where we are today, and how it will benefit organizations deploying automation?

Lee Coulter: Sure. I do want to emphasize that there’s nothing wrong, and in fact I really do encourage folks to move through 1.0 and 2.0 to get to 3.0. Jumping straight to 3.0 is a risky place to be.

There are a lot of organizational capabilities, and organizational learning as you move through the early efforts of task harvesting. Here again, task harvesting is wonderful. Saving money and learning how to automate is important, also as you build your CoE.

When you start to get into Bot 2.0, now you realize that you don’t have as many straight-through processing processes as you thought. As you begin to do the really detailed kinds of business analysis for automation, which it’s a specific discipline. It’s a new discipline. If you follow the CMM process levels, it’s a Level 7 process discovery, which is very, very different. Part of the enterprise learning here is exactly how many times a process starts and stops, and how many different operands and condition sets are needed in order to get a process on an end-to-end basis, through to completion.

You start in Bot 1.0 with picking just chunks of a process that are straight-through processing, or STP. Then when you get to Bot 2.0, you’re really looking at, OK how do I start to engage a rules engine? Can I pause the automation and execute a set of queries, do some assessment against what was returned from the query in order to determine a current condition set? This would be your basic decision making capability in automation.

Bot 3.0 is just really beginning to, even for the early adopters, this is fairly new stuff. It basically, Bot 3.0, is when you have sufficient data to build predictive models that reach a level of confidence and a sufficiently acceptable level of false negatives and false positives that you can use that prediction on the next best action to actually take the next best action. Here again, it’s an iterative process. This isn’t a case of where you build one model and everything is now being aided by machine learning. This is a case of where you’re really, through deliberate planning and execution, begin to introduce prediction and prescription such that over time, some of your most basic decisions for next best action can now be taken over by technology.

I do believe that there will be a Bot 4.0 as well in which certain knowledge domains will be … The data models will be licensable, as opposed to needing for folks to build them. But once we now cross the line into Bot 3.0, what we really have now is the ability for predictive models to begin to orchestrate automated tasks without human intervention. That’s really, that’s the holy grail. That is the intelligence process automation, as opposed to task automation.

Guy Nadivi: Yeah, I think that automation is such a different paradigm, it really requires a different way of thinking, and that really leads into the next question I have for you, which is regarding the fact that your report stated very few organizations today have a Head of Automation. Without one, it will be difficult elevating automation to a strategy from a tactic. In fact, you estimate that today less than 5% of organizations have an automation strategy. What will it take to change market sentiment so that we start seeing more Chief Automation Officers in the enterprise?

Lee Coulter: This is a really interesting one and was kind of a surprise. But once you learn it, then you say, “oh well that kind of makes sense.”

I’ve had a long standing saying as a matter of leadership, which is if you want something done, make it someone’s job. This is very much a case of that idea. In so many enterprises, automation got a foothold in the operation probably somewhere in lower middle management as a tactical response to the relentless incremental cost reduction expectations. You need to take out that … You get no inflation and you need to give me 5% year-on-year, and how are you gonna do that?

In a lot of organizations, automation was brought in as a way to harvest those tasks, introduce digital labor to the enterprise, really as a way to satisfy that need for consistent year over year productivity. What happens to organizations as they begin to mature, they realize that digital labor is capable of a whole lot more. There’s an interesting inversion that happens here.

You start off by asking yourself, where could I use human labor, or digital labor, to do the things that human labor is doing today? When you invert that, you ask the question, where could I deploy digital labor to do jobs that no human could or would ever do? That opens up a box into the world of strategic automation where by use of automation you may change customer experience, which may change the costs of your customer acquisition, may reduce the customer turnover, may improve customer experience at the most basic level, can impact your reputation.

I use the example when a single defect can have a reputational impact, and we’ll use Uber as the example or Tesla, where single defects have a very significant impact. Automation can be used to deliver those sorts of strategic benefits that typically happens after you have achieved a certain level of financial return with it. But what we see is that there’s a correlation between the organization installing a person whose job it is to put automation to good work, to deploy digital labor. When it’s that person’s job, they’re exploring it from both the tactical and the strategic perspective. They’re considering speed, quality, experience, obviously cost, but asking the question about what is the impact to efficiency, and what is the impact to effectiveness of a process? What could digital labor do? That’s really what we see is if you have a Head of Automation somewhere, you’re more likely to have a strategy. Same thing goes if you have a strategy, very often it is a person who has been tasked with leading that strategy.

Guy Nadivi: I found one of the more interesting perspectives your report offered was with regards to procurement departments. You advocate procurement departments start seeing themselves as creators of business value and profit drivers themselves, rather than just as a support function. You advise them to stop viewing automation as an enabler for a specific project, but rather as a catalyst for enterprise-wide digital transformation. In your experience Lee, how prepared are typical corporate procurement departments to adopt this shift in mindset, and can they be incentivized to do so?

Lee Coulter: You know, Guy, this is a really … It ties back to the earlier dialogue with the core reasons that a lot of programs fail. I literally have seen examples where organizations have spent 400 to 500 thousand dollars procuring a 200 thousand dollar piece of software. And because I described an automation platform as a box full of potential, procurement organizations really don’t know how to buy a box full of potential. It’s kind of like buying a box of random Legos, and your kid promises that they’re gonna build an Eiffel Tower, and a model of the White House, and the Golden Gate Bridge, and it’s gonna generate all this value. But really, it’s a tool in the hands of the business.

Procurement organizations wrestle with how do I know what’s an appropriate price? How do I compare licensing models? How do I compare actual technology capability? Interestingly in the new IEEE standard 2755.1, which we are working very, very hard to get published before the end of this calendar year, even to be able to say what differentiates one product from another is a bit of a challenge, and the new standard will bring a lot of clarity to that, defining about 165 features and functions of this category of tool in general.

Coming back to your question on procurement organizations, this is another place where the business really has to drive the process. If the enterprise lets the procurement engine, it’s like putting a train on a track. You know where the track leads, but in this case, you need the track to go somewhere else and you might have to lay some track. So, it can, procurement organizations can rise to this challenge, but they need to do so consciously and with the close alignment of their business sponsor. If the organization already has an IT procurement specialty organization, as many large enterprises do, looking for the right procurement leaders to educate as to the difference in this exercise before the process begins is critical.

Guy Nadivi: There’s a section in the report also about automation anxiety, or something I started referring to with the term robophobia. And, you advise that the way to win over resistance within people is by focusing on influencing rather than conquering them. Lee, can you please describe how best to win over someone who sees automation as an adversary rather than an ally?

Lee Coulter: So, this is…it really, Guy, goes back to the fundamentals of organizational change management, or just human behavior in change management. And what we’re doing here is we’re changing paradigms for people. And, in the beginning there’s lots of excitement. It’s like oh that copy and paste thing that I used to have to do I won’t have to do that anymore, and I’m so excited that I won’t have to do that anymore. And then as time goes by people begin to realize that as people are leaving that department isn’t back-filling anymore. And, every two to four weeks I’m now having to be trained on how the new process works because incremental portions of it have been automated. And now I’ve got a supervisor who is responsible for the output of a process or group of processes who used to be able to know how work was going by going and talking to her team, and being able to see the work being done. That same supervisor also had a sense of value and a sense of contribution based on the number of people being managed.

So, we’re fundamentally changing the nature of work in a way that has a result in something called “cognitive load concentration”. But people that had very transactional jobs suddenly find themselves confronted with the likelihood that they’re going to be asked to do more complex work that has a greater cognitive load. We’re finding that we’re asking supervisors and managers to be responsible for both the humans that they manage that they can see, and the digital labor that they are managing that they can’t see. And, this whole thing is automation anxiety, and it’s very real. And, there are even some charts and graphs out there that kind of show you over time when you should expect to see it. And, it is literally a limiting factor in how quickly automation can be injected into an organization.

So, when you begin a program having your messaging already thought through. Guy, I know hundreds of people in this industry, and outside of some commercial BPO’s I don’t know anybody who’s running a program that has resulted in job eliminations. And, being able to say that to your folks as you begin is a key part of messaging. Talking about retraining, and upskilling, and how the organization will keep your folks relevant as the works changes, these are really important parts of change management and getting the human behavior into this new world of rapidly changing delivery process and this mix of human and digital labor.

Guy Nadivi: And that’s a great lead-in to the last question I wanted to ask you about reskilling or upskilling. You advocate for organizations to include reskilling or upskilling in their automation strategy, as it will instill confidence in the people being asked to incorporate automation into their work. What about the people who want to change their roles by actually delivering automation to an organization? What do you think are the most important new skills they can acquire to make this transition?

Lee Coulter: So, this is a fantastic question because it really speaks to the changing face and work mix that we’re now facing. If you look at the literature from 2014/15, it was 169 million people are going to be out of a job, and here we’ve ranked 700 different jobs in terms of their propensity to be automated away. And that rhetoric has pretty much gone away and been replaced by a much more sane and rational analysis of the tasks within a job that are automated, or the tasks within a role that are automated, or the roles that a person may play throughout the day which have some level of automation potential.

So, no question that becoming more familiar with automation and the role of technology is important, but I would recommend to people that they become versed in process improvement capabilities. Whether it’s Lean, or Six Sigma, or Kaizen, or it’s a business analysis for automation, they all have in them a lot of fundamental tools and skills that cross over here. But, the successful contributors of the future are going to be able to understand how a process gets done and understand how different kinds of optimization can be put to good use. Whether that’s automation, or information, analytics, decision-making visibility, etc. So, becoming more versed in process management overall, learning the skills of the next job up on your job ladder. Whatever that is. Beginning to pay more attention to that. In some cases that means folks will have to take a more active hand in managing their own skill base if their organization isn’t already planning for reskilling and upskilling.

There also needs to be an organizational conversation about the willingness to make an investment in your people. So, some parts of an enterprise are very significantly automatable and you will need significantly less folks in those functions in the future. So, is the organization willing to have somebody work part-time while they return to school to get a certificate, to learn a new skill? Will the organization invest in trainers to come in and on company time give associates and employees new skills? So, I guess becoming involved in the work of process management and process improvement, or looking up the value chain in your own career ladder in your job family are the areas that I would urge folks to concentrate on new skills development.

Guy Nadivi: Alright. And with that it looks like we’ve run out of time for this episode of Intelligent Automation Radio. Lee, thank you so much for joining us today and sharing your thoughts on the global intelligent automation market. It’s been very interesting and I’ve really enjoyed having you as our guest.

Lee Coulter: Thanks so much Guy. I really had a good time.

Guy Nadivi: Lee Coulter, CEO of Ascension Shared Services, Chair of the IEEE Working Group on Standards in Intelligent Process Automation, and Chief Intelligent Automation Officer of the Shared Services and Outsourcing Network. Clearly a very busy guy. Thank you for listening everyone, and remember – don’t hesitate, automate.



CEO of Ascension Shared Services, Chair of the IEEE Working Group on Standards in Intelligent Process Automation, and Chief Intelligent Automation Officer of the Shared Services and Outsourcing Network.

Lee Coulter is a change leader with expertise in Intelligent Process Automation, shared services, BPO and technology innovation developed over 30 years in leadership positions at many large companies including General Electric, AON, Kraft Foods,and Ascension (www.ascension.org)

Coulter is currently Senior Vice President of Ascension and Chief Executive Officer of its shared services subsidiary, the Ascension Ministry Service Center. Ascension,the nation’s largest non-profit health system, tasked Coulter with creating a captive BPO service business as part of its landmark transformation initiative.

Previously, he was Senior Vice President of Global Shared Services at Kraft Foods, leading global IT and implementing more than 100 shared services across nine functions, and completing $4B of global ITO and BPO deals earning Chairman Roger Deromedi’s “Play to Win” award.

Prior to joining Kraft, Coulter served as Vice President of Global IT Service Delivery at AON Corporation. There he cut costs 35% in a global IT transformation leveraging ITO, ITIL, and service excellence.

Coulter’s 15 years at GE culminated as a Vice President in GE’s IT outsourcing business. He spearheaded the company’s global Y2K initiative and help multiple operations leadership positions in GE Healthcare and GE Capital businesses around the world. He received the President’s Award from GE CEO Jeff Immelt. He is a sought after speaker in the areas of automation,shared services, business process transformation, strategy, and service excellence.

• Chair, IEEE Working Group on Standards in Intelligent Process Automation
• Chief Intelligent Automation Officer, Shared Services and Outsourcing Network
• Board, HfS Future of Operations in a Robotic Age (FORA)
• Member, Diamandis’ Abundance360
• Co-Chair World BPO/ITO Forum
• Global Steering Committee, Shared Service and Outsourcing Network
• Executive Advisor, Frontier Strategy Group
• Founder and Advisor, Agilify

Lee can be found at: 

E-Mail:            lee.coulter@ascension.org

Twitter:           @rleecoulter

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

Quotes

 “…it's first important to understand that automation is a business activity. It's not an IT activity. And I try to explain to folks that when purchasing an automation platform, you're not actually purchasing a piece of software that has business logic in it.”

"… an automation platform is…..a box of potential and when that potential is guided by the business leaders, by the folks in the operation, a tremendous amount of configured business logic can now be put to work as digital labor in the enterprise. So successful programs first have business driving the program."

 “…successful programs typically have a CoE (Center of Excellence) around them, which provides design authority, and change management, and interface with controls and audit, interface with IT.

“I've had a long standing saying as a matter of leadership, which is if you want something done, make it someone's job."

“In so many enterprises, automation got a foothold in the operation probably somewhere in lower middle management as a tactical response to the relentless incremental cost reduction expectations.”

 “In a lot of organizations, automation was brought in as a way to harvest those tasks, introduce digital labor to the enterprise, really as a way to satisfy that need for consistent year over year productivity. What happens to organizations as they begin to mature, they realize that digital labor is capable of a whole lot more.”

“I described an automation platform as a box full of potential, procurement organizations really don't know how to buy a box full of potential.”

 “I know hundreds of people in this industry, and outside of some commercial BPO's I don't know anybody who's running a program that has resulted in job eliminations.”

“So, I guess becoming involved in the work of process management and process improvement, or looking up the value chain in your own career ladder in your job family are the areas that I would urge folks to concentrate on new skills development.”

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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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

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

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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

Could Self-Service Automation Be Your Saving Grace?

Self-service automation has become quite the buzzword amongst IT professionals, and for good reason. Simply put, this intelligent technology is revolutionizing the way organizations operate and dramatically improving the way employees perform their jobs. There are a great number of benefits to self-service automation, including helping the IT department save time and money while also empowering end-users to resolve their issues instantly, without the need to involve the helpdesk. As such, productivity and efficiency levels rise across the board.

But what, exactly, is self-service automation? Well, in the most basic of terms, this type of automation allows non-IT workers to proactively perform routine, repetitive and ad-hoc processes. These tasks could involve anything from new-user onboarding and generating reports to resetting passwords and performing system restarts. Previously, employees had to rely on IT each time one of these functions needed to be executed, which resulted in unnecessary delays, subsequent dips in productivity and, of course, frustration on the part of both parties.

With self-service automation in place, IT can establish a library of automated tasks, processes and workflows that can be easily implemented by the end-user community without the assistance of the tech team. By shifting these routine but necessary tasks to the end-user, IT personnel is then freed up to focus time, effort and resources on more critical business matters. Likewise, by eliminating the need for helpdesk involvement, employees are able to get their issues resolved faster, which reduces delays and promotes a greater degree of productivity.

Some folks in the IT realm are still on the fence about this technology, fearing it will ultimately make their jobs obsolete. In reality, while automation will indeed replace at least a portion of tasks and possibly eliminate some lower-tiered roles altogether, it will also create new opportunities for those in IT to further their skills and education, making them more valuable as an employee in the long run. Thus, self-service automation shouldn’t be viewed as a threat, but rather as a tool to make life better for everyone.

Self-service is also addressing the skills gap that currently exists in IT. Whether it’s a smaller to mid-sized company that can’t afford to keep a large IT department on staff due to budget restraints or a larger enterprise that simply cannot keep up with the increasing demand that is stretching even the most well-staffed IT department too thin. Taking those smaller, menial tasks off the plate of the tech team provides for a better allocation of resources.

Are you reaping the many benefits of self-service automation for your company? If not, the time to start doing so is now. Click here to launch your free trial of Ayehu’s automation platform today.

Free eBook! Get Your Own Copy Today

5 Ways Machine Learning is Transforming the Business World

Today’s forward-thinking organizations are leveraging the power of artificial intelligence to automate the decision making process. In fact, corporate investment in AI is predicted to reach $100 billion by the year 2025. As a result of this rapid digital transformation, many changes are underway in the workplace. In particular, there are a number of ways that machine learning is already making an impact for companies in every industry. Here are a few to consider.

Personalizing the customer experience.

One of the most exciting benefits machine learning can have for businesses is the fact that it can help improve the customer experience while also lowering costs. Through things like deep data mining, natural language processing and continuous learning algorithms, customers can receive highly personalized support with little to no human intervention. And people are warming to the idea. In fact, 44% of US consumers say they actually prefer chatbots to human agents.

Improving loyalty and retention.

With machine learning, companies can do a deep dive into customer behavior to identify those who are at a higher risk of churning. This enables organizations to develop and implement next steps designed to target and retain those high-risk customers. The more proactive a company is in this area, the more profitable it will be over time.

Enhancing the hiring process.

When asked about the most difficult part of their job, corporate recruiters and hiring managers almost unanimously list the task of shortlisting qualified candidates for job openings. With dozens and sometimes even hundreds of applicants, sifting through and narrowing down the options can be a monumental task. Machine learning is fundamentally changing the way this process is handled by letting software do the dirty work, quickly identifying and shortlisting those candidates that are the best fit.

Detecting fraud.

Did you know that the average organization loses up to 5% of their total revenue each year due to fraud? Machine learning algorithms can be used to track data and apply pattern recognition to identify anomalies. This can help risk management detect fraudulent transactions in real-time so they can be prevented. This type of “algorithmic security” can also be applied to cybersecurity, leveraging AI to quickly and accurately pinpoint threats so they can be addressed before they are able to do damage.

Streamlining IT operations.

Another way AI and machine learning are revolutionizing how organizations operate is through intelligent automation. Powered by machine learning algorithms, agentless automation and orchestration platforms become force multipliers, driving efficiency and helping enterprises save time on manual and repetitive tasks, accelerate mean time to resolution, and maintain greater control over IT infrastructure.

Want to see machine learning in action? Schedule a free product demo today!

Free eBook! Get Your Own Copy Today

The New Wave of Hybrid AI in the Automation Era – Insights from the Experts

Over the past few years, Gartner has made some pretty bold predictions surrounding artificial intelligence and automation technology. These include such projections as the generation of $2.9 trillion in business value, the recovery of 6.2 billion hours of worker productivity and the creation of some 2.3million new jobs. All of this is expected to take place over the next couple of years, if not sooner.

Whatever you believe about AI, one thing’s for sure – it’s not going away any time soon.In fact, all signs indicate that the not-so-distant future will look a lot different from what we are used to today, with machines performing not only the physical work of humans, but also the thinking, planning, strategizing and decision-making as well.

At Ayehu, we’re always trying to stay a step ahead of the curve in terms of technology and its capabilities. Such was the purpose of our recent panel discussion entitled The New Wave of Hybrid AI in the Automation Era, during which we sat down with several AI and automation thought leaders, including:

We asked these experts to take a look back at some of the emerging trends observed and experienced in 2018 and offer a glimpse into the future. We also asked each of our esteemed panelists to go out on a limb and make a bold prediction for 2019.

Here’s a little taste of what we uncovered.

Evolution in IT Operations

One of the first topics touched upon by our own Brian Boeggeman centered on the creation of automation centers of excellence (COE) and the automation engineers that are driving adoption and proliferation of automation within the enterprise.

“We saw a trend taking place this year where there’s a lot of focus put on the creation on these centers of excellence to really drive automation,” said Brian. “Automation is clearly seen as a business imperative and a clear necessity in order to orient the next phase of enterprise operations, where efficiencies can be uncovered and realized across the business.”

He continued, “Those automation engineers effectively become the change agents within the organizations to drive that cultural change within the enterprise. As we engage with many of our customers, we’re continuing to see that as a high focus for them actually making investments to build out these COEs to push across the entire organization and enterprise as a whole.”

Brian also talked about the acceleration and creation of new services through automation and AI, pointing out that automation has emerged as a key strategy to achieve the delicate balance between innovation and operational excellence.

Download the on-demand recording to learn how to unleash new capabilities of human capital as well as what trend Brian believes will forever change the service levels and operating capabilities of organizations.

How AI and ML are Impacting the Adoption of Automation in the Enterprise  

Next, we turned the floor over to Manish Kothari, who has been involved with artificial intelligence and machine learning since the 70s. Manish opened his statement by pointing how critical data is to extracting the full value of AI.

“I think we’re starting to see a very big transformation take place across all sectors from agriculture, to consumer, and especially in IT where the need is arguably one of the highest,” Manish commented.

He went on to say: “If you are at the IT infrastructure side, you are, actually for one of the first times, really in a position to become an enabler rather an impediment to innovation.”

Manish also shared his insight on what enterprise managers who have never dealt with AI should know before deploying it and how he envisions the roles of managers changing once AI begins to get a foothold.

Find out everything that Manish had to say here.

Top Reasons Organizations Should Automate IT Operations

The next topic on the agenda was turned over to Andrew Brill of Change Healthcare, who was asked to share his thoughts on the top reasons for automating ITOps.

He was quick to point out that while the initial reasons typically revolve around cost savings and efficiency, some really exciting things can begin to happen,particularly in terms of an organization’s ability to remain compliant and consistent in its operating practices. Automation essentially becomes a force multiplier, saving users time and skyrocketing productivity and facilitating innovation.

“When we transform our staff who were doing the more robotic functions and eliminate that from their daily work, it allows them to start thinking about how we advance the organization, how we build better engineered solutions, how we work with partners that add the most value,” Andrew points out.

“Their brains are more actively working on the things that solve business problems as opposed to the functional tasks and work that perhaps we’ve had them do over the past ten years.”

Check out the playback to hear more and learn what Andrew believes are the biggest benefits of automation.

The Impact of AI on Digital Transformation

The discussion then turned to Ross Tisnovsky of McKinsey who was asked what kind of impact he believed technologies such as intelligent automation and AI having on IT digital transformation efforts in the enterprise.

In his response, Ross shared how he approaches the adoption of new technology. In particular, he highlighted the main things that users want from IT which, in his opinion, include:

  • A workspace for collaboration and enhanced productivity
  • Improved decision-making
  • Support for business innovation
  • High efficiency at a minimal cost

According to Ross, the solution to these needs requires a hybrid of AI, automation and human decision making.

“Automation works on both sides of the picture and so does the artificial intelligence. In other words, it’s likely to become part of anything we do on the business side today.”

Watch the on-demand discussion to discover the Ross’ most intriguing thoughts on hybrid AI from his real-world experience.

Key Insights and Future Outlook

During the final portion of the panel discussion, the experts weighed in on such points as:

  • The role of humans in automation adoption
  • Biggest challenges to automating IT operations
  • Skills needed to automate effectively
  • Critical KPIs/success metrics to focus on at different stages of the IT digital transformation journey
  • What enterprise IT leadership should be doing right now to start preparing for the changes to come
  • Bold predictions for AI in 2019 (and beyond)

Don’t miss out on what was certainly one of the timeliest and most informative panel discussions we’ve ever had the pleasure of hosting. Tune in to the full recording today!

Watch the full recorded panel discussion

Episode #6: Insights from IBM: Digital Workforce and a Software-Based Labor Model – IBM Automation’s Gene Chao

Dec 04, 2018    Episodes

Episode #6: Insights from IBM: Digital Workforce and a Software-Based Labor Model

In today’s episode of Ayehu’s podcast we interview Gene Chao, Global Vice President and General Manager at IBM Automation

Automation, Artificial Intelligence, and Machine Learning are disrupting work environments in nearly every major industry and job function around the globe.  This tectonic shift in how work gets done is almost as breath-taking as the pace at which the change is occurring.  Depending on one’s perspective, this digital transformation is either exciting, unsettling, or even both.  If however you run the automation practice for IBM, a Fortune 100 company, your perspective is uniquely broader, and decidedly more enlightened.

As Global Vice President and General Manager for IBM Automation, Gene Chao probably knows as much as anyone else in the world about the current state of these technologies, and the impact they’re having on the enterprise.  Gene joins us to share his views on what a digital workforce with a software-based labor model will look like, why organizations clinging to rigid centralized hierarchies may not remain competitive much longer, and which metric is the worst indicator at gauging the effectiveness of automation.



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 Gene Chao, Global Vice President and General Manager for IBM Automation. Gene leads the entire global spectrum of IBM’s automation business with responsibilities that include building and developing IBM’s offerings across automation, artificial intelligence, and enterprise solutions. Prior to leading IBM’s automation business, Gene had a distinguished career with such global IT leaders as CSC, Hewlett-Packard, and Accenture. And most recently prior to joining IBM, Gene was the Chief Revenue Officer of IPsoft. And I can’t imagine too many other people having as broad an insight on the global automation market as Gene Chao. And so we invited him to come on our show, and we’re very pleased he’s able to take time out of his extremely busy schedule to join us today. Gene, welcome to Intelligent Automation Radio.

Gene Chao: Thank you so much Guy. Really excited to be here, and I think the topic is top-of-mind, and hopefully the audience agrees. We have a lot to talk about, so thank you again.

Guy Nadivi: All right. Let’s dive in. Gene, not too many people know this tidbit, but the company that eventually became IBM started out in the 1880s I believe building scales. So I think it’s appropriate that I start out today by asking you to “weigh in” about intelligent automation, which you talk a lot about in your own presentations, and of course is what our show’s all about. How do you define intelligent automation?

Gene Chao: Well, it’s a great question, Guy. “Automation”, amongst other terms in the industry, have become so broad, just like “AI”, just like “platforms”. We actually mean something pretty specific here at IBM. The way we define it is, think about automation has traditionally been about routines, repetitiveness. It’s about a static way of working, or a static workflow. This holds true across physical robotics, assembly line type of things, as well as what I’ll call the first generation of your knowledge worker automation. Think of things like advancing macros, or even elements like screen scraping. However, with the advancement and the ability to take advantage of new artificial intelligence elements, the advancements of machine learning, really good insights into patterns and anomalies, those micro data and business insights, a workflow rebalancing has shifted that static workflow into a dynamic workflow. To us, this is intelligent automation. Not only being able to create the static aspect of it, but the ability to learn, to adapt, to interpret with the advent of machine learning and AI, to start creating an autonomous workflow. Things that understand, things that it will be able to self-adjust and self-correct. This is the new way of working.

Guy Nadivi: So now speaking of workflow, you’ve stated that people following a process supported by technology is basically a dead-end paradigm, and the future will be one where processes are run by technology, supported by people. Can you elaborate a bit on that, and perhaps give an example where you’ve seen that next generation approach implemented?

Gene Chao: Oh, absolutely. And it’s interesting. What started out as sort of a pithy, hooky type of statement has really become the cornerstone of our methodology. And if you don’t mind, let me work backwards a little bit. You asked about a specific example. The best example I can give is a business, look at Netflix, right? It’s a perfect example where recommendations, the insights into who’s watching what and when, those “jobs” if you will are done by algorithms, by bots. They’re not being interpreted by a human or a person. People are there to support the system. They’re there to support the algorithms. And that’s what we mean by this whole shift into technology running the process, supported by people. It’s not that we don’t need people. People are a very important aspect of development and certainly the management of these things, but processes have traditionally been designed for people to follow. The new sort of paradigm as you call it is really around rebalancing that workflow. What people do, versus what the systems and algorithms can do, that’s fundamentally changed, which is why we’re trying to introduce the concept that those automation technologies, those things that actually trigger those processes, those are done by systems and technologies, rather than people igniting that spark. Does that make sense, Guy?

Guy Nadivi: Yeah. So Gene, in that future where processes are run by technology, you’ve predicted there will be a digital workforce comprised of software-based labor. What will that look like?

Gene Chao: Oh, that’s a really good question. In what I’ll call a classic model, there’s a people-only aspect, and then there’s what I’ll call a people plus machines, or a people augmented by technology aspect. And that was good. That was a necessary step as we took a look at what our computing powers can do, what the software components can do. It’s what created the maturity of things like systems of record or ERP systems. But now you introduce a third part, which are what I’ll call the digital workforce or the virtual co-workers. And now you have a three part framework that looks like people only, people plus machines, and then a software-based labor model. That’s what we mean by the digital workforce. And there’s actually two sides of it. There is a digital workforce which is literally a software-based labor model, and then there’s what I call digitally-skilled people. So how do we move from being a pure back office accountant into an accountant that also manages technology? So those are the things that we comprise as a digital workforce.

Guy Nadivi: You’ve talked about how automated processes that touch multiple departments divided by walls will eventually cause those walls to come down, and that suggests a flattening of the organizational hierarchy away from a centralized paradigm. And as most people know, flatter organizations tend to move quicker than hierarchical ones, and generally also require employees to take on elevated levels of responsibility and greater involvement in decision making. So as automation becomes more ubiquitous, do you think organizations that cling to rigid hierarchical structures will still be competitive?

Gene Chao: That’s a very emphatic no. Those dynamics and those changes are already happening. So a couple sort of, let’s double-click into that. There’s two aspects into what I want to talk about. First is, we have the concept of trying to move everything into a stay through processing model. So we’ve taken a look at all of our work, whether it’s supporting our clients, running our business, even the way we provide consulting type of projects, and we’ve classified that work into where those routines are, what are those repetitions where there’s unique knowledge or unique work, and we’ve tried to reshape those workflows into everything straight through processing. So it becomes at a machine time, a machine speed if you will in terms of how that stuff gets done, how work gets done. That completely takes away the organizational hierarchy, because as you think about vertically integrated processes, and I’ll use the case of a banking environment. Banks have their customers, customers may have a checking and savings account, might have a 401k or investment management. They might also have a mortgage. How do you maintain those stove pipes while saying, “I’m gonna be customer-first,” and really understanding the customer relationship aspect of that? You have to break down those departmental walls. You have to get through an ability to transact, scrape through without hiccups, and you have to tear down those hierarchies.

Now, easier said than done. You touch upon one thing that is critical is the requirement of employees to take on elevated levels of responsibility. Absolutely. You have to become cross-functional which means you have to be cross-trained, and you have to be able to look across the organization and upside-down and sideways to figure out the right organizational dynamic. Those workflows will drive those new organizations. So long winded way to say “no, many businesses have already changed, they’ve already decentralized some of those paradigms”, and those are the ones leading the market today.

Guy Nadivi: It’s pretty clear automation is heralding some dramatic shifts in corporate cultures, and I’m curious, what are currently the biggest bottlenecks IBM is seeing that are preventing wider adoption of automation, and more importantly, how will those be resolved?

Gene Chao: We could probably do another show on that one. And I wouldn’t say anyone’s technically wrong, it’s just that there’s a fear factor in this. But let me land on two key things around the bottlenecks. The first one is, having a belief or trust that the technology can actually work. There’s just a general fear that’s created. So is my job gonna be lost to a bot? I wouldn’t worry about that too much, because we’re not in a place where complete swaths of people are gonna get completely eliminated, right? So there’s a fear over if I adopt this quickly and I adopt this well, we’re gonna lose our human intelligence. We’re gonna lose our human capital. That fear factor paralyzes a lot of companies. And we have this phrase which is around automation and/or AI that is responsible. Those are the types of things that we’re working on.

The second big bottleneck is really understanding the design principles of how to do this. Let me kind of shorten that and say, many of our clients are using these technologies as sort of an extra tool in their backpack as opposed to really taking a look at fundamental charter, role, and job changes in that adoption. They’re just saying, “Hey, maybe there’s an RPA, robotic process automation technology. We’ll just kind of shove it into that process, and we’ll find two hours of efficiency.” Those little points of light does not add up to an economic change. Those little points of light, as we joke around, we call those longer lunch breaks. So we’ve created some efficiencies and some productivity, but that wider adoption, that automation at scale as we say, never really happens. So we start to take a look at the strategies and the blueprinting at a wider adoption level.

Guy Nadivi: Switching gears for a moment, taking a broader view Gene, I’m curious from your vantage point, what industries have you seen benefit the most from intelligent automation?

Gene Chao: That’s a great question. There’s a bit of a complex answer there. Let me answer that in two ways, and really in a pivot. By industry or sector, we’ve noticed that those that are highly digitized, so think about banks or investment management companies, think about companies that use that next set of digital data. Those are the ones that have been the early adopters. So if I could pick the top three in terms of adoption, I would say banking and financial services. I would say the second industry is around industrials and/or manufacturing. I think that’s because they’re already used to the robotics element. And the third one that’s been up and coming very, very quickly for us, funny enough, is consumer goods and retail, because they’ve already introduced the topics on sort of a chat bot or the buying assistant. You go onto websites now and there’s a buying assistant there or a bot to help you through a purchasing process. They’ve now taken that one step further.

So those are the three industry sectors that I would focus on in terms of earliest adoption. However, if I pivot to a different approach to how they use automation, there’s sort of a three-part answer. The first one is around IT service management. How do we fundamentally take back office processes and automate the heck out of it? How do we move to a virtual engineer or a virtual service agent model? The second one is really around the running-the-business processes. Think about accounting, think about HR and recruiting. Think about internal sourcing and procurement. Those are the first two areas that have been almost to the degree of table stakes that you have to go address through an intelligent automation engine. The last one is a focus on that front office. This is where that banking or financial services approach has been leading the way. Think about things like a “robo-adviser” for investment management. They’re now flowing that all the way through from advice given to their customers, making sure this compliance and regulatory aspects on doing things like tax returns and investment management. The front office has really started to adopt this approach. So that’s almost in the stack ranking. Biggest, widest adoption and IT operations and service management. Right underneath that would be the run-the-business processes, your non-core processes, and the third would be in the core business processes that they have. That make sense?

Guy Nadivi: It does. And it makes me wonder, the financial services, manufacturing, consumer goods and retail, those are three very different industries, and the processes you discussed were also very diverse. So I’m curious, from all that diversity, is there a particular metric you like above all others that best captures the effectiveness of automation to those types of enterprises?

Gene Chao: Absolutely. And I’ll actually start on the other end of the spectrum where automation has classically been viewed as a cost or productivity play. So what’s been happening is expense ratios, and I think funny enough the worse one for me is around managing headcount out of the business, so people reduction. Those are to me the worst indicators of effectiveness, because there are disconnects between operating model, use cases, and translating that into your operating expense, in this case people. We try to advise folks to go beyond that and get into taking a look at the turnaround time of a process. Measure the run time of how long a process takes, the effectiveness of the outcome. We’re introducing topics like different types of resource units. I talked about the digital workforce. Think about a human resource unit versus a software-based resource unit. Humans are managed in terms of 2,000 hours a year. Machine time, robotics, or automation is measured in 6-7,000 hours a year. So just by the effectiveness of the software technologies, that entire metric has changed. And we’re in the middle of trying to drive the new commercial terms around that. So those advancing commercial terms are really what’s gonna be able to capture the effectiveness of what we’re doing.

Guy Nadivi: You mentioned unrealistic metrics, and that leads me to wonder what are some of the most unrealistic expectations about automation that you’ve encountered?

Gene Chao: Yeah, it’s sort of coupled, and I’ll pick on the RPA community a little bit. It’s “Hey, adopt RPA and you’re gonna save 40% tomorrow.” I just haven’t seen that happen. Unrealistic expectations are really centered around the speed at which you can get these economic gains. There’s a lot of design principles, there are very many touch points and dependencies along the way before you really see a new operating model in terms of that work flow. So don’t think you’re gonna get sort of an in-quarter return on investment, especially when that bad metric I talked about in terms of people or head count take down, that doesn’t happen very quickly. And when you start looking at the cost benefit of it, you just gotta be careful. So that’s sort of that speed to realization of economics. That’s the worst one.

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

Gene Chao: I think it’s actually two. I quickly mentioned one. The first one is understand the enterprise impacts to what you’re doing. 60-70% of the design principles of intelligent automation are common regardless of domain area. So having an automation element running your infrastructure, the baseline or common service areas are very similar if not the same to your front office processes. Now domains are obviously in question and have a uniqueness to it, but there’s a lot of what I’ll call baseline work that has to get done. So consider your enterprise strategy. The second one is be very specific on what I call “use case hunting”. That economic return, that operational return, be really specific. Can’t just be, put that in your backpack, we’re all good. It’s gotta be mindful, and it’s gotta be thought out across functional design areas.

Guy Nadivi: Use case hunting may be my new favorite term in the automation glossary. All right, looks like that’s all the time we have for on this episode of Intelligent Automation Radio. Gene, thank you very much for coming on the show today and sharing some really intriguing insights about the global automation market. It’s been great having you as our guest.

Gene Chao: My pleasure. Always fun with you, Guy, and look forward to the next one.

Guy Nadivi: Gene Chao, Global Vice President and General Manager for IBM Automation. Thank you for listening everyone, and remember, don’t hesitate, automate.



Gene Chao

Global Vice President and General Manager - IBM Automation

Gene leads the entire global spectrum of IBM’s Automation business, with responsibilities for building/developing offerings, skills, and competencies across automation/autonomics, artificial intelligence, and enterprise solutions.  Gene’s team encompasses this foundational business area through market/client advocacy, thought leadership, delivery assurance, and developing IBM’s ecosystem and client engagement models.

Throughout his career, Gene has held general management, sales, and executive leadership positions in the business & IT services arena.  He most recently arrived from IPsoft, where he was the Chief Revenue Officer.  Gene’s experience spans business and technology consulting, as well as managed/outsourcing services with leading companies such as CSC, Hewlett Packard, and Accenture.  Additionally, he also has broad corporate finance experience as he began his career as a financial analyst with Shearson Lehman, and also resides on the Board of Trustees for Active Weighting Funds (ETF provider).

Gene can be found at: 

E-Mail:             gene.chao@ibm.com

Twitter:          @gene_chao

LinkedIn:       https://www.linkedin.com/in/gene-chao-46474/

Moreinformation about IBM Automation can be found at this link:

https://www.ibm.com/automation

Quotes

“To us, this is intelligent automation. Not only being able to create the static aspect of it, but the ability to learn, to adapt, to interpret with the advent of machine learning and AI, to start creating an autonomous workflow. Things that understand, things that it will be able to self-adjust and self-correct. This is the new way of working.”

"It's not that we don't need people. People are a very important aspect of development and certainly the management of these things, but processes have traditionally been designed for people to follow. The new sort of paradigm as you call it is really around rebalancing that workflow. What people do, versus what the systems and algorithms can do…"

“How do you maintain those stove pipes while saying, "I'm gonna be customer-first," and really understanding the customer relationship aspect of that? You have to break down those departmental walls. You have to get through an ability to transact, scrape through without hiccups, and you have to tear down those hierarchies.”

“You have to become cross-functional which means you have to be cross-trained, and you have to be able to look across the organization and upside-down and sideways to figure out the right organizational dynamic. Those workflows will drive those new organizations."

“So is my job gonna be lost to a bot? I wouldn't worry about that too much, because we're not in a place where complete swaths of people are gonna get completely eliminated…”

“Unrealistic expectations are really centered around the speed at which you can get these economic gains. There's a lot of design principles, there are very many touch points and dependencies along the way before you really see a new operating model in terms of that work flow.”

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
https://www.ibm.com/automation

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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